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HR practitioners continue to tap into data and insights to help their organizations understand and prepare for AI’s impact on jobs and work. With this in mind, I recently shared two useful resources: 1) The Brookings Institution analysis pairing AI exposure with an “adaptive capacity” index to show where worker resilience is high or vulnerability is concentrated; and 2) HBS Working Knowledge research on how AI is shifting job demand, including an interactive tool showing which occupations are more likely to be enhanced versus eliminated. This past week, a new 17-page report from Anthropic was also widely shared. Using a new metric, “observed exposure,” it compares theoretical AI capability with real-world usage data and finds that, in most sectors, actual adoption remains only a fraction of what AI is theoretically capable of doing. One implication I draw for workforce planning and related areas is that organizations should not treat AI’s potential capability as the same thing as immediate workforce impact. Instead, leaders should distinguish between roles/jobs/work with high theoretical but low actual adoption, high actual adoption, and low exposure. Those distinctions matter because they lead to different conclusions, prioritization, and actions. Relying only on theoretical exposure could prematurely lead to workforce reductions or restructuring. A better use of this data is to identify where role redesign, reskilling, and shifts in productivity expectations are most likely to emerge first so efforts can be prioritized more thoughtfully.
