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Dry rot. A Potemkin village. The Wizard of Oz.
What do those things have in common? In each case, they may look good on the surface, but it’s only an illusion. Wood afflicted with dry rot looks just fine until the tree it’s in topples down. Grigory Potemkin is said to have tried to impress Catherine the Great by creating a prosperous Crimean village constructed only of building facades, masking the real town’s crippling poverty. The Wizard of Oz looked great and powerful, until the cowering man behind the curtain was revealed.
Could artificial intelligence do the same to organizations — create an impressive, seamless exterior even as capability completely falls apart inside?
This alarming possibility was raised by the AI experts we interviewed for a joint Anthrome Insight-Axialent study on AI’s impact on behavior and culture inside organizations. Even as those experts uniformly cited AI’s potential to transform work for the better, they cautioned that AI adoption risks unintentionally creating an organizational mirage: workplaces that appear highly capable while people’s real skills quietly erode beneath polished AI-generated output.
Perhaps even more worrisome: When we can no longer reliably tell who truly knows what, the interpersonal trust that teams depend on to function also crumbles.
What are the early signs that the mirage effect is already forming in your organization — and what does it look like when it takes hold? How can leaders avoid the mirage effect and make sure that in the AI age, their people and organizations are as capable as they appear?
Capability Mirages: The Early Signs and Troubling Possibilities
An AI-driven illusion of competence, hiding the absence of genuine understanding, is already appearing in some organizations, the AI experts told us. Stephanie Antonian, founder and CEO of AI product development company Aestora, explained how this can play out in dangerous ways for leaders and organizations: “The upside [of AI tools] is that everyone can produce a level of work that’s pretty good for basic tasks. … It looks pretty good. But then you don’t know what’s underneath it, how resilient that piece of work is, or whether it’s going to give you an additional liability.”
After all, AI is not necessarily an improver of work but, rather, an amplifier. As AI Business Impact CEO Gábor Szórád observed, “At the end of the day, if you are a fantastic software engineer or a great manager, AI allows you to do more with the same energy. If you’re a bad one, you’re just going to create more crappy instructions, longer ones, more bad ideas. It just magnifies whatever you put in.”
The tech vendor marketing narrative stating that AI makes individuals more capable just compounds the mirage issue. In some situations, AI does enable the production of better short-term outputs — but it also breaks the historical link between strong output and strong capability, causing teams to lose sight of who actually possesses strong skills.
Moreover, AI itself doesn’t know when it’s out of its depth, said Amir Michael, professor of accounting and deputy executive dean for executive and professional education at Durham University. Likewise, Antonian observed, people who aren’t capable in a particular subject area can’t spot where AI-generated work has gone wrong — unlike people with subject-matter expertise. They may pass on bad output to others who also can’t tell the difference.
The mechanism through which professional mastery has always been built — productive struggle, error-based learning, the slow accumulation of genuine judgment — may be quietly bypassed before many people realize what is being lost.
But there is a second, less visible problem that, in the long run, may be the more dangerous one: Not everyone is self-aware enough to notice their own skills eroding. There is no single individual — among managers, colleagues, or clients — who can do an accurate, real-time read on how capable an organization is, overall. Individual skills and collective capability could weaken long before anyone notices, and there is absolutely no guarantee that even the best-functioning AI could begin to fill the gap. Leaders should be especially concerned about losses in the sophisticated, critically necessary “muscle of critical thinking,” said Albert Durig, cofounder and partner at Triviam Consulting.
This situation results in a direct and damaging consequence for organizational trust. Teams have traditionally functioned based on knowing, or at least being able to calibrate, who knows what. That calibration determines whose judgment should be relied upon, how managers identify who is ready for greater responsibility, and how organizations know what they are truly good at. When AI makes that calibration unreliable, trust erodes with it. When AI-assisted work is later discovered to have been misrepresented, the trust collapse tends to be swift: “The impact on trust is 0 [doubt] to 100,” said Elisa Farri, vice president at Capgemini Invent Management Lab.
Concerned yet? We all should be.
But if leaders act now, they can avoid organizational dry rot — and maintain true capability fueled by humans and machines alike.
How to Preserve Human Capability and Team Trust
Let’s explore what the experts we spoke with had to say.
1. Choose purpose and business goals over an AI-first mentality.
It’s trendy to announce that your company is thinking “AI first.” But it’s not what these experts would recommend. Antonian put it directly: “When you go AI-first, you have already told your organization it’s not human-first.” That signal, once received, is hard to unsend — and at a moment when people are already anxious about their relevance and job security, it can quietly erode the trust that makes teams function.
One potential outcome of an AI-first future is an unpleasant inversion of the roles of human and machine, said one senior AI executive at a Fortune 500 company. They see a concrete risk that people might let AI do the reasoning, interpreting, and responding only to become “transactional tools” themselves.
“AI can be the lead,” Michael noted. “That’s the problem. As long as AI is your follower — it follows your requests, your orders — we’re fine. The time that AI jumps to be your lead, that’s the downturn.”
AI-first thinking, in the view of our experts, causes people to lose perspective on the utility of AI as a tool — and to deploy AI in comically inappropriate settings. Remember the old saw “If you’re a hammer, everything looks like a nail”? That applies to AI-first: “You don’t walk around the house, holding the biggest drill that you have, asking people if they need their coffee stirred,” said Andrea Jones-Rooy, a data scientist, organizational researcher, and visiting associate professor at New York University’s Center for Data Science.
Even when AI is used for more seemingly appropriate ends, such as measurement and KPIs, Durig noted, it can cause a dominance of measurement over meaning. That leads to “a performance culture without purpose,” he said.
On the flip side, when purpose comes first, enabled by AI tools, our experts see the potential for true progress and even stark disruption. With AI enablement, “Small groups of people that get together for a specific purpose may outperform corporations because they are more nimble, flexible, fast-moving,” said AI entrepreneur Thierr
