The current debate around “AI deskilling” is understandable, but it points in the wrong direction.
What we are observing is not a sudden erosion of skills. It is the exposure of how those skills were built and how fragile many of them actually are.
For years, organizations have optimized for efficiency. We streamlined processes, reduced friction, and scaled output. In doing so, we quietly removed many of the moments where actual expertise is developed: repetition, failure, and problem ownership.
AI is not creating this dynamic; however, it is accelerating it—and making it visible.
When an experienced professional hesitates to step back into their own work after relying on AI, that is not a technology problem. It is a signal that execution has been separated from understanding for longer than we might want to admit.
Looking back, strong operators were never defined by how quickly they produced results but by their ability to navigate ambiguity, to recover when things broke, and to make decisions even without a clear path.
AI does not replace these capabilities; nonetheless, they can quietly atrophy if they are no longer required.
That is where leadership becomes critical.
Many organizations are currently reinforcing the wrong behavior. They reward speed, volume, and visible AI adoption because these metrics are easy to track. However, they say very little about whether someone truly understands what they are doing.
And over time, this creates a structural risk: a workforce that performs well under ideal conditions but struggles when those conditions change.
The question is not whether people use AI; that is inevitable.
The question is whether leaders still demand ownership of outcomes.
Because AI makes it easy to deliver an answer, it does not ensure that the answer is understood, challenged, or even appropriate in context. If that layer of accountability disappears, organizations will not become less skilled overnight—they will become less resilient.
For early-career talent, the stakes are even higher. If the first years of a career are no longer shaped by solving problems firsthand, but by “coordinating” outputs, then expertise becomes theoretical rather than earned.
And theoretical expertise does not hold under pressure.
That is why the conversation needs to shift.
AI is not reducing human capability. It is redefining what we choose to develop and what we allow to fade. Leaders who get this right will not limit AI usage; they will raise the bar for thinking.
Because in the end, competitive advantage will not come from who uses AI faster. It will come from who still knows what to do when AI is no longer enough.
Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice—it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.