What Happens to a Leader’s Trust in AI After One Visible Mistake?
Picture the meeting where an AI-drafted number turns out to be wrong in front of the leadership team. Someone does the math out loud. The room goes quiet. Then someone says, “This is exactly why we can’t trust these tools.”
After one visible mistake, a leader’s trust in AI can drop faster than the error deserves. Research suggests people lose confidence in an algorithm more quickly than in a person who makes the same mistake.
I’m Sahar Andrade, MB.BCh, Neuroleadership Coach and Forbes Coaches Council member. In my experience, the work is to sort the mistake before decidin
g about the tool.
Why does one AI mistake hit trust so hard?
The setting matters. A Stack Overflow survey, reported by IT Brief on June 4, 2026, found that workplace use of AI agents rose to 59% from 31% a year earlier. In the same survey, 82% of respondents said they were concerned about the accuracy of information provided by AI agents.
Use nearly doubled. Concern is the majority view. So when a mistake shows up in public, it does not land on neutral ground. It lands on a worry already in the room, waiting for evidence.
One visible mistake looks a lot like evidence.
The common misread is that the size of the reaction tells you the size of the problem. It often does not. A reaction can carry the timing, the audience, the embarrassment, and every doubt that was there before the slide went up.
What is algorithm aversion?
In 2015, Dietvorst, Simmons and Massey published five studies in the Journal of Experimental Psychology: General. People became less confident in an algorithm after watching it perform, even when it outperformed a human forecaster. The reason: they lost confidence in the algorithm more quickly than in a human after seeing the same mistake.
Same mistake. The machine pays a bigger trust price.
That asymmetry is algorithm aversion. Not “people dislike machines.” Something narrower and more useful.
Here is the honest limit. These were forecasting tasks, not workplace AI. Applying this to leaders is my inference, not their finding.
My read: under pressure, one public error can feel bigger than its size. That is tomorrow’s question. Today’s point is simpler: the drop in trust may be partly about who made the error.
Is losing trust in AI after a mistake ever the right call?
Yes. This is where leaders get hurt in both directions.
Some AI mistakes are ordinary errors: a wrong number, a missed detail, a summary that got a point backward. People make these every day too. Ordinary errors are a reason to improve the process.
Other failures are serious: anything touching safety, privacy, or compliance. There, stopping the tool is the right call, and the organization’s own process leads. A leader should not improvise a response or quietly fix it and move on.
Not every drop in trust is a bias. Sometimes it is a sorting problem. Treating a quality slip like a disaster wastes a useful tool. Treating a serious failure like a quality slip is a real risk.
How should a leader respond after an AI mistake?
Four moves.
Separate the error from the tool. Ask what went wrong, where, and how often this kind of error has happened before. One wrong input, one time, is a different problem from a weakness that keeps coming back.
Sort it. Ordinary error: fix it, add a human check, keep going. Safety, privacy, or compliance failure: stop, follow the organization’s process, do not improvise. If it is unclear which, pause and ask through that process.
Keep a hand on the output. A follow-up by the same researchers found people were considerably more willing to use an imperfect algorithm when they could modify its output, even slightly. Applying that to workplace AI is my inference. In practice, make the human edit step visible: a named person checks AI-drafted figures against the source before they reach leadership.
Say one sentence that holds both truths. For an ordinary error: “The tool got this wrong. We’re keeping it, and we’re adding a check.” For a serious failure: “We’re pausing it until it’s reviewed.”
If someone on the team caught the error, add: “Thank you for catching that.” In my opinion, that line keeps errors coming to the surface.
How can a leader tell judgment from asymmetry?
Run one check. Picture the most trusted person on the team making exactly the same mistake. Same number. Same room.
If the response would be the same, that is judgment. If the person would get “good catch, let’s fix it” and the tool would get “we can’t trust these things,” that gap is worth a second look.
This is not about trusting AI more. It is about trusting it accurately.
Frequently Asked Questions
Why do leaders lose trust in AI so quickly after one mistake?
Research on algorithm aversion offers one explanation. In 2015, Dietvorst, Simmons and Massey found that people lost confidence in an algorithm more quickly than in a human after seeing the same mistake, even when the algorithm outperformed the human forecaster. Those studies used forecasting tasks, not workplace AI, so applying them to leaders is an inference. In my experience, a public setting can add weight too. A mistake in front of peers tends to feel bigger than the same mistake found in private.
What is algorithm aversion?
Algorithm aversion is a term researchers use for a specific asymmetry. When a machine and a person make the same mistake, people tend to lose confidence in the machine faster. In five studies published in 2015 in the Journal of Experimental Psychology: General, people became less confident in an algorithm after watching it perform, even when it beat a human forecaster. The research looked at forecasting, not workplace AI tools, so its use for leaders is an inference rather than a finding.
Should a leader stop using an AI tool after it makes a mistake?
It depends on the kind of mistake. For an ordinary error, like a wrong figure or a missed detail, the usual answer is to fix it, add a human check, and keep using the tool. For a failure that touches safety, privacy, or compliance, stopping the tool is the right call, and the organization’s own process should lead. The key step is sorting the mistake first, so the response matches what actually happened rather than how the moment felt.
How can a leader rebuild trust in AI after a visible error?
One useful step is keeping a human hand on the output. A follow-up study by the same researchers found people were considerably more willing to use an imperfect algorithm when they could modify its output, even slightly. Applying that to workplace AI is my inference. In practice, it can mean a named person reviews AI-drafted work before it reaches leadership. A visible check can change what the next error means: a catch by the process, not a public failure.
What should a leader say to the team after an AI mistake?
One clear sentence that holds both truths. For an ordinary error: “The tool got this wrong. We’re keeping it, and we’re adding a check.” For a serious failure: “We’re pausing it until it’s reviewed.” In my experience, long explanations after a public mistake can sound defensive, while one sentence sounds decided. If a team member caught the error, add: “Thank you for catching that.” That tells the whole team that surfacing AI mistakes is a contribution, not a confrontation.
How does a leader stay grounded when an AI error happens in public?
A grounded leader slows the moment down before deciding anything about the tool. Three questions help: what went wrong, where, and how often has it happened before. Then one more check: would the response be the same if a trusted colleague had made this exact mistake? If not, the reaction may be partly about who made the error. In my opinion, that pause is what keeps a leader from losing a useful tool or keeping a risky one.
Listen to the episode: https://www.saharconsulting.com/ai-cafe-conversations-podcast
The Pressure Room is tomorrow: Thursday, October 29, at 11 AM Pacific. Free, 45 minutes, on Zoom. This session: Why Does a Small Problem Suddenly Feel Like an Emergency Under Pressure? Register for The Pressure Room: https://www.saharandrade.com/pressure-room
About the author: Sahar Andrade, MB.BCh, is a non-practicing physician turned Neuroleadership Coach and a Forbes Coaches Council member. She teaches executives how the nervous system shapes leadership under pressure, and hosts the AI Café Conversations podcast.
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