When AI and Experience Clash: Letting Humans Challenge the Machine

The machine says the patient is low risk. The veteran clinician says something feels wrong. The AI recommends cutting a workstream. The executive team knows that workstream is critical to a customer they are trying to win. The coding assistant produces perfectly functioning code—except it has quietly recreated a function that already exists somewhere else in the codebase.

These aren’t hypothetical scenarios. They are the kinds of moments leaders increasingly face as AI moves from experimentation into decisions that affect customers, employees, operations and the bottom line. Automatically trusting the machine ignores context, intuition and accountability. Automatically siding with the human can mean overlooking patterns and possibilities that AI can uncover. So what should happen when an AI system and an experienced human reach different conclusions?

Members of the Senior Executive AI Think Tank—a curated community of leaders specializing in machine learning, generative AI and enterprise AI applications—have encountered this tension firsthand across healthcare, manufacturing, software development, enterprise transformation, market intelligence and technology strategy. In the examples that follow, they share what happened when AI and human expertise diverged, how their teams responded and what those moments revealed about the roles each should play in high-stakes decision-making.

Don’t Assume Human Judgment Is Better

Edward Morris, CEO and Lead Prompt Engineer of Enigmatica, sees a common leadership mistake: assuming that human judgment should automatically outrank an AI recommendation.

“I think a majority of people go down the route of assuming that human is better when that isn’t always the case,” Morris says. “What actually works is a combination of the two.”

That combination requires leaders to resist reflexive skepticism. AI can surface weaknesses that an experienced professional simply did not notice.

“An AI can point out flaws in a plan that you never considered,” Morris says, “and it would be up to human judgment whether or not you listen to them.”

He points to an example within paid advertising. If an AI system can analyze campaign performance and identify a problem in the analytics, dismissing the recommendation solely because it came from a machine makes little sense.

Morris also cautions leaders against treating today’s AI as if it were still operating at the level of the early generative AI boom. 

“Too many people seem to be stuck in this idea that AI is still the same as it was in 2022 or 2023,” he says. “AI is not just Copilot, ChatGPT and the Google search summary. It’s far more than that.”

In summary: Establish a process for challenging AI outputs, but don’t establish a culture that automatically distrusts them.

“AI is strongest at pattern recognition; leaders are strongest at context, consequence and intent.”

– Rishi Kumar, Chief Transformation Officer of AI and Digital at Matchingfit

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Ask What the Model Cannot See

Rishi Kumar, Chief Transformation Officer of AI and Digital at Matchingfit, describes a transformation program in which AI recommended deprioritizing a workstream because historical data indicated relatively low near-term ROI.

Leadership disagreed.

“They knew it was a strategic dependency for a larger customer and operating-model shift that the historical data could not see,” Kumar says.

The team did not simply override the model. Instead, they investigated the disagreement.

“That exposed the real issue,” he says. “The AI was optimizing for the measurable past, while leaders were accountable for the emerging future.”

That distinction is critical for executives. A model may be highly accurate within the boundaries of its available data while still being poorly suited to a decision involving structural change, emerging markets or a strategic dependency.

Kumar summarizes the division of labor this way: “AI is strongest at pattern recognition; leaders are strongest at context, consequence and intent.”

But the goal, he says, is not to decide who wins an argument: “When the two disagree, that is not a failure; it is a decision signal.”

Look Beyond the Data That Was Measured

Muthukumarapandian Chandrasekaran of CitiusTech offers a particularly high-stakes example.

“An AI model flagged a pattern in patient data as low risk based purely on the numbers,” Chandrasekaran says. “A senior clinician on the team disagreed.”

The clinician had access to information that did not fit neatly into the structured dataset: prior treatment attempts, family circumstances and the patient’s history.

“The AI wasn’t wrong about the pattern,” he says. “It was wrong about what the pattern meant.”

The team decided to investigate rather than treat the disagreement as a contest to win. 

“We navigated it by treating the disagreement as information, not a tiebreaker to resolve quickly,” Chandrasekaran says. “The team dug into why the two views diverged instead of picking a side.”

Chandrasekaran argues that this is where the real insights lie: when organizations examine missing variables, not merely debate the output.

“AI is excellent at finding patterns in what’s measured,” Chandrasekaran says, “but experience and intuition often carry the context that was never measured in the first place.”

The goal, he says, is to build a process where disagreement between experience and AI “triggers deeper scrutiny instead of getting smoothed over.”

“Tip 20 jigsaws into a pile and the AI pulls pieces from all of them, confident and wrong. Hand it the right pieces and it builds a perfect puzzle.”

– Jason Barnard, Founder and CEO at Kalicube

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Give AI the Right Pieces to Work With

Jason Barnard, Founder and CEO at Kalicube, sees another form of disagreement emerging in software development.

“As we deployed AI coders, over the last year, our developers have been finding multiple functions doing the same job in our codebase,” Barnard says. “Every version was working perfectly, which is what made it hard to spot.”

The problem is not necessarily that an AI coder produces broken software. It can produce software that works while still making the overall system worse.

“Our codebase is too big for an AI to hold in memory at once,” Barnard says. “So it selects the parts that look relevant to your task and glues those together.”

What the system cannot see becomes the central problem.

“The trouble lies in what it didn’t select: It has no way of knowing anything is missing,” he says. “So it rebuilds the centralized function it never saw, slightly differently, again and again.”

Barnard’s experience points to a broader principle for AI deployment: Better prompts and better documentation can reduce errors, but leaders also need systems for maintaining context and detecting omissions.

“Documentation partly solves that,” he says, “but however hard we’ve pushed, the machine still misses something, and at scale, that is unsustainable.”

His metaphor captures the leadership role particularly well: “Tip 20 jigsaws into a pile and the AI pulls pieces from all of them, confident and wrong. Hand it the right pieces and it builds a perfect puzzle.”

In other words, human expertise may be most valuable before the AI makes its recommendation—by determining which information belongs in the decision.

Use Domain Expertise to Challenge AI

Lynn Comp, Head of AI Center of Excellence at Intel, recently built six competitive market intelligence agents using extensively researched techniques, including academic work on methodology. The systems were sophisticated. But sophistication did not eliminate the need for domain expertise.

“Thankfully I had mastered the domain and knew a fair amount about the companies I researched,” Comp says, “because at one point the agent asserted a conclusion that I knew from experience was flawed.”

Her response was not to discard the technology but to improve it. 

“Because I was able to redirect and challenge it with my experience, I got a useful analysis from the agent,” she says.

Comp also describes the episode as a warning about the persuasive quality of modern AI. 

“It opened my eyes to the risks of relying on non-deterministic tools that respond anthropomorphically by design,” she says.

For executives, this means domain expertise should be treated as part of the AI system—not as an obstacle to adoption. Teams should identify who has the expertise to recognize an implausible conclusion and give those people explicit authority to challenge outputs.

Combine Data With Front-Line Experience

Sathish Anumula, Enterprise and Business Architect at IBM Corporation, describes a predictive maintenance project in which an AI model analyzed equipment sensor data and predicted that a key machine was likely to fail within days.

“The AI model looked at sensor data and predicted a key machine was likely to fail in a few days and recommended immediate maintenance,” Anumula says. “But veteran engineers who had worked with that equipment for years disagreed.”

The engineers recognized subtle operating patterns that the available data did not capture. Rather than treating the engineers as biased or the model as authoritative, the team combined the two perspectives.

“We didn’t accept either side at face value, but went deeper, blending the AI insights with expert observations,” he says. “We discovered a nascent problem that neither method had fully uncovered.”

This is the kind of outcome executives should seek when AI and humans disagree. The purpose of a review should not merely be to select the more credible party. It should be to determine what the disagreement reveals about the system, the data and the operating environment.

“The experience reinforced a key lesson: AI is great at finding patterns at scale, but human expertise adds context, judgment and intuition,” Anumula says.

“The answer was not trying to resolve the disagreement, but to leverage the difference as a signal.”

– Maitrik Patel, Sr. Engineering Manager at Apple

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Test the Evaluation System, Not Just the Output

Maitrik Patel, Sr. Engineering Manager at Apple, describes an evaluation pipeline that rated AI outputs as high quality even though it did not assign confidence to them. Human reviewers, meanwhile, noticed small but meaningful errors.

“The agent was trying to optimize for shallow coherence and task completion,” Patel says. “Engineers found semantic flaws—knowledge only revealed in downstream context, and information only domain experts could surface.”

The critical insight came from resisting the urge to declare either the AI or the humans correct.

“The answer was not trying to resolve the disagreement, but to leverage the difference as a signal,” Patel says.

That means examining the assumptions embedded in the evaluation framework itself.

“When an AI and humans seem to get it wrong,” Patel says, “it’s the implicit assumptions within the evaluation framework—especially in agentic pipelines that multiply on and on.”

Preserve Human Authority in High-Stakes Decisions

Will Conaway, President of Tuxedo Cat Consulting, shares a healthcare scenario in which an AI tool classified a patient as low risk based on stable vital signs and historical information.

“The patient’s tone, fatigue and subtle change in behavior did not match the model’s assessment,” Conaway says. “Instead of choosing one over the other, the team paused, reviewed the data, reassessed the patient and ordered additional evaluation.”

The human concern proved to be important.

“The lesson was that AI is strongest when it supports clinical judgment, not replaces it,” Conaway says.

The distinction matters because clinical environments often contain signals that are difficult to quantify. A clinician may recognize a change in behavior that is meaningful even when it is not yet represented as a discrete data point.

Conaway recommends designing workflows around that reality: “I recommend healthcare leaders create workflows where AI prompts better questions, while clinicians retain the space and authority to think critically, challenge outputs and act in the patient’s best interest.”

Plan for What the Model Hasn’t Seen

Dileep Rai, Manager of Oracle Cloud Technology at Hachette Book Group (HBG), describes a demand forecasting initiative for new product launches in which AI recommended lower initial inventory because historical data suggested similar products would ramp slowly.

Experienced planners saw a different picture.

“Experienced planners recognized signals the model couldn’t fully capture, including retailer commitments, marketing momentum and category shifts,” Rai says.

Instead of overriding the model, the team used a human-in-the-loop review and scenario planning process.

“Rather than override either perspective, we combined them through a human-in-the-loop review and scenario planning process,” Rai says.

The launch ultimately tracked much closer to the planners’ expectations than the original model forecast.

“The best outcomes come when AI informs decisions and experts make the final call,” Rai says, “especially for high-impact or novel situations.”

Make Consequence Part of the Decision Rule

Rishi Katdare, Senior Technology Executive at Amazon Web Services, identifies a recurring enterprise pattern: AI recommends the statistically strongest action while experienced leaders hesitate because the downside is difficult to reverse.

“The right response is not to choose human or machine,” Katdare says. “It is to examine what each side knows, identify missing context, then weigh consequence, reversibility and ownership.”

That adds an important dimension to human-AI governance. The question to ask is “Which prediction is more accurate?”, but it’s also “What happens if we are wrong?”

“AI is strong at surfacing patterns and options,” Katdare says. “Experience is strong at recognizing when the cost of being wrong changes the decision.”

That principle can help executives determine where automation belongs. A low-risk, reversible decision may be appropriate for automation even if the model is not perfect. A high-impact decision with an irreversible downside may require a much stronger human review regardless of model performance.

Katdare’s final point is particularly relevant for enterprise governance: “Disagreement should trigger a review of the decision system itself. The best balance comes from explicit decision rights and knowing when a recommendation is too consequential to automate.”

Leadership Rules for Human-AI Decision-Making

• Don’t assume human judgment is better. Human expertise is valuable, but leaders should not allow experience to become an automatic veto against machine-generated insights.
• Ask what the model cannot see. When AI and leaders disagree, investigate whether historical data is missing emerging conditions, strategic dependencies or other context that matters to the decision.
• Look beyond the data that was measured. Examine missing context, unstructured information, intuition and variables that were never represented in the training or decision data.
• Give AI the right pieces to work with. AI performance depends heavily on the information and context it receives, so leaders should ensure that relevant information is accessible and that AI systems have the context needed to make sound decisions.
• Use domain expertise to challenge AI. Organizations should identify subject-matter experts who can recognize when an AI conclusion is plausible on the surface but flawed in context—and give them authority to challenge it.
• Combine data with front-line experience. Pair AI-generated patterns with observations from people who understand the equipment, process or operating environment firsthand.
• Evaluate the evaluation system. When humans and AI disagree about whether an output is good, examine the assumptions, metrics and downstream context used to define “good” in the first place.
• Preserve human authority in high-stakes decisions. Healthcare, safety, financial and other consequential decisions should retain clearly defined human oversight, critical review and escalation paths.
• Use scenario planning when the future doesn’t resemble the past. Combine AI forecasts with expert scenarios when launches, markets or other novel situations contain signals that historical data may not capture.
• Factor consequence and reversibility into the decision rule. The more difficult or costly a decision is to undo, the stronger the case for human review—even when the AI recommendation appears statistically sound.

Turning Disagreement Into Better Decisions

When AI and human expertise disagree, the most useful question is not, “Which one is right?” It is, “What is the disagreement telling us?” Sometimes AI sees a pattern experience missed. Sometimes an expert recognizes context the data cannot capture. And sometimes the disagreement exposes a missing variable, flawed assumption or decision whose consequences the model was never designed to weigh.

The experiences shared by the Senior Executive AI Think Tank suggest that leaders should not design AI workflows to eliminate disagreement, but to learn from it. Challenge AI without reflexively distrusting it, give models the context they need, bring domain expertise into the process and preserve human authority when the stakes demand it.

The goal isn’t to choose between human judgment and machine intelligence. It’s to build decision-making systems where each makes the other better.