Printer-friendly version
About the Author

Mayank Kejriwal, Ph.D. is research team lead and research assistant professor at the University of Southern California. He is the Principal Investigator on two Artificial Intelligence projects funded by the U.S. Defense Advanced Research Projects Agency, and co-author of an authoritative MIT Press textbook on knowledge graphs, published in 2021.


On Preparing for the Future of Work through Proactively Inclusive Lifelong Learning Frameworks

The effects of automation and international competition on the future of work for administrative and professional-services employees have been the subject of much recent research, with studies indicating that emerging technologies could lead up to 375 million workers worldwide1 to switch jobs or face unemployment. Other analyses reveal that 14% of existing jobs may disappear in the next two decades, with an additional 32% likely to change radically.2 A recent article by Deloitte argued that the “notion of the job” is rapidly becoming a “relic of the industrial era” and will likely become outmoded in the near future.3

Minimizing the impacts of technological disruption on employment requires efficient education and re-skilling systems that foster seamless lifelong learning, enabling individuals at different stages of their careers to adapt to the economy’s structural changes. We focus our discussion in this article on work that is primarily administrative and professional services-oriented in nature, which includes the majority of white-collar and knowledge-based jobs predominant in the modern economy. Sectors such as agriculture and mining are left for a future discussion.

Lifelong learning has been recognized as both an opportunity, and as important to the global economy, by multiple global initiatives and organizations. It is directly mentioned in Goal 4 (Quality Education) of the United Nations’ 17 Sustainable Development Goals (SDGs). In its Centenary Declaration for the Future of Work,4 the International Labor Organization (ILO) stated that in “discharging its constitutional mandate … and further developing its human-centered approach to the future of work,” its members must work collectively and individually on “effective lifelong learning and quality education for all.” Another example of a multi-stakeholder initiative to address these economic shifts is the Global Deal Partnership,5 which aims to create “favorable conditions for collaboration between employers, workers and governments” through effective social dialogue and voluntary commitments by partners. Making lifelong learning and re-training seamless for employees could be one such commitment.

We argue that emerging technologies, which are both cause and effect of an increasingly knowledge-based economy6 with high automation and shorter shelf life of technical skills, is a necessary part of the solution for enabling an inclusive future of work. For example, as virtual reality (VR) headsets become cheaper and more widely available,7 and with rapid advancements in “EdTech,”8 new possibilities may arise for engaging globally dispersed audiences with different learning styles9 (e.g., visual versus auditory), differential access to resources, and varying degrees of knowledge. Such technologies do far more than just “automate,” as discussed in a recent special issue10 of the journal Organization Science on emerging technologies and organizing. For this reason, the issue focuses on significant challenges to the workforce of the future, and to organization science. Yet, it is still unclear whether such technologies have, in fact, led to their purported benefits for populations historically disenfranchised in the workplace, such as women, older workers and members of underrepresented groups, which include minorities and Indigenous Peoples, undocumented immigrants, LGBTQ+, and workers with disabilities.  

With these observations in mind, our thesis is that policymakers globally need to work together to formulate the principles of a lifelong learning framework that is proactively inclusive. A framework is a basic structure, plan, or system. Without making inclusion a fundamental feature of such a structure, we risk widening the systemic post-colonial inequities that are already manifest between industrialized and developing economies.11 Unchecked, such inequity can lead to global disruption, both geopolitical and economic. As part of such a framework, we identify three interrelated research areas that could be systematically studied by a sufficiently broad set of stakeholders, including (among others) political scientists, diplomats, and policymakers, to inform evidence-driven policymaking and diplomacy that proactively ensure the inclusive design of lifelong learning frameworks:

Using emerging technologies for ‘frictionless’ re-skilling of workers. Key research question: As some jobs disappear and others are created, often at varying rates in different parts of the world, how can emerging technologies like artificial intelligence (AI) and VR be effectively used to frictionlessly match and train all workers (and especially including members of underrepresented groups) to maximally use their unique aptitudes and strengths in new jobs?12

Developing and implementing a global credentialing framework for lifelong learning. Key research questions: What are the critical features of a credentialing framework that the international civil sector and educators can agree upon, to certify workers for completing massive open online courses (MOOCs) or similar training, on platforms offered by different (often competing) public and private organizations?13 Is technology a necessary part of such training, or are there viable non-tech options that can be scaled and certified? How can the unique, experiential skills of members of underrepresented groups be recognized? What formal role can international organizations such as the Organisation for Economic Co-operation and Development (OECD) and the ILO play in certifying the quality of credentials?

Incentivizing lifelong learning for currently employed workers. Key research questions: How can both governments and the private sector incentivize lifelong learning through tax credits and (potentially tax-deductible) tuition reimbursements, and what are the costs associated with such incentives? Can collective bargaining play a role in developing and implementing such initiatives, such that workers do not face the impossible task of staying competitive in their present jobs while training for future opportunities?

Even within a single nation and economic sector, there is no one-size-fits-all answer to these questions. Encouragingly, institutions like the University of Florida have been recently funded14 under future-of-work research programs launched by the US National Science Foundation,15 to rigorously address such questions at the fine-grained levels of individual sectors and communities. Their research is a welcome complement to the growing body of research on this subject at the level of the single nation and economic sector, typically conducted by public policy organizations, such as the Brookings Institution, and government agencies such as the Bureau of Labor Statistics (BLS). Early empirical data from several such initiatives contradict an early, and somewhat controversial, premise, that re-skilling programs focused on software engineering (“coding”) might be the panacea to the economic woes caused by trends such as globalization and outsourcing. Indeed, the data is suggesting that such programs, especially for individuals formerly employed in manufacturing, coal mining, or truck driving, can fail in drastic ways.16 Although our focus here is on white-collar and knowledge-based workers, the lesson from these studies is an important one; namely, re-skilling accountants or architects to become coders may turn out to be equally problematic. We note that the research is still in its infancy on why such re-skilling initiatives have failed, e.g., are they fundamentally misguided or are they poorly implemented? More to the point, coding itself may eventually become automated due to development of AI that can now write code with increasing accuracy,17 and the commercialization of “no-code” tools.18

In addressing the first research area, researchers must consider these ongoing studies generating evidence-based practices that allow disenfranchised workers to make use of their existing abstract and mechanical abilities. In other words, such initiatives need to be designed in ways that holistically draw upon workers’ aptitude, employment history, age, other demographic factors, professional aspirations, and other relevant attributes identified by social science research. As more evidence-based research continues to be proposed, evaluated, and replicated, policymakers and diplomats should carefully heed their conclusions in forming their responses whether legislative, executive, diplomatic, or otherwise.

In addressing the second research area, policymakers and diplomats face the difficult task of establishing robust international coalitions in an era when increasingly populist electorates have become skeptical of globalization and its benefits. Working transparently and designing such coalitions for mutual benefit will both be essential for converging upon standards and practices acceptable across nation-states. To be truly inclusive, such coalitions should also consider including the occupied Palestinian territories, First Nations, refugees, and other groups that may currently be stateless. Economic and political alliances, such as the G20 and OECD, offer pragmatic and well-respected blueprints for organizing such coalitions. However, inclusive frameworks must resist the neo-colonial tendency of imposing Western standards and practices on the African and South Asian sub-continents. A guiding set of principles is offered by the aforementioned SDGs, particularly in Goal 10 (reduce inequality within and among countries). Political science scholarship on such issues must take a similarly informed and equitable view.

Finally, concerning the third research area of incentives and costs, we note that both the time and money needed for lifelong learning must be considered. Interestingly, there is precedent for offsetting such costs. Technology companies, such as Google, are well known for allocating discretionary time (e.g., 20% of the work week) to spend on projects and initiatives of workers’ choosing.19 Other companies, including Starbucks and Amazon, have instituted tuition reimbursement policies for their workers.20 Government incentives also exist, such as the Lifetime Learning Credit in the US,21 but they are of limited utility given the rising (and already high) costs of college tuition and student loans that can lead to sizeable debt.22

To conclude, the rapid implementation and declining costs of AI, VR, robotics, and other emerging technologies make it highly likely that they will form the backbone of the future of work. Developing the infrastructure to reskill workers within proactively inclusive lifelong learning frameworks is a critical problem of our times. Properly done, such frameworks have the potential to make the global economy more resilient by reducing ageism, unemployment and underemployment, and post-colonial inequities.



  1. Raphael Bick, Eric Hazan, Hamza Khan, Sébastien Lacroix, Hugo Sarrazin, and Tom Welchman, “The Future of Work: Reskilling and Remote Working to Recover in the Next Normal,” McKinsey Digital, July 7, 2020,
  2. James Manyika, Susan Lund, Michael Chui, Jacques Bughin, Jonathan Woetzel, Parul Batra, Ryan Ko, and Saurabh Sanghvi, “Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation,” McKinsey Global Institute, November 28, 2017,
  3. Susan Cantrell, “Beyond the Job,” Deloitte Insights, October 26, 2021,
  4. International Labor Organization, “ILO Centenary Declaration for the Future of Work,” June 2019,
  5. The partnership was initiated by the Swedish Prime Minister Stefan Löfven and developed in cooperation with the ILO and the Organisation for Economic Co-operation and Development (OECD)
  6. Svetlana Igorevna Ashmarina and Valentina Vyacheslavovna Mantulenko, eds. Current Achievements, Challenges and Digital Chances of Knowledge Based Economy (Cham: Springer, 2021).
  7. “Headset Technology is Cheaper and Better than Ever,” Economist, October 3, 2020,
  8. Dylan Peterson, “Edtech and Student Privacy: California Law as a Model,” Berkeley Technology Law Journal 31 (2016): 961–996.
  9. Meryem Yilmaz-Soylu and Buket Akkoyunlu, “The Effect of Learning Styles on Achievement in Different Learning Environments,” Turkish Online Journal of Educational Technology 8, no. 4 (2009): 43–50.
  10. Special Issue Editors: Diane Bailey, Samer Faraj, Pamela Hinds, Georg von Krogh, Paul Leonardi, Special Issue of Organization Science: Emerging Technologies and Organizing, Organization Science 30, no. 3 (2019): 642–646.
  11. Paul Krugman, and Anthony J. Venables. "Globalization and the Inequality of Nations." The quarterly journal of economics 110, no. 4 (1995): 857-880.
  12. Cyrus Hodes, Niki Iliadis, and Mayank Kejriwal, “Using AI and Virtual Reality/Augmented Reality to Deliver Tailored and Effective Jobs Training,” Day One Project, July 2021,
  13. Krystal Laryea, Andreas Paepcke, Kathy Mirzaei, and Mitchell L. Stevens, “Ambiguous Credentials: How Learners Use and Make Sense of Massively Open Online Courses,” Journal of Higher Education 92, no. 4 (2021): 596–622.
  14. “Collaborative Research: The Future of Remanufacturing: Human-Robot Collaboration for Disassembly of End-of-Use Products,” National Science Foundation Award Abstract #2026276,, accessed January 10, 2021,
  15. Future of Work at the Human-Technology Frontier,” National Science Foundation,
  16. Lauren Weber, “Why Companies are Failing at Reskilling,” Wall Street Journal, April 19, 2019,
  17. Cade Metz, “A.I. Can Now Write Its Own Computer Code. That’s Good News for Humans,” New York Times, September 9, 2021,
  18. Examples of such tools are provided in which follows this trend. Such tools allow code generation using intuitive visual interfaces that can be mastered without the kind of specialized training required of software engineers today.
  19. Bill Murphy, Jr., “Google Says It Still Uses the ‘20 Percent Rule,’ and You Should Totally Copy It.” Inc., November 1, 2020,
  20. Kelia Washington, “Starbucks, Walmart, and Amazon Offer ‘Free’ College – but Read the Fine Print,” The Century Foundation, October 15, 2018,
  21. “Lifetime Learning Credit,” US Internal Revenue Service, accessed September 22, 2021,
  22. Zack Friedman, “Student Loan Debt Statistics in 2018: A $1.5 Trillion Crisis,” Forbes, June 13, 2018,