AI Can Compress Tasks. It Can’t Shortcut Expertise.
Stop for a minute and think about where expertise actually comes from.
Not information. I mean the kind of expertise that lets someone sit across from a CEO and explain why a product can’t launch on time because of geopolitical supply chain issues. The kind that understands which stakeholder needs a call before the meeting, which “minor” issue is about to become a reputational crisis, which solution sounds good in theory but will fail the moment it hits operational reality.
That capability doesn’t magically appear once someone gets a senior title. It’s developed over years of exposure to ambiguity, pressure, repetition, tradeoffs, relationships, consequences, and unforeseen circumstances. People learn by being close to the work, watching experienced operators navigate difficult situations, making mistakes, correcting them, and slowly building judgment.
That’s why the conversation around AI and white collar work needs to be way more nuanced than “the technology will replace x% of jobs / make us x% more efficient.”
Yes, AI can accelerate analysis, drafting, coding, design, synthesis, planning, and research. In many cases, it performs those tasks remarkably well. But organizations still need people who can exercise judgment, carry accountability, maintain trust, and make decisions when conditions change in ways the process couldn’t anticipate.
If companies aggressively eliminate too much entry- and mid-level work in the name of efficiency, where exactly will the next generation of experienced operators come from? Who develops the pattern recognition to know when the output is subtly wrong, incomplete, strategically dangerous, or disconnected from how organizations and people actually behave?
The model can identify the supply chain issue. It can summarize the risks and draft the mitigation plan. But it’s not going to walk into the boardroom, absorb the emotional temperature of the room, renegotiate priorities across functions, preserve trust with a client, and explain why the launch date has to move.
That’s still human work.
The more AI capabilities advance, the more important it becomes to think carefully about which work we automate, which work we redesign, and which developmental experiences organizations cannot afford to lose. Are you doing that now? If not, why not?
