"Labor is the largest line item for most companies. When AI enters the picture, it is natural to look there first. If technology can do more, we must need fewer people. But there is little evidence that AI is delivering productivity at a level that justifies the speed of workforce reduction. What I see instead is pressure, particularly in public companies, to show immediate returns on significant AI investments. Cutting travel or discretionary spending does not move the needle. Headcount does. So it becomes the most visible lever."
"Recently, I spoke with a young analyst who just finished a rotation program. His advice was simple: Do not let new hires rely on AI too early. That runs counter to what most CEOs say. Every company wants employees to be AI-fluent. However, if you rely on AI before you understand the business, you lose the ability to judge the work. You may produce answers faster, but you cannot assess their quality, relevance, or risk."
"Judgment is built through repetition. By doing the work yourself, you learn what good looks like, where things break, and how decisions hold up in practice. Without that foundation, you defer to AI instead of using it as a tool."
"I have been in boardrooms where AI is discussed as both an opportunity and a justification. Leaders talk about transformation, and in the same breath talk about reducing headcount. The connection feels automatic, as if one must follow the other. Here's what's missing from the conversation: What is the work we actually want done, and how should it be done?"
AI is increasingly treated as a justification for reducing headcount, with leaders linking transformation to workforce cuts. Labor is a major cost line item, so companies often target staffing first when adopting AI. Evidence for productivity gains that warrant rapid reductions is limited, while public companies face pressure to show immediate returns on large AI investments. Cutting travel or discretionary spending has less impact, making headcount the most visible lever. New hires are advised not to rely on AI too early because understanding the business is required to judge quality, relevance, and risk. Judgment develops through repetition and hands-on work, which enables better decision-making and use of AI as a tool rather than a substitute.
#ai-adoption #workforce-reduction #productivity-measurement #employee-training #software-development
Read at Fast Company
Unable to calculate read time
Collection
[
|
...
]