Extrapolating from the figures shared in the Future of Jobs Survey 2020, employers expect that by 2025, increasingly redundant roles will decline from being 15.4% of the workforce to 9% (6.4% decline), and that emerging professions will grow from 7.8% to 13.5% (5.7% growth) of the total employee base of company respondents. Based on these figures, we estimate that by 2025, 85 million jobs may be displaced by a shift in the division of labour between humans and machines, while 97 million new roles may emerge that are more adapted to the new division of labour between humans, machines and algorithms, across the 15 industries and 26 economies covered by the report.
The 2020 version of the Future of Jobs Survey also reveals similarities across industries when looking at increasingly strategic and increasingly redundant job roles. Similar to the 2018 survey, the leading positions in growing demand are roles such as Data Analysts and Scientists, AI and Machine Learning Specialists, Robotics Engineers, Software and Application developers as well as Digital Transformation Specialists. However, job roles such as Process Automation Specialists, Information Security Analysts and Internet of Things Specialists are newly emerging among a cohort of roles which are seeing growing demand from employers. The emergence of these roles reflects the acceleration of automation as well as the resurgence of cybersecurity risks.
In addition, as presented in the Industry Profiles in Part 2 of this report, a set of roles are distinctively emerging within specific industries. This includes Materials Engineers in the Automotive Sector, Ecommerce and Social Media Specialists in the Consumer sector, Renewable Energy Engineers in the Energy Sector, FinTech Engineers in Financial Services, Biologists and Geneticists in Health and Healthcare as well as Remote Sensing Scientists and Technicians in Mining and Metals. The nature of these roles reflects the trajectory towards areas of innovation and growth across multiple industries.
At the opposite end of the scale, the roles which are set to be increasingly redundant by 2025 remain largely consistent with the job roles identified in 2018 and across a range of research papers on the automation of jobs.34 These include roles which are being displaced by new technologies: Data Entry Clerks, Administrative and Executive Secretaries, Accounting and Bookkeeping and Payroll Clerks, Accountant and Auditors, Assembly and Factory Workers, as well as Business Services and Administrative Managers.
Such job disruption is counter-balanced by job creation in new fields, the ‘jobs of tomorrow’. Over the coming decade, a non-negligible share of newly created jobs will be in wholly new occupations, or existing occupations undergoing significant transformations in terms of their content and skills requirements. The World Economic Forum’s Jobs of Tomorrow report, authored in partnership with data scientists at partner companies LinkedIn and Coursera, presented for the first time a way to measure and track the emergence of a set of new jobs across the economy using real-time labour market data.35 The data from this collaboration identified 99 jobs that are consistently growing in demand across 20 economies. Those jobs were then organized into distinct professional clusters according to their skills similarity.
This resulting set of emerging professions reflects the adoption of new technologies and increasing demand for new products and services, which are driving greater demand for green economy jobs, roles at the forefront of the data and AI economy, as well as new roles in engineering, cloud computing and product development. In addition, the emerging professions showcase the continuing importance of human interaction in the new economy through roles in the care economy; in marketing, sales and content production; as well as roles where a facility or aptitude for understanding and being comfortable working with different types of people from different backgrounds is critical. Figure 23 displays the set of roles which correspond to each professional cluster, organized according to the scale of each opportunity.36 Due to constraints related to data availability, the Care and Green Jobs cluster are not currently covered by the following analysis.
Note: Job transitions refers to any job transition while job pivots refers to individuals moving away from their current occupation. Job Families are groups of occupations based upon work performed, skills, education, training, and credentials. Data derived from the following countries: Argentina, Australia, Brazil, Canada, France, Germany, India, Ireland, Italy, Mexico, Netherlands, New Zealand, Saudi Arabia, Singapore, South Africa, Spain, Sweden, United Arab Emirates, United Kingdom and United States.
In this report we present a unique extension of this analysis which examines key learnings gleaned from job transitions into those emerging clusters using LinkedIn data gathered over the past five years. For this analysis the LinkedIn data science team analysed the job transitions of professionals who moved into emerging jobs over the period of 2015 to 2020. The researchers analysed when professionals transitioned into any new role as well as when they transitioned to a wholly new occupation—here called ‘pivots’. To understand the skill profile of each occupation, analysts first identified a list of the most representative skills associated with an occupation, based on LinkedIn’s Skills Genome Metric which calculates the ‘most representative’ skills across roles, using the TF-IDF method. To examine the extent to which certain skills groups of interest are associated with a particular occupation, a ‘skill penetration’ figure is calculated. This indicates the share of individual skills associated with that occupation that belong to a given skill group. To understand the skill profile of each occupation, analysts calculated the ‘skill penetration’ score for each skill associated with an occupation. That is, the ‘skill penetration’ figure indicates the individuals from that occupation who list the specific skill as a share of all individuals employed in that occupation.
The aggregate skills similarity between two occupations is then calculated as the cosine similarity of those two occupations. In addition, for each skill group, a skills gap measure is calculated by expressing the skill penetration of the destination job as a share of the same indicator in the source job.
The evidence indicates that some emerging job clusters present significant opportunities for transitions into growing jobs (jobs in increasing demand) through effective career pivots. As demonstrated in Figure24 A, among the transitions into Data and AI professions, 50% of the shifts made are from non-emerging roles. That figure is much higher at 75% in Sales, 72% in content roles and 67% of Engineering roles. One could say that such field are easier to break into, while those such as Data and AI and People and Culture present more challenges. These figures suggest that some level of labour force reallocation is already underway.
By analysing these career pivots—instances where professionals transition to wholly new occupations—it becomes apparent that some of these so-called ‘jobs of tomorrow’ present greater opportunities for workers looking to fully switch their job family and therefore present more options to reimagine one’s professional trajectory, while other emerging professions remain more fully bounded. As presented in Figure 24 C only 19 % and 26% of job transitions into Engineering and People and Culture, respectively, come from outside the job family in which those roles are today. In contrast, 72% of Data and AI bound transitions originate from a different job family and 68% of transitions into emerging jobs within Sales. As illustrated in Figure 25 emerging job clusters are typically staffed by workers starting in a set of distinctive job families, but the diversity of those source job families varies by emerging profession. While emerging roles in Product Development draw professionals from a range of job families, emerging roles in People and Culture job cluster typically transition from the Human Resources job family. The emerging Cloud Computing job cluster is primarily populated by professionals transitioning from IT and Engineering.
Finally, a number of jobs of tomorrow present greater opportunities to pivot into professions with a significant change in skills profile. In Figure24 B it is possible to observe that transitions into People and Culture and into Engineering have typically been ones with high skills similarity while Marketing and Content Development have been more permissive of low skills similarity. Among the emerging professions outlined in this report, transitions into Data and AI allow for the largest variation in skills profile between source and destination job title.
Figure 25 demonstrates that the newer emerging professions such as Data and AI, Product Development and Cloud Computing present more opportunities to break into these frontier fields, and that, in fact, such transitions do not require a full skills match between the source and destination occupation. However, some job clusters of tomorrow remain more ‘closed’ and tend to recruit staff with a very specific skill set. It is not possible to observe whether those limitations are necessary or simply established practice. It may be the case that such ‘siloed’ professional clusters can be reinvigorated by experimentation with relaxing the constraints for entry into some emerging jobs alongside appropriate reskilling and upskilling.