A number of recent studies have taken a close look at the the future of work over the next 10 to 15 years. For example, a December, 2017 report by McKinsey examined which jobs will likely be displaced by automation through 2030, as well as which jobs are likely to be created over the same period, based on data from 46 countries.
McKinsey’s overall conclusion was that a growing technology-based economy will create a significant number of new occupations which will more than offset declines in occupations displaced by automation. However, “while there may be enough work to maintain full employment to 2030 under most scenarios, the transitions will be very challenging – matching or even exceeding the scale of shifts out of agriculture and manufacturing we have seen in the past.”
Another report, The Changing Nature of Work, – published by the World Bank in October, 2018, – concluded that our problem is not that there won’t be enough work in the future. The key problem is that, in many countries, the workforce isn’t prepared for a fast unfolding future. Neglecting investments in human capital, – i.e., the sum total of a population’s health, skills, knowledge, and experience, – will dramatically weaken a country’s competitiveness.
Let me now discuss Eight Futures of Work Scenarios and their Implications, a white paper published in January, 2018 by the World Economic Forum (WEF) in collaboration with the Boston Consulting Group (BCG). The paper considered eight distinct scenarios on what work might be like by 2030, based on different combinations of three of the most impactful and uncertain variables affecting the future of work: the rate of technological change; the evolution of learning in the workforce; and the magnitude of talent mobility across geographies.
For simplicity, the study considered two possible outcomes for each variable:
Technological Change. Developments in data science, AI, robotics, IoT, blockchain and other advanced technologies will have a major impact on labor markets over the next 10 – 15 years. The two possible outcomes are:
- Steady: change proceeds at the current (or slower) pace, with large-scale automation of blue- and white-collar routine tasks, but higher skilled tasks remain relatively untouched.
- Accelerated: In addition to routine tasks, machines become capable of performing non-routine tasks requiring cognitive skills, as well as a wide range of physical tasks.
Learning Evolution. An even larger challenge will be ensuring that workers have the skills and support needed to transition to new jobs. The growing demand for expertise in rapidly advancing technologies will require continuous training. We’ll see increased demand for human skills like creativity, originality, critical thinking, emotional intelligence, leadership, reasoning, problem solving and ideation. The two outcomes considered are:
- Slow: Many displaced workers are competing for fewer roles that match their skills, while companies face increasing talent shortages.
- Fast: Concerns about technological change and talent gaps have led to reforms in education systems; companies invest heavily in the training and reskilling of their workers, which along with lifelong learning help create a dynamic, creative workforce.
Talent Mobility. The third major variable, worker’s movement within and across borders, will be affected by a number of factors, including economic opportunities, travel regulations, crises and conflicts. The two possible outcomes are:
- Low: National as well as local governments have imposed restrictions on migration to protect jobs in the short term; talent shortages impact economic growth, while low levels of mobility dampen the exchange of new ideas and the expansion of markets.
- High: Large scale movements of people searching for better opportunities has become the norm; high-skilled workers flow to high-income enclaves, which are generally concentrated in large urban areas all around the world.
The eight future of work scenarios are based on different combinations of these three variables. Let me briefly summarize the salient qualities of each.
Workforce Autarkies: steady technological change; slow learning evolution; low talent mobility.
Workforce autarkies are nationalist economies that aim to be self-sufficient. “Reacting to the worries of displaced workers, governments have imposed restrictions on international labour mobility and sought to fulfil their economies’ talent needs internally.” State protectionism can provide some relief to lower-skilled workers, but forces employers to move work requiring higher-skilled talent to countries with unrestricted markets. “The resulting reduction in knowledge transfer and continued talent shortfalls for local companies has reduced growth and dynamism over time, reducing the capacity of local labour markets.”
Mass Movement: steady technological change; slow learning evolution; high talent mobility
In this scenario, both lower- and higher-skilled workers are on the move searching for better opportunities. This helps businesses access the best talent, but increases competition between workers at all skills levels, potentially leading to social tensions.
Robot Replacement: accelerated technological change; slow learning evolution; low talent mobility
In this scenario, technology and machines advance rapidly while many in the workplace are unable to keep pace and face shrinking opportunities, further leading to increased automation. This hollowing out of the labour market will potentially lead to “deep and growing inequalities, polarized values and divided views about technology,” with conflict on the rise.
Empowered Entrepreneurs: steady technological change; fast learning evolution; low talent mobility
This scenario is characterized by a highly skilled, motivated workforce, leading to a dynamic market for workers to create entrepreneurial opportunities for themselves. It might also lead to governments restricting labor mobility to protect their investments in high-skilled talent.
Polarized World: accelerated technological change, slow learning evolution; high talent mobility
The combination of accelerated technological change and slow learning has led to large sections of the workforce being unemployable, while the lack of human skills is increasing the pressure to automate. Deep and growing inequalities dominate society, with large-scale movements of people within and across countries in search of opportunities. Affluent, globally-dispersed urban super-economies trade ideas, goods and services with each other.
Skilled Flows: steady technological change, fast learning evolution; high talent mobility
The fast pace of learning has led to a highly skilled, motivated, dynamic workforce across a range of industries and sectors. Labor mobility within and cross borders has become the norm, with credentials and degrees increasingly standardized across countries and regions.
Productive Locals: accelerated technological change, fast learning evolution; low talent mobility
The combination of accelerated technological changes and fast learning leads to a strong demand for high-skilled human workers to work with and complement increasingly smart machines. However, low mobility lead to talent shortages and dampen the exchange of new ideas and expansion of markets, causing companies and workers to focus on their local economies.
Agile Adapters: accelerated technological change, fast learning evolution; high talent mobility.
“There is a strong demand for human workers to complement machines, manage the shifts underway and specialize in new kinds of roles… High talent mobility within countries and across borders, combined with widespread opportunities for online platform work that crosses borders, has created a global workforce that is highly agile, productive and globalized, rapidly diffusing values, ideas, technologies, goods and services around the world.”
The white paper makes it clear that scenarios are not predictions, which, in the end would be an impossible feat. Rather the scenarios are designed to stimulate discussions among policy-makers, businesses, academic institutions and individuals so they’re better prepared for the potentially big changes to come. “Indeed, our intention in this work is to demonstrate that the future is not pre-determined. All of the scenarios we present are possible, but none is certain. The most likely outcome is a combination, with different scenarios playing out simultaneously in different geographies, industries, age cohorts and socio-economic groups.”
