Managers Are Fixing AI's Mistakes More Than Anyone Else
Fifty-seven percent of managers have had to fix or redo work from colleagues who relied too heavily on AI, according to a survey of more than 2,000 American workers. Only 38% of individual contributors faced the same burden. The percentage climbs further up the organizational chart: 63% of VPs and C-suite executives said they've had to rework AI-generated output that fell short of their standards.
The data comes from Founder Reports' 2026 AI in the Workplace report, conducted in April 2026. It reveals a gap between AI's promised speed gains and the actual cost of ensuring quality.
Why Managers Bear the Burden
Eighty-nine percent of survey respondents use AI tools for work. Sixty-one percent use them regularly-38% daily and 23% weekly. But speed comes with a tradeoff: spotty accuracy.
Managers own outcomes. When AI increases productivity and output, there's simply more work to review. Two factors compound the problem:
- Spotting mistakes in AI output requires advanced expertise. Errors often look correct on the surface.
- Forty-four percent of respondents say their company lacks a clear AI policy, or they're unsure one exists. Without guidance, workers make assumptions about how to use these tools.
Kirill Meshyk, head of AI data collection at Unidata, explained the nature of AI errors: "The most typical AI error we encounter is when it produces something correct-looking but false. It's well-formatted and sounds plausible, but the reviewer will only realize it is wrong when looking into details."
Riken Shah, founder and CEO of OSP, a healthcare technology firm, described how his review work has shifted. "I spend less time checking for completeness and more time interrogating the reasoning," he said. "Was the output validated? Did the person using the tool apply domain knowledge, or did they accept the model's framing wholesale?"
Adoption Is Outpacing Governance
McKinsey's 2025 Superagency in the Workplace report found that 92% of companies plan to increase AI investments over the next three years. Yet only 1% of business leaders call their company "mature" in AI deployment.
Companies are moving faster at adopting AI than at building systems to manage it. The cost falls on managers responsible for reviewing the work.
Teresa Tran, COO of LaGrande Marketing, addressed this directly. "The best thing I did was write down exactly what a finished piece of work looks like before anyone on my team uses AI to produce it," she said. Her solution: a one-page submission standard with specific requirements. "Managers who skip that step end up doing the hard work twice, once when they review and again when they send it back for a full rewrite."
Executive coach Louise Spinks sees a predictable pattern. "If the policy's too vague, people just make it up as they go," she said. Without clear direction, workers guess at expectations. Their assumptions don't match what managers expect. The result: more careful review, more revisions.
Verification Is Inconsistent
Even when AI output meets expectations, reviewing it takes more time. Seventy-seven percent of survey respondents said they review AI-assisted work more carefully than fully human work. Thirty-six percent said they review it "much more carefully."
Yet many don't thoroughly verify facts. A Clear Spark Digital survey found that only 17% always verify AI-generated information. Less than half (46%) ever check the sources AI tools provide.
Aimen Hallou, chief technology officer at Floxy, explained why: "Unlike a human, AI tools will not hesitate. They always state their answers as definitively as possible. To successfully review AI output, people need to possess even greater expertise in the relevant topic."
With details going unchecked by those using the tools and significant expertise needed to spot mistakes, review work concentrates at the management level.
Building Better Policies
Experts point to clarity as the foundation of effective AI governance. Hallou said an effective policy must "explicitly state which data may and may not be fed into an AI tool." Shah added that requirements must be clear about where human verification is needed and who owns the final product.
But policy alone isn't enough. Spinks stressed that training matters equally. "One can't assume people understand hallucinations, bias, data leakage or the difference between using AI as a drafting tool versus using it as a decision-making authority," she said.
Without training, responsibility for catching errors continues to flow upward. Meshyk put it plainly: "AI doesn't remove your professional responsibility for what is done by the machine."
Learn more about AI for Management and how to build effective governance structures for your team.
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