El hijo.

El retrato del Hijo

Un hombre rico y su hijo tenían una gran pasión por el arte. Tenían de todo en su colección, desde Rafael hasta Picasso. Muy a menudo, se sentaban juntos a admirar las grandes obras de arte. Desgraciadamente el hijo se fue a la guerra. Fue muy valiente y murió en la batalla mientras rescataba a otro soldado. El padre recibió la noticia y sufrió profundamente la muerte de su único hijo. Un mes más tarde, justo antes de la Navidad, alguien tocó la puerta. Un joven con un gran paquete en sus manos dijo al padre: Señor, usted no me conoce, pero yo soy el soldado por quien su hijo dio la vida. El salvó muchas vidas ese día; me estaba llevando a un lugar seguro cuando una bala le atravesó el pecho, muriendo así instantáneamente.

Él hablaba muy a menudo de usted y de su amor por el arte. El muchacho extendió los brazos para entregar el paquete: “Yo sé que esto no es mucho. Yo no soy un gran artista, pero creo que a su hijo le hubiera gustado que usted recibiera esto.” El padre abrió el paquete. Era un retrato de su hijo pintado por el joven soldado. El contempló con profunda admiración la manera en que el soldado había capturado la personalidad de su hijo en la pintura. El padre estaba tan atrapado por la expresión de los ojos de su hijo, que los suyos propios se arrasaron de lágrimas.

Le agradeció al joven soldado y ofreció pagarle por el cuadro. “¡Oh no señor mío, yo nunca podría pagarle lo que su hijo hizo por mí. Es un regalo!

El padre colgó el retrato arriba de la repisa de su chimenea. Cada vez que los visitantes e invitados llegaban a su casa, les mostraba el retrato de su hijo antes de mostrar su famosa galería.

El hombre murió unos meses más tarde y se anunció una subasta con todas las pinturas que poseía. Mucha gente importante e influyente acudió con grandes expectativas de hacerse de un famoso cuadro de la colección.

Sobre la plataforma estaba el retrato del hijo.

El subastador golpeó su mazo para dar inicio a la subasta: “Empecemos los remates con este retrato del hijo, ¿quién ofrece por este retrato?” Hubo un gran silencio. Entonces una voz del fondo de la habitación gritó: “queremos ver la pintura famosa, olvídese de esa.” Sin embargo el subastador persistió: “¿alguien ofrece algo por esta pintura? ¿100.000, 200.000?

Otra vez gritó con enojo: “¡No venimos por esa pintura, venimos por… los Van Goghs, los Rembrandts. Vamos a la oferta de verdad!” Pero aun así el subastador continuaba con su labor: “EL HIJO, EL HIJO, EL HIJO… ¿quién se lleva al HIJO?” Finalmente una voz se oyó desde atrás; el viejo jardinero del padre y del hijo, siendo un hombre muy pobre, ofreció lo único que podía ofrecer, $ 10. “Tenemos $10, ¿Quién da 20?” gritó el subastador. La multitud se estaba enojando mucho. No querían la pintura de “El Hijo”, querían las que representaban una valiosa inversión para sus propias colecciones. El subastador golpeó por fin el mazo: “Va una, van dos, VENDIDA POR $10.” ¡Empecemos por la colección! Gritó uno. El subastador soltó su mazo y dijo: “lo siento mucho, damas y caballeros, pero la subasta llegó a su final.” “Pero… ¿y las pinturas?” dijeron los interesados. “Lo siento” contestó el subastador. “Cuando me llamaron para conducir esta subasta, se me dijo de un secreto estipulado en el testamento del dueño. Yo no tenía permitido revelar esta estipulación hasta este preciso momento. Solamente la pintura de “El Hijo” sería subastada. Aquel que la aceptara, heredará absolutamente todas las pinturas del hombre incluyendo las famosas pinturas. El hombre que aceptó quedarse con “El Hijo” se queda con TODO.”

REFLEXIÓN: Dios nos ha entregado a su Hijo, quien murió en una cruz hace más de 2000 años. Así como el subastador, su mensaje hoy es: EL HIJO, EL HIJO, ¿Quién se lleva EL HIJO? Quien ama al Hijo lo tiene todo.  Evangelio de Mateo 6:33: “Busquen primero el Reino de Dios y su justicia, y todo lo demás se les dará por añadidura.”  En Juan 14:6 “Jesús le dijo: Yo soy el camino, y la verdad, y la vida; nadie viene al Padre, sino por mí”. Es un lindo mensaje para compartir. Solo repite esta frase y mira como se mueve Dios: “Señor, te amo y te necesito, estas en mi corazón. Bendíceme a mí, a mi familia, mi casa, mi hogar, mi empleo, mis finanzas, mis sueños y mis proyectos, y a mis amigos, en el nombre de Jesús, Amén. No hay sino un solo camino.

 

El viejo truco del falso mesías.

Los muchachos que votaron por Hugo Chávez, hoy son los mismos arrepentidos venezolanos, padres, madres y hermanos separados en muchos países, que prefieren venir a Colombia, ecuador o Perú, y muchos otros lugares, a pasar necesidades, para poder mandarle cualquier cosa a sus familias, habiendo salido de su país, porque allá solo hay hambre, pobreza, desolación, narcotráfico e indiferencia; porque allá no hay futuro.

Habiendo tenido un país pleno de riquezas, tan boyante que, de hace 30 años hacia atrás, a donde quiera que llegaba un venezolano, llegaba la alegría, la fiesta y la abundancia. Eran felices, y no lo sabían; eran ricos, y no lo sabían, lo tenían todo, y no lo sabían; tenían grandes empresas, producían en Venezuela todas aquellas cosas que los demás países latinoamericanos apenas soñábamos con tener. Fue por esa época cuando les llegó un falso mesías, que les vendía la idea de que estaban pobres, desarrapados, que no tenían nada, que todo el mundo estaba mejor que ellos, que estaban comiendo mierda, mientras los políticos se estaban robando todas las riquezas que a ellos les correspondían. Así, les envenenó el alma, les llenó el alma de odio y de envidias; se dejaron meter cucarachas en la cabeza, en lugar de pensar en trabajar para mejorar lo que tenían.

Venezuela no era el paraíso. No hay país perfecto, pero sin duda, era el país que mejor estaba en Latinoamérica; para allá iba gente de habla hispana de todos nuestros países, iban a trabajar y a mandar plata para sus familias, vivían unos años allá, conseguían para comprar casa acá, y hasta les alcanzaba para disfrutar de la vida en un país ajeno. Lo anterior, permite darse una pequeña idea de lo que tenían en Venezuela, de lo que había allí y que perdieron. Hoy, quienes no conocieron esa Venezuela, o a esos venezolanos, ni siquiera la pueden imaginar, viendo la miseria que vemos ahora.

Si no se han dado cuenta, ese mismo discurso barato de Chávez, nos lo lleva metiendo desde hace varios años, otro dizque mesías aquí en Colombia. No contento con los avances que ha tenido en las urnas, convoca a las calles a nuestros jóvenes, para que unan su rebeldía juvenil, con las malas mañas de hampones profesionales que como él mismo, demonio, parece que vinieron a la tierra, solo para mentir, robar, matar y destruir. Llevan a nuestros menores a arriesgar sus vidas y las de sus familias, haciéndoles creer la idea de que son mártires por un mundo mejor. ¿Cuál mundo mejor? Uno como el que Chávez le vendió a quienes lo llevaron al poder en Venezuela, y que nunca llegó? Queramos o no, quien consigue un mundo mejor, siempre lo hace a punta de trabajo y sacrificio, porque ni siquiera la plata de las loterías luce.

Este falso mesías colombiano, que tiene un ego del tamaño del Everest, cuando como cualquier personaje público, saca un video en sus redes sociales, no lo anuncia como un mensaje, ó cosa parecida; para él es una, “alocución presidencial,” y lleva años enseñando así a nuestra juventud, en contubernio con mucha gente influyente pero equivocada, tratando de borrar de nuestros jóvenes, y aún de nuestra niñez, la esperanza de un futuro mejor, y llenándoles sus inexpertas almas de odio contra todo el que no se muestre de acuerdo con ellos. Este odio lo ves en las redes, cuando, si tratas de mostrar un punto de vista diferente a esos jóvenes, te matonean, porque el odio que les han inoculado sus mesías, no soporta que alguien pueda tener una historia de vida, que le haya llevado a pensar que sí es posible salir adelante, y colaborar para que otros lo hagan, y para que el país entero mejore, sin importar que haya gente maligna en todas partes; finalmente, malos siempre ha habido, y en todas partes, y sin embargo, los buenos somos mas.

No pierdo la esperanza de que en Colombia sí podamos aprender del error ajeno, y que quienes aquí están siendo engañados y manipulados, caigan en cuenta de que esta historia del falso mesías, no solo la compraron en Venezuela y Nicaragua, que son nuestros espejos mas cercanos, sino que los primeros que lo hicieron fueron los cubanos. En Cuba, los primeros en caer en el engaño, llevan décadas de miseria, en Nicaragua ya llevan varios años, en ambos se mantienen en el poder mediante represión, encarcelamientos y asesinatos. No hay ley para ellos, ni siquiera puedes hablar en su contra.  Argentina, Mexico, Ecuador y otros, llevan cerrando el trato desde hace algunos años, pero para redondearlo necesitan a Colombia, para así poder tener la ruta completa. Si, Colombia les ha sido por piedra en el zapato, por montaña atravesada entre ellos y sus megalómanos sueños de poder, porque aquí, aunque no seamos todos, la mayoría sí creemos en el trabajo como forma de vida digna, y que las riquezas mal ganadas pronto se esfuman; en tanto que las obtenidas poco a poco, se multiplican.

El Castrochavismo No Existe.

El castrochavismo no existe.

 

Discutiendo hoy con un muchacho cuyos argumentos pasaban por copiarlos y pegarlos desde alguna página, además de utilizar stickers y palabras con las que pretendió amordazar mis ideas, uno de sus argumentos fue decir que el castro-chavismo no existe. Y en algo tiene razón, sostener que existe, es como decir que el ciclomontañismo existe, alguien podría decir que es imposible. Sin embargo, es un hecho que las bicicletas y las montañas existen, o no? Tampoco se puede negar que existen fanáticos de usar sus bicicletas en las montañas, lo cual resulta un deporte extremadamente agradable.

De la misma manera, no se puede negar la existencia de los Castro en Cuba, y la de Chávez en Venezuela, los cuales a su vez, han generado la existencia de los "castristas" y asimismo, de los chavistas, y de la misma manera que existen los ciclo-montañistas, también existen los castro-chavistas, que se distinguen por promover, más o menos descaradamente las políticas internas y externas de dichos personajes; las internas, resumidas en la miseria y el hambre de sus pueblos, así como la poco creíble insistencia de sus gobiernos en culpar de ello a todo el mundo, menos a ellos mismos, que son los que mandan allí. Las políticas externas de los castristas y los chavistas, se han unido en torno al llamado foro de Sao Paulo ó de Puebla, México, donde se han reunido todos los comunistas de Iberoamérica desde hace décadas, incluidos los políticos y los terroristas, y algunos aventajados de sus seguidores, estultos más o menos útiles a esas causas, pero la mayoría de las veces con gran olfato para detectar el dinero fácil. La idea es exportar desde Cuba, como ya lo hicieron hacia Venezuela y Nicaragua, el imperio de su modelo generador de hambre y miseria que les parece tan conveniente a ellos (porque mientras ellos se enriquecen, el pueblo lo sufre). Para ello, han usado desde intentos de  invasiones a otras naciones latinoamericanas después de mediados del siglo pasado, hasta el entrenamiento y el ocultamiento de terroristas en ambos países, que después van a delinquir en países como Colombia, realizando atentados con miles de muertos, sembrando cultivos ilícitos y enviando drogas a otros países, con las consabidas alianzas con el narcotráfico que generan la muerte de muchos compatriotas (véase alianzas con el cartel de Sinaloa actualmente).

Pese a que la lucha armada ya no es útil para lograr el poder, los castristas y los chavistas unidos, continúan respaldando veladamente a las guerrillas narcotraficantes por dos cosas: primero, porque generan desestabilización en los gobiernos, y segundo, generan millones y millones de dólares y euros en riquezas para sus dirigentes. Aun así, la estrategia para conseguir el poder ahora es otra, usar los políticos con ideas afines, financiados por ultra-multi-millonarios como George Soros; generar el odio entre diferentes grupos humanos, incluso naturales (léase ideologías de género y feminismo radical); y usando de "pequeñas ayudas" como las máquinas de voto electrónico que han asegurado el triunfo chavista en las elecciones venezolanas, aún desde la época del tristemente célebre Hugo Chávez.

Dado que todas estas cosas no se pueden realizar sin controlar a una parte de la población, estos personajes, patrocinan toda clase de injerencias en los territorios nacionales de otros países, respaldando muy diferentes iniciativas; desde los grupos que defienden el medio ambiente, hasta los que promueven la aprobación del aborto aún hasta los 9 meses, sin siquiera contemplar la idea de que se trata de un ser humano indefenso, al que están asesinando. La anterior injerencia, ya de por sí, es algo inadmisible.

Ahora, imagínese que usted vive en una casa, y los hijos de sus vecinos son viciosos, venden drogas, son ladrones, violadores etc., mientras tanto, usted se ha dedicado a criar los suyos, con los más altos principios, les ha inculcado los más altos valores del trabajo y el estudio, y su vecino, pretende metérsele a la fuerza a su casa, a adoctrinar a sus hijos con las ideas que dieron a luz en los hijos de él, toda clase de pensamientos criminales. Le parece a usted que esto debería permitirse? De la misma forma, los señores de Cuba y Venezuela, castristas y chavistas respectivamente, una vez que han acabado con todo en sus respectivos patios, llevan décadas ya, pretendiendo apoderarse de las patrias ajenas y de las riquezas de los países vecinos, promoviendo el narcotráfico, el desfalco, el robo, el abuso de autoridad legítima o ilegítima (véase las denuncias de las violaciones a niñas y niños perpetradas por las farc y denunciadas por la "Corporación Rosa Blanca"). A lo anterior, se suman ahora las decenas de miles de Castristas cubanos y chavistas venezolanos, a los que se les ha abierto las puertas de Colombia, con la intención nuestra de que puedan buscar un futuro mejor para sus familias, pero resulta que la intención de éstos, no es venir a trabajar ó estudiar, sino a traer todos los comportamientos que han hecho "grandes" a sus movimientos en sus países de origen, la idea es espiar, y desde allí, controlar las células adoctrinadoras, que convertirán a los juiciosos hijos de los vecinos en depredadores de las riquezas de sus propios países, así como en depredadores sociales que al estilo de los que han sido adoctrinados en Cuba, desde hace ya mas de 60 años, acaben con la inocencia de los niños, roben, maten, violen y trafiquen a sus anchas, cuando ya tengan consolidado su régimen del terror, sin que nadie se les pueda oponer.

Como se puede ver, es lo mismo decir, “los castristas de cuba y los chavistas de Venezuela”, que decir los “castrochavistas”, cuando uno se refiere a la unión de los dos en un grupo que promueve los mas bajos intereses de ambos. La importancia del asunto no está en usar una palabra, ó 9 para decir lo mismo, el asunto está en no dejarse desviar la atención de lo realmente importante que es, que se nos metan al rancho y acaben con todo, hasta con nuestras familias, sin que nosotros ni siquiera podamos chistar, porque en el momento que lo hagamos, nos vamos a convertir en blanco de descalificadores sin autoridad alguna para descalificar a nadie.

Me tiene sin cuidado quién usó por primera vez el término ciclomontañismo ó castrochavismo, no soy seguidor ni admirador de ninguno. Lo que me importa son las consecuencias de ambos, deporte en el primero, y destrucción y muerte en el segundo, me preocupa tanto que haya dineros del narcotráfico colombiano en las elecciones, como que hayan dineros de Soros, ó del narcotráfico de Venezuela y Cuba también. Pero también me preocupa que se haga trampa con los votos electrónicos de máquinas creadas precisamente para ello, y que vengan a metérseme en mi casa a hacer toda clase de desmanes con mis cosas y con mi familia, sin que yo siquiera alce mi voz de protesta. Yo siempre lo haré, al menos mientras que tenga vida, porque en Venezuela como en Cuba, pensar y disentir, es un crimen que se paga con la cárcel ó con la vida, y espero que esa situación no llegue a mi patria.

Mi país está lejos de ser perfecto, pero traer millones de males peores, nada va a solucionar. Pretender que el dinero del narcotráfico de Venezuela ó Cuba, es mejor que el del narcotráfico colombiano, es estúpido. Hacerse el ciego frente a todos los males que tenemos no sirve de nada, soy consciente de ellos, lucho contra ellos y los denuncio, como denuncio al otro bando, que es peor, porque usar esos males como excusa para atraer más males, y miles de veces peores, no tiene el menor sentido. Muchos viven resaltando la desigualdad social en Colombia, pero convenientemente se olvidan de que aquí, muchos han salido adelante, y hasta han llegado a ser ricos e importantes ó ambas cosas, cosa que no sucede en esos países, donde sí que hay igualdad entre el pueblo: todos son miserables y mantenidos, mientras que los dirigentes muy comunistas y todo, pero viven ellos y sus familias, paseando por el mundo, gastando a manos llenas, en mansiones y yates comprados con los dineros de la robolución.

Aquel que defienda la política de los castro en Cuba, con su más de medio siglo de indebida injerencia en Latinoamérica, todas las muertes, robos, secuestros, violaciones, y demás malignidades que nos ha traído, puede ser que simplemente esté equivocado, pero lo está de la peor manera. Ahora, si respalda estas cosas no por estar equivocado, sino que lo hace aun sabiendo todo esto, entonces no es un equivocado ni un distraído, ni un desorientado; es un cómplice de todos los crímenes que se han perpetrado desde que la Cuba de los Castro existe.

 

 

 

 

The emerging contours of the new world of work in the Fourth Industrial Revolution are rapidly becoming a lived reality for millions of workers and companies around the world. The inherent opportunities for economic prosperity, societal progress and individual flourishing in this new world of work are enormous, yet depend crucially on the ability of all concerned stakeholders to instigate reform in education and training systems, labour market policies, business approaches to developing skills, employment arrangements and existing social contracts. Catalysing positive outcomes and a future of good work for all will require bold leadership and an entrepreneurial spirit from businesses and governments, as well as an agile mindset of lifelong learning from employees.

The fundamental pace of change has only accelerated

further since the World Economic Forum published its initial report on this new labour market—The Future of Jobs: Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution—in January 2016. With an increased need for tangible evidence and reliable information from

the frontlines of this change, this new edition of the Future of Jobs Report once again taps into the collective knowledge of those who are best placed to observe the dynamics of workforces—executives, especially Chief Human Resources Officers, of some of the world’s largest employers—by asking them to reflect on the latest employment, skills and human capital investment trends across industries and geographies.

A particular focus of this new edition of the report is on arriving at a better understanding of the potential of new technologies, including automation and algorithms, to create new high-quality jobs and vastly improve the job quality and productivity of the existing work of human employees. As has been the case throughout economic history, such augmentation of existing jobs through technology is expected to create wholly new tasks—from

app development to piloting drones to remotely monitoring patient health to certified care workers—opening up opportunities for an entirely new range of livelihoods for workers. At the same time, however, it is also clear that the Fourth Industrial Revolution’s wave of technological advancement is set to reduce the number of workers required for certain work tasks. Our analysis finds that

increased demand for new roles will offset the decreasing demand for others. However, these net gains are not a foregone conclusion. They entail difficult transitions for millions of workers and the need for proactive investment in developing a new surge of agile learners and skilled talent globally.

To prevent an undesirable lose-lose scenario— technological change accompanied by talent shortages, mass unemployment and growing inequality—it is critical that businesses take an active role in supporting their existing workforces through reskilling and upskilling,

that individuals take a proactive approach to their own lifelong learning and that governments create an

enabling environment, rapidly and creatively, to assist in these efforts. Our analysis indicates that, to date, many employers’ retraining and upskilling efforts remain focused on a narrow set of current highly-skilled, highly-valued employees. However, in order to truly rise to the challenge of formulating a winning workforce strategy for the Fourth Industrial Revolution, businesses will need to recognize human capital investment as an asset rather than a liability. This is particularly imperative because there is a virtuous cycle between new technologies and upskilling. New technology adoption drives business growth, new job creation and augmentation of existing jobs, provided it can fully leverage the talents of a motivated and agile workforce who are equipped with futureproof skills to take advantage of new opportunities through continuous retraining and upskilling. Conversely, skills gaps—both among workers and among an organization’s senior leadership—may significantly hamper new technology adoption and therefore business growth.

At the World Economic Forum’s Centre for the New

Economy and Society, we provide a platform for leaders to understand current socio-economic transformations and shape a future in which people are at the heart of

economic growth and social progress. A significant portion of our activities aim to support leaders in managing the future of work. This biannual report provides a five-year outlook based on the latest thinking inside companies and is designed to inform other businesses, governments and workers in their decision-making. Additionally the Centre

is working across multiple industries to design sector-level

roadmaps to respond to the new opportunities and challenges of managing workforce transitions. The Centre is also supporting developed and emerging economies

in setting up large-scale public private collaborations to close skills gaps and prepare for the future of work. Finally, the Centre acts as a test bed for early-stage work at the frontier of managing the future of work, ranging from the development of new principles for the gig economy to the adoption of common skills taxonomies across business and education.

We would like to express our appreciation to Vesselina Ratcheva, Data Lead, Centre for the New Economy and Society; Till Alexander Leopold, Project Lead, Centre

for the New Economy and Society; and Saadia Zahidi, Head, Centre for the New Economy and Society for their leadership of this report. Additional thanks to Genesis Elhussein, Specialist, and Piyamit Bing Chomprasob, Project Lead, for their work on the report’s survey collection phase, and the support of other members of the Centre for the New Economy and Society team for its integration into a comprehensive platform for managing workforce change. We greatly appreciate, too, the

innovative data collaboration with LinkedIn and the support of the report’s regional survey partners, which enhanced its geographical coverage. Finally, we continue to count on the proactive leadership of the Stewards and Partners of the System Initiative on Shaping the Future of Education, Gender and Work under the umbrella of the Forum’s Centre for the New Economy and Society.

Workforce transformations are no longer an aspect of the distant future. As shown in the five-year outlook of this report, these transformations are a feature of today’s workplaces and people’s current livelihoods and are set to continue in the near term. We hope this report is a call to action to governments, businesses, educators and individuals alike to take advantage of a rapidly closing window to create a new future of good work for all.

Key Findings

As technological breakthroughs rapidly shift the frontier between the work tasks performed by humans and those performed by machines and algorithms, global labour markets are undergoing major transformations. These transformations, if managed wisely, could lead to a new age of good work, good jobs and improved quality of life for all, but if managed poorly, pose the risk of widening skills gaps, greater inequality and broader polarization.

As the Fourth Industrial Revolution unfolds, companies are seeking to harness new and emerging technologies

to reach higher levels of efficiency of production and consumption, expand into new markets, and compete on new products for a global consumer base composed increasingly of digital natives. Yet in order to harness the

transformative potential of the Fourth Industrial Revolution, business leaders across all industries and regions will increasingly be called upon to formulate a comprehensive workforce strategy ready to meet the challenges of this new era of accelerating change and innovation.

This report finds that as workforce transformations accelerate, the window of opportunity for proactive management of this change is closing fast and business, government and workers must proactively plan and implement a new vision for the global labour market. The report’s key findings include:

•             Drivers of change: Four specific technological advances—ubiquitous high-speed mobile internet; artificial intelligence; widespread adoption of big data analytics; and cloud technology—are set to dominate the 2018–2022 period as drivers positively affecting business growth. They are flanked by a range of socio-economic trends driving business opportunities in tandem with the spread of new technologies, such as national economic growth trajectories; expansion of education and the middle classes, in particular

in developing economies; and the move towards a greener global economy through advances in new energy technologies.

•             Accelerated technology adoption: By 2022, according to the stated investment intentions of companies surveyed for this report, 85% of respondents are likely or very likely to have expanded their adoption

of user and entity big data analytics. Similarly, large proportions of companies are likely or very likely

to have expanded their adoption of technologies such as the internet of things and app- and web- enabled markets, and to make extensive use of cloud computing. Machine learning and augmented and virtual reality are poised to likewise receive considerable business investment.

•             Trends in robotization: While estimated use cases for humanoid robots appear to remain somewhat more limited over the 2018–2022 period under consideration in this report, collectively, a broader range of recent robotics technologies at or near commercialization— including stationary robots, non-humanoid land robots and fully automated aerial drones, in addition to machine learning algorithms and artificial intelligence— are attracting significant business interest in adoption. Robot adoption rates diverge significantly across sectors, with 37% to 23% of companies planning this investment, depending on industry. Companies across all sectors are most likely to adopt the use of stationary robots, in contrast to humanoid, aerial or underwater robots, however leaders in the Oil & Gas industry report the same level of demand for stationary and aerial and underwater robots, while employers in the Financial Services industry are most likely to signal the planned adoption of humanoid robots in the period up to 2022.

•             Changing geography of production, distribution and value chains: By 2022, 59% of employers surveyed for this report expect that they will have significantly

modified how they produce and distribute by changing the composition of their value chain and nearly half expect to have modified their geographical base of operations. When determining job location decisions, companies overwhelmingly prioritize the availability

of skilled local talent as their foremost consideration, with 74% of respondents providing this factor as their key consideration. In contrast, 64% of companies cite labour costs as their main concern. A range of

additional relevant factors—such as the flexibility of local labour laws, industry agglomeration effects or proximity of raw materials—were considered of lower importance.

•             Changing employment types: Nearly 50% of companies expect that automation will lead to some reduction in their full-time workforce by 2022, based on the job profiles of their employee base today. However, 38% of businesses surveyed expect to extend their workforce to new productivity-enhancing roles, and more than a quarter expect automation to lead to the creation of new roles in their enterprise.

In addition, businesses are set to expand their use of contractors doing task-specialized work, with many respondents highlighting their intention to engage workers in a more flexible manner, utilizing remote staffing beyond physical offices and decentralization of operations.

•             A new human-machine frontier within existing tasks: Companies expect a significant shift on the frontier between humans and machines when it comes to existing work tasks between 2018 and 2022. In 2018, an average of 71% of total task hours across the 12 industries covered in the report are performed by humans, compared to 29% by machines. By 2022 this average is expected to have shifted to 58% task hours performed by humans and 42% by machines. In 2018, in terms of total working hours, no work task was yet estimated to be predominantly performed

by a machine or an algorithm. By 2022, this picture is projected to have somewhat changed, with machines and algorithms on average increasing their contribution to specific tasks by 57%. For example, by 2022, 62% of organization’s information and data processing and information search and transmission tasks will be performed by machines compared to 46% today. Even those work tasks that have thus far

remained overwhelmingly human—communicating and interacting (23%); coordinating, developing, managing and advising (20%); as well as reasoning and decision- making (18%)—will begin to be automated (30%, 29%, and 27% respectively). Relative to their starting point today, the expansion of machines’ share of work task performance is particularly marked in the reasoning and decision-making, administering, and looking for and receiving job-related information tasks.

•             A net positive outlook for jobs: However this finding is tempered by optimistic estimates around emerging tasks and growing jobs which are expected to offset declining jobs. Across all industries, by 2022, growth in emerging professions is set to increase their share of employment from 16% to 27% (11% growth) of

the total employee base of company respondents, whereas the employment share of declining roles is set to decrease from currently 31% to 21% (10% decline). About half of today’s core jobs—making up the bulk of employment across industries—will remain stable in the period up to 2022. Within the set of companies surveyed, representing over 15

million workers in total, current estimates would suggest a decline of 0.98 million jobs and a gain of

1.74 million jobs. Extrapolating these trends across those employed by large firms in the global (non- agricultural) workforce, we generate a range of estimates for job churn in the period up to 2022.  One set of estimates indicates that 75 million jobs may be displaced by a shift in the division of labour between humans and machines, while 133 million new roles may emerge that are more adapted to the new division of labour between humans, machines and algorithms. While these estimates and the assumptions behind them should be treated with caution, not least because they represent a subset of employment globally, they are useful in highlighting the types of adaptation strategies that must be put

in place to facilitate the transition of the workforce to

the new world of work. They represent two parallel and interconnected fronts of change in workforce transformations: 1) large-scale decline in some roles as tasks within these roles become automated or redundant, and 2) large-scale growth in new products and services—and associated new tasks and jobs— generated by the adoption of new technologies and other socio-economic developments such as the

rise of middle classes in emerging economies and demographic shifts.

•             Emerging in-demand roles: Among the range of established roles that are set to experience increasing demand in the period up to 2022 are Data Analysts and Scientists, Software and Applications Developers, and Ecommerce and Social Media Specialists, roles that are significantly based on and enhanced by

the use of technology. Also expected to grow are roles that leverage distinctively ‘human' skills, such as Customer Service Workers, Sales and Marketing Professionals, Training and Development, People and Culture, and Organizational Development Specialists

as well as Innovation Managers. Moreover, our analysis finds extensive evidence of accelerating demand

for a variety of wholly new specialist roles related to understanding and leveraging the latest emerging technologies: AI and Machine Learning Specialists, Big Data Specialists, Process Automation Experts, Information Security Analysts, User Experience and Human-Machine Interaction Designers, Robotics Engineers, and Blockchain Specialists.

•             Growing skills instability: Given the wave of new technologies and trends disrupting business models and the changing division of labour between workers and machines transforming current job profiles, the vast majority of employers surveyed for this report expect that, by 2022, the skills required to perform most jobs will have shifted significantly. Global average skills stability—the proportion of core skills required to

perform a job that will remain the same—is expected to be about 58%, meaning an average shift of 42% in required workforce skills over the 2018–2022 period.

•             A reskilling imperative: By 2022, no less than 54% of all employees will require significant re- and upskilling. Of these, about 35% are expected to require additional training of up to six months, 9% will require reskilling lasting six to 12 months, while 10% will require additional skills training of more than a year. Skills continuing to grow in prominence by 2022 include analytical thinking and innovation as well as active learning and learning strategies. Sharply increasing importance of skills such as technology design and programming highlights the growing demand for various forms of technology competency identified

by employers surveyed for this report. Proficiency in new technologies is only one part of the 2022 skills equation, however, as ‘human’ skills such as creativity, originality and initiative, critical thinking, persuasion and negotiation will likewise retain or increase their value, as will attention to detail, resilience, flexibility and complex problem-solving. Emotional intelligence, leadership and social influence as well as service orientation also see an outsized increase in demand relative to their current prominence.

•             Current strategies for addressing skills gaps: Companies highlight three future strategies to manage the skills gaps widened by the adoption of new technologies. They expect to hire wholly new permanent staff already possessing skills relevant to new technologies; seek to automate the work tasks

concerned completely; and retrain existing employees. The likelihood of hiring new permanent staff with relevant skills is nearly twice the likelihood of strategic redundancies of staff lagging behind in new skills adoption. However, nearly a quarter of companies

are undecided or unlikely to pursue the retraining of existing employees, and two-thirds expect workers to adapt and pick up skills in the course of their changing jobs. Between one-half and two-thirds are likely to

turn to external contractors, temporary staff and freelancers to address their skills gaps.

•             Insufficient reskilling and upskilling: Employers indicate that they are set to prioritize and focus their re- and upskilling efforts on employees currently performing high-value roles as a way of strengthening their enterprise’s strategic capacity, with 54% and 53% of companies, respectively, stating they intend to target employees in key roles and in frontline roles which will be using relevant new technologies. In addition, 41% of employers are set to focus their reskilling provision on high-performing employees while a much smaller proportion of 33% stated that they would prioritize

at-risk employees in roles expected to be most

The Future of Jobs Report 2018

affected by technological disruption. In other words, those most in need of reskilling and upskilling are least likely to receive such training.

There are complex feedback loops between new technology, jobs and skills. New technologies can drive business growth, job creation and demand for specialist skills but they can also displace entire roles when certain tasks become obsolete or automated. Skills gaps—both among workers and among the leadership of organizations—can speed up the trends towards automation in some cases but can also pose barriers to the adoption of new technologies and therefore impede business growth.

The findings of this report suggest the need for a comprehensive ‘augmentation strategy’, an approach where businesses look to utilize the automation of some job tasks to complement and enhance their human workforces’ comparative strengths and ultimately to enable and empower employees to extend to their full potential.

Rather than narrowly focusing on automation-based labour cost savings, an augmentation strategy takes into account the broader horizon of value-creating activities that can be accomplished by human workers, often in complement to technology, when they are freed of the need to perform routinized, repetitive tasks and better able to use their distinctively human talents.

However, to unlock this positive vision, workers will need to have the appropriate skills enabling them to thrive in the workplace of the future and the ability to continue to retrain throughout their lives. Crafting a sound in-company lifelong learning system, investing in human capital and collaborating with other stakeholders on workforce strategy should thus be key business imperatives, critical to companies’ medium to long-term growth, as well as

an important contribution to society and social stability. A mindset of agile learning will also be needed on the part of workers as they shift from the routines and limits of today’s jobs to new, previously unimagined futures. Finally, policy-makers, regulators and educators will need to play a fundamental role in helping those who are displaced repurpose their skills or retrain to acquire new skills and to invest heavily in the development of new agile learners in future workforces by tackling improvements to education and training systems, as well as updating labour policy to match the realities of the Fourth Industrial Revolution.

Part 1

Preparing the Future Workforce

The Future of Jobs Report 2018

Introduction

A significant volume of research on the theme of the future of work has emerged since the World Economic Forum published its initial report on the subject—The Future

of Jobs: Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution1—at the Forum’s Annual Meeting in January 2016. What the future of work might hold is a concern that resonates broadly and that has fuelled extensive discussion among policy-makers, business leaders and individual workers.2 Over the past few years, academics, think tanks, strategy consultants and policy-makers have debated what the future of work might look like, how it can be productively shaped for the benefit of economies and societies, and the implications of changes to work for individuals, for their livelihoods, and for the youngest generations studying to enter the future workforce.3

Common to these recent debates is an awareness that, as technological breakthroughs rapidly shift the frontier between the work tasks performed by humans and those performed by machines and algorithms, global

labour markets are likely to undergo major transformations. These transformations, if managed wisely, could lead to a new age of good work, good jobs and improved quality of life for all, but if managed poorly, pose the risk of widening skills gaps, greater inequality and broader polarization. In many ways, the time to shape the future of work is now.

To support responses to the critical questions confronting businesses, governments and workers over the coming years, and to reassess its 2016 findings, the World Economic Forum has conducted a second iteration of the Future of Jobs Survey. While much valuable analysis has been authored over the past two years by a broad range of analysts and researchers, the debate has often focused on

the far-term horizon, looking to the future of work in 2030, 2040 or 2050. Those approaches can be complemented by an operational time horizon—with the potential to hold up a mirror to current practises, to provide an opportunity for leaders to re-asses their current direction and its likely outcomes, and to consider potential adjustments. As forecasts of the extent of structural change across global labour markets depend on taking into consideration the time horizon, this report—and future editions—aim to provide a (rolling) five-year outlook. This edition covers the 2018–2022 period.

A particular focus of this new edition of the report is to arrive at a better understanding of the potential of new technologies to create as well as disrupt jobs and to improve the quality and productivity of the existing

work of human employees. Our findings indicate that, by 2022, augmentation of existing jobs through technology may free up workers from the majority of data processing and information search tasks—and may also increasingly support them in high-value tasks such as reasoning and decision-making as augmentation becomes increasingly common over the coming years as a way to supplement and complement human labour. The changes heralded by the use of new technologies hold the potential to expand labour productivity across industries, and to shift the axis of competition between companies from a focus on automation-based labour cost reduction to an ability to leverage technologies as tools to complement and enhance human labour.

The data in this report represents the current

understanding of human resources leaders—primarily of large employers with operations in multiple geographic locations—of the factors informing their planning, hiring, training and investment decisions at present and through to the report’s 2022 time horizon. The findings described

Figure 1: Sample overview by number of locations and number of employees, 2018

1a: Number of locations              1b: Number of employees

Source: Future of Jobs Survey 2018, World Economic Forum.

throughout the report are not foregone conclusions but trends emerging from the collective actions and investment decisions taken or envisaged by companies today. The usefulness of this focused perspective lies precisely

in its operational concreteness, shedding light on the understanding and intentions of companies that are often setting the pace of global labour market change within their sectors and geographies as well as shaping demand for talent across global value chains and fast-growing online talent platforms.

Since the publication of the 2016 edition of the report, business leaders’ view of the human resources function has begun to shift decisively—continuing a broader rethinking that has been going on for some time. Talent management and workforce analytics are increasingly integral elements of companies’ future-readiness plans.

Yet relatively few organizations have so far formulated comprehensive workforce strategies for the Fourth Industrial Revolution. Therefore, this report also aims to serve as a call to action. Rapid adaptation to the new labour market is possible, provided there is concerted effort by all stakeholders. By evaluating the issues at hand from the perspective of some of the world’s largest

employers, we hope to improve current knowledge around anticipated skills requirements, recruitment patterns

and training needs. Furthermore, it is our hope that this knowledge can incentivize and enhance partnerships between governments, educators, training providers, workers and employers in order to better manage the transformative workforce impact of the Fourth Industrial Revolution.

Survey and research design

The Future of Jobs Report 2018, and the corresponding survey and research framework, represent an evolution of the approach taken in the report’s 2016 edition. The

original research framework was developed in collaboration with leading experts from the World Economic Forum’s Global Future Councils, including representatives from academia, international organizations, professional

service firms and the heads of human resources of major organizations. The 2018 edition reflects lessons learned from the design and execution of the original survey. The employer survey at the heart of this report was conducted in the first half of 2018 through the World Economic Forum’s global membership community—covering a comprehensive range of industries and geographies

(for details, see Appendix B: Industry and Regional Classifications)—and in close collaboration with a number of leading research institutes and industry associations worldwide.

The survey focused on gathering the views of business executives—principally Chief Human Resources Officers (CHROs) facing the workforce changes afoot in today’s enterprises. The questions asked can be briefly outlined in three parts: (1) questions aimed at mapping the transformations currently underway; (2) questions focused on documenting shifting work tasks and therefore skills requirements in the job roles performed by individuals

in the workplace of 2022; and (3) questions aimed at understanding the priorities and objectives companies have set themselves in terms of workforce training and reskilling and upskilling (Appendix  A:  Report  Methodology provides a detailed overview of the report’s survey design and research methodology).

The resulting data set represents the operational understanding of strategic human resources professionals, specifically those of large employers operating in multiple locations (Figures 1a and 1b). While only a minority of the world’s global workforce of more than three billion people is directly employed by large multinational employers, these companies often act as anchors for local firm ecosystems. Therefore, in addition to their own significant share of

Table 1: Employees represented by companies surveyed

employment, workforce-planning decisions by these                   firms have the potential to transform local labour markets

through indirect employment effects and spillovers, and by setting the pace for adoption of new technologies and changing skills and occupational requirements.

In total, the report’s data set contains 313 unique responses by global companies from a wide range of industry sectors, collectively representing more than 15 million employees (Table 1). In addition, the report’s regional analysis is based on a diversified sample with a focus on balanced representation of company-level responses for 20 developed and emerging economies—Argentina, Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Mexico, Philippines, Russian Federation, Singapore, South Africa, Korea, Rep., Switzerland, Thailand, United Kingdom,

United States and Vietnam—collectively representing about 70% of global GDP. Two sections in the latter part of the report are dedicated to industry- and country-level analysis: The Future of Jobs across Industries and The Future of Jobs across Regions. Appendix B: Industry and Regional Classifications provides an overview of categorizations used.

Structure of the report

This report consists of two parts. Part 1 explores the future of jobs, work tasks, skills and workforce strategies over the 2018 to 2022 period, as reflected

in the operational understanding of CHROs and others                                at the frontlines of workforce transformation in some

of the world’s largest employers. It touches first on expected trends, technological disruptions and strategic drivers of change transforming business models. It then explores a range of priority issues with regard to the development of comprehensive workforce strategies

for the Fourth Industrial Revolution, including employee reskilling and workforce augmentation. Next, it examines specific implications for a range of different industries and geographies. Part 1 concludes with a set of recommendations for upgrading and reviewing existing

talent and workforce strategies. Part 2 of the report

presents detailed industry-by-industry and country-by- country trends and provides a range of industry-specific and country-specific practical information to decision- makers and experts through dedicated Industry Profiles and Country Profiles. In addition, the reader may refer to the report’s methodological appendix for further

information on our survey design, sample selection criteria and research methodology.

Source: Future of Jobs Survey 2018, World Economic Forum.

Table 2: Trends set to impact business growth positively/negatively up to 2022, top ten

Trends set to positively impact business growth up to 2022       Trends set to negatively impact business growth up to 2022

Increasing adoption of new technology               Increasing protectionism

Increasing availability of big data             Increase of cyber threats

Advances in mobile internet      Shifts in government policy

Advances in artificial intelligence             Effects of climate change

Advances in cloud technology   Increasingly ageing societies

Shifts in national economic growth         Shifts in legislation on talent migration

Expansion of affluence in developing economies            Shifts in national economic growth

Expansion of education               Shifts of mindset among the new generation

Advances in new energy supplies and technologies       Shifts in global macroeconomic growth

Expansion of the middle classes              Advances in artificial intelligence

Source: Future of Jobs Survey 2018, World Economic Forum.

Strategic Drivers of New Business Models

As the Fourth Industrial Revolution unfolds, companies are seeking to harness new and emerging technologies to reach higher levels of efficiency of production and consumption, expand into new markets, and compete on new products for a global consumer base composed increasingly of digital natives. More and more, employers are therefore also seeking workers with new skills

from further afield to retain a competitive edge for their enterprises and expand their workforce productivity. Some workers are experiencing rapidly expanding opportunities in a variety of new and emerging job roles, while others are experiencing a rapidly declining outlook in a range of job roles traditionally considered ‘safe bets’ and gateways to a lifetime career.

Even as technological advancements pose challenges to existing business models and practices, over the coming years, these same dynamics of technological change are set to become the primary drivers of opportunities for new growth. For example, based on one recent estimate, even a somewhat moderately paced rollout of new automation technologies over the next 10 to 20 years would lead to an investment surge of up to US$8 trillion in the United States alone.4

According to the global employers surveyed for this report, four specific technological advances—ubiquitous high-speed mobile internet; artificial intelligence; widespread adoption of big data analytics; and cloud technology—are set to dominate the 2018–2022 period as drivers positively affecting business growth (Table 2). They are flanked by a range of socio-economic trends driving business opportunities in tandem with the spread of new technologies, such as national economic growth trajectories; expansion of education and the middle classes, in particular in developing economies; and the

move towards a greener global economy through advances in new energy technologies. By contrast, technological

and social trends expected to negatively impact business

growth include increasing protectionism; cyber threats; shifts in government policies; the effects of climate change; and increasingly ageing societies.

By 2022, according to the stated investment intentions of companies surveyed for this report, 85% of respondents are likely or very likely to have expanded their adoption

of user and entity big data analytics (Figure 2). Similarly, large proportions of companies are likely or very likely to have expanded their adoption of technologies such as the internet of things and app- and web-enabled markets, and to make extensive use of cloud computing. Machine learning and augmented and virtual reality are poised to likewise receive considerable business investment. While estimated use cases for humanoid robots, a fixture of the current media discourse on the future of jobs, appear

to remain somewhat more limited over the 2018–2022 period under consideration in this report,5 collectively, a broader range of recent robotics technologies at or near commercialization—including stationary robots, non- humanoid land robots and fully automated aerial drones, in addition to machine learning algorithms and artificial intelligence—are attracting significant business interest in adoption.6

There are complex feedback loops between new technology, jobs and skills. New technologies can drive business growth, job creation and demand for specialist skills but they can also displace entire roles when certain tasks become obsolete or automated. Skills gaps—both among workers and among the leadership of organizations—can speed up the trends towards automation in some cases but can also pose barriers to the adoption of new technologies and therefore impede business growth.

Opportunities for new and emerging technologies to drive inclusive economic and business growth over the 2018–2022 period are manifold, yet concrete and viable mechanisms for preparing the global labour market— thereby enabling employers to better leverage these opportunities across industries and regions—remain

Figure 2: Technologies by proportion of companies likely to adopt them by 2022 (projected)

User and entity big data analytics            85%

App- and web-enabled markets              75%

Internet of things           75%

Machine learning            73%

Cloud computing             72%

Digital trade       59%

Augmented and virtual reality  58%

Encryption          54%

New materials  52%

Wearable electronics    46%

Distributed ledger (blockchain) 45%

3D printing         41%

Autonomous transport 40%

Stationary robots            37%

Quantum computing     36%

Non-humanoid land robots        33%

Biotechnology  28%

Humanoid robots            23%

Aerial and underwater robots   19%

Source: Future of Jobs Survey 2018, World Economic Forum.

elusive. A mindset of agile learning on the part of both company leaders and workers will be needed, starting with an ability to reimagine the routines and limits of today’s jobs as part of a comprehensive workforce strategy for the Fourth Industrial Revolution.

Workforce Trends and Strategies for the Fourth Industrial Revolution

In order to harness the transformative potential of the Fourth Industrial Revolution, business leaders across all industries and regions will increasingly be called upon to formulate a comprehensive workforce strategy ready to meet the challenges of this new era of accelerating

change and innovation. Policy-makers, educators, labour unions and individual workers likewise have much to gain from deeper understanding of the new labour market and proactive preparation for the changes underway.

Key factors to consider include mapping the scale of occupational change underway and documenting emerging and declining job types; highlighting

opportunities to use new technologies to augment human work and upgrade job quality; tracking the evolution of

job-relevant skills; and, finally, documenting the business case for investment in retraining, upskilling and workforce transformation. The following three sub-sections of the report aim to provide informative data and evidence to support such an endeavour.

The 2022 jobs landscape

As discussed in the report’s Introduction, recent projections of the extent of structural change in the global labour market depend significantly on the time horizon taken into consideration.7 In addition to the rate of technological advancement itself, a range of other

considerations—such as ease of commercialization, public adoption of new technologies8 and existing labour laws— influence the rate at which these developments accelerate workforce transformation.

In the estimates of employers surveyed for this report, global labour markets are set to undergo significant transformation over the coming five years.

A cluster of emerging roles will gain significantly in importance over the coming years, while another cluster of job profiles are set to become increasingly redundant (Figure 3). Across all industries, by 2022, the cluster

Figure 3: Share of stable, new and redundant roles, 2018 vs. 2022 (projected)

Source: Future of Jobs Survey 2018, World Economic Forum.

of emerging professions is set to increase its share of employment from 16% to 27% of the total employee base of our company respondents, whereas the employment share of declining roles is set to decrease from currently 31% to 21% (Figure 3). In purely quantitative terms, therefore, the expectation emerging from the estimates

of employers surveyed for this report is that, by 2022, structural decline of certain types of jobs (10% decline) will be fully counter-balanced by job creation and the emergence of new professions (11% growth).About half of today’s core jobs—making up the bulk of employment across industries—will remain somewhat stable in the period up to 2022.

Applied to our sample, representing over 15 million workers in total, the above numbers would suggest a decline of 0.98 million jobs and a gain of 1.74 million jobs. Extrapolating from these trends for the global

(non-agricultural) workforce employed by large firms, we generate a range of estimates for job churn in the period up to 2022. One of these indicates that 75 million jobs may be displaced by the above trends, while 133 million additional new roles may emerge concurrently.9

It should be noted, however, that these projections primarily represent the share of roles within the remit

of large multinational employers. A complementary perspective might emerge from analysis that focuses on small- and medium-sized enterprises, or more fully takes into account employment sectors such as health, care and education. In particular such segments of economic activity hold the promise for further job creation opportunities.

As they stand today responses to the Future of Jobs Survey indicate the potential for a positive outlook for the future of jobs. Yet that outlook is underscored by the need to manage a series of workforce shifts, set to accompany the adoption of new technologies. By 2022, 59% of employers surveyed for this report expect that they will have significantly modified the composition of their value chain, and nearly half expect to have modified

their geographical base of operations. In addition, 50% of companies expect that automation will lead to some reduction in their full-time workforce, based on the job profiles of their employee base today.

Also by 2022, 38% of businesses surveyed expect to extend their workforce to new productivity-enhancing roles, and more than a quarter expect automation to lead to the creation of new roles in their enterprise. In addition,

businesses are set to expand their use of contractors doing task-specialized work, with many respondents highlighting their intention to engage workers in a more flexible

manner, utilizing remote staffing beyond physical offices and decentralization of operations. Respondents expect increased job creation in such project-based, temporary and freelancing roles, pointing to structural labour market transformations in terms of contractual arrangements and employment relations as well as occupational profiles. In summary, while overall job losses are predicted to be offset by job gains, there will be a significant shift in the quality, location, format and permanency of new roles.

Among the range of roles that are set to experience increasing demand in the period up to 2022 are established roles such as Data Analysts and Scientists, Software and Applications Developers, and Ecommerce and Social Media Specialists that are significantly based on and enhanced by the use of technology. Also expected to grow are roles that leverage distinctively ‘human’ skills such as Customer Service Workers, Sales and Marketing Professionals, Training and Development, People and Culture, and Organizational Development Specialists as well as Innovation Managers. Moreover, our analysis finds extensive evidence of accelerating demand for a variety

of wholly new specialist roles related to understanding and leveraging the latest emerging technologies: AI and Machine Learning Specialists, Big Data Specialists, Process Automation Experts, Information Security

Analysts, User Experience and Human-Machine Interaction

Table 3: Examples of stable, new and redundant roles, all industries

Stable Roles       New Roles          Redundant Roles

Managing Directors and Chief Executives General and Operations Managers* Software and Applications Developers and

Analysts*

Data Analysts and Scientists* Sales and Marketing Professionals*

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

Human Resources Specialists Financial and Investment Advisers Database and Network Professionals Supply Chain and Logistics Specialists Risk Management Specialists Information Security Analysts*

Management and Organization Analysts Electrotechnology Engineers Organizational Development Specialists* Chemical Processing Plant Operators University and Higher Education Teachers Compliance Officers

Energy and Petroleum Engineers Robotics Specialists and Engineers Petroleum and Natural Gas Refining Plant

Operators           Data Analysts and Scientists*

AI and Machine Learning Specialists General and Operations Managers* Big Data Specialists

Digital Transformation Specialists Sales and Marketing Professionals* New Technology Specialists

Organizational Development Specialists* Software and Applications Developers and

Analysts*

Information Technology Services Process Automation Specialists Innovation Professionals Information Security Analysts*

Ecommerce and Social Media Specialists User Experience and Human-Machine

Interaction Designers

Training and Development Specialists Robotics Specialists and Engineers People and Culture Specialists

Client Information and Customer Service Workers*

Service and Solutions Designers

Digital Marketing and Strategy Specialists           Data Entry Clerks

Accounting, Bookkeeping and Payroll Clerks Administrative and Executive Secretaries Assembly and Factory Workers

Client Information and Customer Service Workers* Business Services and Administration Managers Accountants and Auditors

Material-Recording and Stock-Keeping Clerks General and Operations Managers*

Postal Service Clerks Financial Analysts Cashiers and Ticket Clerks

Mechanics and Machinery Repairers Telemarketers

Electronics and Telecommunications Installers and Repairers

Bank Tellers and Related Clerks Car, Van and Motorcycle Drivers

Sales and Purchasing Agents and Brokers Door-To-Door Sales Workers, News and Street

Vendors, and Related Workers Statistical, Finance and Insurance Clerks Lawyers

Source: Future of Jobs Survey 2018, World Economic Forum.

Note: Roles marked with * appear across multiple columns. This reflects the fact that they might be seeing stable or declining demand across one industry but be in demand in another.

Designers, Robotics Engineers and Blockchain Specialists (Table 3).

Across the industries surveyed, jobs expected to become increasingly redundant over the 2018–2022 period are routine-based, middle-skilled white-collar roles—such as Data Entry Clerks, Accounting and Payroll Clerks, Secretaries, Auditors, Bank Tellers and Cashiers (Table 3)— that are susceptible to advances in new technologies

and process automation. These shifts reflect unfolding and accelerating trends that have evolved over a number of recent years—continuing developments that have impacted roles in retail banking (ATMs), consumer sales (self-checkout kiosks) and other sectors.10 Given that the skills requirements of emerging roles frequently look very different from those of roles experiencing redundancy, proactive, strategic and targeted efforts will be needed to map and incentivize workforce redeployment.

Industries are set to take diverse routes in the adoption of new technologies, and the distinctive nature of the work performed within each sector will result in disruption to jobs and skills that will demand industry-specific adaptation.

For example, given comparatively high levels of education in the financial services industry, displaced roles may be somewhat more easily offset by redeploying workers in alternative, higher value-added functions. In contrast, the two largest job roles in the consumer industry, Cashiers and Sales Associates—accounting for no less than 45%

of total industry employment—have a comparatively small share of workers with advanced education.11 Cross- industry analysis of the roles experiencing falling and rising demand suggests the possibility of leveraging those industry-specific differences for the benefit of displaced workers, by expanding the search for new opportunities across the industry landscape.

While the labour market shifts described in this section are not foregone conclusions, they are reasonable forecasts emerging from the actions and investment decisions taken by companies in response to global trends today. As new technology adoption builds momentum, companies feel competitive pressures similar to the way they felt compelled to create global supply chains in the 1990s and 2000s.12 These trends affecting business leaders’ decision environments are prompting a wide range of company responses that collectively shape the future nature of jobs (Figure 4).

While individual companies may not have the option to disconnect their corporate strategy from the

fundamental trajectory of these wider trends, such as the unfolding Fourth Industrial Revolution, they do, however, have the possibility of formulating a proactive response.

Two investment decisions, in particular, will be crucial to shaping the future of jobs: the question of whether to

prioritize automation or augmentation and the question of whether or not to invest in workforce reskilling.

Figure 4: Projected (2022) effects on the workforce of current growth strategy, by proportion of companies

Modified composition of value chain     59%      

                                                                                             

Reduced current workforce due to automation               50%                                                                     

                                                                                             

Modified the locations of operation      48%                                                                     

                                                                                             

Expanded use of contractors doing task-specialized work           48%                                                                     

                                                                                             

Expanded current workforce    38%                                      

                                                              

Brought new financing on-board to manage transition 36%                                      

                                                              

Expanded current workforce due to automation            28%                                      

Source: Future of Jobs Survey 2018, World Economic Forum.                                                  

These two crucial dimensions are examined further in the following two sub-sections.

From automation to augmentation

Some forecasts project that advances in automation will result in the wholesale replacement of the human workforce. Encompassing the near- or medium-term timeframes, our analysis suggests another perspective: that work currently performed by humans is being

augmented by machine and algorithmic labour. Responses from employers surveyed for this report can be interpreted as evidence for the increasing viability of what a number

of experts have called an ‘augmentation strategy’. Namely, it has been suggested that businesses can look to utilize the automation of some job tasks to complement and enhance the human workforces’ comparative strengths and ultimately to enable and empower employees to extend to their full potential and competitive advantage.13 Rather than narrowly focusing on automation-based labour cost savings, an augmentation strategy takes into account the broader horizon of value creating activities that can be accomplished by human workers, often in complement to technology, when they are freed of the need to perform routinized, repetitive tasks and better able to use their distinctively human talents.14

Importantly, most automation occurs at the level of specific work tasks, not at the level of whole jobs.15 For example, according to one recent study, whereas

nearly two-thirds of today’s job roles entail at least 30% of tasks that could be automated based on currently available technology, only about one-quarter of today’s job roles can be said to have more than 70% of tasks that are automatable.16 A similar recent analysis finds that workforce automation is likely to play out in three waves

between today and the mid-2030s, increasing the share of fully automatable manual tasks in the most affected current job roles from less than 5% today to nearly 40% by the mid-2030s, and the share of automatable tasks

involving social skills from less than 5% today to about 15% in the same time horizon.17 The most relevant question to businesses, governments and individuals is not to what extent automation will affect current employment numbers, but how and under what conditions the global labour market can be supported in reaching a new equilibrium in the division of labour between human workers, robots and algorithms. Workforce planning and investment decisions taken today will play a crucial role in shaping this process.

Waves of automation have reshaped the global economy throughout history. Since the first and second industrial revolutions, organizations have bundled specific work tasks into discrete job roles, giving rise to distinct occupational profiles and optimizing the process of economic value creation based on the most efficient division of labour between humans and machines technologically available at the time.18 As technological change and progress have increased workforce productivity by ‘re-bundling’ work tasks into new kinds of jobs, so they have seen the decline of obsolete job profiles and the dynamic rise of wholly new ones, historically leaving the balance of net job and economic value creation firmly on the positive side.19

While the Fourth Industrial Revolution’s wave of technological advancement will reduce the number of workers required to perform certain work tasks, responses by the employers surveyed for this report indicate that

it will create increased demand for the performance of others, leading to new job creation. Moreover, while the

Figure 5: Ratio of human-machine working hours, 2018 vs. 2022 (projected)

Human                                                                                               Machine              Human                                                                Machine

Reasoning and decision-making                                                                                                             19%                                                                                     28%

                                                                                                                                                                                                           

Coordinating, developing, managing and advising                                                                                                         19%                                                                                      29%

                                                                                                                                                                                                           

Communicating and interacting                                                                                                             23%                                                                                     31%

                                                                                                                                                                                                           

Administering                                                                                                 28%                                                                                      44%

                                                                                                                                                                                                           

Performing physical and manual work activities                                                                                                             31%                                                                                      44%

                                                                                                                                                                                                           

Identifying and evaluating job-relevant information                                                                                                    29%                                                                                     

46%

                                                                                                                                                                                                           

Performing complex and technical activities                                                                                                     34%                                                                                      46%

                                                                                                                                                                                                           

Looking for and receiving job-related information                                                                                                        36%                                                                                      55%

                                                                                                                                                                                                           

Information and data processing                                                                                                           47%                                                                                     62%

Source: Future of Jobs Survey 2018, World Economic Forum.    2018                                                                                                                                   2022                                     

current popular discourse is often fixated on technology that substitutes for humans, technology will also create new tasks—from app development to piloting drones

to remotely monitoring patient health20—opening up opportunities for work never previously done by human workers,21 highlighting that different types of new technology may bring about very different outcomes for workers.22

The rise of workplace automation in its many forms has the potential to vastly improve productivity and augment the work of human employees. Automation technology can help remove the burden of repetitive administrative work and enable employees to focus

on solving more complex issues while reducing the risk of error, allowing them to focus on value-added tasks.23 Examples of now well-established and almost

unremarkable automation-based augmentation technology that hardly existed 25 years ago range from computer- aided design and modelling software used by architects, engineers and designers, to robotic medical tools used

by doctors and surgeons, through to search engine technology that allows researchers to find more relevant information. In theory, these technologies take away tasks from workers, but in practice their overall effect is to vastly amplify and augment their abilities.24

The estimates of companies surveyed for this report provide a nuanced view of how human-machine

collaboration might evolve in the time horizon up to 2022 (Figure 5). In today’s enterprise, machines and algorithms most often complement human skills in information and data processing. They also support the performance of

complex and technical tasks, as well as supplementing more physical and manual work activities. However, some work tasks have thus far remained overwhelmingly human: Communicating and interacting; Coordinating, developing, managing and advising; as well as Reasoning and decision-making. Notably, in terms of total working hours, in the aggregate no work task was yet estimated to be predominantly performed by a machine or an algorithm.

By 2022, this picture is projected to change somewhat. Employers surveyed for this report expect a deepening across the board of these existing trends,

with machines and algorithms on average increasing their contribution to specific tasks by 57%. Relative to their starting point today, the expansion of machines’ share of work task performance is particularly marked in Reasoning and decision-making; Administering; and Looking for

and receiving job-related information. The majority of an organization’s information and data processing

and information search and transmission tasks will be performed by automation technology (Figure 5).

Based on one recent estimate, the next wave of labour-augmenting automation technology could lead to an average labour productivity increase across sectors of about 30% compared to 2015, with some significant

variation by industry.25 For employers, optimally integrating humans and automation technology will require an analytical ability to deconstruct the work performed in their organizations today into discrete elements—that is, seeing the work tasks of today’s job roles as independent and fungible components—and then reconfiguring these components to reveal human-machine collaboration

Table 4: Comparing skills demand, 2018 vs. 2022, top ten

Today, 2018       Trending, 2022  Declining, 2022

Analytical thinking and innovation Complex problem-solving

Critical thinking and analysis

Active learning and learning strategies Creativity, originality and initiative Attention to detail, trustworthiness Emotional intelligence

Reasoning, problem-solving and ideation Leadership and social influence

Coordination and time management     Analytical thinking and innovation Active learning and learning strategies Creativity, originality and initiative Technology design and programming Critical thinking and analysis

Complex problem-solving Leadership and social influence Emotional intelligence

Reasoning, problem-solving and ideation

Systems analysis and evaluation              Manual dexterity, endurance and precision Memory, verbal, auditory and spatial abilities Management of financial, material resources Technology installation and maintenance Reading, writing, math and active listening Management of personnel

Quality control and safety awareness Coordination and time management Visual, auditory and speech abilities

Technology use, monitoring and control

Source: Future of Jobs Survey 2018, World Economic Forum.

opportunities that are more efficient, effective and impactful.26  Among other things, success in this domain will require a strategic repositioning of the corporate human resource function and expanded organizational capabilities in data analysis and workforce analytics.27

For workers, improved productivity may allow them to re-focus their work on high-value activities that play to the distinctive strengths of being human. However, to unlock this positive vision, workers will need to have the appropriate skills that will enable them to thrive in the

workplace of the future. And as discussed in detail in the next section, even for those who currently have these skills, the pace at which tasks are being augmented and skills are changing continues to accelerate.

The reskilling imperative

Current shifts underway in the workforce will displace some workers while at the same time create new opportunities for others. However, maximizing the gains and minimizing the losses requires attention not just from policy-makers, but also coherent responses from companies to find win-win solutions for workers and for their bottom line. Leading research documents the potentially divergent impact of the introduction of automation technology, demonstrating how both job

design (how tasks are organized into jobs) and employee’s possession (or lack thereof) of skills complementing newly introduced technologies contribute to eventual outcomes for companies and workers.28 Workers with in-demand skills ready for augmentation may see their wages and

job quality increase considerably. Conversely, even if automation only affects a subset of the tasks within their job role, workers lacking appropriate skills to adapt to new technologies and move on to higher value tasks may see their wages and job quality suppressed by technology steadily eroding the value of their job, as it encroaches on the tasks required to perform it.29 Therefore, central to the success of any workforce augmentation strategy is the buy-in of a motivated and agile workforce, equipped with futureproof skills to take advantage of new opportunities through continuous retraining and upskilling.30 Given the wave of new technologies and trends disrupting business

models and the changing division of labour between workers and machines transforming current job profiles, the vast majority of employers surveyed for this report expect that, by 2022, the skills required to perform most jobs will have shifted significantly. While these skill shifts are likely to play out differently across different industries and regions,31 globally, our respondents expect average skills stability—the proportion of core skills required to perform a job that will remain the same—to be about 58%, meaning an average shift of 42% in required workforce skills over the 2018–2022 period.32

Key skills demand trends identified by our analysis include, on the one hand, a continued fall in demand for manual skills and physical abilities and, on the other hand, a decrease in demand for skills related to the management of financial and other resources as well as basic technology installation and maintenance skills (Table 4). Skills continuing to grow in prominence by 2022 include Analytical thinking and innovation as well as Active learning and learning strategies. The sharply

increased importance of skills such as Technology design and programming highlights the growing demand for various forms of technology competency identified by employers surveyed for this report. Proficiency in new technologies is only one part of the 2022 skills equation, however, as ‘human’ skills such as creativity, originality and initiative, critical thinking, persuasion, and negotiation will likewise retain or increase their value, as will attention to detail, resilience, flexibility and complex problem-solving.

Emotional intelligence, leadership and social influence as well as service orientation also see an outsized increase in demand relative to their current prominence.

Companies will need to pursue a range of organizational strategies in order to stay competitive in the face of rapidly changing workforce skills requirements. To do this, the skills of executive leadership and the human resources function will also need to evolve to successfully lead the transformation. With regard to likely approaches towards workers facing shifting skills demand, companies surveyed for this report specifically highlight three future strategies: hiring wholly new permanent staff already

Figure 6: Projected (2022) strategies to address shifting skills needs, by proportion of companies (%)

Hire new permanent staff with skills relevant to new technologies

Look to automate the work

Retrain existing employees

Expect existing employees to pick up skills on the job

Outsource some business functions to external contractors

Hire new temporary staff with skills relevant to new technologies

Hire freelancers with skills relevant to new technologies

Strategic redundancies of staff who lack the skills to use new technologies

n Likely n Equally likely  n Unlikely

Source: Future of Jobs Survey 2018, World Economic Forum.

Note: The bars represent the proportion of responses by companies that stated that specific strategies were likely, equally likely or unlikely. Some companies abstained from answering the question. In such cases part of the bar remains blank (typically, 0–1% in the graph above).

possessing skills relevant to new technologies; seeking to completely automate the work tasks concerned; and retraining existing employees (Figure 6). The likelihood of hiring new permanent staff with relevant skills is nearly twice the likelihood of strategic redundancies of staff lagging behind in new skills adoption. However nearly one-quarter of companies are undecided or unlikely to pursue the retraining of existing employees. Two-thirds

expect workers to adapt and pick up skills in the course of their changing jobs. Between one-half and two-thirds are likely to turn to external contractors, temporary staff and freelancers to address their skills gaps.

Employers surveyed for this report estimate that, by 2022, no less than 54% of all employees will require significant reskilling and upskilling (Figure 7). Of these, about 35% are expected to require additional training of

up to six months, 9% will require reskilling lasting six to 12 months, while 10% will require additional skills training of more than a year.

Respondents to our survey further indicate that

expected to be most affected by technological disruption. In other words, those most in need of reskilling and upskilling are least likely to receive such training.

Our findings corroborate a range of recent research indicating that, currently, only about 30% of employees in today’s job roles with the highest probability of technological disruption have received any kind of

professional training over the past 12 months. In addition, they are on average more than three times less likely than

Figure 7: Expected average reskilling needs across companies, by share of employees, 2018–2022

Reskilling needs

of less than 1 month, 13%

Reskilling needs

of 1–3 months, 12%

they are set to prioritize and focus their reskilling and upskilling efforts on employees currently performing high value roles as a way of strengthening their enterprise’s strategic capacity, with 54% and 53% of companies, respectively, stating they intend to target employees in key roles and in frontline roles which will be using relevant new technologies. In addition, 41% of employers are

set to focus their reskilling provision on high-performing employees while a much smaller proportion of 33%

No reskilling needed, 46%

Reskilling needs

Reskilling needs of over 1 year, 10%

Reskilling needs

of 3–6 months, 10%

Reskilling needs

of 6–12 months, 9%

stated that they would prioritize at-risk employees in roles

Source: Future of Jobs Survey 2018, World Economic Forum.

Figure 8: Preferred partners in managing the integration of new technologies and workforce transition

Specialized departments in my firm                      85%      

Professional services firms                        75%      

Industry associations                    66%      

Academic experts                          63%      

International educational institutions                   52%      

Local educational institutions                   50%      

Government programs                47%      

Labour unions                  23%      

Source: Future of Jobs Survey 2018, World Economic Forum.

employees in less exposed roles to have participated in any on-the-job training or distance learning and about twice less likely to have participated in any formal education.33 Other recent research similarly finds that, currently, reskilling and upskilling efforts are largely focused on already highly-skilled and highly-valued employees.34

These findings are a cause for concern, given that making an inclusive culture of lifelong learning a reality is increasingly imperative for organizations and for workers whose growth strategies and job roles are being affected by technological change. In particular, they highlight that the bottom-line impact and business case for reskilling and upskilling investments remain somewhat unclear and require much greater attention. Time requirements,

costs, success cases and appropriate delivery models for reskilling and upskilling are likely to look very different for different categories of job roles and workers.

To provide a preliminary picture, companies surveyed for this report highlight that, overwhelmingly, their key success metric for reskilling and upskilling initiatives is increased workforce productivity—chosen by 90% of respondent employers—followed by retention of high- skilled workers, enabling workers in frontline roles to make the best use of new technologies and increased employee satisfaction. Significantly smaller proportions of companies regard reskilling as a means of lowering recruitment costs, redeploying employees in disrupted job roles or as a way to increase the skills base of their medium- and lower-skilled workforce. In short, to date reskilling has been regarded

by employers as a narrow strategy focused on specific subsets of employees, not as a comprehensive strategy to drive workforce transformation.

Finally, while companies themselves will need to take the lead in creating capacity within their organizations

to support their transition towards the workforce of the future, the economic and societal nature of these

challenges means that they will also increasingly need to learn to partner with other stakeholders for managing the large-scale retraining and upskilling challenges ahead.

Tangible collaboration opportunities include partnering with educators to reshape school and college curricula, intra- and inter-industry collaboration on building talent pipelines, and partnerships with labour unions to enhance cross-industry talent mobility. Governments may likewise become key partners in creating incentives for lifelong learning, ensuring shared standards for retraining and strengthening safeguards for workers in transition.35 However, more guidance and good practice learning opportunities will be needed. Currently, respondents to our survey expect to continue to primarily look to specialized internal departments to meet their retraining needs for

the period up to 2022, with some supplementary support from professional services firms, industry associations and academic experts (Figure 8). Less than half of companies actively consider partnering with government programmes and slightly more than a fifth see labour unions as preferred partners.

Companies surveyed for this report anticipate that, over the 2018–2022 period, on average, around half of all retraining will be delivered through internal departments, about one quarter through private training providers and about one-fifth through public education institutions. About 34% of the retraining to be delivered directly by employers is expected to result in an accreditation recognized outside of the company in question. Expanding such systems for certifiable skills recognition could significantly promote

the marketplace for corporate reskilling and upskilling in the near future and improve outcomes for workers. These findings highlight both the future role of companies as learning organizations and the range of possible reskilling and upskilling multistakeholder collaboration arrangements.

The Future of Jobs Across Industries

The future of jobs is not singular. It will diverge by industry and sector, influenced by initial starting conditions around the distribution of tasks, different investments in technology adoption, and the skills availability and adaptability of the

workforce. As a consequence, different industries experience variation in the composition of emerging roles and in the nature of roles that are set to have declining demand.

Among the trends driving growth across industries over the 2018–2022 period, advances in mobile internet are likely to have a distinct impact in the Aviation, Travel & Tourism industry, the Financial Services & Investors industries, and in the Consumer industry. The rapid adoption of new technologies by consumers as well as

advancements in cloud technology are set to drive growth in the Information & Communication Technologies industry, while the availability of big data is expected to have an even broader impact on the Financial Service & Investors and the Energy Utilities & Technologies industries. New energy supplies and technologies, in tandem with advances in computing power, are set to drive gains in the Energy Utilities & Technologies sector. Among non-technological drivers of business growth, increasing affluence in developing economies is poised to drive growth in the Aviation, Travel &Tourism; Global Health & Healthcare; and Chemistry, Advanced Materials & Biotechnology industries.

Table 5 on page 16 demonstrates the range of demand for the adoption of specific technologies. Robotic technology is set to be adopted by 37% to 23% of the companies surveyed for this report, depending on industry. Companies across all sectors are most likely to adopt the use of stationary robots, in contrast to humanoid, aerial

or underwater robots. However, leaders in the Oil & Gas industry report the same level of demand for stationary and aerial and underwater robots, while employers in the Financial Services & Investors industry are most likely to signal the planned adoption of humanoid robots in the

period up to 2022. Distributed ledger technologies are set to have a particular impact in the Financial Services industry, which promises to be an early adopter of the technology. In fact, 73% of respondents expect their enterprise to adopt its use. Another industry set to scale its adoption of distributed ledger technologies will be the Global Health & Healthcare industry. Machine learning is expected to be adopted across a range of industries, including banking and insurance, where it may disrupt risk prediction; in the medical field, where it may be used for advanced diagnosis; across the energy sector, where it may lead to predictive maintenance; and in the consumer sector, where it may enhance the industry’s ability to model demand.

While technologies have the capacity to automate and potentially augment a variety of tasks across enterprises, this will vary by industry-specific capital investment, the risks associated with automating sensitive tasks, the unknown knock-on-effects of how machines and algorithms will perform the task, the presence of

a longer-term workforce strategy, and the managerial challenges of re-orienting the operations of different enterprises. Additionally, many sectors face disruption and shifts in demand through non-technological factors, such as the effect of ageing in the Global Health & Healthcare industry. Efficiencies in healthcare technologies will thus become necessary innovations to meet the demographic changes afoot, freeing time spent in administration and record keeping for caregiving activities.36

The growth potential of new technological expansion is buffered by multi-dimensional skills gaps across local and global labour markets, and among the leadership

of enterprises. Skills gaps among the local labour market are among the most cited barriers to appropriate technology adoption for a number of industries, but they

are particularly strong concerns for business leaders in the Aviation Travel & Tourism, Information & Communication Technologies, Financial Services & Investors, and Mining

& Metals industries. Companies in Global Health & Healthcare as well as Infrastructure industries are most likely to cite leadership skills gaps as significant barriers, while the Chemistry, Advanced Materials & Biotechnology and Information & Communication Technologies sectors report broad global labour market skills shortages.

There is a distinctive footprint of tasks performed across each industry. For example, on average, workers in the Mining & Metals industry spend the majority of their time in physical and manual tasks, while those in the Professional Services industry spend the majority of their time on tasks related to communicating and interacting. In the Oil & Gas, Infrastructure, and Chemistry, Advanced Materials & Biotechnology industries, the tasks that occupy today’s workers for the largest proportion of their time focus on the performance of complex and technical

activities. Administrative activities are particularly prominent in the Infrastructure industry as well in the Mining & Metals and Financial Services & Investors industries.

As industries make investments in new technologies, the impact on each industry as a whole is determined by the task composition of each sector and the desirability of automating or augmenting specific tasks. Existing research has highlighted that some industries remain labour-intensive in the production of goods and services, leading to low productivity growth.37 If managed well,

the augmentation of a range of tasks today can create the opportunity for new, higher productivity growth. For example, administering and physical tasks are often low

value and low productivity tasks. In the current projections of companies surveyed for this report, administrative tasks in the Financial Services & Investors sector are set to

be significantly replaced by machine labour. While today machines and algorithms perform 36% of the collective hours spent on this task, by 2022 this share will rise

to 61%, with knock-on effects on the demand for Data Entry Clerks, Secretarial staff and Accounting staff. In the Energy and Consumer sectors, physical and manual

Table 5: Technology adoption by industry and share of companies surveyed, 2018–2022 (%)

               

Overall Automotive, Aerospace, Supply Chain & Transport       

Aviation, Travel & Tourism          Chemistry, Advanced Materials & Biotechnology           

Consumer         

Energy Utilities & Technologies

Financial Services & Investors  

Global Health & Healthcare        Information & Communication Technologies    

Infrastructure  

Mining & Metals             

Oil & Gas            

Professional Services

User and entity big data analytics            85           84           89           79           85           85           86           87           93           65                62           87           85

App- and web-enabled markets              75           76           95           71           88           65           89           80           93           53                50           61           74

Internet of things           75           82           95           58           73           85           65           67           86           76           50           83                74

Machine learning            73           87           79           58           82           77           73           80           91           53           69           70                74

Cloud computing             72           76           79           67           67           73           65           73           91           71           62           78                76

Digital trade       59           68           68           62           82           58           70           53           70           47           50           57           59

Augmented and virtual reality  58           71           68           50           48           65           59           67           72           59           62                65           53

Encryption          54           58           53           25           42           38           73           67           67           41           25           57           53

New materials  52           71           32           79           79           65           22           60           30           82           62           83           41

Wearable electronics    46           61           53           46           45           42           49           73           49           24           25           70                35

Distributed ledger (blockchain) 45           32           37           29           39           54           73           67           67           18           38                48           50

3D printing         41           61           21           58           42           54           19           53           35           41           50           57           29

Autonomous transport 40           74           58           54           39           46           16           20           44           41           50           30                41

Stationary robots            37           53           37           50           42           35           27           47           35           35           38           52                29

Quantum computing     36           29           32           25           33           46           43           33           44           24           19           43                41

Non-humanoid land robots        33           42           26           21           36           27           32           40           37           29           25                30           24

Biotechnology  28           18           0             42           52           42           11           87           23           12           44           39           24

Humanoid robots            23           29           26           17           18           8             35           13           33           12           25           13                24

Aerial and underwater robots   19           18           16           17           12           35           5             0             19           29           25                52           21

Source: Future of Jobs Survey 2018, World Economic Forum.

Table 6: Projected (2022) effects on the workforce by industry and proportion of companies (%)

               

Overall Automotive, Aerospace, Supply Chain & Transport       

Aviation, Travel & Tourism          Chemistry, Advanced Materials & Biotechnology           

Consumer         

Energy Utilities & Technologies

Financial Services & Investors  

Global Health & Healthcare        Information & Communication Technologies    

Infrastructure  

Mining & Metals             

Oil & Gas            

Professional Services

Modify value chain         59           82           44           71           83           78           56           67           55           78           44           87                60

Reduce workforce due to automation  50           48           50           38           57           56           56           47           55           33                72           52           37

Expand task-specialized contractors      48           52           50           42           51           52           44           33           57           56                56           52           51

Modify locations of operation   48           42           50           58           54           52           67           73           55           28           44                57           54

Expand the workforce  38           50           39           38           34           19           31           27           41           28           22           35                71

Bring financing on-board for transition 36           38           33           29           40           37           31           20           34           56                22           30           37

Expand workforce due to automation  28           20           50           29           23           19           25           20           52           22                33           26           57

Source: Future of Jobs Survey 2018, World Economic Forum.

work activities will also be replaced. Today, respectively 38% and 30% of such tasks in these two sectors are performed by machines and algorithms. By 2022, those rates are expected to be 56% and 50% respectively, with knock-on effects on demand for Assembly and Factory Workers, Cashiers, and Stock-Keeping Clerks.

Distinctively, the Aviation Travel & Tourism and Information & Communication Technologies sectors are those most likely to venture into automating some complex and technical activities. For example, today 25% of labour in the Information & Communications Technology industry

is performed by machines and algorithms, while 46% is projected for 2022.

All industries expect sizable skills gaps, stating that at least 50% of their workforce will require reskilling of some duration. According to respondents to the Future of Jobs Survey, more than 55% of workers across the Aviation, Travel & Tourism; Financial Services & Investors; Chemistry, Advanced Materials & Biotechnology; and Global Health & Healthcare sectors will need some reskilling. The Aviation, Travel & Tourism industry outlines the largest demand for reskilling, projecting that 68% of its workforce will require some reskilling. Further, companies in that industry project that 18% of the workforce will require reskilling lasting more than one year.

While most industry respondents expect to observe declining demand for a set of, often labour-intensive roles dominated by manual and routinized work, that decline is often counter-balanced by growth across other specializations. A critical concern that will affect all

industries will be the imperative to reskill workers currently in roles that have declining prospects into ones with expanding prospects.

Many of the companies surveyed for this report project that, by 2022, they will both expand and contract parts of their current workforce, with expansion likely

to offset the contraction. However, this balance looks different across different industry sectors. Mining & Metals industry respondents, alongside those from the

Consumer and Information & Communication Technologies industries, expect to see a reduction in their workforce

due to automation, while Professional Services industry respondents expect that the changes afoot are more likely to lead to an expansion of their workforce.

Projected adaptations specific to the skilling needs associated with these changes include the potential to buy, build, borrow or automate talent. In particular, many of the Future of Jobs Survey respondents highlighted that they are likely to hire new permanent staff with skills that are relevant to the adopted technologies. The broad mobility sector is most likely to look to automation as a way to solve its projected talent challenges, and is least likely to look

to reskill current employees. In contrast, companies in the Global Health & Healthcare industry—in addition to the Chemistry, Advanced Materials & Biotechnology industry— are most likely to look to retrain existing workers.

The trusted partners with the potential to support industries in their transformation vary across three key groups: specialized departments within the companies in question, professional services firms and industry associations. A series of other potential stakeholders—

education institutions, government programmes and labour unions—received less emphasis as possible partners in these transitions. The Oil & Gas, Mining & Metals, and Energy Utilities & Technology industries are more likely to look to industry associations to support their workforce transition. Companies in the Global Health & Healthcare sector name professional services firms as their primary support mechanism, but also name academic experts as their third-most important support pillar. Finally, Aviation, Travel & Tourism firms are most likely to name local education institutions as their third-most important support structure. Part 2 of this report contains distinct Industry Profiles that offer a deeper look at technology, jobs, tasks and skills trends within different sectors.

The Future of Jobs Across Regions

As the Fourth Industrial Revolution unfolds across the globe, the future of jobs can be expected to develop with both common and differentiated characteristics across different countries and regions of the world.38 In the near term, our data shows that the mix of prevalent industries in different countries will result in different national combinations of the effects described in the

previous section, The Future of Jobs across Industries. Additionally, as global companies choose to differentiate and locate specific job roles and economic activities in certain countries over others due to a range of strategic considerations, there will be a secondary effect on the future of jobs in a range of developed and emerging markets, highlighting the ongoing importance of global supply chains and multinational companies in shaping the structure of the global economy.39,40

With regard to the factors determining job location decisions, companies surveyed for this report overwhelmingly cite availability of skilled local talent as their foremost consideration, with 74% of respondents

providing this factor as their key consideration. In contrast, 64% of companies cite labour costs as their main

concern (Table 7). Notably, while we find some evidence of pure labour cost considerations being more important in emerging economies—with, for example, 74% of companies operating in South Africa and a similar share of companies operating in the Philippines highlighting this

rationale, compared to 57% in the United Kingdom—skilled local talent availability remains the single most important factor behind job location decisions in these economies

as well. A range of additional relevant factors—such as the flexibility of local labour laws, industry agglomeration effects or proximity of raw materials—were considered of lower importance relative to skilled local talent availability and labour cost considerations.

Table 7: Factors determining job location decisions, 2018–2022, by industry

Industry              Primary                Secondary          Tertiary

Overall Talent availability            Labour cost        Production cost

Automotive, Aerospace, Supply Chain & Transport        Talent availability            Labour cost        Quality of the supply chain

Aviation, Travel & Tourism          Talent availability            Organization HQ              Labour cost

Chemistry, Advanced Materials & Biotechnology            Talent availability            Production cost               Labour cost

Consumer          Labour cost        Talent availability            Quality of the supply chain

Energy Utilities & Technologies Talent availability            Labour cost        Production cost

Financial Services & Investors   Talent availability            Labour cost        Organization HQ

Global Health & Healthcare        Talent availability            Labour cost        Production cost

Information & Communication Technologies     Talent availability            Labour cost        Geographic concentration

Infrastructure   Labour cost        Talent availability            Production cost

Mining & Metals              Labour cost        Production cost               Talent availability

Oil & Gas             Talent availability            Production cost               Labour cost

Professional Services    Labour cost        Talent availability            Geographic concentration

Source: Future of Jobs Survey 2018, World Economic Forum.

Furthermore, our analysis finds some industry-specific variation with regard to overall labour cost sensitivity relative to skilled local talent availability considerations. For example, across countries and regions, Consumer, Energy Utilities & Technologies, Financial Services & Investors,

Infrastructure, and Mining & Metals are industries that tend to emphasize labour cost over skilled local talent availability. In contrast, the Automotive, Aerospace, Supply Chain & Transport; Chemistry, Advanced Materials &

Biotechnology; Global Health & Healthcare; and Information & Communication Technologies industries tend to place a larger priority on skilled local talent availability (Table 7).

While a detailed discussion of the potential impact of automation on manufacturing in different countries and regions—and the potential for ‘re-shoring’—is beyond the scope of this report, it is worth noting the link

between labour costs, skills and investment in automation technologies in advanced and emerging economies.

For example, according to one recent study, in 1997, manufacturing value-added per dollar of labour cost was twice as high in Mexico than in the United States. By 2013, this gap had shrunk to less than 15%.41 Provided simultaneous investment in automation technology and labour augmentation in advanced economies continues apace over the 2018–2022 period, it is not inconceivable that shifting comparative advantage in labour costs will

affect the industrial structure of economies such as Vietnam through re-shoring of work tasks in sectors such as textiles, apparel, footwear or electronics assembly.42 Indeed, more than half of companies surveyed for this report expected that by 2022 they would be considering adjusting the composition of their value chains in response to the adoption of new technologies, and just under half expected targeting new talent by modifying the location of their operations.

At least two key factors suggest that the grounds for optimism may outweigh concerns. Firstly, even if

factory automation and labour augmentation in advanced industrial economies might lead to some re-shoring over the 2018–2022 period, many emerging economies are increasingly shifting toward a domestic consumption driven growth model, with rising local middles classes generating increased demand for goods and services traditionally intended for export.43 Secondly, as discussed in the section From Automation to Augmentation, new technologies give rise to new job roles, occupations and industries, with wholly new types of jobs emerging to perform new work tasks related to new technologies.

Comparing occupational structures across advanced and emerging economies suggests that there is considerable scope for job growth in many sectors in the latter. For example, healthcare and education jobs provide 15%

of total employment in the United States, and business services such as finance and real estate provide 19%, whereas, in emerging economies in East Asia and the Pacific, the respective shares are 3.5%–6.0% and 1.5%– 6.0%, suggesting considerable scope for job growth.44

However, in order to result in a positive outcome for workers and businesses alike in the midst of these geographically differentiated shifts, lifelong learning and

national reskilling and upskilling plans for countries at every stage of economic development are paramount. Part 2 of this report offers a deeper look at technology, jobs, tasks and skills trends within different regions and countries through distinct Country and Regional Profiles. They are intended as a practical guide to exploring these issues in greater granularity and identifying opportunities for countries to build up their future talent pool in a targeted manner. The information provided might also prove useful to evaluate

shifting comparative advantage due to new technologies that might affect future company and industry location decisions in relation to various countries in question.

Some of the most frequently cited job roles expected to experience an increase in demand across the geographies covered by the report over the 2018–2022 period—as highlighted by surveyed employers with operations in the respective country or region—include Software and Applications Developers, Data Analysts

and Scientists, as well as Human Resources Specialists, Sales and Marketing Professionals and specialized Sales Representatives in virtually all world regions. Region- specific roles expected to be in demand include Financial and Investment Advisers in East Asia and the Pacific and Western Europe; Information Security Analysts in Eastern Europe; Assembly and Factory Workers in Latin America and the Caribbean, Middle East and North Africa, South Asia and Sub-Saharan Africa; and Electrotechnology Engineers in North America.

Crucial to taking advantage of these emerging job creation opportunities across countries and regions will be the existence of a well-skilled local workforce and of national reskilling and upskilling ecosystems equipped to support local workers to keep abreast of technological change and shifting skills needs. As discussed in the section The Reskilling Imperative (see Figure 7 on  page 13), across all countries and regions, employers surveyed for this report expect that significant reskilling will be needed by a large share of the global workforce over the 2018–2022 period. The expected average

timeframe required to retrain or upskill affected workers— either in order to equip the country’s workforce with

the skills needed to seize new opportunities created by the trends and disruptions experienced by businesses operating in the country in question, or in order to avoid losing competitiveness due to the obsolescence of the workforce’s existing skillsets——ranges from 83 day

for companies located in Switzerland to 105 days for companies located in France (Figure 11).

A Look to the Recent Past (in Collaboration with LinkedIn)

While the Future of Jobs Survey is designed to look to the

near-term future based on the views of the leaders shaping the decisions affecting the future of work, it is equally important

to develop a clear sense of recent trends and consider their projections into the future. The World Economic Forum’s data collaboration with LinkedIn helps trace trends in hiring for a range of roles across the period 2013–2017. This data reveals the recent past and the adaptation that has already occurred across roles, impacting the lives and livelihoods of a variety of professionals.

An average rate of change was calculated to reveal the share of hiring for each role from LinkedIn’s 653 codified occupations. LinkedIn analysts expressed the monthly hires of any one job as a proportion of all hires across jobs in each relevant industry within any one calendar month. A linear

regression line was fitted to aggregate the generalized trend and to reveal multi-year trends that point to the prioritization of hiring across industries. The resulting lists of roles and scale of change are featured in Figures 9 and 10 (on pages 20 and 21)

and reveal, across industries and geographies, the roles that in the aggregate experienced the greatest upward or downward trend in demand from 2013–2017. The trends highlight business prioritization of new hires, namely the roles which employers believed to be the most appropriate investments to prepare their enterprises for success over the relevant period.

The data reveals that the Basics and Infrastructure industry has experienced a boom in real estate brokerage hires, but

a decreasing relative demand for engineering roles and for technicians of various kinds. In the Consumer industry, the demand for Sales Managers was outpaced by demand for Marketing Managers and Software Engineers, while the inverse was true for the Energy industry cluster, where the demand

for Managerial and Sales personnel has outpaced demand for Technicians and Engineers. A similar trend can be observed in the Information and Communication Technology industry. Here, relative demand for Systems Administrators has been outpaced by an increase in hires specializing in Experience Design and Marketing. In the Healthcare sector, more specialized roles in nutrition and mental health have experienced rising demand

in contrast to generalist roles such as Nursing staff or Medical Officers. A slowdown in hiring trends within the Professional Services sector appears to have distinctively impacted creative, editorial and journalistic roles, all reflecting recent disruptions

to the publishing industry. A downward trend among the hiring profile of journalistic professions has seen a matching increase in new hires across broader content writing roles.

Across all regions, digital, marketing and talent-related professions dominate the list of roles that have experienced upward hiring trends alongside marketing specialists, and professionals specializing in software engineering, Data Analysts, User Experience Designers and Human Resources Specialists.

The East Asia and the Pacific region has experienced falling demand for more traditional technical professions such as Engineering, and that trend is mirrored in the Middle East and North Africa region. In a similar fashion, historic hiring trends reveal a decline in hires of technical professions, such as Database Administrators and Electrical Engineers in South Asia. The Latin America and Caribbean and Sub-Saharan Africa regions saw a decline in new hires into roles focused on accounting, administrative activities and in supply chain specialization. Finally, Western Europe has experienced a

slowdown in the relative hiring of creative professionals, reflecting recent disruptions in the publishing industry.

(Continued on next page)

A Look to the Recent Past (in Collaboration with LinkedIn) (cont’d.)

Figure 9: Top ten most emerging and declining roles between 2013–2017 as observed in hiring trends, by industry

(rate of change)

Basics and Infrastructure             Healthcare

Real Estate Agent Real Estate Consultant Real Estate Broker Marketing Specialist Software Engineer

Human Resources Specialist

Civil Engineer Account Manager Sales Executive Marketing Manager Construction Worker Electrical Engineer

Manager of Construction Civil Engineering Technician Manager of Engineering

Accountant Environment Health Safety Manager

Mechanical Technician Electrical Technician Administrative Assistant

-2            -1            0             1             2

Software Engineer Rehabilitation Therapist Healthcare Assistant Mental Health Practitioner

Human Resources Specialist Marketing Specialist

Nutritionist Nursing Student

Mental Health Technician

Data Analyst Medical Officer

Lifeguard Sports Instructor Administrative Office Manager

Alternative Medicine Practitioner

Nurse Food and Beverage Server

Medical Doctor Salesperson Administrative Assistant

-2            -1            0             1             2

Consumer          Information and Communication Technology

Marketing Specialist Software Engineer Marketing Manager Marketing Representative Human Resources Specialist Food and Beverage Server

Sales Consultant Manager of Marketing Account Manager

Driver Manager of Customer Service

Accountant

Artist       Sales Manager

Customer Service Specialist

Merchandiser Manager of Retail

Customer Service Representative Administrative Assistant

Salesperson

-2            -1            0             1             2

Software Engineer Marketing Specialist

Recruiter           Human Resources Specialist

Data Analyst

Driver                            User Experience Designer Customer Experience Manager

Account Executive Marketing Manager

Information Technology Manager Information Technology Specialist

Sales Manager Customer Service Representative Technical Support Specialist Information Technology Analyst Information Technology Consultant

System Administrator Administrative Assistant

Project Manager

-2            -1            0             1             2

Energy  Mobility

Software  Engineer

Salesperson Business Development Manager

Sales Manager Energy Manager Project Manager Marketing Specialist Manager of Sales Account Manager

Business Development Specialist

Electrical  Engineer

Accountant Chemical Engineer

Driller  Electrical Technician Mechanical Technician Administrative Assistant

Geologist Mechanical Engineer Petroleum Engineer

-2            -1            0             1             2

Software Engineer

Driver Marketing Specialist

Human Resources Specialist Supply Chain Associate Mechanical Engineer Marketing Manager

Recruiter Sales Consultant Sales Executive

Chef Supply Chain Manager

Food and Beverage  Specialist

Accountant Lifeguard

Manager of Food Services Mechanical Technician

Customer Service Representative Food and Beverage Server Administrative Assistant

-2            -1            0             1             2

Financial Services            Professional Services

Software Engineer Finance Analyst Financial Advisor Finance Specialist Data Analyst Insurance Agent

Manager of Product Management

Finance Officer Human Resources Specialist Marketing Specialist

Food and Beverage Server Accounting Assistant

Accountant Project Manager

Financial Services Associate Manager of Finance

Banker Salesperson

Customer Service Representative Administrative Assistant

Source: LinkedIn.

-2            -1            0             1             2

Marketing Specialist

Recruiter Human Resources Consultant Human Resources Specialist

Marketing Manager Accounting Associate Software Engineer Account Manager Data Analyst Financial Auditor

Customer Service Representative

Law Clerk Manager of Creative Services

Editor Food and Beverage Server

Accountant Journalist Salesperson Architect

Administrative Assistant

-2            -1            0             1             2

(Continued on next page)

A Look to the Recent Past (in Collaboration with LinkedIn) (cont’d.)

Figure 10: Top ten most emerging and declining roles between 2013–2017 as observed in hiring trends, by region

(rate of change)

East Asia and the Pacific               North America

Marketing Specialist Software Engineer

Human Resources Specialist Human Resources Consultant

Account Manager

Driver Data Analyst

Writer User Experience Designer Finance Specialist Electrical Technician Electrical Engineer Mechanical  Technician

Customer Service Representative

Accountant Journalist Sales Manager

Mechanical Engineer Project Manager Administrative Assistant

-2            -1            0             1             2

Real Estate Agent Software Engineer Marketing Specialist

Recruiter Marketing Manager

Driver Data Analyst Account Executive Finance Analyst

Human Resources Specialist

Chef Food and Beverage Server

Sports Instructor

Editor Manager of Retail Administrative Office Manager

Lifeguard          Customer Service Representative

Salesperson Administrative Assistant

-2            -1            0             1             2

Eastern Europe and Central Asia              South Asia

Software Engineer Human Resources Specialist

Recruiter Marketing Specialist Business Strategy Analyst

Data Analyst User Experience Designer Manager of Product Management

Accounting Specialist Human Resources Consultant Food and Beverage Server

Economist Translator

System Administrator

Editor Manager of Sales

Journalist Salesperson Administrative Assistant

Sales Manager

-2            -1            0             1             2

Marketing Specialist

Recruiter Writer

Marketing Manager Manager of Business Development Human Resources Specialist

Data Analyst Software Engineer Graphic Designer

Business Development Manager

Manager of Retail Technical Support Engineer Database Administrator Manager of Sales Administrative Assistant Electrical Engineer

Accountant  Information Technology Consultant

System Administrator Project Manager

-2            -1            0             1             2

Latin America and the Caribbean             Sub-Saharan Africa

Software Engineer Marketing Specialist

Salesperson Sales Consultant Strategic Advisor

Lawyer Sales Executive Real Estate Agent

Manager of Marketing

Data Analyst Mechanical Technician Supply Chain Assistant

Environment Health Safety Manager

Journalist Administrative Assistance Specialist Information Technology Analyst Technical Support Analyst Accounting Assistant

Accountant Administrative Assistant

-2            -1            0             1             2

Software Engineer Marketing Specialist Marketing Manager

Writer Financial Advisor Data Analyst

Human Resources Specialist

Salesperson Business Development Manager

Lawyer Civil Engineering Technician

Electrical Engineer Finance Officer

Supply Chain Manager Technical Support Technician

Electrical Technician

Journalist    Mechanical Technician Administrative Assistant

Accountant

-2            -1            0             1             2

Middle East and North Africa     Western Europe

Software Engineer Marketing Specialist Marketing Manager

Human Resources Specialist Real Estate Consultant

Writer Lawyer

Civil Engineer Nutritionist Mechanical Engineer

Journalist Civil Engineering Technician

Nurse Sales Executive

Customer Service Representative

Electrical  Engineer

Salesperson Project Manager Administrative Assistant

Accountant

Source: LinkedIn.

-2            -1            0             1             2

Software Engineer Marketing Manager

Human Resources Specialist Marketing Specialist

Recruiter Human Resources Consultant Business Development Specialist Manager of Product Management

Data Analyst User Experience Designer

Architect Entertainer Marketing Assistant

Photographer Graphic Designer

Editor Food and Beverage Server Administrative Assistant

Journalist Salesperson

-2            -1            0             1             2

Figure 11: Average reskilling needs in days, by country and region, 2018–2022

France Philippines Singapore Germany India

East Asia and the Pacific

Australia Japan Thailand Mexico South Africa Argentina

Russian  Federation

Brazil Vietnam

Middle East and North Africa

North America

China Central Asia

Latin America and the Caribbean

Western Europe Korea, Rep. United States

Sub-Saharan Africa

Indonesia United Kingdom Eastern Europe South Asia

Switzerland       

0             20           40           60           80           100

Source: Future of Jobs Survey 2018, World Economic Forum.

For governments and businesses alike, there is a significant opportunity in strengthening cross-sectoral multistakeholder collaboration to promote corporate reskilling and upskilling among employers in affected countries and regions. Responses by the companies surveyed for this report indicate that, currently, employers expect to primarily seek out the support of their own internal departments as well as private training providers to deliver required retraining and upskilling programmes over the 2018–2022 period. In contrast, across many regions, the least sought-after partners are local education institutions, government programmes and labour unions. This somewhat narrow field of envisaged collaboration partners highlights both an opportunity and a clear

need for expanding the range of creative and innovative

multistakeholder solutions.

Conclusions

The new labour market taking shape in the wake of the Fourth Industrial Revolution holds both challenges and

opportunities. As companies begin to formulate business transformation and workforce strategies over the course of the 2018–2022 period, they have a genuine window

of opportunity to leverage new technologies, including automation, to enhance economic value creation through new activities, improve job quality in traditional and newly emerging occupations, and augment their employees’ skills to reach their full potential to perform new high value- added work tasks, some of which will have never before been performed by human workers. The business case for such an ‘augmentation strategy’ is becoming increasingly clear—and, we expect, will receive progressively more attention over the coming years, including through upcoming work by the World Economic Forum’s Centre for the New Economy and Society.

At the same time, technological change and shifts in job roles and occupational structures are transforming the demand for skills at a faster pace than ever before. Therefore, imperative for achieving such a positive vision of the future of jobs will be an economic and societal

move by governments, businesses and individuals towards agile lifelong learning, as well as inclusive strategies and programmes for skills retraining and upgrading across

the entire occupational spectrum. Technology-related and non-cognitive soft skills are becoming increasingly more important in tandem, and there are significant opportunities for innovative and creative multistakeholder partnerships of governments, industry employers, education providers and others to experiment and invest in new types of education and training provision that will be most useful to individuals in this new labour market context.

As this new labour market takes shape over the 2018– 2022 period, governments, businesses and individuals

will also find themselves confronted with a range of wholly new questions. For example, as employment relationships increasingly shift towards temporary and freelancing arrangements, how can we ensure that individuals receive the support and guidance they need to acquire the right skills throughout their working lives? As employers are deconstructing traditional job roles and re-bundling work tasks in response to new technologies, how can they minimize the risks and best leverage new partnerships with resources such as online freelancers and talent platforms?45 And how can they best ensure such task re- bundling does not inadvertently lead to new forms of job polarization through ‘task segregation’, whereby specific groups of workers are disproportionately allocated the most or least rewarding work tasks?46

While it is beyond the scope of this report to attempt to provide comprehensive answers to all of the above questions, a range of immediate implications and priorities stand out for different stakeholders.

For governments, firstly, there is an urgent need to address the impact of new technologies on labour markets through upgraded education policies aimed at rapidly raising education and skills levels of individuals of all ages,

particularly with regard to both STEM (science, technology, engineering and mathematics) and non-cognitive soft skills, enabling people to leverage their uniquely human capabilities. Relevant intervention points include school curricula, teacher training and a reinvention of vocational training for the age of the Fourth Industrial Revolution, broadening its appeal beyond traditional low- and medium-

skilled occupations.47 Secondly, improvements in education and skills provision must be balanced with efforts on

the demand side. Governments can help stimulate job creation through additional public investment as well as by leveraging private investments through blended finance or government guarantees. The exact nature of desirable investments will vary from country to country.

However, over the coming years, there is enormous scope and a clear unmet need in creating the hard and soft infrastructure to power the Fourth Industrial Revolution— from digital communication networks to renewable and smart energy grids to smart schools and hospitals to improved care homes and childcare facilities.48 Thirdly, to the extent that new technologies and labour augmentation will boost productivity, incomes and wealth, governments may find that increased tax revenues provide scope to enhance social safety nets to better support those who may need support to adjust to the new labour market.

This could be achieved through reforming and extending existing social protection schemes, or through moving to a wholly new model such as the idea of basic income and

basic services. Learning from pilot schemes of this kind—in addition to those currently underway in places such as

the Netherlands, various American and Canadian states, Kenya, India and Brazil—will be critical for all governments over the course of the 2018–2022 period.49

For industries, firstly, it will pay to realize that—as competition for scarce skilled talent equipped to seize the opportunities of the Fourth Industrial Revolution intensifies and becomes more costly over the coming years—there is an opportunity to support the upskilling of their current workforce toward new (and technologically reorganized) higher-skilled roles to ensure that their workforce achieves its full potential. Our findings indicate that, to date, many companies intend to mostly limit their skills training provision over the 2018–2022 period to

employees performing today’s in-demand job roles, rather than thinking more long-term and creatively. Clearly, a more inclusive and proactive approach will be needed—to both increase the availability of future skills and address impending skills scarcity, and to enable a wider range

of workers to share in the gains from new technologies and work more effectively with them through skills augmentation. Secondly, the need to ensure a sufficient pool of appropriately skilled talent creates an opportunity for businesses to truly reposition themselves as learning organizations and to receive support for their reskilling and upskilling efforts from a wide range of stakeholders. One promising model involves new forms of professional

skills certification similar to existing schemes delivered by a range of companies in the information technology sector. By establishing objective and marketable credentials for a large variety of emerging job roles, such schemes could help improve the focus of corporate training programmes, increase labour market flexibility, and create clear skills and performance measures to help employers screen candidates and certified workers to command skills premiums.50 Thirdly, with the increasing importance

of talent platforms and online workers, conventional industries, too, should be thinking strategically how these action items could be applied to the growing ‘gig’ and platform workforces as well.51

For workers, there is an unquestionable need to take personal responsibility for one’s own lifelong learning and career development. It is also equally clear that many individuals will need to be supported through periods of job transition and phases of retraining and upskilling by

governments and employers. For example, lifelong learning is becoming a rich area of experimentation, with several governments and industries looking for the right formula to encourage individuals to voluntarily undergo periodic skills upgrading.52 Similarly, while a fully-fledged universal basic income may remain politically and economically unfeasible or undesirable over the 2018–2022 period, some variants or aspects of the idea—such as providing a ‘universal lifelong learning fund’ for individuals to draw on as needed—might receive increasing attention over the coming years.53 Solutions are likely to vary by country and to depend on local political, economic and social circumstances.

Ultimately, the core objective for governments, industries and workers alike should be to ensure that tomorrow’s jobs are fairly remunerated, entail treatment with respect and decency and provide realistic scope for personal growth, development and fulfilment.54 It is our hope that this new edition of the World Economic Forum’s Future of Jobs Report provides both a call to action and

a useful tool for proactively shaping the future of jobs to realize this vision.

Notes

1             World Economic Forum, The Future of Jobs: Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution, 2016. For

an overview of some of this recent research, see: Balliester, Thereza and Adam Elsheikhi, The Future of Work: A Literature Review, ILO Research Department Working Paper No. 29, International Labour Organization, 2018.

2             African Development Bank (AFDB), Asian Development Bank (ADB), European Bank for Reconstruction and Development (EBRD),

and Inter-American Development Bank (IDB), The Future of Work: Regional Perspectives, 2018.

3             According to the International Labour Organization’s literature review, existing research on the future of work covers a wide range of topics, with a particular focus on technological innovations and inequality. Aspects that would merit additional analysis include the impact of demographics and environmental changes and, ‘[with] regard to

the future of job creation and destruction, projections on the impact  of automation on agriculture would be essential … particularly for developing countries’; Balliester, and Elsheikhi, The Future of Work: A Literature Review.

4             Bain & Company, Labor 2030: The Collision of Demographics, Automation and Inequality, 2018.

5             According to an estimate by Bain & Company, based primarily on the rapidly declining cost of robotic dexterity for service applications, humanoid robots are likely to reach commercialization in the

early 2020s, specifically creating ‘a strong business case for the automation of many tasks in restaurant kitchens and bars’; see: Bain & Company, Labor 2030: The Collision of Demographics, Automation and Inequality.

6             For example, ‘cobots’—robotic helper units installed alongside human workers to enhance their productivity and often costing less than

one-quarter the price of traditional robots—are set to have a large commercial and workforce impact over the coming years, being

well-placed for deployment in many parts of the service sector as yet largely untouched by workplace automation; see: Bain & Company, Labor 2030: The Collision of Demographics, Automation and Inequality.

7             See, for example, the differing perspectives provided by: Bain & Company, Labor 2030: The Collision of Demographics, Automation and Inequality; McKinsey & Company, Jobs lost, jobs gained: Workforce Transitions in a Time of Automation, McKinsey Global Institute (MGI), 2017; and PwC, Will robots really steal our jobs? An international analysis of the potential long-term impact of automation, 2018.

8             As noted by a recent Bain & Company study, while public reaction  to new technologies is likely to vary substantially from one country to the next, thereby accelerating or decelerating their adoption, differences in public policies toward new technologies such as automation may be harder to sustain if their applications are tradeable. For example, if London were to deregulate the application

of fully autonomous machine learning algorithms in financial markets, competitive forces are likely to put greater pressure on technology regulators in New York to follow suit. By contrast, if London were to permit coffee shops more generous labour automation leeway than New York, differences are more likely to remain localized; see: Bain & Company, Labor 2030: The Collision of Demographics, Automation and Inequality.

9             These extrapolated figures are based on employers’ current projections for the set of roles with increasing, declining and stable demand in the period up to 2022, which were estimated  by employers as a share of each enterprise’s total workforce. The

figures were then applied to the International Labour Organization’s estimates and projections of global non-agricultural employment

in both 2018 and 2022, adjusted for the estimated share of total employment represented by this report’s respondent data, i.e. large businesses. The figures used for estimating the global share of large business employment are based on established estimates by the World Bank, US Bureau of Labor Statistics and Eurostat, holding the distribution of firm size constant between 2018 and 2022.

10           Barclays, Robots at the gate: Humans and technology at work, 2018.

11           Ibid.

12           Bain & Company, Labor 2030: The Collision of Demographics, Automation and Inequality.

13           See: Ton, Zeynep and Sarah Kalloch, Transforming Today’s Bad Jobs into Tomorrow’s Good Jobs, Harvard Business Review, June 2017; Deloitte, Reconstructing Jobs: Creating good jobs in the age of artificial intelligence, 2018.

14           Davenport, Thomas and Julia Kirby, Beyond Automation, Harvard Business Review, June 2015.

15           See for example: Arntz, Melanie, Terry Gregory and Ulrich Zierahn, The risk of automation for jobs in OECD countries: a comparative analysis, OECD Social, Employment and Migration Working Papers No 189, Organisation for Economic Cooperation and Development (OECD), 2016; McKinsey Global Institute, A Future That Works: Automation, Employment, and Productivity, McKinsey Global Institute (MGI), 2017; PwC, Will robots really steal our jobs? An international analysis of the potential long term impact of automation. For a range of relevant additional considerations, see: van der Zande, Jochem, et al., The Substitution of Labor: From technological feasibility to other factors influencing job automation, Innovative Internet: Report 5, Stockholm School of Economics Institute for Research, 2018.

16           McKinsey Global Institute, A Future That Works: Automation, Employment, and Productivity.

17           PwC, Will robots really steal our jobs? An international analysis of the potential long term impact of automation; the three waves of workforce automation identified by the report consist of an algorithmic wave (to early 2020s; involving ‘automation of simple

computational tasks and analysis of structured data, affecting data- driven sectors such as financial services’); an augmentation wave (to late 2020s; involving ‘dynamic interaction with technology for clerical support and decision making … including robotic tasks in semi- controlled environments such as moving objects in warehouses); and an autonomous wave (to mid-2030s; involving ‘automation of physical labour and manual dexterity, and problem-solving in dynamic real- world situations that require responsive actions, such as in transport and construction’).

18           A thought-provoking empirical perspective on this process is provided by: Cohen, Lisa, “Assembling Jobs: A Model of How Tasks Are Bundled Into and Across Jobs”, Organization Science, vol. 24, no. 2, 2012.

19           Autor, David, “Why Are There Still So Many Jobs? The History and Future of Workplace Automation”, Journal of Economic Perspectives, vol. 29, no. 3, 2015, pp. 3–30.

20           For example, since its launch in 2008, developers have earned more than US$86 billion through Apple’s App Store platform, and app development is estimated to have created more than 1.7 million jobs in the United States and more than 2 million jobs in Europe; see: Apple, App Store kicks off 2018 with record-breaking holiday season, https://www.apple.com/newsroom/2018/01/app-store-kicks-off- 2018-with-record-breaking-holiday-season, 2018; Mandel, M., U.S. App Economy Jobs Update, Progressive Policy Institute, http://www. progressivepolicy.org/blog/u-s-app-economy-update, 2017; and Mandel, M., Update on European App Economy jobs, Progressive Policy Institute, http://www.progressivepolicy.org/blog/update-on- european-app-economy-jobs, 2018.

21           Dellot, Benedict, “Why automation is more than just a job killer”, RSA Blog, 20 July 2018, https://www.thersa.org/discover/publications- and-articles/rsa-blogs/2018/07/the-four-types-of-automation- substitution-augmentation-generation-and-transference. The

RSA, a British think tank, accordingly distinguishes four types of automation: (1) substitution (‘technology taking on a task that would [otherwise have been] be undertaken by a worker’; (2) augmentation (‘[technology] expand[ing] the capability of workers, allowing them to achieve more and better-quality work in a shorter space of time’);

(3) generation (‘[technology] generat[ing] tasks that were never done by humans previously … creat[ing] work rather than captur[ing] it from others’); (4) transference (‘technology shift[ing] responsibility for undertaking a task from workers to consumers. Self-service checkouts, for instance, have not done away with the job of processing items through tills. Instead they’ve merely passed on the

responsibility to shoppers. … This form of automation typically relies on … sophisticated UX and UI Design’); ibid.

22           An innovative effort to distinguish between labour-substituting and labour-augmenting technologies—based on 78 individual tools and technologies—is provided by: Nedelkoska, Ljubica and Glenda Quintini, Automation, skills use and training, OECD Social,

Employment and Migration Working Papers, No. 202, OECD, http:// dx.doi.org/10.1787/2e2f4eea-en, 2018.

23           KPMG, The augmented workforce; Cognizant, The Robot and I: How New Digital Technologies Are Making Smart People and Businesses Smarter by Automating Rote Work, 2015.

24           Dellot, Why automation is more than just a job killer.

25           Measured in incremental additional US$ of gross output per worker,

i.e. excluding baseline forecasts of labour productivity growth; Bain & Company, Labor 2030: The Collision of Demographics, Automation and Inequality.

26           Jesuthasan, Ravin and John Boudreau, Thinking Through How Automation Will Affect Your Workforce, Harvard Business Review, April 2017; also see: Jesuthasan, Ravin, “You may not be a disrupter, but you might find opportunities in the gig economy”, Willis Towers Watson Blog, 24 July 2017, https://www.willistowerswatson.com/en/ insights/2017/07/insights-gig-economy.

27           Shook, Ellyn and Mark Knickrehm, Harnessing Revolution: Creating the Future Workforce, Accenture Strategy, 2017.

28           Autor, David, Frank Levy and Richard Murnane, Upstairs, Downstairs: Computer-Skill Complementarity and Computer-Labor Substitution on Two Floors of a Large Bank, NBER Working Paper No. 7890, National Bureau of Economic Research, 2000.

29           Barclays, Robots at the gate: Humans and technology at work.

30           Shook and Knickrehm, Harnessing Revolution: Creating the Future Workforce.

31           For a detailed analysis, see the sections The Future of Jobs across Industries and The Future of Jobs across Regions; also see: McKinsey & Company, Skill Shift: Automation and the Future of the Workforce, Discussion Paper, McKinsey Global Institute (MGI), 2018.

32           For a more extensive discussion of the concept of skills stability, see: World Economic Forum, The Future of Jobs: Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution, 2016 and CEDEFOP, Briefing Note: Preventing skill obsolescence, 2012.

33           Nedelkoska and Quintini, Automation, skills use and training.

34           McKinsey & Company, Skill Shift: Automation and the Future of the Workforce.

35           Ibid.

36           Ibid.

37           See: Bain & Company, Labor 2030: The Collision of Demographics, Automation and Inequality; McKinsey & Company, Skill Shift: Automation and the Future of the Workforce; Barclays, Robots at the gate: Humans and technology at work.

38           For a recent comprehensive overview, see: African Development Bank (AFDB), Asian Development Bank (ADB), European Bank for Reconstruction and Development (EBRD), Inter-American

Development Bank (IDB), The Future of Work: Regional Perspectives,

2018.

39           Nedelkoska and Quintini, Automation, skills use and training.

40           See, for example: Baldwin, Richard, The Great Convergence: Information Technology and the New Globalization, Harvard University Press, 2016; Reijnders, Laurie S.M. and Gaaitzen de Vries, Job Polarization in Advanced and Emerging Countries: The Role of Task Relocation and Technological Change within Global Supply Chains, GGDC Research Memorandum 167, University of Groningen- Groningen Growth and Development Centre, 2017.

41           Bain & Company, Labor 2030: The Collision of Demographics, Automation and Inequality.

42           International Labour Organization (ILO), Inception Report for the Global Commission on the Future of Work, 2017.

43           Asian Development Bank (ADB), Asian Development Outlook 2018: How Technology Affects Jobs, 2018.

44           Ibid.

45           Jesuthasan, “You may not be a disrupter, but you might find opportunities in the gig economy”.

46           Chan, Curtis and Michael Anteby, “Task Segregation as a Mechanism for Within-job Inequality: Women and Men of the Transportation Security Administration”, Administrative Science Quarterly, vol. 61, no. 2, 2016, pp. 184–216.

47           The Economist Intelligence Unit and ABB, The Automation Readiness Index: Who is Ready for the Coming Wave of Automation, 2018.

48           Bain & Company, Labor 2030: The Collision of Demographics, Automation and Inequality; PwC, Will robots really steal our jobs? An international analysis of the potential long term impact of automation.

49           PwC, Will robots really steal our jobs? An international analysis of the potential long term impact of automation.

50           Bain & Company, Labor 2030: The Collision of Demographics, Automation and Inequality.

51           Taylor, Matthew, Good work: The Taylor Review of Modern Working Practices, Report for the UK Government, 2017.

52           “Singapore, for example, is experimenting with funding ‘individual learning accounts’, which adults use to support training courses throughout their lives. Germany’s Federal Ministry of Labour and Social Affairs is examining a similar scheme, as well as a modified form of “employment insurance” to fund skills upgrading throughout people’s lives”; see: The Economist Intelligence Unit and ABB, The Automation Readiness Index: Who is Ready for the Coming Wave of Automation.

53           PwC, Will robots really steal our jobs? An international analysis of the potential long term impact of automation.

54           Taylor, Good work: The Taylor Review of Modern Working Practices.

References and Further Reading

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Accenture, New Skills Now: Inclusion in the Digital Economy, 2017.

———, Creating South Africa’s Future Workforce, 2018.

Acemoglu, Daron, “Labor- and Capital-Augmenting Technical Change”, Journal of the European Economic Association, vol. 1, no.1, 2003, pp. 1–37.

Acemoglu, Daron and Pascual Restrepo, The Race between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment, NBER Working Paper no. 22252, National Board of Economic Research, 2016.

Acemoglu, Daron and Robert Shimer, “Productivity gains from unemployment insurance”, European Economic Review, vol. 44, 2000, pp. 1195–1224.

African Development Bank (AFDB), Asian Development Bank (ADB), European Bank for Reconstruction and Development (EBRD), and Inter-American Development Bank (IDB), The Future of Work: Regional Perspectives, 2018.

Alphabeta, The Automation Advantage: How Australia can seize a $2 trillion opportunity from automation and create millions of safer, more meaningful and more valuable jobs, 2017.

Arntz, Melanie, Terry Gregory and Ulrich Zierahn, The risk of automation for jobs in OECD countries: a comparative analysis, OECD Social, Employment and Migration Working Papers No 189, Organisation for Economic Cooperation and Development (OECD), 2016.

Asian Development Bank (ADB), Asian Development Outlook 2018: How Technology Affects Jobs, 2018.

Autor, David, “Why Are There Still So Many Jobs? The History and Future of Workplace Automation”, Journal of Economic Perspectives, vol. 29, no. 3, 2015, pp. 3–30.

Autor, David, Frank Levy and Richard Murnane, Upstairs, Downstairs: Computer-Skill Complementarity and Computer-Labor Substitution on Two Floors of a Large Bank, NBER Working Paper No. 7890, National Bureau of Economic Research, 2000.

Avent, Ryan, The Wealth of Humans: Work and its Absence in the Twenty- first Century, Penguin, 2016.

Babcock, Linda, et al., “Gender Differences in Accepting and Receiving Requests for Tasks with Low Promotability”, American Economic Review, vol. 107, no. 3, 2017, pp. 714–747.

Bain & Company, Labor 2030: The Collision of Demographics, Automation and Inequality, 2018.

Bakhshi, Hasan, et al., The Future of Skills: Employment in 2030, Pearson, Nesta and The Oxford Martin School, 2017.

Baldwin, Richard, The Great Convergence: Information Technology and the New Globalization, Harvard University Press, 2016.

Balliester, Thereza and Adam Elsheikhi, The Future of Work: A Literature Review, ILO Research Department Working Paper No. 29, International Labour Organization, 2018.

Barclays, Robots at the gate: Humans and technology at work, 2018.

Behrendt, Christina and Quynh Anh Nguyen, Innovative Approaches for Ensuring Universal Social Protection for the Future of Work, ILO Future of Work Research Paper Series No. 1, International Labour Organization, 2018.

Berg, Andrew, Edward Buffie and Luis-Felipe Zanna, Should We Fear the Robot Revolution? (The Correct Answer is Yes), IMF Working Paper No. 18/116, International Monetary Fund, 2018.

Bessen, James, Toil and Technology: Innovative technology is displacing workers to new jobs rather than replacing them entirely, IMF Finance and Development Magazine, March 2015.

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Cognizant, 21 Jobs of the Future: A Guide to Getting – and Staying – Employed over the Next Ten Years, 2017.

———, The Robot and I: How New Digital Technologies Are Making Smart People and Businesses Smarter by Automating Rote Work, 2015.

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International Federation of Robotics, The Impact of Robots on Productivity, Employment and Jobs: A positioning paper by the International Federation of Robotics, 2017.

International Labour Organization (ILO), Inception Report for the Global Commission on the Future of Work, 2017.

———, Synthesis Report of the National Dialogues on the Future of Work, 2017.

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———, A Future That Works: Automation, Employment, and Productivity, McKinsey Global Institute (MGI), 2017.

———, Jobs lost, jobs gained: Workforce Transitions in a Time of Automation, McKinsey Global Institute (MGI), 2017.

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– Harnessing the Maker Movement to Transform GCC Economies, Oliver Wyman, 2017.

World Economic Forum, Towards a Reskilling Revolution: A Future of Jobs for All, 2018.

———, Accelerating Gender Parity in the Fourth Industrial Revolution, 2017.

———, Accelerating Workforce Reskilling for the Fourth Industrial Revolution, 2017.

———, Eight Futures of Work: Scenarios and their Implications, 2018.

———, The Future of Jobs: Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution, 2016.

———, The Future of Jobs and Skills in Africa, 2017.

———, The Future of Jobs and Skills in MENA, 2017.

———, The Global Gender Gap Report 2017, 2017.

———, The Global Human Capital Report 2017, 2017.

———, How to Prevent Discriminatory Outcomes in Machine Learning, 2018.

———, Realizing Human Potential in the Fourth Industrial Revolution, 2017.

Appendix A:

Report Methodology

Changes to jobs and skills are set to have large-scale effects on companies, government and individuals across the global community. What does the future hold? How can you find the right talent to ensure growth? How can you make informed and socially conscious decisions when faced with major disruptions to jobs and skills?

The analysis that forms the basis of this report is the result of an extensive survey of Chief Human Resources and Chief Executive Officers of leading global employers which aims to give specificity to these discussions. The survey aims to capture executives’ current planning and projections related to jobs and skills in the period leading up to 2022.

Survey Design

There are three core concepts that are key to the construction of the Future of Jobs Survey: job roles, tasks and skills. Task are defined as the actions necessary to turn a set of inputs into valuable outputs. As such, tasks can be considered to form the content of jobs. Skills, on the other hand, are defined as the capabilities that are needed to complete a task. In essence, tasks are what needs to be done and skills define the capacity to do them.

The original Future of Jobs Survey employed to produce the first Future of Jobs Report, in 2016, was informed by an extensive literature review on the various dimensions covered by the survey, and by continuous consultation with leading experts from academia, international organizations, business and civil society through the World Economic Forum’s Global Agenda Council on the Future of Jobs and Global Agenda Council on Gender Parity, which served as partners and advisory bodies to the study. This second edition of the survey

Figure A1: Future of Jobs Survey 2018 framework

Part I

Transformations

Part II

Occupations, Skills and Tasks

Part III

Training and Reskilling

Source: Future of Jobs Survey 2018, World Economic Forum.

adjusted that approach on the basis of lessons learned from that first endeavour.

The updated 2018 survey now consists of three interrelated parts. Part I maps the trends that are set to positively and negatively impact business growth, the technologies that are likely to play a part in that expansion, the rationale and barriers related to this technology expansion, employers’ preferred ecosystem for support, and the workforce shifts that will be needed to effect those changes. Part II maps three interlocking pillars of the labour market—occupations, skills and tasks—and provides employers with an opportunity to share the jobs that are set

to experience stable, declining and rising demand. Part II also asks employers to estimate the current and future composition of their workforce, and the division of labour between humans, machines and algorithms. Part III gives survey respondents an opportunity to share their current plans for the period up to 2022 as they pertain to closing key skills gaps in their enterprises. In particular, the survey asks employers to rate the likelihood of employing a variety of strategies aimed at ensuring their businesses have the right talent to grow, to give specificity to the scale of their future reskilling needs, and to share a range of detailed information about their current and future reskilling provision.

Representativeness

The survey collection process was conducted via an online questionnaire, with data collection spanning a nine-month period from November 2017 to July 2018. The survey

set out to represent the current strategies, projections and estimates of global business, with a focus on large multinational companies and more localized companies

which are of significance due to their employee or revenue size. As such there are two areas of the future of jobs that remain out of scope for this report—namely, the future of jobs as it relates to the activities of small and medium-sized enterprises and as it relates to the informal sectors of, in particular, developing economies.

The Future of Jobs Survey was distributed to relevant companies through extensive collaboration between the World Economic Forum and its constituents, amplified

by regional survey partners. The survey is also the result of extensive cross-departmental coordination within the

World Economic Forum during which the Forum’s Business Engagement Team, Centre for Global Industries and Centre for Regional and Geopolitical Affairs supported the report team’s efforts to sub-select relevant samples. For key partners in the survey distribution process, please refer to the Survey Partners and Acknowledgements sections.

Detailed sample design specifications were shared with survey partners, requesting that the sample of companies targeted for participation in the survey should be drawn from a cross-section of leading companies that make up a country or region’s economy, and should include—although not necessarily be limited to—national

and multinational companies that are among the country’s top 100 employers (either by number of employees or by revenue size). In cases where we worked with a regional partner organization we requested additional focus on strong representation from key sectors represented in that geography. To ensure that the survey was representative of the relevant population, the report team conducted additional analysis, confirming the number of responses as well as the size of each respondent’s revenue and employee pool.

The final sub-selection of countries with data of sufficient quality to be featured in the report was based

on the overall number of responses from companies with a presence in each country—and within that subset, was based on the number of companies headquartered in the relevant location and the diversity of the sample in relation to the companies’ number of locations. In particular, the

aim was to arrive at a sample in which more than two-fifths of the companies were large multinational firms, and a reasonable range of companies maintained a focused local or regional presence. The final sub-selection of industries included was based on the overall number of responses

by industry, in addition to a qualitative review of the pool of named companies represented in the survey data.

After relevant criteria were applied, the sample was found to be composed of 12 industry clusters and 20 economies. Industry clusters include Aviation, Travel & Tourism; Chemistry, Advanced Materials & Biotechnology; Consumer; Energy; Financial Services & Investors; Global Health & Healthcare; Information & Communication Technologies; Infrastructure; Mining & Metals; Mobility;

Oil & Gas; and Professional Services. Economies include Argentina, Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Mexico, Philippines, Russian Federation, Singapore, South Africa, Republic of Korea, Switzerland, Thailand, United Kingdom, United States and Vietnam— collectively representing about 70% of global GDP. In total, the report’s data set contains 313 unique responses by global companies, collectively representing more than 15 million employees (see Table 1 in Part 1).

Classification Frameworks for Jobs and Skills

Similar to the initial report, this year’s report employed the Occupational Information Network (O*NET) framework

for its categories of analysis for jobs, skills and tasks. O*NET was developed by the US Department of Labor in collaboration with its Bureau of Labor Statistics’ Standard Classification of Occupations (SOC) and remains the most extensive and respected classification of its kind. In its unabridged form, the O*NET-SOC taxonomy includes detailed information on 974 individual occupations in the United States, grouped into approximately 20 broader job families, which are regularly revised and updated for new and emerging occupations to keep up with the changing occupational landscape.

For this edition of the report, the Generalized Work Activities segment of the O*NET methodology was used to form the list of tasks used in the survey. In addition, for the classification of skills, the report team employed an abridged version of the “Worker Characteristics” and Worker Requirement classifications; in particular, bundles 1.A., 1.C., 2.A., and 2.B. Additional details about the composition of the skills list used in this report can be found in Table A1.

Table A1: Classification of skills used, based on O*NET content model

The Future of Jobs Report 2018

Competency bundle      Competencies, O*NET Description

Active learning and learning strategies Active Learning Understanding the implications of new information for both current and future problem- solving and decision-making.

                Learning Strategies        Selecting and using training/instructional methods and procedures appropriate for the situation when learning or teaching new things.

Reading, writing, math, active listening Active Listening               Giving full attention to what other people are saying, taking time to understand the points being made, asking questions as appropriate, and not interrupting at inappropriate times.

                Mathematics     Using mathematics to solve problems.

                Reading Comprehension             Understanding written sentences and paragraphs in work related documents.

                Science Using scientific rules and methods to solve problems.

                Speaking             Talking to others to convey information effectively.

                Writing Communicating effectively in writing as appropriate for the needs of the audience.

Analyticial thinking and innovation         Analytical Thinking          Job requires analyzing information and using logic to address work-related issues and problems.

                Innovation         Job requires creativity and alternative thinking to develop new ideas for and answers to work-related problems.

Attention to detail, trustworthiness      Attention to Detail         Job requires being careful about detail and thorough in completing work tasks.

                Dependability   Job requires being reliable, responsible, and dependable, and fulfilling obligations.

                Integrity              Job requires being honest and ethical.

Complex problem- solving          Complex Problem-Solving          Identifying complex problems and reviewing related information to develop and evaluate options and implement solutions.

Coordination and time management     Time Management        Managing one's own time and the time of others.

                Coordination     Adjusting actions in relation to others' actions.

Creativity, originality and initative           Initiative              Job requires a willingness to take on responsibilities and challenges.

                Creativity            Workers on this job try out their own ideas.

                Responsibility   Workers on this job make decisions on their own.

                Autonomy          Workers on this job plan their work with little supervision.

                Originality           The ability to come up with unusual or clever ideas about a given topic or situation, or to develop creative ways to solve a problem.

Critical thinking and analysis       Critical Thinking                Using logic and reasoning to identify the strengths and weaknesses of alternative solutions, conclusions or approaches to problems.

                Monitoring         Monitoring/assessing performance of yourself, other individuals, or organizations to make improvements or take corrective action.

Emotional intelligence  Concern for Others        Job requires being sensitive to others' needs and feelings and being understanding and helpful on the job.

                Cooperation      Job requires being pleasant with others on the job and displaying a good-natured, cooperative attitude.

                Social Orientation           Job requires preferring to work with others rather than alone, and being personally connected with others on the job.

                Social Perceptiveness   Being aware of others' reactions and understanding why they react as they do.

Instruction, mentoring and teaching      Instructing          Teaching others how to do something.

                Training and Teaching Others    Identifying the educational needs of others, developing formal educational or training programs or classes, and teaching or instructing others.

Leadership and social influence               Leadership         Job requires a willingness to lead, take charge, and offer opinions and direction.

                Social Influence               Job requires having an impact on others in the organization, and displaying energy and leadership

Management of financial, material resources   Management of Financial Resources     Determining how money will be spent to get the work done, and accounting for these expenditures.

                Management of Material Resources     Obtaining and seeing to the appropriate use of equipment, facilities, and materials needed to do certain work.

Management of personnel        Management of Personnel Resources  Motivating, developing, and directing people as they work, identifying the best people for the job.

(Continued on next page)

Table A1: Classification of skills used, based on O*NET content model (cont’d.)

Competency bundle      Competencies, O*NET Description

Manual dexterity, endurance and precision       Endurance          The ability to exert oneself physically over long periods without getting out of breath.

                Flexibility, Balance, and Coordination    Abilities related to the control of gross body movements.

                Physical Strength Abilities           Abilities related to the capacity to exert force.

                Control Movement Abilities       Abilities related to the control and manipulation of objects in time and space.

                Fine Manipulative Abilities         Abilities related to the manipulation of objects.

                Reaction Time and Speed Abilities          Abilities related to speed of manipulation of objects.

Memory, verbal, auditory and spatial abilities   Attentiveness   Abilities related to application of attention.

                Memory              Abilities related to the recall of available information.

                Perceptual Abilities        Abilities related to the acquisition and organization of visual information.

                Spatial Abilities Abilities related to the manipulation and organization of spatial information.

                Verbal Abilities Abilities that influence the acquisition and application of verbal information in problem- solving.

Persuasion and negotiation       Negotiation       Bringing others together and trying to reconcile differences.

                Persuasion         Persuading others to change their minds or behavior.

Quality control and safety awareness   Quality Control Analysis               Conducting tests and inspections of products, services, or processes to evaluate quality or performance.

Reasoning, problem solving and ideation            Idea Generation and Reasoning Abilities             Abilities that influence the application and manipulation of information in problem-solving.

                Quantitative Abilities     Abilities that influence the solution of problems involving mathematical relationships.

Resiliance, stress tolerance and flexibility           Adaptability/Flexibility  Job requires being open to change (positive or negative) and to considerable variety in the workplace.

                Self Control        Job requires maintaining composure, keeping emotions in check, controlling anger, and avoiding aggressive behavior, even in very difficult situations.

                Stress Tolerance              Job requires accepting criticism and dealing calmly and effectively with high stress situations.

Service orientation         Service Orientation        Actively looking for ways to help people.

Systems analysis and evaluation              Judgment and Decision Making               Considering the relative costs and benefits of potential actions to choose the most appropriate one.

                Systems Analysis             Determining how a system should work and how changes in conditions, operations, and the environment will affect outcomes.

                Systems Evaluation        Identifying measures or indicators of system performance and the actions needed to improve or correct performance, relative to the goals of the system.

Technology design and programming    Programming    Writing computer programs for various purposes.

                Technology Design         Generating or adapting equipment and technology to serve user needs.

Technology installation and maintenance           Equipment Maintenance            Performing routine maintenance on equipment and determining when and what kind of maintenance is needed.

                Installation         Installing equipment, machines, wiring, or programs to meet specifications.

                Repairing            Repairing machines or systems using the needed tools.

Technology selection, monitoring and control   Equipment Selection     Determining the kind of tools and equipment needed to do a job.

                Operation and Control  Controlling operations of equipment or systems.

                Operation Monitoring   Watching gauges, dials, or other indicators to make sure a machine is working properly.

                Operations Analysis       Analyzing needs and product requirements to create a design.

Troubleshooting and user experience  Troubleshooting              Determining causes of operating errors and deciding what to do about them.

Visual, auditory and speech abilities      Auditory and Speech Abilities   Abilities related to auditory and oral input.

                Visual Abilities  Abilities related to visual sensory input.

Appendix B:

Industry and Regional Group Classifications

Table B1: Classification of industries featured in the report

Industry Cluster               Industry

Automotive, Aerospace, Supply Chain and Transport    Automotive

                Aerospace

                Supply Chain and Transport

Aviation, Travel and Tourism     Aviation, Travel and Tourism

Chemistry, Advanced Materials and Biotechnology        Chemistry, Advanced Materials and Biotechnology

Consumer          Retail, Consumer Goods and Lifestyle

                Agriculture, Food and Beverage

Energy Utilities and Technologies            Energy Utilities

                Energy Technologies

Financial Services and Investors               Insurance and Asset Management

                Banking and Capital Markets

                Private Investors

                Institutional Investors

Global Health and Healthcare    Global Health and Healthcare

Information and Communication Technologies Information Technology

                Telecommunications

                Electronics

Infrastructure   Infrastructure and Urbanisation

Mining and Metals         Mining and Metals

Oil and Gas         Oil and Gas

                Oil Field Services and Equipment

Professional Services    Professional Services

Table B2: Classification of regions, by country elegible for inclusion in the analysis

EAST ASIA AND

THE PACIFIC       EASTERN EUROPE AND

CENTRAL ASIA  LATIN AMERICA AND THE CARIBBEAN  MIDDLE EAST AND

NORTH AFRICA

NORTH AMERICA           

SOUTH ASIA     

SUB-SAHARAN AFRICA

WESTERN EUROPE

Australia Brunei Darussalam Cambodia China

Fiji Indonesia Japan Korea, Rep. Lao PDR Malaysia Mongolia Myanmar

New Zealand Philippines Singapore Thailand Timor-Leste Vietnam        Albania Armenia Azerbaijan Belarus Bosnia and Herzegovina Bulgaria Croatia

Czech Republic Estonia Georgia Hungary Kazakhstan

Kyrgyz Republic Latvia

Lithuania Macedonia Moldova Montenegro Poland Romania

Russian Federation Serbia

Slovak Republic Slovenia Tajikistan Ukraine Uzbekistan               Argentina Bahamas Barbados Belize Bolivia Brazil Chile Colombia

Costa Rica Cuba Dominican Republic Ecuador

El Salvador Guatemala Guyana Haiti Honduras Jamaica Mexico Nicaragua Panama Paraguay Peru Suriname Trinidad and Tobago Uruguay Venezuela              Algeria Bahrain Egypt

Iran, Islamic Rep. Iraq

Israel Jordan Kuwait Lebanon Mauritania Morocco Oman Qatar

Saudi Arabia Syria

Tunisia Turkey United Arab Emirates Yemen     Canada United States   Bangladesh Bhutan India Maldives Nepal Pakistan

Sri Lanka              Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Chad

Côte d'Ivoire Eritrea Ethiopia Gabon Gambia, The Ghana Guinea Kenya Lesotho Liberia Madagascar Malawi

Mali Mauritius Mozambique Namibia Nigeria Rwanda Senegal Sierra Leone South Africa Swaziland Tanzania Uganda Zambia Zimbabwe          Austria Belgium Cyprus Denmark Finland France Germany Greece Iceland Ireland Italy

Luxembourg Malta Netherlands Norway Portugal Spain Sweden Switzerland

United Kingdom

Part 2

Industry and Country/Region Profiles

User’s Guide: How to Read the Industry and Country/Region Profiles

Part 2 of the report presents findings through an industry and country lens, with the aim of providing specific practical information to decision-makers and experts from academia, business, government and civil society. Complementing the cross-industry and cross-country analysis of results in Part 1, it provides deeper granularity for a given industry, country or region through dedicated Industry Profiles and Country/Region Profiles. Profiles are intended to provide interested companies and policy- makers with the opportunity to benchmark themselves relative to the range of expectations prevalent in their industry and/or country. This User’s Guide provides

an overview of the information contained in the various Industry Profiles and Country/Region Profiles and its appropriate interpretation.

Industry Profiles

   Trends driving industry growth

The first section of each Industry Profile provides an overview of the top socio-economic trends and

technological disruptions expected to positively affect the growth of the industry over the 2018–2022 period, ranked according to the share of survey respondents from the industry who selected the stated trend as one of the top drivers of growth for their industry. For a more detailed discussion of each trend, please refer to the Strategic Drivers of New Business Models section in Part 1 of the report.

   Technology adoption in industry

The bar chart represents the share of survey respondents from the industry who indicated that, by 2022, their company was “likely” or “very likely” (on a 5-point scale) to have adopted the stated technology as part of its growth strategy. For a more detailed discussion of each technology, please refer to the section Strategic Drivers of New Business Models section in Part 1 of the report.

   Expected impact on workforce

This bar chart represents the share of survey respondents from the industry who expect their company to have adopted the stated measure(s) over the 2018–2022 period as part of their current growth strategy. For a more detailed discussion of each measure, please refer to the The 2022 Jobs Landscape section in Part 1 of the report.

   Barriers to adoption of new technologies

This bar chart represents the five biggest perceived barriers to adopting new technologies across the industry, as measured by the share of survey respondents from the industry who selected the stated obstacle as one of the top

impediments to successful new technology adoption faced by their company. The data featured in the Industry Profile represents additional supplementary information beyond the high-level overview provided in Part 1 of the report.

   Projected adaptation partners

The bar chart in the first section of the second page of the Industry Profile represents the share of survey respondents from the industry who indicated that their company was “likely” or “very likely” (on a 5-point scale) to collaborate with the stated partner entity over the 2018–2022 period

to develop measures and strategies for adaptation to the trends and disruptions expected to affect the industry.

For a more detailed discussion of adaptation partner collaboration intentions, please refer to the The Reskilling Imperative section in Part 1 of the report.

   Augmentation of key job tasks in 2018 and 2022  Bar charts in this section represent the expected evolution of human-machine collaboration over the 2018–2022 period across the industry. The column labels on the left- hand side of the section report the three most common job tasks, in terms of total task hours, performed across the totality of jobs in the industry. The 2018 column reports the total share of task hours contributed to the achievement

of the job task by human workers on the one hand, and  by machines or algorithms on the other. For example, if the respective shares were 75% and 25%, respectively, for every hour spent on performing the task in the industry, 45 minutes would have been expended by human workers and 15 minutes by machines or algorithms. The 2022 column reports the expected evolution of this human- machine division of labour across the industry by the stated year.

Note that the diagrams measure the relative change in contribution by human workers and machines, not the absolute underlying number of task hours—meaning that there is no “zero-sum” competition between the two. For example, a reduction in the relative share of task hours contributed to a specific task by human workers could be entirely due to increased machine productivity over the 2018–2022 period, rather than a reduction in the absolute number of work hours spent on the task by human workers. For a more detailed discussion of this issue, please refer to the From Automation to Augmentation section in Part 1 of the report.

Roles such as:  

Roles such as:

               

               

               

               

               

               

               

               

               

General and Operations Managers        Accountants and Auditors

   Average reskilling needs

This section highlights the expected reskilling needs over the 2018–2022 period across the industry. The diagram represents the distribution of the industry workforce according to the expected average timeframe required to retrain or upskill affected workers—either in order to equip the industry’s workforce with the skills needed

to seize new opportunities created by the trends and disruptions expected to affect the industry, or in order to avoid losing competitiveness due to the obsolescence of the workforce’s existing skillsets. For a more detailed

discussion of expected reskilling needs, please refer to the

The Reskilling Imperative section in Part 1 of the report.

   Workforce in 2018 and 2022

This table provides an overview of expected developments in the industry-specific job roles most frequently mentioned by survey respondents from the industry. The blue column highlights emerging job roles for the industry in question and indicates their expected total employment share within the industry workforce in 2018 and 2022. Analogously, the grey column highlights declining job roles for the industry in question and indicates their expected total employment share within the industry workforce in 2018 and 2022.

The individual job roles listed underneath each category are for illustrative purposes and report the job roles most frequently cited by survey respondents from the industry. Categorization of job roles is adapted from the O*NET labour market information system (please see Appendix A: Report Methodology for details).

Country/Region Profiles

   Factors determining job location decisions

The first section of each Country/Region Profile provides an overview of the factors determining job location decisions at a global level for companies operating in the country or region. On the one hand, policy-makers may use the information provided to benchmark the country on the priority factors identified by each industry to determine opportunities for the country to build up its future talent pool in a targeted manner. On the other hand, the information provided might also prove useful to evaluate the potential risk posed by new technologies and shifting comparative advantage that might affect future company and industry location decisions in relation to the country. For a more detailed discussion of this issue, please refer to the The Future of Jobs across Regions section in Part 1  of the report.

   Technology adoption

This bar chart represents the share of survey respondents from companies operating in the country in question

who indicated that, by 2022, their company was “likely” or “very likely” (on a 5-point scale) to have adopted the

stated technology as part of its growth strategy. For a more detailed discussion of each technology, please refer to the Strategic Drivers of New  Business  Models  section  in Part 1 of the report.

   Emerging job roles

This table provides an overview of job roles expected to experience an increase in demand across the country over the 2018–2022 period. The individual job roles listed are for illustrative purposes and report the job roles most frequently cited by survey respondents from companies operating in the country. Categorization of job roles is

adapted from the O*NET labour market information system (please see Appendix A:  Report  Methodology  for details).

   Average reskilling needs

The first section of the second page of the Country/ Region Profile highlights the expected reskilling needs over the 2018–2022 period across the country. The diagram represents the distribution of the country’s workforce according to the expected average timeframe required to retrain or upskill affected workers—either in order to equip the country’s workforce with the skills needed to seize new opportunities created by the trends and disruptions expected to affect businesses operating in the country in

question, or in order to avoid losing competitiveness due to the obsolescence of the workforce’s existing skillsets. For

a more detailed discussion of expected reskilling needs,

The Future of Jobs Report 2018

please refer to the The Reskilling Imperative section in Part 1 of the report.

   Responses to shifting skills needs

This stacked bar chart is a diagrammatic representation of the share of survey respondents from companies operating in the country in question who indicated that, by 2022, their company was either “likely” or “very likely” (on a 5-point scale) to have implemented the stated response measure to shifting skills needs within its industry, that their company was yet “undecided” about introducing the response measure in question, or who questioned the need for introducing the stated response measure and therefore indicated that their company was “unlikely” or “very unlikely” (on a 5-point scale) to adopt it. The stacked bars are ordered by the overall proportion of survey respondents from companies operating in the country who considered introduction of the respective response measures “likely” or “very likely”—providing a sense of

the total shifting skills needs response profile across

companies operating in the country. Underlying responses have been rounded and may therefore not exactly add

up to 100%. For a more detailed discussion of expected

reskilling response strategies, please refer to the The Reskilling Imperative section in Part 1 of the report.

   Emerging skills

This table provides an outlook on the expected evolution of workforce skills demand over the 2018–2022 period across the country. The individual skills listed are for illustrative purposes and report the skills most frequently cited by survey respondents from companies operating in the country. Categorization of skills is adapted from the O*NET labour market information system. For a detailed description of each skill, please see Table A1 in the Appendix A: Report Methodology section in v of the report.

   Projected use of training providers

This bar chart represents the share of survey respondents from companies operating in the country who expect their company to make use of the stated education and training provider(s) over the 2018–2022 period to deliver reskilling and upskilling opportunities to their current workforce.

For a more detailed discussion of companies’ retraining and upskilling intentions, please refer to The Reskilling Imperative section in Part 1 of the report.

Index of Profiles

Industry Profiles

Automotive, Aerospace, Supply Chain & Transport        42

Aviation, Travel & Tourism          44

Chemistry, Advanced Materials & Biotechnology            46

Consumer          48

Energy Utilities & Technologies 50

Financial Services & Investors   52

Global Health & Healthcare        54

Information & Communication Technologies     56

Infrastructure   58

Mining & Metals              60

Oil & Gas             62

Professional Services    64

Country/Region Profiles

Argentina           68

Australia              70

Brazil     72

China    74

France  76

Germany            78

India      80

Indonesia           82

Japan    84

Korea, Rep         86

Mexico 88

Philippines         90

Russian Federation        92

Singapore           94

South Africa       96

Switzerland        98

Thailand              100

United Kingdom              102

United States    104

Vietnam              106

Central Asia       108

East Asia and the Pacific               110

Eastern Europe 112

Latin America and the Caribbean             114

Middle East and North Africa     116

North America  118

South Asia          120

Sub-Saharan Africa         122

Western Europe              124

Industry Profiles

Automotive, Aerospace, Supply Chain & Transport

Trends driving industry growth Technology adoption in industry (share of companies surveyed)

1.            Increasing adoption of new technology

2.            Advances in artificial intelligence

3.            Increasing availability of big data

4.            Shifts in national economic growth

5.            Advances in new energy supplies and technologies

6.            Advances in mobile internet

7.            Advances in cloud technology

8.            Expansion of affluence in developing economies

9.            Advances in computing power

10.          Advances in devices bridging the human-machine divide

Expected impact on workforce (share of companies surveyed)               Barriers to adoption of new technologies (share of companies surveyed)

Modify value chain         82%

Expand task-specialized contractors      52%

Expand the workforce  50%

Reduce workforce due to automation  48%

Modify locations of operation   42%

Bring financing on-board for transition 38%

Expand workforce due to automation  20%

Skills gaps, local labour market  Don’t understand opportunties               Skills gaps, leadership   Shortage of investment capital   Lack of flexibility, hiring and firing

Automotive, Aerospace, Supply Chain & Transport

Projected adaptation partners Augmentation of key job tasks in 2018 and 2022 (share of task hours)

Specialized departments in my firm       84%

Professional services firms         71%

Industry associations     66%

1.            Communicating and interacting

2.            Performing complex and technical activities

3.            Performing physical and manual work activities

Human Machine

Human

Machine

2018      2022

Average reskilling needs (share of workforce)  Workforce in 2018 and 2022

8% in 2018          21% in 2022

n Less than 1 month      13%

n 1 to 3 months                11%

n 3 to 6 months                8%

n 6 to 12 months             11%

n Over 1 year    12%

n No reskilling needed  45%

Aviation, Travel & Tourism

Trends driving industry growth Technology adoption in industry (share of companies surveyed)

1.            Advances in mobile internet

2.            Increasing adoption of new technology

3.            Expansion of affluence in developing economies

4.            Advances in artificial intelligence

5.            Expansion of the middle classes

6.            Expansion of education

7.            Increasing availability of big data

8.            Increasing frequency of new working arrangements

9.            Shifts in national economic growth

10.          Advances in cloud technology

Expected impact on workforce (share of companies surveyed)               Barriers to adoption of new technologies (share of companies surveyed)

Reduce workforce due to automation  50%

Modify locations of operation   50%

Expand workforce due to automation  50%

Expand task-specialized contractors      50%

Modify value chain         44%

Expand the workforce  39%

Bring financing on-board for transition 33%

Skills gaps, local labour market  Don’t understand opportunties               Skills gaps, leadership   Shortage of investment capital   Skills gaps, global labour market

Aviation, Travel & Tourism

Projected adaptation partners Augmentation of key job tasks in 2018 and 2022 (share of task hours)

Specialized departments in my firm       94%

Industry associations     71%

Local educational institutions    65%

1.            Communicating and interacting

2.            Coordinating, developing, managing and advising

3.            Performing complex and technical activities

Human Machine

Human

Machine

2018      2022

Average reskilling needs (share of workforce)  Workforce in 2018 and 2022

8% in 2018          13% in 2022

n Less than 1 month      13%

n 1 to 3 months                13%

n 3 to 6 months                12%

n 6 to 12 months             11%

n Over 1 year    18%

n No reskilling needed  32%

Chemistry, Advanced Materials & Biotechnology

Trends driving industry growth Technology adoption in industry (share of companies surveyed)

1.            Increasing adoption of new technology

2.            Expansion of affluence in developing economies

3.            Increasing availability of big data

4.            Advances in new energy supplies and technologies

5.            Shifts in global macroeconomic growth

6.            Shifts in national economic growth

7.            Advances in artificial intelligence

8.            Advances in computing power

9.            Expansion of the middle classes

10.          Increasing urbanization

Expected impact on workforce (share of companies surveyed)               Barriers to adoption of new technologies (share of companies surveyed)

Modify value chain         71%

Modify locations of operation   58%

Expand task-specialized contractors      42%

Reduce workforce due to automation  38%

Expand the workforce  38%

Expand workforce due to automation  29%

Bring financing on-board for transition 29%

Don’t understand opportunties               Skills gaps, local labour market  Skills gaps, global labour market              Skills gaps, leadership              Lack of flexibility, hiring and firing

Chemistry, Advanced Materials & Biotechnology

Projected adaptation partners Augmentation of key job tasks in 2018 and 2022 (share of task hours)

Specialized departments in my firm       86%

Professional services firms         83%

Industry associations     65%

1.            Coordinating, developing, managing and advising

2.            Performing complex and technical activities

3.            Performing physical and manual work activities

Human Machine

Human

Machine

2018      2022

Average reskilling needs (share of workforce)  Workforce in 2018 and 2022

10% in 2018        19% in 2022

n Less than 1 month      10%

n 1 to 3 months                15%

n 3 to 6 months                10%

n 6 to 12 months             9%

n Over 1 year    15%

n No reskilling needed  42%

Consumer

Trends driving industry growth Technology adoption in industry (share of companies surveyed)

1.            Advances in mobile internet

2.            Advances in artificial intelligence

3.            Shifts of mindset among the new generation

4.            Increasing adoption of new technology

5.            Increasing availability of big data

6.            Increasing urbanization

7.            Shifts in national economic growth

8.            Advances in new energy supplies and technologies

9.            Expansion of affluence in developing economies

10.          Expansion of the middle classes

Expected impact on workforce (share of companies surveyed)               Barriers to adoption of new technologies (share of companies surveyed)

Modify value chain         83%

Reduce workforce due to automation  57%

Modify locations of operation   54%

Expand task-specialized contractors      51%

Bring financing on-board for transition 40%

Expand the workforce  34%

Expand workforce due to automation  23%

Consumer

Projected adaptation partners Augmentation of key job tasks in 2018 and 2022 (share of task hours)

Professional services firms         88% Specialized departments in my firm 84% Academic experts              53%

1.            Communicating and interacting

2.            Coordinating, developing, managing and advising

3.            Performing physical and manual work activities

Human Machine

Human

Machine

2018      2022

Average reskilling needs (share of workforce)  Workforce in 2018 and 2022

15% in 2018        28% in 2022

n Less than 1 month      8%

n 1 to 3 months                12%

n 3 to 6 months                10%

n 6 to 12 months             10%

n Over 1 year    9%

n No reskilling needed  50%

Energy Utilities & Technologies

Trends driving industry growth Technology adoption in industry (share of companies surveyed)

1.            Advances in new energy supplies and technologies

2.            Increasing availability of big data

3.            Advances in artificial intelligence

4.            Advances in cloud technology

5.            Advances in computing power

6.            Increasing adoption of new technology

7.            Expansion of education

8.            Advances in mobile internet

9.            Effects of climate change

10.          Expansion of affluence in developing economies

Expected impact on workforce (share of companies surveyed)               Barriers to adoption of new technologies (share of companies surveyed)

Modify value chain         78%

Reduce workforce due to automation  56%

Modify locations of operation   52%

Expand task-specialized contractors      52%

Bring financing on-board for transition 37%

Expand workforce due to automation  19%

Expand the workforce  19%

Energy Utilities & Technologies

Projected adaptation partners Augmentation of key job tasks in 2018 and 2022 (share of task hours)

Specialized departments in my firm       80%

Industry associations     76%

Professional services firms         62%

1.            Coordinating, developing, managing and advising

2.            Performing complex and technical activities

3.            Performing physical and manual work activities

Human Machine

Human

Machine

2018      2022

Average reskilling needs (share of workforce)  Workforce in 2018 and 2022

n Less than 1 month      14%

n 1 to 3 months                8%

n 3 to 6 months                8%

n 6 to 12 months             9%

n Over 1 year    9%

n No reskilling needed  51%

Financial Services & Investors

Trends driving industry growth Technology adoption in industry (share of companies surveyed)

1.            Advances in mobile internet

2.            Increasing availability of big data

3.            Increasing adoption of new technology

4.            Advances in artificial intelligence

5.            Advances in cloud technology

6.            Advances in computing power

7.            Expansion of affluence in developing economies

8.            Expansion of education

9.            Expansion of the middle classes

10.          Shifts of mindset among the new generation

Expected impact on workforce (share of companies surveyed)               Barriers to adoption of new technologies (share of companies surveyed)

Modify locations of operation   67%

Reduce workforce due to automation  56%

Modify value chain         56%

Expand task-specialized contractors      44%

Expand the workforce  31%

Bring financing on-board for transition 31%

Expand workforce due to automation  25%

Skills gaps, local labour market  Don’t understand opportunties               Skills gaps, leadership   Skills gaps, global labour market   Lack of flexibility, hiring and firing

Financial Services & Investors

Projected adaptation partners Augmentation of key job tasks in 2018 and 2022 (share of task hours)

Specialized departments in my firm       79%

Professional services firms         76%

Industry associations     73%

1.            Administering

2.            Communicating and interacting

3.            Information and data processing

Human Machine

Human

Machine

2018      2022

Average reskilling needs (share of workforce)  Workforce in 2018 and 2022

15% in 2018        29% in 2022

n Less than 1 month      13%

n 1 to 3 months                9%

n 3 to 6 months                10%

n 6 to 12 months             11%

n Over 1 year    13%

n No reskilling needed  44%

Global Health & Healthcare

Trends driving industry growth Technology adoption in industry (share of companies surveyed)

1.            Increasingly ageing societies

2.            Advances in artificial intelligence

3.            Expansion of affluence in developing economies

4.            Expansion of the middle classes

5.            Increasing adoption of new technology

6.            Increasing availability of big data

7.            Shifts in global macroeconomic growth

8.            Shifts in national economic growth

9.            Advances in mobile internet

10.          Expansion of education

Expected impact on workforce (share of companies surveyed)               Barriers to adoption of new technologies (share of companies surveyed)

Modify locations of operation   73%

Modify value chain         67%

Reduce workforce due to automation  47%

Expand task-specialized contractors      33%

Expand the workforce  27%

Expand workforce due to automation  20%

Bring financing on-board for transition 20%

Don’t understand opportunties               Skills gaps, leadership   Skills gaps, local labour market  Shortage of investment capital   Other (Please specify)

Global Health & Healthcare

Projected adaptation partners Augmentation of key job tasks in 2018 and 2022 (share of task hours)

Professional services firms         93% Specialized departments in my firm 93% Academic experts              67%

1.            Communicating and interacting

2.            Coordinating, developing, managing and advising

3.            Performing complex and technical activities

Human Machine

Human

Machine

2018      2022

Average reskilling needs (share of workforce)  Workforce in 2018 and 2022

n Less than 1 month      11%

n 1 to 3 months                15%

n 3 to 6 months                12%

n 6 to 12 months             11%

n Over 1 year    10%

n No reskilling needed  41%

Information & Communication Technologies

Trends driving industry growth Technology adoption in industry (share of companies surveyed)

1.            Increasing adoption of new technology

2.            Advances in cloud technology

3.            Increasing availability of big data

4.            Advances in mobile internet

5.            Advances in computing power

6.            Advances in artificial intelligence

7.            Advances in devices bridging the human-machine divide

8.            Expansion of affluence in developing economies

9.            Expansion of education

10.          Advances in new energy supplies and technologies

Expected impact on workforce (share of companies surveyed)               Barriers to adoption of new technologies (share of companies surveyed)

Expand task-specialized contractors      57%

Reduce workforce due to automation  55%

Modify value chain         55%

Modify locations of operation   55%

Expand workforce due to automation  52%

Expand the workforce  41%

Bring financing on-board for transition 34%

Skills gaps, local labour market  Don’t understand opportunties               Skills gaps, leadership   Skills gaps, global labour market   Lack of flexibility, hiring and firing

Information & Communication Technologies

Projected adaptation partners Augmentation of key job tasks in 2018 and 2022 (share of task hours)

Specialized departments in my firm 88% Professional services firms      69% International educational institutions 64%

1.            Administering

2.            Communicating and interacting

3.            Performing complex and technical activities

Human Machine

Human

Machine

2018      2022

Average reskilling needs (share of workforce)  Workforce in 2018 and 2022

17% in 2018        33% in 2022

n Less than 1 month      12%

n 1 to 3 months                8%

n 3 to 6 months                10%

n 6 to 12 months             10%

n Over 1 year    10%

n No reskilling needed  50%

Infrastructure

Trends driving industry growth Technology adoption in industry (share of companies surveyed)

1.            Increasing urbanization

2.            Increasing availability of big data

3.            Advances in new energy supplies and technologies

4.            Expansion of the middle classes

5.            Shifts in national economic growth

6.            Advances in artificial intelligence

7.            Expansion of affluence in developing economies

8.            Advances in cloud technology

9.            Shifts in global macroeconomic growth

10.          Advances in devices bridging the human-machine divide

Expected impact on workforce (share of companies surveyed)               Barriers to adoption of new technologies (share of companies surveyed)

Modify value chain         78%

Expand task-specialized contractors      56%

Bring financing on-board for transition 56%

Reduce workforce due to automation  33%

Modify locations of operation   28%

Expand the workforce  28%

Expand workforce due to automation  22%

Skills gaps, local labour market  Skills gaps, leadership   Don’t understand opportunties               Shortage of investment capital   No interest among leadership

Infrastructure

Projected adaptation partners Augmentation of key job tasks in 2018 and 2022 (share of task hours)

Specialized departments in my firm       82%

Industry associations     73%

Professional services firms         71%

1.            Administering

2.            Communicating and interacting

3.            Performing complex and technical activities

Human Machine

Human

Machine

2018      2022

Average reskilling needs (share of workforce)  Workforce in 2018 and 2022

16% in 2018        19% in 2022

n Less than 1 month      14%

n 1 to 3 months                11%

n 3 to 6 months                7%

n 6 to 12 months             9%

n Over 1 year    11%

n No reskilling needed  47%

Mining & Metals

Trends driving industry growth Technology adoption in industry (share of companies surveyed)

1.            Increasing adoption of new technology

2.            Advances in devices bridging the human-machine divide

3.            Advances in new energy supplies and technologies

4.            Advances in artificial intelligence

5.            Shifts in national economic growth

6.            Expansion of education

7.            Expansion of gender parity

8.            Increasing availability of big data

9.            Shifts in global macroeconomic growth

10.          Advances in cloud technology

Expected impact on workforce (share of companies surveyed)               Barriers to adoption of new technologies (share of companies surveyed)

Reduce workforce due to automation  72%

Expand task-specialized contractors      56%

Modify value chain         44%

Modify locations of operation   44%

Expand workforce due to automation  33%

Expand the workforce  22%

Bring financing on-board for transition 22%

Skills gaps, local labour market  Don’t understand opportunties               Skills gaps, leadership   Shortage of investment capital   Lack of flexibility, hiring and firing

Mining & Metals

Projected adaptation partners Augmentation of key job tasks in 2018 and 2022 (share of task hours)

Specialized departments in my firm       94%

Professional services firms         88%

Industry associations     80%

1.            Administering

2.            Communicating and interacting

3.            Performing physical and manual work activities

Human Machine

Human

Machine

2018      2022

Average reskilling needs (share of workforce)  Workforce in 2018 and 2022

15% in 2018        22% in 2022

n Less than 1 month      12%

n 1 to 3 months                9%

n 3 to 6 months                10%

n 6 to 12 months             11%

n Over 1 year    8%

n No reskilling needed  50%

Oil & Gas

Trends driving industry growth Technology adoption in industry (share of companies surveyed)

1.            Advances in cloud technology

2.            Advances in computing power

3.            Increasing availability of big data

4.            Increasing adoption of new technology

5.            Advances in artificial intelligence

6.            Advances in new energy supplies and technologies

7.            Shifts in national economic growth

8.            Advances in mobile internet

9.            Expansion of education

10.          Expansion of gender parity

Expected impact on workforce (share of companies surveyed)               Barriers to adoption of new technologies (share of companies surveyed)

Modify value chain         87%

Modify locations of operation   57%

Reduce workforce due to automation  52%

Expand task-specialized contractors      52%

Expand the workforce  35%

Bring financing on-board for transition 30%

Expand workforce due to automation  26%

Don’t understand opportunties               Skills gaps, local labour market  Skills gaps, leadership   Lack of flexibility, hiring and firing            Skills gaps, global labour market

Oil & Gas

Projected adaptation partners Augmentation of key job tasks in 2018 and 2022 (share of task hours)

Specialized departments in my firm       91%

Industry associations     87%

Professional services firms         74%

1.            Communicating and interacting

2.            Performing complex and technical activities

3.            Performing physical and manual work activities

Human Machine

Human

Machine

2018      2022

Average reskilling needs (share of workforce)  Workforce in 2018 and 2022

17% in 2018        26% in 2022

n Less than 1 month      10%

n 1 to 3 months                12%

n 3 to 6 months                10%

n 6 to 12 months             10%

n Over 1 year    8%

n No reskilling needed  50%

Professional Services

Trends driving industry growth Technology adoption in industry (share of companies surveyed)

1.            Increasing adoption of new technology

2.            Advances in artificial intelligence

3.            Increasing availability of big data

4.            Advances in cloud technology

5.            Advances in mobile internet

6.            Expansion of education

7.            Shifts in national economic growth

8.            Expansion of affluence in developing economies

9.            Increasing frequency of new working arrangements

10.          Shifts of mindset among the new generation

Expected impact on workforce (share of companies surveyed)               Barriers to adoption of new technologies (share of companies surveyed)

Expand the workforce  71%

Modify value chain         60%

Expand workforce due to automation  57%

Modify locations of operation   54%

Expand task-specialized contractors      51%

Reduce workforce due to automation  37%

Bring financing on-board for transition 37%

Skills gaps, local labour market  Don’t understand opportunties               Skills gaps, leadership   Shortage of investment capital   Lack of flexibility, hiring and firing

Professional Services

Projected adaptation partners Augmentation of key job tasks in 2018 and 2022 (share of task hours)

Specialized departments in my firm       82%

Professional services firms         67%

Industry associations     66%

1.            Communicating and interacting

2.            Coordinating, developing, managing and advising

3.            Reasoning and decision-making

Human Machine

Human

Machine

2018      2022

Average reskilling needs (share of workforce)  Workforce in 2018 and 2022

17% in 2018        37% in 2022

n Less than 1 month      12%

n 1 to 3 months                10%

n 3 to 6 months                10%

n 6 to 12 months             9%

n Over 1 year    10%

n No reskilling needed  50%

Country and Region Profiles

Argentina

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Production cost               Labour cost        Talent availability

Aviation, Travel & Tourism          Talent availability            Ease of importing talent              Organization HQ

Chemistry, Advanced Materials & Biotechnology            Talent availability            Labour cost          Production cost            

Consumer          Labour cost        Talent availability            Quality of the supply chain

Energy Utilities & Technologies Talent availability            Production cost               Organization HQ

Financial Services & Investors   Talent availability            Organization HQ              Labour cost

Global Health & Healthcare        Talent availability            Labour cost        Production cost

Information & Communication Technologies     Talent availability            Labour cost        Geographic concentration

Oil & Gas             Production cost               Talent availability            Organization HQ

Professional Services    Talent availability            Labour cost        Strong local ed. provision

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Software and Applications Developers and Analysts Managing Directors and Chief Executives

Data Analysts and Scientists

Sales and Marketing Professionals General and Operations Managers

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

Assembly and Factory Workers Financial and Investment Advisers Database and Network Professionals Human Resources Specialists

Argentina

                 

Average reskilling needs (share of workforce)

n Less than 1 month      13%

n 1 to 3 months                13%

n 3 to 6 months                10%

n 6 to 12 months             10%

n Over 1 year    9%

n No reskilling needed  47%

Responses to shifting skills needs (share of companies surveyed)

Hire new permanent staff with skills relevant to new technologies        84%                                       12%

                                                              

                                                              

Look to automate the work       83%                                       13%

                                                              

Hire new temporary staff with skills relevant to new technologies         74%                       14%      

Retrain existing employees        72%

Emerging skills  Projected use of training providers (share of training)

Analytical thinking and innovation          Critical thinking and analysis       Internal department     47%

Creativity, originality and initiative          Complex problem-solving           Private training providers            32%

Active learning and learning strategies Resilience, stress tolerance and flexibility           Private educational institutions                23%

Technology design and programming    Emotional intelligence  Public training provider 14%

Reasoning, problem-solving and ideation

Leadership and social influence                               Public educational institutions  14%

Australia

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Talent availability            Quality of the supply chain                Production cost

Aviation, Travel & Tourism          Talent availability            Organization HQ              Ease of importing talent

Chemistry, Advanced Materials & Biotechnology            Talent availability            Labour cost        Geographic concentration

Consumer          Talent availability            Labour cost        Geographic concentration

Energy Utilities & Technologies Geographic concentration          Production cost               Talent availability

Financial Services & Investors   Talent availability            Labour cost        Organization HQ

Global Health & Healthcare        Talent availability            Labour cost          Production cost            

Information & Communication Technologies     Talent availability            Labour cost        Geographic concentration

Oil & Gas             Production cost               Geographic concentration          Talent availability

Professional Services    Talent availability            Strong local ed. provision            Labour cost

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Software and Applications Developers and Analysts Sales and Marketing Professionals

Managing Directors and Chief Executives Data Analysts and Scientists

General and Operations Managers

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

Human Resources Specialists Assembly and Factory Workers Financial and Investment Advisers

Business Services and Administration Managers

Australia

                 

Average reskilling needs (share of workforce)

n Less than 1 month      11%

n 1 to 3 months                13%

n 3 to 6 months                9%

n 6 to 12 months             9%

n Over 1 year    10%

n No reskilling needed  49%

Responses to shifting skills needs (share of companies surveyed)

Look to automate the work       87%

Hire new permanent staff with skills relevant to new technologies        84% Retrain existing employees              74%

Hire new temporary staff with skills relevant to new technologies         73%

Expect existing employees to pick up skills on the job   71%

Hire freelancers with skills relevant to new technologies            67%

Outsource some business functions to external contractors      61% Strategic redundancies of staff who lack the skills to use new technologies             55%

n Likely n Equally likely  n Unlikely

                 

Emerging skills  Projected use of training providers (share of training)

Creativity, originality and initiative          Leadership and social influence               Internal department     50%

Analytical thinking and innovation          Emotional intelligence  Private training providers            29%

Active learning and learning strategies Reasoning, problem-solving and ideation           Private educational institutions                21%

Technology design and programming    Resilience, stress tolerance and flexibility           Public educational institutions                18%

Complex problem-solving

Critical thinking and analysis                      Public training provider 16%

Brazil

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Talent availability            Production cost               Labour cost

Aviation, Travel & Tourism          Talent availability            Organization HQ              Ease of importing talent

Chemistry, Advanced Materials & Biotechnology            Talent availability            Labour cost        Production cost

Consumer          Labour cost        Talent availability            Quality of the supply chain

Energy Utilities & Technologies Production cost               Talent availability            Quality of the supply chain

Financial Services & Investors   Talent availability            Geographic concentration          Organization HQ

Global Health & Healthcare        Talent availability            Labour cost          Production cost            

Information & Communication Technologies     Talent availability            Labour cost        Organization HQ

Oil & Gas             Production cost               Talent availability            Organization HQ

Professional Services    Talent availability            Strong local ed. provision            Labour cost

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Software and Applications Developers and Analysts Managing Directors and Chief Executives

Data Analysts and Scientists

Sales and Marketing Professionals General and Operations Managers

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

Human Resources Specialists Financial Analysts

Database and Network Professionals Financial and Investment Advisers

Brazil

                 

Average reskilling needs (share of workforce)

n Less than 1 month      12%

n 1 to 3 months                14%

n 3 to 6 months                10%

n 6 to 12 months             9%

n Over 1 year    9%

n No reskilling needed  47%

Responses to shifting skills needs (share of companies surveyed)

Hire new permanent staff with skills relevant to new technologies        88% Look to automate the work              86%

Retrain existing employees        79%

Hire new temporary staff with skills relevant to new technologies         74%

Expect existing employees to pick up skills on the job   68%

Hire freelancers with skills relevant to new technologies            62%

Outsource some business functions to external contractors      61% Strategic redundancies of staff who lack the skills to use new technologies             54%

n Likely n Equally likely  n Unlikely

                 

Emerging skills  Projected use of training providers (share of training)

Analytical thinking and innovation          Critical thinking and analysis       Internal department     48%

Creativity, originality and initiative          Complex problem-solving           Private training providers            28%

Active learning and learning strategies Resilience, stress tolerance and flexibility           Private educational institutions                18%

Technology design and programming    Emotional intelligence  Public educational institutions  16%

Reasoning, problem-solving and ideation

Leadership and social influence                               Public training provider 12%

China

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Talent availability            Production cost               Quality of the supply chain

Aviation, Travel & Tourism          Talent availability            Organization HQ              Ease of importing talent

Chemistry, Advanced Materials & Biotechnology            Talent availability              Labour cost                      Production cost

Consumer          Talent availability            Quality of the supply chain         Production cost

Energy Utilities & Technologies Production cost               Labour cost        Location of raw materials

Financial Services & Investors   Talent availability            Labour cost        Organization HQ

Global Health & Healthcare        Talent availability            Labour cost          Production cost            

Information & Communication Technologies     Talent availability            Labour cost        Geographic concentration

Oil & Gas             Production cost               Talent availability            Geographic concentration

Professional Services    Talent availability            Strong local ed. provision            Labour cost

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Software and Applications Developers and Analysts Sales and Marketing Professionals

Managing Directors and Chief Executives Data Analysts and Scientists

General and Operations Managers

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

Human Resources Specialists Assembly and Factory Workers Financial and Investment Advisers Database and Network Professionals

China

                 

Average reskilling needs (share of workforce)

n Less than 1 month      12%

n 1 to 3 months                13%

n 3 to 6 months                9%

n 6 to 12 months             9%

n Over 1 year    9%

n No reskilling needed  48%

Responses to shifting skills needs (share of companies surveyed)

Look to automate the work       86%

Hire new permanent staff with skills relevant to new technologies        86% Retrain existing employees              79%

Hire new temporary staff with skills relevant to new technologies         68%

Outsource some business functions to external contractors      65%

Expect existing employees to pick up skills on the job   64%

Hire freelancers with skills relevant to new technologies            58% Strategic redundancies of staff who lack the skills to use new technologies             47%

n Likely n Equally likely  n Unlikely

                 

Emerging skills  Projected use of training providers (share of training)

Creativity, originality and initiative          Emotional intelligence  Internal department     52%

Analytical thinking and innovation          Leadership and social influence               Private training providers            28%

Active learning and learning strategies Systems analysis and evaluation              Private educational institutions                21%

Technology design and programming    Reasoning, problem-solving and ideation           Public educational institutions                18%

Complex problem-solving

Critical thinking and analysis                      Public training provider 14%

France

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Talent availability            Quality of the supply chain                Production cost

Aviation, Travel & Tourism          Talent availability            Organization HQ              Ease of importing talent

Chemistry, Advanced Materials & Biotechnology            Talent availability            Production cost               Labour cost

Consumer          Labour cost        Geographic concentration          Talent availability

Energy Utilities & Technologies Labour cost        Production cost               Talent availability

Financial Services & Investors   Talent availability            Labour cost        Organization HQ

Global Health & Healthcare        Talent availability            Labour cost          Production cost            

Information & Communication Technologies     Talent availability            Labour cost        Organization HQ

Oil & Gas             Geographic concentration          Talent availability            Organization HQ

Professional Services    Talent availability            Strong local ed. provision            Labour cost

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Managing Directors and Chief Executives

Software and Applications Developers and Analysts Sales and Marketing Professionals

General and Operations Managers Data Analysts and Scientists

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

Assembly and Factory Workers Human Resources Specialists Financial and Investment Advisers Financial Analysts

France

Average reskilling needs (share of workforce)  Responses to shifting skills needs (share of companies surveyed)

n Less than 1 month      11%

n 1 to 3 months                12%

n 3 to 6 months                9%

n 6 to 12 months             9%

n Over 1 year    11%

n No reskilling needed  48%

Look to automate the work       83%

Hire new permanent staff with skills relevant to new technologies        82% Retrain existing employees              72%

Hire new temporary staff with skills relevant to new technologies         71%

Expect existing employees to pick up skills on the job   71%

Hire freelancers with skills relevant to new technologies            66%

Outsource some business functions to external contractors      59% Strategic redundancies of staff who lack the skills to use new technologies             58%

n Likely n Equally likely  n Unlikely

Emerging skills  Projected use of training providers (share of training)

Creativity, originality and initiative          Leadership and social influence               Internal department     50%

Analytical thinking and innovation          Emotional intelligence  Private training providers            31%

Active learning and learning strategies Reasoning, problem-solving and ideation           Private educational institutions                21%

Technology design and programming    Resilience, stress tolerance and flexibility           Public educational institutions                17%

Complex problem-solving

Critical thinking and analysis                      Public training provider 16%

Germany

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Talent availability            Production cost               Quality of the supply chain

Aviation, Travel & Tourism          Talent availability            Organization HQ              Ease of importing talent

Chemistry, Advanced Materials & Biotechnology            Labour cost        Talent availability            Production cost

Consumer          Labour cost        Talent availability            Quality of the supply chain

Energy Utilities & Technologies Labour cost        Talent availability            Production cost

Financial Services & Investors   Talent availability            Geographic concentration          Labour cost

Global Health & Healthcare        Talent availability            Labour cost        Production cost

Information & Communication Technologies     Talent availability            Labour cost        Geographic concentration

Oil & Gas             Geographic concentration          Talent availability            Production cost

Professional Services    Talent availability            Strong local ed. provision            Geographic concentration

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Software and Applications Developers and Analysts Managing Directors and Chief Executives

Sales and Marketing Professionals General and Operations Managers Data Analysts and Scientists

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

Assembly and Factory Workers Human Resources Specialists Financial and Investment Advisers Financial Analysts

Germany

                 

Average reskilling needs (share of workforce)

n Less than 1 month      13%

n 1 to 3 months                13%

n 3 to 6 months                9%

n 6 to 12 months             9%

n Over 1 year    10%

n No reskilling needed  46%

Responses to shifting skills needs (share of companies surveyed)

Look to automate the work       85%

Hire new permanent staff with skills relevant to new technologies        83% Retrain existing employees              73%

Hire new temporary staff with skills relevant to new technologies         70%

Expect existing employees to pick up skills on the job   70%

Hire freelancers with skills relevant to new technologies            63%

Outsource some business functions to external contractors      60% Strategic redundancies of staff who lack the skills to use new technologies             54%

n Likely n Equally likely  n Unlikely

                 

Emerging skills  Projected use of training providers (share of training)

Analytical thinking and innovation          Leadership and social influence               Internal department     47%

Creativity, originality and initiative          Emotional intelligence  Private training providers            27%

Active learning and learning strategies Resilience, stress tolerance and flexibility           Private educational institutions                19%

Technology design and programming    Systems analysis and evaluation              Public educational institutions  15%

Critical thinking and analysis

Complex problem-solving                          Public training provider 13%

India

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Talent availability            Labour cost        Quality of the supply chain

Aviation, Travel & Tourism          Talent availability            Organization HQ              Ease of importing talent

Chemistry, Advanced Materials & Biotechnology            Talent availability            Production cost               Labour cost

Consumer          Talent availability            Labour cost        Quality of the supply chain

Energy Utilities & Technologies Talent availability            Labour cost        Production cost

Financial Services & Investors   Talent availability            Organization HQ              Ease of importing talent

Global Health & Healthcare        Talent availability            Labour cost        Production cost

Information & Communication Technologies     Talent availability            Labour cost        Geographic concentration

Oil & Gas             Labour cost        Production cost               Other (please specify)

Professional Services    Talent availability            Labour cost        Strong local ed. provision

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

User and entity big data analytics            89%

Internet of things           77%

App- and web-enabled markets              76%

Machine learning            75%

Cloud computing             72%

Digital trade       64%

Augmented and virtual reality  63%

New materials  58%

Encryption          57%

Wearable electronics    53%

3D printing         52%

Autonomous transport 50%

Distributed ledger (blockchain) 48%

Stationary robots            44%

Managing Directors and Chief Executives Sales and Marketing Professionals

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

Software and Applications Developers and Analysts General and Operations Managers

Data Analysts and Scientists Assembly and Factory Workers Human Resources Specialists Financial Analysts

Financial and Investment Advisers

Quantum computing     41%

Non-humanoid land robots        40%

Biotechnology  31%

Humanoid robots            27%

Aerial and underwater robots   21%

India

Average reskilling needs (share of workforce)  Responses to shifting skills needs (share of companies surveyed)

n Less than 1 month      13%

n 1 to 3 months                13%

n 3 to 6 months                9%

n 6 to 12 months             9%

n Over 1 year    10%

n No reskilling needed  46%

Hire new permanent staff with skills relevant to new technologies        78% Expect existing employees to pick up skills on the job          70%

Outsource some business functions to external contractors      67%

17%

18%

Emerging skills  Projected use of training providers (share of training)

Analytical thinking and innovation          Leadership and social influence               Internal department     51%

Active learning and learning strategies Reasoning, problem-solving and ideation           Private training providers                29%

Creativity, originality and initiative          Emotional intelligence  Private educational institutions                20%

Technology design and programming    Systems analysis and evaluation              Public educational institutions  18%

Critical thinking and analysis

Complex problem-solving                          Public training provider 14%

Indonesia

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Talent availability            Quality of the supply chain                Production cost

Aviation, Travel & Tourism          Talent availability            Ease of importing talent              Organization HQ

Chemistry, Advanced Materials & Biotechnology            Talent availability            Labour cost        Geographic concentration

Consumer          Talent availability            Labour cost        Production cost

Energy Utilities & Technologies Production cost               Talent availability            Quality of the supply chain

Financial Services & Investors   Talent availability            Labour cost        Organization HQ

Global Health & Healthcare        Talent availability            Production cost                 Labour cost     

Information & Communication Technologies     Talent availability            Labour cost        Geographic concentration

Oil & Gas             Talent availability            Production cost               Geographic concentration

Professional Services    Talent availability            Strong local ed. provision            Labour cost

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Software and Applications Developers and Analysts Sales and Marketing Professionals

Data Analysts and Scientists

Managing Directors and Chief Executives General and Operations Managers

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

Human Resources Specialists Financial and Investment Advisers Financial Analysts

Robotics Specialists and Engineers

Indonesia

                 

Average reskilling needs (share of workforce)

n Less than 1 month      12%

n 1 to 3 months                12%

n 3 to 6 months                9%

n 6 to 12 months             9%

n Over 1 year    9%

n No reskilling needed  50%

Responses to shifting skills needs (share of companies surveyed)

Look to automate the work       88%

Hire new permanent staff with skills relevant to new technologies        87% Retrain existing employees              83%

Expect existing employees to pick up skills on the job   70%

Outsource some business functions to external contractors      65%

Hire new temporary staff with skills relevant to new technologies         65%

Hire freelancers with skills relevant to new technologies            60% Strategic redundancies of staff who lack the skills to use new technologies             52%

n Likely n Equally likely  n Unlikely

                 

Emerging skills  Projected use of training providers (share of training)

Creativity, originality and initiative          Emotional intelligence  Internal department     48%

Analytical thinking and innovation          Critical thinking and analysis       Private training providers            25%

Active learning and learning strategies Reasoning, problem-solving and ideation           Private educational institutions                20%

Technology design and programming    Systems analysis and evaluation              Public educational institutions  20%

Complex problem-solving

Leadership and social influence                               Public training provider 14%

Japan

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Talent availability            Quality of the supply chain                Production cost

Aviation, Travel & Tourism          Talent availability            Ease of importing talent              Organization HQ

Chemistry, Advanced Materials & Biotechnology            Labour cost        Talent availability            Production cost

Consumer          Talent availability            Labour cost        Geographic concentration

Energy Utilities & Technologies Geographic concentration          Talent availability            Production cost

Financial Services & Investors   Talent availability            Organization HQ              Labour cost

Global Health & Healthcare        Talent availability            Labour cost        Production cost

Information & Communication Technologies     Talent availability            Labour cost        Geographic concentration

Oil & Gas             Geographic concentration          Talent availability            Production cost

Professional Services    Talent availability            Strong local ed. provision            Labour cost

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Software and Applications Developers and Analysts Sales and Marketing Professionals

Managing Directors and Chief Executives Data Analysts and Scientists

General and Operations Managers

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

Human Resources Specialists Financial and Investment Advisers Assembly and Factory Workers Financial Analysts

Japan

                 

Average reskilling needs (share of workforce)

n Less than 1 month      12%

n 1 to 3 months                13%

n 3 to 6 months                9%

n 6 to 12 months             9%

n Over 1 year    10%

n No reskilling needed  48%

Responses to shifting skills needs (share of companies surveyed)

Look to automate the work       85%

Hire new permanent staff with skills relevant to new technologies        83% Retrain existing employees              75%

Expect existing employees to pick up skills on the job   67%

Hire new temporary staff with skills relevant to new technologies         64%

Outsource some business functions to external contractors      61%

Hire freelancers with skills relevant to new technologies            58% Strategic redundancies of staff who lack the skills to use new technologies             51%

n Likely n Equally likely  n Unlikely

                 

Emerging skills  Projected use of training providers (share of training)

Creativity, originality and initiative          Emotional intelligence  Internal department     52%

Analytical thinking and innovation          Leadership and social influence               Private training providers            27%

Active learning and learning strategies Reasoning, problem-solving and ideation           Private educational institutions                22%

Technology design and programming    Systems analysis and evaluation              Public educational institutions  18%

Critical thinking and analysis

Complex problem-solving                          Public training provider 15%

Korea, Rep.

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Production cost               Talent availability            Labour cost

Aviation, Travel & Tourism          Talent availability            Ease of importing talent              Organization HQ

Chemistry, Advanced Materials & Biotechnology            Labour cost        Talent availability            Production cost

Consumer          Labour cost        Geographic concentration          Talent availability

Energy Utilities & Technologies Talent availability            Labour cost        Production cost

Financial Services & Investors   Talent availability            Labour cost        Organization HQ

Global Health & Healthcare        Talent availability            Labour cost          Production cost            

Information & Communication Technologies     Talent availability            Labour cost        Geographic concentration

Oil & Gas             Talent availability            Production cost               Labour cost

Professional Services    Talent availability            Strong local ed. provision            Labour cost

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Sales and Marketing Professionals

Software and Applications Developers and Analysts Data Analysts and Scientists

Managing Directors and Chief Executives

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

General and Operations Managers Human Resources Specialists Assembly and Factory Workers Risk Management Specialists Financial Analysts

Korea, Rep.

                 

Average reskilling needs (share of workforce)

n Less than 1 month      13%

n 1 to 3 months                13%

n 3 to 6 months                10%

n 6 to 12 months             8%

n Over 1 year    9%

n No reskilling needed  46%

Responses to shifting skills needs (share of companies surveyed)

Look to automate the work       89%

Hire new permanent staff with skills relevant to new technologies        87% Retrain existing employees              82%

Outsource some business functions to external contractors      65%

Hire new temporary staff with skills relevant to new technologies         63%

Expect existing employees to pick up skills on the job   61%

Hire freelancers with skills relevant to new technologies            52% Strategic redundancies of staff who lack the skills to use new technologies             44%

n Likely n Equally likely  n Unlikely

                 

Emerging skills  Projected use of training providers (share of training)

Analytical thinking and innovation          Leadership and social influence               Internal department     50%

Creativity, originality and initiative          Reasoning, problem-solving and ideation           Private training providers                23%

Active learning and learning strategies Systems analysis and evaluation              Private educational institutions                19%

Critical thinking and analysis       Emotional intelligence  Public educational institutions  18%

Technology design and programming

Complex problem-solving                          Public training provider 10%

Mexico

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Talent availability            Production cost               Labour cost

Aviation, Travel & Tourism          Talent availability            Ease of importing talent              Organization HQ

Chemistry, Advanced Materials & Biotechnology            Talent availability            Labour cost        Quality of the supply chain

Consumer          Labour cost        Talent availability            Quality of the supply chain

Energy Utilities & Technologies Production cost               Labour cost        Talent availability

Financial Services & Investors   Talent availability            Labour cost        Strong local ed. provision

Global Health & Healthcare        Talent availability            Labour cost        Production cost

Information & Communication Technologies     Talent availability            Labour cost        Ease of importing talent

Oil & Gas             Talent availability            Production cost               Location of raw materials

Professional Services    Talent availability            Strong local ed. provision            Labour cost

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Managing Directors and Chief Executives

Software and Applications Developers and Analysts Data Analysts and Scientists

Sales and Marketing Professionals General and Operations Managers

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

Human Resources Specialists Financial and Investment Advisers Assembly and Factory Workers Financial Analysts

Mexico

Average reskilling needs (share of workforce)  Responses to shifting skills needs (share of companies surveyed)

n Less than 1 month      12%

n 1 to 3 months                11%

n 3 to 6 months                10%

n 6 to 12 months             10%

n Over 1 year    9%

n No reskilling needed  48%

Look to automate the work       84%

Hire new permanent staff with skills relevant to new technologies        84% Retrain existing employees              78%

Hire new temporary staff with skills relevant to new technologies         74%

Expect existing employees to pick up skills on the job   70%

Hire freelancers with skills relevant to new technologies            62%

Outsource some business functions to external contractors      61% Strategic redundancies of staff who lack the skills to use new technologies             54%

10%

17%

  

n Likely n Equally likely  n Unlikely

Emerging skills  Projected use of training providers (share of training)

Analytical thinking and innovation          Leadership and social influence               Internal department     49%

Creativity, originality and initiative          Critical thinking and analysis       Private training providers            33%

Active learning and learning strategies Resilience, stress tolerance and flexibility           Private educational institutions                24%

Technology design and programming    Emotional intelligence  Public training provider 16%

Reasoning, problem-solving and ideation                           Public educational institutions  16%

Philippines

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Talent availability            Quality of the supply chain                Production cost

Aviation, Travel & Tourism          Talent availability            Organization HQ              Ease of importing talent

Chemistry, Advanced Materials & Biotechnology            Labour cost          Talent  availability                         Production cost

Consumer          Talent availability            Quality of the supply chain         Production cost

Financial Services & Investors   Labour cost        Talent availability            Ease of importing talent

Global Health & Healthcare        Talent availability            Labour cost        Production cost

Information & Communication Technologies     Talent availability            Labour cost        Geographic concentration

Professional Services    Talent availability            Labour cost        Strong local ed. provision

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Software and Applications Developers and Analysts Sales and Marketing Professionals

Managing Directors and Chief Executives Data Analysts and Scientists

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

General and Operations Managers Human Resources Specialists Financial and Investment Advisers Assembly and Factory Workers Database and Network Professionals

Philippines

                 

Average reskilling needs (share of workforce)

n Less than 1 month      10%

n 1 to 3 months                12%

n 3 to 6 months                9%

n 6 to 12 months             10%

n Over 1 year    10%

n No reskilling needed  49%

Responses to shifting skills needs (share of companies surveyed)

Look to automate the work       86%

Hire new permanent staff with skills relevant to new technologies        84% Retrain existing employees              80%

Expect existing employees to pick up skills on the job   74%

Outsource some business functions to external contractors      65%

Hire new temporary staff with skills relevant to new technologies         64%

Hire freelancers with skills relevant to new technologies            61% Strategic redundancies of staff who lack the skills to use new technologies             54%

n Likely n Equally likely  n Unlikely

                 

Emerging skills  Projected use of training providers (share of training)

Analytical thinking and innovation          Leadership and social influence               Internal department     49%

Active learning and learning strategies Emotional intelligence  Private training providers            27%

Creativity, originality and initiative          Reasoning, problem-solving and ideation           Private educational institutions                20%

Technology design and programming    Resilience, stress tolerance and flexibility           Public educational institutions                19%

Critical thinking and analysis                      Public training provider 15%

Russian Federation

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Production cost               Talent availability            Labour cost

Aviation, Travel & Tourism          Talent availability            Organization HQ              Ease of importing talent

Chemistry, Advanced Materials & Biotechnology            Labour cost        Production cost               Talent availability

Consumer          Labour cost        Geographic concentration          Production cost

Energy Utilities & Technologies Talent availability            Labour cost        Production cost

Global Health & Healthcare        Talent availability            Labour cost        Production cost

Information & Communication Technologies     Talent availability            Labour cost        Organization HQ

Oil & Gas             Geographic concentration          Talent availability            Production cost

Professional Services    Talent availability            Strong local ed. provision            Labour cost

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Managing Directors and Chief Executives

Software and Applications Developers and Analysts Sales and Marketing Professionals

General and Operations Managers

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

Data Analysts and Scientists Human Resources Specialists Assembly and Factory Workers Financial and Investment Advisers Risk Management Specialists

Russian Federation

Average reskilling needs (share of workforce)  Responses to shifting skills needs (share of companies surveyed)

n Less than 1 month      12%

n 1 to 3 months                15%

n 3 to 6 months                10%

n 6 to 12 months             7%

n Over 1 year    10%

n No reskilling needed  46%

1

Hire new temporary staff with skills relevant to new technologies         74%                                       20%

                                                              

Expect existing employees to pick up skills on the job   71%                                       20%

Retrain existing employees        68%

Emerging skills  Projected use of training providers (share of training)

Creativity, originality and initiative          Complex problem-solving           Internal department     47%

Analytical thinking and innovation          Leadership and social influence               Private training providers            26%

Active learning and learning strategies Reasoning, problem-solving and ideation           Private educational institutions                19%

Technology design and programming    Systems analysis and evaluation              Public educational institutions  19%

Critical thinking and analysis

Emotional intelligence                  Public training provider 16%

Singapore

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Talent availability            Labour cost        Quality of the supply chain

Aviation, Travel & Tourism          Talent availability            Organization HQ              Ease of importing talent

Chemistry, Advanced Materials & Biotechnology            Talent availability            Labour cost        Quality of the supply chain

Consumer          Labour cost        Talent availability            Quality of the supply chain

Energy Utilities & Technologies Production cost               Talent availability            Labour cost

Financial Services & Investors   Talent availability            Organization HQ              Labour cost

Global Health & Healthcare        Talent availability            Labour cost        Production cost

Information & Communication Technologies     Talent availability            Labour cost        Geographic concentration

Oil & Gas             Talent availability            Production cost               Geographic concentration

Professional Services    Talent availability            Strong local ed. provision            Labour cost

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

User and entity big data analytics            92%

Internet of things           82%

App- and web-enabled markets              81%

Machine learning            78%

Cloud computing             73%

Digital trade       63%

Augmented and virtual reality  62%

Encryption          62%

Wearable electronics    58%

Distributed ledger (blockchain) 54%

New materials  52%

3D printing         47%

Emerging job roles         Autonomous transport 46%

Stationary robots            43%

Software and Applications Developers and Analysts      Sales Representatives, Wholesale and Manufacturing,                Quantum computing

Sales and Marketing Professionals         Technical and Scientific Products             41%

Data Analysts and Scientists       Financial and Investment Advisers         Non-humanoid land robots        39%

Managing Directors and Chief Executives Human Resources Specialists

General and Operations Managers

Financial Analysts

Database and Network Professionals

Biotechnology  27%

Humanoid robots            24%

Aerial and underwater robots   21%

Singapore

                 

Average reskilling needs (share of workforce)

n Less than 1 month      11%

n 1 to 3 months                13%

n 3 to 6 months                9%

n 6 to 12 months             9%

n Over 1 year    10%

n No reskilling needed  47%

Responses to shifting skills needs (share of companies surveyed)

Look to automate the work       86%

Hire new permanent staff with skills relevant to new technologies        85% Retrain existing employees              77%

Expect existing employees to pick up skills on the job   71%

Hire new temporary staff with skills relevant to new technologies         69%

Outsource some business functions to external contractors      62%

Hire freelancers with skills relevant to new technologies            57% Strategic redundancies of staff who lack the skills to use new technologies             53%

n Likely n Equally likely  n Unlikely

                 

Emerging skills  Projected use of training providers (share of training)

Analytical thinking and innovation          Leadership and social influence               Internal department     49%

Active learning and learning strategies Emotional intelligence  Private training providers            27%

Creativity, originality and initiative          Reasoning, problem-solving and ideation           Private educational institutions                21%

Technology design and programming    Systems analysis and evaluation              Public educational institutions  19%

Critical thinking and analysis

Complex problem-solving                          Public training provider 17%

South Africa

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Production cost               Talent availability            Quality of the supply chain

Aviation, Travel & Tourism          Talent availability            Organization HQ              Labour cost

Chemistry, Advanced Materials & Biotechnology            Talent availability              Labour cost                      Geographic concentration

Consumer          Talent availability            Quality of the supply chain         Production cost

Energy Utilities & Technologies Labour cost        Geographic concentration          Talent availability

Financial Services & Investors   Talent availability            Ease of importing talent              Strong local ed. provision

Global Health & Healthcare        Talent availability            Labour cost          Production cost            

Information & Communication Technologies     Talent availability            Labour cost        Geographic concentration

Oil & Gas             Production cost               Geographic concentration          Talent availability

Professional Services    Talent availability            Geographic concentration          Strong local ed. provision

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Software and Applications Developers and Analysts Sales and Marketing Professionals

Managing Directors and Chief Executives General and Operations Managers

Data Analysts and Scientists Financial and Investment Advisers

Assembly and Factory Workers

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

Industrial and Production Engineers Human Resources Specialists

South Africa

                 

Average reskilling needs (share of workforce)

n Less than 1 month      12%

n 1 to 3 months                11%

n 3 to 6 months                10%

n 6 to 12 months             10%

n Over 1 year    9%

n No reskilling needed  47%

Responses to shifting skills needs (share of companies surveyed)

Hire new permanent staff with skills relevant to new technologies        88% Look to automate the work              83%

Hire new temporary staff with skills relevant to new technologies         75%

Expect existing employees to pick up skills on the job   72%

Retrain existing employees        67%

Outsource some business functions to external contractors      62%

Hire freelancers with skills relevant to new technologies            62% Strategic redundancies of staff who lack the skills to use new technologies             56%

n Likely n Equally likely  n Unlikely

                 

Emerging skills  Projected use of training providers (share of training)

Analytical thinking and innovation          Reasoning, problem-solving and ideation           Internal department     48%

Creativity, originality and initiative          Critical thinking and analysis       Private training providers            31%

Active learning and learning strategies Resilience, stress tolerance and flexibility           Private educational institutions                20%

Technology design and programming    Emotional intelligence  Public training provider 15%

Complex problem-solving

Leadership and social influence                               Public educational institutions  14%

Switzerland                                      

Factors determining job location decisions         Technology adoption (share of companies surveyed)  

                User and entity big data analytics            93%                                                                                                                                    

                                                                                                                                                                           

                App- and web-enabled markets              83%                                                                                                                                    

                                                                                                                                                                           

                Machine learning            81%                                                                                                                                    

                                                                                                                                                                           

                                                                                                                                                                           

                Internet of things           81%                                                                                                                                    

                                                                                                                                                                           

                                                                                                                                                                           

                Cloud computing             75%                                                                                                                                    

                                                                                                                                                                           

                Augmented and virtual reality  72%                                                                                                                                    

                                                                                                                                                                           

                                                                                                                                                                           

                Digital trade       71%                                                                                                                                    

                                                                                                                                                                           

                                                                                                                                                                           

Wearable electronics    61%                                                                                                                                    

                                                                                                                                                                           

New materials  60%                                                                                                                                    

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.         Encryption         

57%                                                                                                                                    

                Autonomous transport 54%                                                                                                                                    

                3D printing         54%                                                                                                                                    

Emerging job roles

Managing Directors and Chief Executives Sales and Marketing Professionals

Software and Applications Developers and Analysts Sales Representatives, Wholesale and Manufacturing,

Technical and Scientific Products General and Operations Managers

Data Analysts and Scientists Human Resources Specialists Assembly and Factory Workers

Database and Network Professionals Information Security Analysts

Distributed ledger (blockchain) 50%

Stationary robots            47%

Non-humanoid land robots        46%

Quantum computing     39%

Biotechnology  31%

Humanoid robots            24%

Aerial and underwater robots   19%

Switzerland

                 

Average reskilling needs (share of workforce)

n Less than 1 month      12%

n 1 to 3 months                12%

n 3 to 6 months                9%

n 6 to 12 months             9%

n Over 1 year    7%

n No reskilling needed  51%

Responses to shifting skills needs (share of companies surveyed)

Look to automate the work       81%

Hire new permanent staff with skills relevant to new technologies        81% Retrain existing employees              74%

Hire new temporary staff with skills relevant to new technologies         74%

Expect existing employees to pick up skills on the job   71%

Hire freelancers with skills relevant to new technologies            65% Strategic redundancies of staff who lack the skills to use new technologies 58% Outsource some business functions to external contractors        56%

n Likely n Equally likely  n Unlikely

                 

Emerging skills  Projected use of training providers (share of training)

Analytical thinking and innovation          Complex problem-solving           Internal department     48%

Creativity, originality and initiative          Critical thinking and analysis       Private training providers            27%

Active learning and learning strategies Resilience, stress tolerance and flexibility           Private educational institutions                18%

Technology design and programming    Systems analysis and evaluation              Public educational institutions  15%

Leadership and social influence

Emotional intelligence                  Public training provider 13%

Thailand

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Talent availability            Production cost               Labour cost

Aviation, Travel & Tourism          Talent availability            Organization HQ              Ease of importing talent

Chemistry, Advanced Materials & Biotechnology            Talent availability              Labour cost                      Production cost

Consumer          Labour cost        Quality of the supply chain         Production cost

Energy Utilities & Technologies Production cost               Labour cost        Talent availability

Financial Services & Investors   Talent availability            Labour cost        Geographic concentration

Global Health & Healthcare        Talent availability            Labour cost          Production cost            

Information & Communication Technologies     Talent availability            Labour cost        Geographic concentration

Oil & Gas             Production cost               Talent availability            Labour cost

Professional Services    Talent availability            Labour cost        Geographic concentration

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Software and Applications Developers and Analysts Managing Directors and Chief Executives

Sales and Marketing Professionals Data Analysts and Scientists

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

General and Operations Managers Human Resources Specialists Financial and Investment Advisers Assembly and Factory Workers Financial Analysts

Thailand

                 

Average reskilling needs (share of workforce)

n Less than 1 month      11%

n 1 to 3 months                12%

n 3 to 6 months                9%

n 6 to 12 months             9%

n Over 1 year    10%

n No reskilling needed  49%

Responses to shifting skills needs (share of companies surveyed)

Look to automate the work       90%

Hire new permanent staff with skills relevant to new technologies        85% Retrain existing employees              79%

Expect existing employees to pick up skills on the job   76%

Hire new temporary staff with skills relevant to new technologies         70%

Outsource some business functions to external contractors      63% Strategic redundancies of staff who lack the skills to use new technologies 56% Hire freelancers with skills relevant to new technologies               55%

n Likely n Equally likely  n Unlikely

                 

Emerging skills  Projected use of training providers (share of training)

Analytical thinking and innovation          Critical thinking and analysis       Internal department     49%

Creativity, originality and initiative          Systems analysis and evaluation              Private training providers            29%

Active learning and learning strategies Reasoning, problem-solving and ideation           Private educational institutions                23%

Technology design and programming    Emotional intelligence  Public educational institutions  21%

Complex problem-solving

Leadership and social influence                               Public training provider 20%

United Kingdom

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Talent availability            Quality of the supply chain                Production cost

Aviation, Travel & Tourism          Talent availability            Organization HQ              Ease of importing talent

Chemistry, Advanced Materials & Biotechnology            Talent availability            Labour cost        Production cost

Consumer          Talent availability            Quality of the supply chain         Geographic concentration

Energy Utilities & Technologies Talent availability            Labour cost        Production cost

Financial Services & Investors   Talent availability            Organization HQ              Labour cost

Global Health & Healthcare        Talent availability            Labour cost        Production cost

Information & Communication Technologies     Talent availability            Labour cost        Geographic concentration

Oil & Gas             Geographic concentration          Talent availability            Production cost

Professional Services    Talent availability            Strong local ed. provision            Geographic concentration

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Software and Applications Developers and Analysts Managing Directors and Chief Executives

Sales and Marketing Professionals Data Analysts and Scientists General and Operations Managers

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

Assembly and Factory Workers Human Resources Specialists Financial and Investment Advisers Financial Analysts

United Kingdom

                 

Average reskilling needs (share of workforce)

n Less than 1 month      13%

n 1 to 3 months                13%

n 3 to 6 months                10%

n 6 to 12 months             8%

n Over 1 year    9%

n No reskilling needed  47%

Responses to shifting skills needs (share of companies surveyed)

Hire new permanent staff with skills relevant to new technologies        86% Look to automate the work              84%

Retrain existing employees        75%

Hire new temporary staff with skills relevant to new technologies         71%

Expect existing employees to pick up skills on the job   71%

Outsource some business functions to external contractors      61%

Hire freelancers with skills relevant to new technologies            60% Strategic redundancies of staff who lack the skills to use new technologies             50%

n Likely n Equally likely  n Unlikely

                 

Emerging skills  Projected use of training providers (share of training)

Analytical thinking and innovation          Leadership and social influence               Internal department     49%

Creativity, originality and initiative          Systems analysis and evaluation              Private training providers            28%

Active learning and learning strategies Reasoning, problem-solving and ideation           Private educational institutions                20%

Technology design and programming    Emotional intelligence  Public educational institutions  17%

Complex problem-solving

Critical thinking and analysis                      Public training provider 15%

United States

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Talent availability            Quality of the supply chain         Labour cost

Aviation, Travel & Tourism          Talent availability            Organization HQ              Ease of importing talent

Chemistry, Advanced Materials & Biotechnology            Talent availability            Labour cost        Production cost

Consumer          Talent availability            Labour cost        Quality of the supply chain

Energy Utilities & Technologies Labour cost        Talent availability            Production cost

Financial Services & Investors   Talent availability            Organization HQ              Labour cost

Global Health & Healthcare        Talent availability            Labour cost        Production cost

Information & Communication Technologies     Talent availability            Labour cost        Organization HQ

Infrastructure   Talent availability            Labour cost        Production cost

Oil & Gas             Talent availability            Labour cost        Production cost

Professional Services    Talent availability            Labour cost        Strong local ed. provision

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Software and Applications Developers and Analysts Data Analysts and Scientists

Managing Directors and Chief Executives General and Operations Managers

Sales and Marketing Professionals

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

Human Resources Specialists Financial Analysts

Financial and Investment Advisers Database and Network Professionals

United States

                 

Average reskilling needs (share of workforce)

n Less than 1 month      13%

n 1 to 3 months                14%

n 3 to 6 months                10%

n 6 to 12 months             8%

n Over 1 year    9%

n No reskilling needed  46%

Responses to shifting skills needs (share of companies surveyed)

Look to automate the work       84%

Hire new permanent staff with skills relevant to new technologies        84% Retrain existing employees              81%

Hire new temporary staff with skills relevant to new technologies         68%

Outsource some business functions to external contractors      65%

Expect existing employees to pick up skills on the job   65%

Hire freelancers with skills relevant to new technologies            58% Strategic redundancies of staff who lack the skills to use new technologies             46%

n Likely n Equally likely  n Unlikely

                 

Emerging skills  Projected use of training providers (share of training)

Analytical thinking and innovation          Leadership and social influence               Internal department     52%

Creativity, originality and initiative          Reasoning, problem-solving and ideation           Private training providers                27%

Active learning and learning strategies Emotional intelligence  Private educational institutions                21%

Technology design and programming    Systems analysis and evaluation              Public educational institutions  17%

Complex problem-solving

Critical thinking and analysis                      Public training provider 14%

Vietnam

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Production cost               Talent availability            Labour cost

Aviation, Travel & Tourism          Talent availability            Organization HQ              Ease of importing talent

Chemistry, Advanced Materials & Biotechnology            Labour cost        Talent availability            Quality of the supply chain

Consumer          Labour cost        Talent availability            Quality of the supply chain

Energy Utilities & Technologies Labour cost        Geographic concentration          Talent availability

Financial Services & Investors   Talent availability            Ease of importing talent              Labour cost

Global Health & Healthcare        Talent availability            Labour cost        Production cost

Information & Communication Technologies     Talent availability            Labour cost        Geographic concentration

Oil & Gas             Talent availability            Production cost               Organization HQ

Professional Services    Talent availability            Strong local ed. provision            Labour cost

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Sales and Marketing Professionals Data Analysts and Scientists

Managing Directors and Chief Executives

Software and Applications Developers and Analysts Sales Representatives, Wholesale and Manufacturing,

Technical and Scientific Products

General and Operations Managers Human Resources Specialists Financial and Investment Advisers Financial Analysts

Assembly and Factory Workers

Vietnam

Average reskilling needs (share of workforce)  Responses to shifting skills needs (share of companies surveyed)

n Less than 1 month      11%

n 1 to 3 months                14%

n 3 to 6 months                10%

n 6 to 12 months             9%

n Over 1 year    9%

n No reskilling needed  47%

Retrain existing employees        82%

Outsource some business functions to external contractors      69%

Expect existing employees to pick up skills on the job   68%

13%

25%

Emerging skills  Projected use of training providers (share of training)

Analytical thinking and innovation          Technology design and programming    Internal department     47%

Creativity, originality and initiative          Emotional intelligence  Private training providers            24%

Active learning and learning strategies Reasoning, problem-solving and ideation           Private educational institutions                21%

Critical thinking and analysis       Systems analysis and evaluation              Public educational institutions  17%

Leadership and social influence

Complex problem-solving                          Public training provider 16%

Central Asia

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Talent availability            Labour cost        Production cost

Aviation, Travel & Tourism          Talent availability            Organization HQ              Ease of importing talent

Chemistry, Advanced Materials & Biotechnology            Talent availability            Quality of the supply chain         Labour cost

Consumer          Labour cost        Geographic concentration          Talent availability

Energy Utilities & Technologies Talent availability            Production cost               Labour cost

Financial Services & Investors   Talent availability            Labour cost        Organization HQ

Global Health & Healthcare        Talent availability            Labour cost          Production cost            

Information & Communication Technologies     Talent availability            Labour cost        Organization HQ

Oil & Gas             Production cost               Talent availability            Location of raw materials

Professional Services    Talent availability            Geographic concentration          Labour cost

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Managing Directors and Chief Executives

Software and Applications Developers and Analysts Sales and Marketing Professionals

Data Analysts and Scientists General and Operations Managers

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

Human Resources Specialists Financial and Investment Advisers Assembly and Factory Workers

Financial Analysts

Central Asia

                 

Average reskilling needs (share of workforce)

n Less than 1 month      11%

n 1 to 3 months                13%

n 3 to 6 months                10%

n 6 to 12 months             9%

n Over 1 year    9%

n No reskilling needed  49%

Responses to shifting skills needs (share of companies surveyed)

Look to automate the work       84%

Hire new permanent staff with skills relevant to new technologies        83% Expect existing employees to pick up skills on the job          76%

Hire new temporary staff with skills relevant to new technologies         74%

Retrain existing employees        73%

Hire freelancers with skills relevant to new technologies            59%

Outsource some business functions to external contractors      56% Strategic redundancies of staff who lack the skills to use new technologies             53%

n Likely n Equally likely  n Unlikely

                 

Emerging skills  Projected use of training providers (share of training)

Analytical thinking and innovation          Complex problem-solving           Internal department     44%

Creativity, originality and initiative          Leadership and social influence               Private training providers            29%

Active learning and learning strategies Reasoning, problem-solving and ideation           Private educational institutions                21%

Technology design and programming    Resilience, stress tolerance and flexibility           Public educational institutions                20%

Critical thinking and analysis

Emotional intelligence                  Public training provider 15%

East Asia and the Pacific

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Talent availability            Labour cost        Quality of the supply chain

Aviation, Travel & Tourism          Talent availability            Organization HQ              Ease of importing talent

Chemistry, Advanced Materials & Biotechnology            Talent availability            Labour cost        Quality of the supply chain

Consumer          Talent availability            Labour cost        Production cost

Energy Utilities & Technologies Labour cost        Geographic concentration          Talent availability

Financial Services & Investors   Talent availability            Organization HQ              Geographic concentration

Global Health & Healthcare        Talent availability            Labour cost          Production cost            

Information & Communication Technologies     Talent availability            Labour cost        Geographic concentration

Infrastructure   Labour cost        Talent availability            Organization HQ

Mining & Metals              Production cost               Labour cost        Quality of the supply chain

Oil & Gas             Talent availability            Production cost               Geographic concentration

Professional Services    Talent availability            Labour cost        Strong local ed. provision

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Software and Applications Developers and Analysts Managing Directors and Chief Executives

Data Analysts and Scientists

Sales and Marketing Professionals General and Operations Managers

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

Human Resources Specialists Financial Analysts

Financial and Investment Advisers

Database and Network Professionals

East Asia and the Pacific

                 

Average reskilling needs (share of workforce)

n Less than 1 month      13%

n 1 to 3 months                12%

n 3 to 6 months                10%

n 6 to 12 months             9%

n Over 1 year    10%

n No reskilling needed  47%

Responses to shifting skills needs (share of companies surveyed)

Look to automate the work       83%

Hire new permanent staff with skills relevant to new technologies        83% Retrain existing employees              73%

Outsource some business functions to external contractors      63%

Hire new temporary staff with skills relevant to new technologies         63%

Expect existing employees to pick up skills on the job   63%

Hire freelancers with skills relevant to new technologies            50% Strategic redundancies of staff who lack the skills to use new technologies             46%

n Likely n Equally likely  n Unlikely

                 

Emerging skills  Projected use of training providers (share of training)

Analytical thinking and innovation          Systems analysis and evaluation              Internal department     49%

Active learning and learning strategies Leadership and social influence               Private training providers            26%

Creativity, originality and initiative          Emotional intelligence  Private educational institutions                21%

Technology design and programming    Reasoning, problem-solving and ideation           Public educational institutions                20%

Critical thinking and analysis

Complex problem-solving                          Public training provider 17%

Eastern Europe

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Talent availability            Production cost               Labour cost

Aviation, Travel & Tourism          Talent availability            Organization HQ              Ease of importing talent

Chemistry, Advanced Materials & Biotechnology            Talent availability            Labour cost        Production cost

Consumer          Labour cost        Talent availability            Quality of the supply chain

Energy Utilities & Technologies Labour cost        Talent availability            Production cost

Financial Services & Investors   Talent availability            Labour cost        Organization HQ

Global Health & Healthcare        Talent availability            Labour cost        Production cost

Information & Communication Technologies     Talent availability            Labour cost        Geographic concentration

Oil & Gas             Talent availability            Geographic concentration          Production cost

Professional Services    Talent availability            Strong local ed. provision            Labour cost

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Software and Applications Developers and Analysts Managing Directors and Chief Executives

Sales and Marketing Professionals Data Analysts and Scientists General and Operations Managers

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

Human Resources Specialists Financial Analysts

Assembly and Factory Workers Information Security Analysts

Eastern Europe

                 

Average reskilling needs (share of workforce)

n Less than 1 month      11%

n 1 to 3 months                14%

n 3 to 6 months                9%

n 6 to 12 months             8%

n Over 1 year    9%

n No reskilling needed  48%

Responses to shifting skills needs (share of companies surveyed)

Hire new permanent staff with skills relevant to new technologies        86% Look to automate the work              85%

Retrain existing employees        72%

Hire new temporary staff with skills relevant to new technologies         72%

Expect existing employees to pick up skills on the job   70%

Outsource some business functions to external contractors      62%

Hire freelancers with skills relevant to new technologies            60% Strategic redundancies of staff who lack the skills to use new technologies             53%

n Likely n Equally likely  n Unlikely

                 

Emerging skills  Projected use of training providers (share of training)

Creativity, originality and initiative          Leadership and social influence               Internal department     48%

Analytical thinking and innovation          Complex problem-solving           Private training providers            24%

Active learning and learning strategies Systems analysis and evaluation              Public educational institutions  18%

Technology design and programming    Reasoning, problem-solving and ideation           Private educational institutions                17%

Emotional intelligence                  Public training provider 14%

Latin America and the Caribbean

Factors determining job location decisions         Technology adoption (share of companies surveyed)

                User and entity big data analytics            89%

                App- and web-enabled markets             

79%

                Machine learning            78%

                Internet of things           77%

                Cloud computing            

72%

                Augmented and virtual reality  69%

                Digital trade       62%

                New materials 

61%

                Encryption          57%

                Wearable electronics    54%

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.         Distributed ledger (blockchain)

52%

                Autonomous transport 52%

Emerging job roles         3D printing         47%

Stationary robots            43%

Software and Applications Developers and Analysts      Sales Representatives, Wholesale and Manufacturing,                Quantum computing

Managing Directors and Chief Executives            Technical and Scientific Products             39%

Data Analysts and Scientists       Financial and Investment Advisers         Non-humanoid land robots        38%

General and Operations Managers Sales and Marketing Professionals

Financial Analysts

Human Resources Specialists Assembly and Factory Workers

Biotechnology  29%

Humanoid robots            24%

Aerial and underwater robots   23%

Latin America and the Caribbean

                 

Average reskilling needs (share of workforce)

n Less than 1 month      12%

n 1 to 3 months                13%

n 3 to 6 months                10%

n 6 to 12 months             9%

n Over 1 year    9%

n No reskilling needed  48%

Responses to shifting skills needs (share of companies surveyed)

Hire new permanent staff with skills relevant to new technologies        85% Look to automate the work              83%

Retrain existing employees        76%

Hire new temporary staff with skills relevant to new technologies         66%

Expect existing employees to pick up skills on the job   65%

Outsource some business functions to external contractors      61%

Hire freelancers with skills relevant to new technologies            59% Strategic redundancies of staff who lack the skills to use new technologies             52%

n Likely n Equally likely  n Unlikely

                 

Emerging skills  Projected use of training providers (share of training)

Analytical thinking and innovation          Leadership and social influence               Internal department     50%

Creativity, originality and initiative          Complex problem-solving           Private training providers            30%

Active learning and learning strategies Emotional intelligence  Private educational institutions                21%

Technology design and programming    Resilience, stress tolerance and flexibility           Public educational institutions                16%

Reasoning, problem-solving and ideation                           Public training provider 13%

Middle East and North Africa

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Talent availability            Production cost               Labour cost

Aviation, Travel & Tourism          Talent availability            Organization HQ              Ease of importing talent

Chemistry, Advanced Materials & Biotechnology            Talent availability            Labour cost        Quality of the supply chain

Consumer          Labour cost        Talent availability            Quality of the supply chain

Energy Utilities & Technologies Labour cost        Talent availability            Production cost

Financial Services & Investors   Talent availability            Organization HQ              Labour cost

Global Health & Healthcare        Talent availability            Labour cost        Production cost

Information & Communication Technologies     Talent availability            Labour cost        Geographic concentration

Oil & Gas             Talent availability            Production cost               Location of raw materials

Professional Services    Talent availability            Labour cost        Geographic concentration

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Software and Applications Developers and Analysts Data Analysts and Scientists

Sales and Marketing Professionals Managing Directors and Chief Executives General and Operations Managers

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

Human Resources Specialists Financial Analysts

Assembly and Factory Workers Financial and Investment Advisers

Middle East and North Africa

                 

Average reskilling needs (share of workforce)

n Less than 1 month      12%

n 1 to 3 months                13%

n 3 to 6 months                9%

n 6 to 12 months             9%

n Over 1 year    9%

n No reskilling needed  47%

Responses to shifting skills needs (share of companies surveyed)

Look to automate the work       89%                       9%

                                              

Hire new permanent staff with skills relevant to new technologies        84%      

12%

Retrain existing employees        76%

Expect existing employees to pick up skills on the job   73%                                                      17%

                                                                             

                                                                             

Hire new temporary staff with skills relevant to new technologies         72%                                                      19%

                                                                             

                                                                             

Outsource some business functions to external contractors      69%                                                      25%

                                                                             

                                                                             

Hire freelancers with skills relevant to new technologies            56%                                       27%      

                                                                             

                                                                             

Strategic redundancies of staff who lack the skills to use new technologies       53%                                       28%      

n Likely n Equally likely  n Unlikely

                 

Emerging skills  Projected use of training providers (share of training)

Analytical thinking and innovation          Critical thinking and analysis       Internal department     50%

Active learning and learning strategies Reasoning, problem-solving and ideation           Private training providers                28%

Creativity, originality and initiative          Emotional intelligence  Private educational institutions                18%

Technology design and programming    Systems analysis and evaluation              Public educational institutions  16%

Complex problem-solving

Leadership and social influence                               Public training provider 15%

North America

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Talent availability            Quality of the supply chain         Labour cost

Aviation, Travel & Tourism          Talent availability            Organization HQ              Ease of importing talent

Chemistry, Advanced Materials & Biotechnology            Talent availability            Labour cost        Production cost

Consumer          Talent availability            Labour cost        Quality of the supply chain

Energy Utilities & Technologies Labour cost        Production cost               Talent availability

Financial Services & Investors   Talent availability            Organization HQ              Geographic concentration

Global Health & Healthcare        Talent availability            Labour cost          Production cost            

Information & Communication Technologies     Talent availability            Labour cost        Geographic concentration

Infrastructure   Talent availability            Labour cost        Geographic concentration

Oil & Gas             Talent availability            Production cost               Labour cost

Professional Services    Talent availability            Labour cost        Strong local ed. provision

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Software and Applications Developers and Analysts Data Analysts and Scientists

Managing Directors and Chief Executives General and Operations Managers

Sales and Marketing Professionals

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

Human Resources Specialists Financial Analysts Electrotechnology Engineers Financial and Investment Advisers

North America

                 

Average reskilling needs (share of workforce)

n Less than 1 month      13%

n 1 to 3 months                14%

n 3 to 6 months                10%

n 6 to 12 months             9%

n Over 1 year    9%

n No reskilling needed  46%

Responses to shifting skills needs (share of companies surveyed)

Look to automate the work       84%

Hire new permanent staff with skills relevant to new technologies        83% Retrain existing employees              81%

Hire new temporary staff with skills relevant to new technologies         66%

Expect existing employees to pick up skills on the job   65%

Outsource some business functions to external contractors      63%

Hire freelancers with skills relevant to new technologies            59% Strategic redundancies of staff who lack the skills to use new technologies             46%

n Likely n Equally likely  n Unlikely

                 

Emerging skills  Projected use of training providers (share of training)

Analytical thinking and innovation          Leadership and social influence               Internal department     52%

Creativity, originality and initiative          Reasoning, problem-solving and ideation           Private training providers                27%

Active learning and learning strategies Emotional intelligence  Private educational institutions                21%

Technology design and programming    Systems analysis and evaluation              Public educational institutions  17%

Critical thinking and analysis

Complex problem-solving                          Public training provider 15%

South Asia

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Talent availability            Quality of the supply chain         Labour cost

Aviation, Travel & Tourism          Talent availability            Organization HQ              Ease of importing talent

Chemistry, Advanced Materials & Biotechnology            Talent availability            Production cost               Labour cost

Consumer          Quality of the supply chain         Labour cost        Talent availability

Energy Utilities & Technologies Talent availability            Organization HQ              Labour cost

Financial Services & Investors   Talent availability            Labour cost        Ease of importing talent

Global Health & Healthcare        Talent availability            Production cost               Labour cost

Information & Communication Technologies     Talent availability            Labour cost        Geographic concentration

Oil & Gas             Production cost               Labour cost        Talent availability

Professional Services    Talent availability            Labour cost        Geographic concentration

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Managing Directors and Chief Executives Sales and Marketing Professionals

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

General and Operations Managers

Software and Applications Developers and Analysts

Data Analysts and Scientists Human Resources Specialists Financial and Investment Advisers Financial Analysts

Assembly and Factory Workers

South Asia

                 

Average reskilling needs (share of workforce)

n Less than 1 month      13%

n 1 to 3 months                13%

n 3 to 6 months                9%

n 6 to 12 months             8%

n Over 1 year    9%

n No reskilling needed  48%

Responses to shifting skills needs (share of companies surveyed)

Look to automate the work       83%                                       13%

                                                              

                                                              

Hire new permanent staff with skills relevant to new technologies        81%                                       14%

                                                              

                                                              

Retrain existing employees        80%                                       15%

                                                              

                                                              

Expect existing employees to pick up skills on the job   73%                                       16%

                                                              

Outsource some business functions to external contractors      66%                                       25%

Emerging skills  Projected use of training providers (share of training)

Analytical thinking and innovation          Leadership and social influence               Internal department     52%

Active learning and learning strategies Emotional intelligence  Private training providers            28%

Creativity, originality and initiative          Reasoning, problem-solving and ideation           Private educational institutions                21%

Technology design and programming    Systems analysis and evaluation              Public educational institutions  19%

Critical thinking and analysis

Complex problem-solving                          Public training provider 17%

Sub-Saharan Africa

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Talent availability            Production cost               Quality of the supply chain

Aviation, Travel & Tourism          Talent availability            Organization HQ              Ease of importing talent

Chemistry, Advanced Materials & Biotechnology            Talent availability              Labour cost                      Geographic concentration

Consumer          Talent availability            Quality of the supply chain         Labour cost

Energy Utilities & Technologies Labour cost        Geographic concentration          Talent availability

Financial Services & Investors   Talent availability            Strong local ed. provision            Ease of importing talent

Global Health & Healthcare        Talent availability            Labour cost        Production cost

Information & Communication Technologies     Talent availability            Labour cost        Ease of importing talent

Oil & Gas             Talent availability            Production cost               Geographic concentration

Professional Services    Talent availability            Geographic concentration          Labour cost

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Software and Applications Developers and Analysts Managing Directors and Chief Executives

Sales and Marketing Professionals Data Analysts and Scientists General and Operations Managers

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

Human Resources Specialists Financial and Investment Advisers Assembly and Factory Workers Electrotechnology Engineers

Sub-Saharan Africa

                 

Average reskilling needs (share of workforce)

n Less than 1 month      13%

n 1 to 3 months                12%

n 3 to 6 months                9%

n 6 to 12 months             9%

n Over 1 year    9%

n No reskilling needed  48%

Responses to shifting skills needs (share of companies surveyed)

Hire new permanent staff with skills relevant to new technologies        85% Look to automate the work              84%

Hire new temporary staff with skills relevant to new technologies         75%

Expect existing employees to pick up skills on the job   72%

Retrain existing employees        70%

Outsource some business functions to external contractors      65%

Hire freelancers with skills relevant to new technologies            58% Strategic redundancies of staff who lack the skills to use new technologies             52%

n Likely n Equally likely  n Unlikely

                 

Emerging skills  Projected use of training providers (share of training)

Analytical thinking and innovation          Leadership and social influence               Internal department     48%

Creativity, originality and initiative          Reasoning, problem-solving and ideation           Private training providers                29%

Active learning and learning strategies Emotional intelligence  Private educational institutions                20%

Technology design and programming    Resilience, stress tolerance and flexibility           Public training provider 15%

Complex problem-solving

Critical thinking and analysis                      Public educational institutions  14%

Western Europe

Factors determining job location decisions         Technology adoption (share of companies surveyed)

Industry              Primary                Secondary          Tertiary

Automotive, Aerospace, Supply Chain & Transport        Talent availability            Quality of the supply chain                Production cost

Aviation, Travel & Tourism          Talent availability            Organization HQ              Labour cost

Chemistry, Advanced Materials & Biotechnology            Talent availability              Production cost                             Labour cost

Consumer          Talent availability            Quality of the supply chain         Production cost

Energy Utilities & Technologies Talent availability            Labour cost        Production cost

Financial Services & Investors   Talent availability            Organization HQ              Labour cost

Global Health & Healthcare        Talent availability            Labour cost        Production cost

Information & Communication Technologies     Talent availability            Labour cost        Organization HQ

Oil & Gas             Geographic concentration          Talent availability            Production cost

Professional Services    Talent availability            Strong local ed. provision            Geographic concentration

Range of options: Flexibility of labour laws, Geographic spread, Quality of the supply chain, Ease of importing talent, Labour cost, Location of raw materials, Organization HQ, Production cost, Strong local education provision, Talent availability.

Emerging job roles

Software and Applications Developers and Analysts Managing Directors and Chief Executives

Sales and Marketing Professionals Data Analysts and Scientists General and Operations Managers

Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products

Human Resources Specialists Financial and Investment Advisers Financial Analysts

Assembly and Factory Workers

Western Europe

                 

Average reskilling needs (share of workforce)

n Less than 1 month      13%

n 1 to 3 months                13%

n 3 to 6 months                10%

n 6 to 12 months             9%

n Over 1 year    9%

n No reskilling needed  47%

Responses to shifting skills needs (share of companies surveyed)

Hire new permanent staff with skills relevant to new technologies        86% Look to automate the work              84%

Retrain existing employees        75%

Expect existing employees to pick up skills on the job   71%

Hire new temporary staff with skills relevant to new technologies         69%

Outsource some business functions to external contractors      63%

Hire freelancers with skills relevant to new technologies            60% Strategic redundancies of staff who lack the skills to use new technologies             52%

n Likely n Equally likely  n Unlikely

                 

Emerging skills  Projected use of training providers (share of training)

Creativity, originality and initiative          Leadership and social influence               Internal department     48%

Analytical thinking and innovation          Emotional intelligence  Private training providers            27%

Active learning and learning strategies Systems analysis and evaluation              Private educational institutions                20%

Technology design and programming    Reasoning, problem-solving and ideation           Public educational institutions                18%

Complex problem-solving

Critical thinking and analysis                      Public training provider 16%

Till Alexander Leopold is a Project Lead in the World Economic Forum’s Centre for the New Economy and Society. His responsibilities include co-leadership of the insights workstream of the System Initiative on Education, Gender and Work; co-authorship of the Forum’s Global Gender Gap Report, Global Human Capital Report, Future of Jobs Report and Industry Gender Gap Report; and management of the Forum’s Global Future Council on Education, Gender and Work. He has presented the System Initiative’s insights work at a number of high-level events and in the media, and has

co-organized activities at the World Economic Forum’s Annual Meeting and regional summits. Till previously served as an economist and project manager at the United Nations and International Labour Organization, where his work focused

on policy analysis, research and technical cooperation in the fields of entrepreneurship, labour economics, and innovation ecosystems, and as a consultant and analyst in the fields of impact investing and social entrepreneurship, with first-hand research and consulting experience in Sub-Saharan Africa and South Asia. He holds master’s degrees in Social Anthropology and Finance and Development Economics from the University of Cambridge and SOAS (University of London), and is currently pursuing a PhD at the United Nations University— Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT).

Vesselina Ratcheva is a Data Lead in the World Economic Forum’s Centre for the New Economy and Society. Her responsibilities include co-leading the insights workstream of the System Initiative on Education, Gender and Work, with

a particular focus on data and innovation in that domain. Ratcheva is a co-author of the Forum’s Global Gender Gap Report, Global Human Capital Report, Future of Jobs Report and Industry Gender Gap Report, and in the past has led and collaborated on research projects spanning topics such as skills, identity (gender, ethnic), organizational culture, political mobilization and international migration. Ratcheva has consistently employed quantitative and qualitative research methods in endeavours aimed at finding the

best ways to ensure more just social and political systems. Ratcheva previously led on research and evaluation in skills and has specialized on the Balkan region. She holds a PhD in Social Anthropology and an MSc in Comparative and Cross-Cultural Research Methods from Sussex University, and a BA in Social Anthropology and Mathematics from the University of Cambridge.

Saadia Zahidi is a Member of the Managing Board and Head of the Centre for the New Economy and Society at the World Economic Forum. Through the System Initiative on Economic Progress and the System Initiative on Education, Gender

and Work, her teams work with leaders from business, government, civil society and academia to understand and shape the new economy, advance competitiveness, drive social mobility and inclusion, close skills gaps, prepare for the future of work and foster gender equality and diversity. Saadia founded and co-authors the Forum’s Future of Jobs Report, Global Gender Gap Report, and Global Human Capital Report. Her book, Fifty Million Rising, charts the rise of working women in the Muslim world and is longlisted for the FT/McKinsey Business Book of the Year 2018. She has been selected as one of the BBC’s 100 Women and won the inaugural FT/McKinsey Bracken Bower Prize for prospective authors under 35. She holds a BA in Economics from

Smith College, an MPhil in International Economics from the

Graduate Institute of Geneva and an MPA from the Harvard Kennedy School. Her interests include the future of work, the impact of technology on employment, education and skills gaps, income inequality and using big data for public good.

The World Economic Forum would like to thank the Partners of the System Initiative on Shaping the Future of Education, Gender and Work for their guidance and support to the System Initiative and this report.

•             A.T. Kearney

•             AARP

•             Accenture

•             Adecco Group

•             African Rainbow Minerals

•             Alghanim Industries

•             AlixPartners

•             AT&T

•             Bahrain Economic Development Board

•             Bank of America

•             Barclays

•             Bill and Melinda Gates Foundation

•             Bloomberg

•             Booking.com

•             Boston Consulting Group

•             Centene Corporation

•             Centrica

•             Chobani

•             Dentsu Aegis Network

•             Dogan Broadcasting

•             EY

•             GEMS Education

•             Genpact International

•             Google

•             GSK

•             Guardian Life Insurance Company

•             HCL Technologies

•             Heidrick & Struggles

•             Hewlett Packard Enterprise

•             Home Instead

•             HP Inc.

•             HSBC

•             Hubert Burda Media

•             IKEA Group

•             Infosys

•             JD.com

•             JLL

•             Lego Foundation

•             LinkedIn

•             LRN Corporation

•             ManpowerGroup

•             Mercer (MMC)

•             Microsoft Corporation

•             Nestlé

•             Nokia Corporation

•             NYSE

•             Omnicom Group

•             Ooredoo

•             PayPal

•             Pearson

•             PhosAgro

•             Prince Mohammed bin Salman bin Abdulaziz (MiSK) Foundation

•             Procter and Gamble

•             Publicis Group

•             PwC

•             QI Group

•             Recruit Holdings

•             Salesforce

•             SAP

•             Saudi Aramco

•             SeverGroup

•             Tata Consultancy Services

•             The Rockefeller Foundation

•             Turkcell

•             UBS

•             Unilever

•             VMware

•             Willis Towers Watson

•             Workday

•             WPP

In addition to our Partners, the leadership of the System Initiative on Shaping the Future of Education, Gender and Work includes leading representatives of the following organizations: Council of Women World Leaders; Department for Planning, Monitoring and Evaluation of the Presidency of South Africa; Endeavor; Haas School of Business, University of California, Berkeley; International Finance Corporation (IFC); International Labour Organization (ILO); International Trade Union Confederation (ITUC); JA Worldwide; London Business School; Ministry of Education of the Government of Singapore; Ministry of Employment of the Government of Denmark; Ministry of Employment, Workforce Development and Labour of the Government of Canada; MIT Initiative on the Digital Economy; Office of the Chief of the Cabinet of Ministers of Argentina; Office of the Deputy Prime Minister of the Russian Federation; The Wharton School, University of Pennsylvania; and United Way Worldwide.

To learn more about the System Initiative, please refer to the System Initiative website: https://www.weforum.org/system-initiatives/ shaping-the-future-of-education-gender-and-work.

The Future of Jobs Report 2018 is the result of extensive collaboration between the World Economic Forum and its constituents, amplified by key regional survey partners. We would like to recognize the following organizations for their contribution to the World Economic Forum’s Future of Jobs Survey and this report.

                 

INDIA

Confederation of Indian Industry (CII) Observer Research Foundation (ORF)

REPUBLIC OF KOREA

Korean Development Institute (KDI)

LATIN AMERICA

Inter-American Development Bank (IDB)

RUSSIAN FEDERATION

Eurasia Competitiveness Institute (ECI)

SOUTH AFRICA

Business Leadership South Africa

SWITZERLAND

EconomieSuisse

UNITED KINGDOM

Confederation of British Industry (CBI)

VIETNAM

Ministry of Labour, Invalids and Social Affairs

PROJECT TEAM

Till Alexander Leopold

Project Lead, Centre for the New Economy and Society

Vesselina Stefanova Ratcheva

Data Lead, Centre for the New Economy and Society

Saadia Zahidi

Head, Centre for the New Economy and Society; Member of the Managing Board

A special thank you to colleagues who made distinctive contributions to the development of this report: Genesis Elhussein, Project Specialist and Ilaria Marchese, Data Specialist. Additional thanks to our colleagues in the Education, Gender and Work System Initiative, including Piyamit Bing Chomprasob, Rigas Hadzilacos, Elselot Hasselaar, Valerie Peyre, Pearl Samandari and Lyuba Spagnoletto.

This report would not have been possible without the support of our colleagues across the Forum’s Business Engagement Team, Centre for Global Industries and Centre for Regional and Geopolitical Affairs. In particular, we would like to express our deep appreciation to Nour Chabaane, Emma Skov Christiansen, David Connolly, Renee van Heusden, Nikolai Khlystov, Julien Lederman, Wolfgang Lehmacher, Tiffany Misrahi, Andrew Moose and Julia Suit in the Forum’s Centre for Global Industries. In the Centre for Regional and Geopolitical Affairs, expansion of the report’s geographical coverage was made possible by the support of Elsie Kanza, Bertrand Assamoi, Nontle Kabanyane and Dieynaba Tandian for the Africa region; Justin Wood, Oliver Hess and Thuy Nguyen for the ASEAN region; Liam Foran for Australia; Martina Larkin, Anastasia Kalinina, Anna Knyazeva, Verena Kuhn, Rosanna Mastrogiacomo and Mark O’Mahoney for the wider Europe region, Denise Burnet and Fabienne Chanavat for France and Michèle Mischler for Switzerland; Sriram Gutta and Suchi Kedia for India; Joo- Ok Lee for the Republic of Korea; Marisol Argueta, Diego Bustamante and Ana del Barrio for the Latin America region; and Malik Faraoun and Teresa Belardo for the MENA region. Finally, a special thank you to Oliver Cann and the World Economic Forum’s Media and Publications team for their invaluable collaboration on the production of this report.

We gratefully acknowledge the excellent collaboration with LinkedIn’s Economic Graph team under the leadership of Sue Duke, with contributions from Nick Eng and Kristin Keveloh.

A special thank you to Michael Fisher for his excellent copyediting work and Neil Weinberg for his superb graphic design and layout. We greatly appreciate the work of design firm Graphéine, which created the cover.

The World Economic Forum is the International Organization for Public-Private Cooperation and engages the foremost political, business and other leaders of society to shape global, regional and industry agendas.