You can also access this resource, and many others, in Issue 369 of my Talent Edge Weekly newsletter.
This new 65-page report examines the shift from AI adoption (deploying and using AI in existing jobs) to AI workforce transformation (redesigning workflows, roles, and workforce practices around AI). Nine cases across firms such as Salesforce, PwC, and BNY cover role redesign, early-career development, reskilling, and AI agents. It offers a nine-domain diagnostic to identify workforce gaps (pp. 46–47) and an illustrative 12-month roadmap (pp. 48–50). Figure 6 (p. 24) caught my attention as it connects to my posts on skill erosion, based on IBM and BCG research. Skill erosion occurs when employees lose proficiency in existing skills as AI reduces opportunities to practice them (e.g., experienced analysts becoming less adept at questioning assumptions as AI interprets data for them). This new report from the AIGW Policy Observatory introduces Learning Debt, when AI removes developmental work without replacing its learning value (e.g., junior analysts missing foundational analysis that builds judgment). The “debt” is that AI productivity gains today may create skill and capability gaps tomorrow. Although these may sound interchangeable, I see a distinction beyond terminology: skill erosion is losing skills you already have, whereas Learning Debt is failing to build skills you and your organization will need in the future. That matters because each risk may require different tactics. Next week, I will share a one-pager with tactics for addressing both while capturing AI’s benefits.
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