In the 21st Century workforce, it pays to be creative, adaptable, and constantly learning—all assets in a hyperconnected economy where a growing number of tasks are being completed by machines.
Whether it’s well-compensated employees or lower wage earners, anyone whose job involves repetition, analytics and predictability is likely to find at least some portion of tasks automated or taken on by human-like artificial intelligence (AI). Livable Wage Jobs (LWJ) collaborates with partners in industry, education, nonprofits, and government agencies to educate, train and upskill youth and adults, fostering social mobility in the evolving global labor force. Strategies include connecting young people and adults to relevant, in-demand local industry needs, and promoting job stability and asset building through work ethic, postsecondary degree and certificate completion, and social-emotional growth in cultivating relationships and critical thinking.
Across industries, automation and new iterations of AI are ramping up productivity and powering the innovation economy, creating an imbalance between investment in ventures that skew wealth to a slim minority, and support for displaced or disconnected workers. Workforce development through multiple on-ramps for continued learning remains a key strategy to counteract inequity by supporting economic growth and inclusiveness (Qureshi & Woo, 2022).
Even as productivity rises, “the great divergence” between increased outputs and stagnant median wages for U.S. workers in recent decades, continues to challenge LWJ and partners to seek solutions in facilitating social mobility. Further research is warranted to determine the extent of current and potential employment loss or gains, and to assess proposed policy shifts to provide holistic support for employees, including middle skill workers whose tasks can be easily automated (Autor, Mindell, & Reynolds, 2020).
Recently, even the work of traditionally higher-paid employees—market research analysts and sales managers, programmers, management analysts, and engineers— who are heavily involved in predictive work “may be susceptible to the data-driven inroads of AI, even though they seemed relatively immune in earlier analyses” (Muro, Maxim, & Whiton, 2019). In addition to policy shifts, advancing widespread computer science skills mastery K-12 and fostering assets difficult to replicate in machines—communication mastery, the qualities of discernment and judgment, and creativity in producing original ideas—are instrumental in maximizing potential in the future workforce (Caporal, 2022).
Autor, D., Mindell, D., & Reynolds, E. (2020). MIT. Retrieved February 2022, from The Work of the Future: https://workofthefuture.mit.edu/wp-content/uploads/2021/01/2020-Final-Report4.pdf
Caporal, J. (2022, January 4). Motley Fool. Retrieved from https://www.fool.com/research/which-jobs-automated-10-years/
Muro, M., Maxim, R., & Whiton, J. (2019, January). Brookings.edu. Retrieved February 2022, from Brookings: https://www.brookings.edu/wp-content/uploads/2019/01/2019.01_BrookingsMetro_Automation-AI_Report_Muro-Maxim-Whiton-FINAL-version.pdf#page=29
Qureshi, Z., & Woo, C. (2022). Shifting Paradigms: Growth, Finance, Jobs and Inequality in the Digital Economy. Brookings Institution Press.