When consulting firms announce that AI agents can now perform work once done by consultants, many clients hear a different message: you may no longer need us. That’s the reaction sparked when McKinsey & Company revealed it was rolling out tens of thousands of AI agents, complete with employee-style IDs and emails. The move reignited fears about AI replacing white-collar jobs, but the deeper issue is subtler. This is not just about automation. It’s about how language reframes value in professional services. By calling AI “employees,” firms may be accelerating a shift that undercuts their own relevance.
Why Calling AI “Employees” Changes the Value Conversation
Language shapes perception, and perception shapes pricing power. When AI is framed as a worker, clients naturally compare its output to human labor costs. If software can generate analyses, slides, or forecasts at scale, the question becomes unavoidable: why pay a premium for people? In trying to signal technological leadership, firms risk flattening their value proposition. What once looked like expertise starts to resemble a commodity. The framing subtly teaches clients how to think about replacing effort rather than buying insight.
AI Is Commoditizing Knowledge Work Across Industries
What’s happening is not unique to consulting. AI is rapidly commoditizing knowledge work across finance, marketing, HR, and law. Forecasts, decks, surveys, research, and first drafts were once markers of expertise. Today, they are baseline outputs—fast, cheap, and widely available. These deliverables justified fees, headcount, and long timelines. Now, abundance has stripped them of scarcity. When firms define themselves by volume of output, they struggle to explain why they’re different from the tools everyone else has.
The White T-Shirt Problem in Professional Services
This is the white T-shirt problem. A white T-shirt is everywhere and easy to replace. Its value doesn’t come from the fabric alone, but from how it’s styled and what it signals. On its own, it’s generic. In context, it can be iconic. AI has turned large parts of professional work into white T-shirts. Without a clear point of view, output becomes interchangeable, and firms blend into the background.
When Output Becomes Abundant, Judgment Becomes the Product
If AI can do what junior consultants once did, then that work was never the product. Judgment was. Context was. The ability to decide what matters, what to ignore, and how to act within messy human systems has always been the real value. AI doesn’t remove that value—it exposes where it was never clearly articulated. Many professionals built identities around producing the thing: the model, the report, the deck. AI reveals that production was only the entry ticket.
AI-Heavy vs. AI-Enabled Organizations
A divide is emerging between AI-heavy and AI-enabled firms. AI-heavy organizations use machines to produce more work faster, with humans reviewing and refining outputs. It looks efficient, but it’s easy to copy. AI-enabled organizations use AI to support fewer, harder decisions. Human work moves up the stack toward authority, trade-offs, and accountability. This work is slower, riskier, and deeply contextual—but it’s also defensible. It’s what clients still pay for.
Why Language Matters More Than the Technology Itself
Calling AI agents “employees” reinforces the idea that value lies in production. Consulting was never just production. It was judgment layered on information. By mislabeling AI as workers rather than leverage, firms shift attention away from what actually makes them valuable. Ironically, the more they emphasize AI output, the more replaceable they appear. When the basics are free, differentiation has to be explicit.
The Leadership Choice Firms Can’t Avoid
AI makes everyone capable of producing the same white T-shirt. Only some will turn it into a signature. Leaders are already making a choice in how they talk about AI—whether they realize it or not. They can use AI to amplify judgment, authority, and accountability, or to optimize output and commoditize their own people. How they frame AI today will determine whether they become harder to replace or teach the market how to do without them. This isn’t a technology decision. It’s a leadership one.
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