Protect human expertise before machine imitation takes over

GenAI is reshaping who can do the work, not necessarily who understands it. That’s the paradox leaders are now facing. We’ve entered an era where anyone with a prompt can perform tasks that once required years of hard-won experience. That’s powerful, but it’s also risky. When machines make it look easy, we stop appreciating what’s truly hard.

The result? A generation of professionals who know just enough to be dangerous— producing content, summaries, and even decisions that appear authoritative but lack the nuance, judgment, and embedded context that true expertise requires.

Enterprise leaders should step back and ask: What are we gaining, and what might we be losing?

GenAI may perform like an expert, but it doesn’t think like one

There’s a dangerous comfort in GenAI’s outputs. The prose is clean. The logic seems sound. It responds with confidence. But what’s missing is the understanding…the why behind the answer (See Exhibit 1). GenAI synthesizes patterns; it doesn’t reason. It mimics expertise; it doesn’t possess it.

Exhibit 1: The illusion of expertise: surface-level AI vs. deep human understanding

Source: HFS Research, 2025

This creates a subtle but serious problem: we start trusting the surface, not the substance. This isn’t just about skill gaps; it’s about cognitive offloading. The more we rely on GenAI to make decisions for us, the more we lose our ability to do the hard thinking.

This is a cultural issue as much as a technical one. Our research shows that 45% of employees are already worried about job loss or resistance to change with GenAI. That fear creates a dangerous dynamic: people disengage, defer to machines, and stop questioning the logic behind decisions.

GenAI is built on what’s documented, but enterprise life runs on what isn’t

One of the biggest myths about AI is that it learns the same way humans do. It doesn’t. GenAI is trained on massive public, formalized, and well-documented datasets. That’s great for FAQs, knowledge bases, and standard process guides. But it leaves out the messy, unwritten, and deeply contextual knowledge that drives real enterprise performance.

GenAI can’t see what’s behind the scenes. It isn’t trained on the lessons learned after a botched rollout, the nuance of a tense client negotiation, or the informal mentorship between meetings. It only knows what’s been documented and codified. But most enterprise know-how doesn’t live in systems. It lives in tribal knowledge.

To lead responsibly, executives must recognize that not all knowledge is created equal or equally accessible to machines. In most organizations, knowledge exists in three distinct layers: explicit, tacit, and tribal (See Exhibit 2).

Exhibit 2: Types of knowledge in organizations: Explicit, tacit and tribal knowledge

Source: HFS Research, 2025

The hype around AI-led work transformation and automation is real, but organizations will need to pick the workstreams that benefit the most from the current wave of AI, GenAI, and Agentic AI technologies. On top of that list will be business processes that rely on explicit knowledge bases and data to drive decision-making.

This matters because enterprise decision-making isn’t just about documented information; it’s about lived experiences. And right now, GenAI can’t replicate that. It can only echo what’s already been formalized, which means it will always be biased toward mainstream, consensus-based thinking.

The deeper danger is that leaders who overly rely on GenAI outputs risk institutionalizing the sanitized version of knowledge while the real insights quietly disappear.

Retaining real expertise is your competitive edge if you act fast

GenAI won’t eliminate the need for your best people; in fact, it will make them more important than ever. Subject matter experts, critical thinkers, and cross-functional connectors may no longer have a monopoly on surface-level knowledge. However, they do have something machines still can’t replicate: lived experience, intuition, context, and, most importantly, relationships.

The ability to build trust, read a room, navigate politics, and influence informal networks is uniquely human. GenAI can summarize a stakeholder map but can’t understand how the decision maker feels about a past failure or whether a post-work catch-up drink just shifted a strategic priority.  In an enterprise, what you know is only part of the equation. Who you know and how you move together is often what gets the real work done.

Imagine a global services firm preparing for a strategy session with a long-standing insurance client. A senior executive uses GenAI to generate a polished overview of industry trends, regulatory shifts, and typical client challenges based on documented knowledge. However, the tool misses critical details: the client recently had a leadership shake-up, and the new CIO is under pressure to consolidate vendors rather than expand digital pilots.

Someone on the team knows this. Having worked with the client for years, they’ve built trust, navigated internal politics, and understood the history behind failed initiatives. But they’re not included in the prep session. The team walks in with a confident, GenAI-fueled proposal and completely misfires—not because the data was wrong, but because the machine lacked the unspoken context and relationships that shape decisions.

This isn’t a cautionary tale about bad AI. It’s about how easily we ignore the value that lives inside our people when we treat GenAI as a replacement for experience rather than a complement to it.

This moment demands a reset in our thinking about expertise. It is not just a talent strategy; it is a leadership test. Ask yourself:

• Are we creating space for our experts to challenge what the machine suggests?
• Are we building systems that capture and protect experiential knowledge before it disappears?
• Are we valuing the human relationships and informal networks that actually move work forward and making sure GenAI doesn’t displace or ignore them?
• Are we investing in people who can interpret, contextualize, and question the outputs?
• Are we assessing the feasibility and success of AI solutions against the broader context of what our people bring to the table in expertise and relationships?

Because once that kind of knowledge walks out the door, GenAI won’t save you. It won’t even know what you’ve lost.

The Bottom Line: As GenAI takes over tasks, human expertise and relationships become what you can’t afford to lose.

The promise of GenAI is massive, but only if we build the right human scaffolding around it. This is a moment to rethink how knowledge flows, decisions are made, and how we preserve expertise that can’t be trained into a model.

The tools themselves won’t write the next chapter of AI adoption. It’ll be shaped by the leaders who ask harder questions about talent, trust, and the role of human judgment in an increasingly automated enterprise.