I have recently noticed a concerning trend in the professional services sector. While we are accelerating the adoption of AI in law, consulting, finance, and accounting, something is happening behind the scenes — we are losing something more valuable than efficiency. We are losing genuine expertise.

Numbers look great on the surface. Thompson Reuters found that companies' use of generative AI doubled in 2025. 95% of professionals say it will become a core part of their work. Productivity is rising, turnaround times are decreasing. But an unexpected cost is coming.

The real problem isn’t technical — it’s cognitive. When we automate everything for speed and efficiency, we remove the experiences through which professionals learn how to think critically. Experts don’t become experts by getting answers quickly. They become experts by dealing with uncertainty, weighing trade-offs, and watching decisions unfold in real time.

Here’s the issue: most current AI tools provide answers, summaries, and recommendations. Rarely do they push people to think deeply. This means that junior staff see the results without witnessing the thinking process behind them. They become faster, but not necessarily better.

Genuine expertise develops through something called “lateral learning” — sitting near experts, listening to conversations, understanding how they make decisions. But hybrid work and automation have eliminated this. Now, juniors don’t see much of this expert thinking.

There’s another gap: current knowledge management systems document how things are done, but they lack the unspoken cues — what experts notice, when they change course, which signals matter. This invisible thinking exists in the gap between “work as perceived” and “work as actually done.” Large language models don’t have this knowledge because it’s never documented.

55% of professionals reported significant changes in their way of working due to AI, and 88% say they prefer specialized AI assistants. But improving tools and efficiency alone doesn’t solve the core problem.

Smart companies in 2026 will differentiate between two types of AI: AI designed for automation and AI designed to support cognition. The first focuses on efficiency. The second — rooted in behavioral science — focuses on better questions rather than faster answers. It encourages people to pause and think aloud about their work.

When expert thinking becomes visible — both to themselves and others — it becomes transferable. Teams can learn it. Clients can understand it. This is where we protect expertise instead of replacing it.

The coming danger isn’t whether AI can do the work. It’s what gets lost when AI makes work so easy that people stop learning how to think and judge for themselves.

Companies that see AI as just a tool for efficiency will see their expertise quietly erode. Those that use it to foster judgment and critical thinking will develop a stronger next generation of professionals. The competitive advantage won’t be for those who adopt AI faster, but for those who adopt it smarter.