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Drexel professor warns that workers who fix AI errors may lack the skills to do it

Published
Score
14

Why it matters

A Fast Company essay published today by a Drexel University professor of management information systems who directs the Center for Applied AI & Business Analytics argues that AI oversight carries a hidden danger it calls "latent risk." The author's concern is that people can lose skills that their organizations still need them to use. The essay opens with a worker who reviews a reliable AI system, rarely changes its output, and is then suddenly asked to take over when the system fails. The core mechanism is skill erosion: a system that performs well can leave human overseers less practiced at independent judgment and less ready to intervene. The author draws on older automation research showing that people miss problems or follow automated recommendations even when other evidence points the other way. The new element, the author argues, is that AI now handles more of the analysis and decision-making that people once did.

The essay is commentary, not reporting on a specific event. No incident, company action, or legislation is tied to it. It also sits within a broader pattern. A 2025 NBC report described writers hired to polish ChatGPT's prose, artists patching AI images, and developers fixing buggy code written by AI assistants. A January 2026 Axios article on a Workday survey found that workers still spend significant time cleaning up after AI.

For attorneys, the practical signal is in the guidance now emerging. A Progressive Robot article dated October 15, 2026, recommends that firms assess error detection, the quality of human reviews, and whether staff can resolve problems when AI is unavailable. It notes that accuracy and productivity metrics alone do not show whether employees can still perform critical tasks independently. It also advises periodic reviews, since processes that once required frequent human intervention may become more automated over time and shift risk elsewhere. Counsel advising clients that deploy AI in review or decision roles should consider whether human-override capability is tested, documented, and maintained.

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