The dumbest AI decision you can make in 2026

But one decision stands above the rest. It's the mistake that guarantees failure. The choice that turns AI from competitive advantage into expensive liability.

And it's not what you think.

It's not moving too slow. It's not moving too fast. It's not picking the wrong vendor or hiring the wrong team.

The dumbest AI decision you can make is deploying AI to avoid thinking instead of deploying AI to enhance thinking.

Let me explain.

AI makes production cheap. A report that took four days now takes four hours. A presentation that required a team now requires a prompt. A research task that demanded expertise now demands wifi.

This feels like progress. Executives see output volume increase. Dashboards turn green. Everyone celebrates.

But here's what's actually happening: people are using AI to skip the hard parts. The analysis. The synthesis. The judgment. The thinking.

They paste a question into ChatGPT, accept the first answer, and move on. They generate a document without reading it closely. They automate a process without understanding why it existed in the first place.

The output looks fine. Sometimes it looks better than fine. But nobody in the loop actually engaged their brain.

Multiply this across an organization. Multiply it across months. Multiply it across every department.

You end up with a company that produces more and understands less. A company where nobody can explain why decisions were made. A company where institutional knowledge evaporates because nobody is building new knowledge anymore.

I call this "cognitive offloading." And it's an organizational disease.

Our research lab Section 9 recently conducted a study across 370+ professionals working in medium to large enterprises. We wanted to understand how people actually use AI in their daily work, not how they claim to use it in surveys designed to impress their managers.

The findings should alarm every executive reading this.

46% of professionals admitted to using AI output without checking the work at least once in the past three months. Nearly half. These aren't entry-level employees padding their productivity stat, these are mid-level professionals at established companies making real decisions based on outputs they never verified.

It gets worse.

18% said they use AI output without review at least once a day. One in five professionals, daily, are inserting unverified AI-generated content into their workflows. Into reports. Into emails. Into analyses that inform business decisions.

And the most concerning finding: 1 in 10 admitted to doing this more than five times in the last week alone.

Think about what that means. Across your organization right now, roughly ten percent of your workforce is routinely skipping verification on AI outputs multiple times per week. They're not using AI as a thinking tool. They're using it as a replacement for thinking entirely.

These aren't bad employees, they are rational actors responding to incentives. Overworked in some cases, incentivized in all to output more. And AI offers a way to keep up.

But the cumulative effect is an organization building its decisions on a foundation nobody has inspected.

The marketing team generates campaign copy with AI. Nobody on the team can explain why that messaging would resonate with the target customer. When results disappoint, they generate more copy. The cycle continues.

The strategy team uses AI to summarize market research. Nobody actually reads the underlying reports. The AI summary becomes the basis for a major investment decision. The summary missed the three paragraphs that would have changed everything.

The product team feeds customer feedback into AI for analysis. The AI identifies patterns. The team builds features based on those patterns. Six months later, customers still aren't happy because the AI couldn't detect what customers were too polite to say directly.

The finance team automates reporting with AI. The reports are accurate. They're also useless because nobody asks what the numbers actually mean. Red flags sit in spreadsheets for months because a human never looked closely enough to notice.

In every case, AI did exactly what it was asked to do. The failure was human. The humans stopped thinking.

Because thinking is hard.

Thinking is slow.

Thinking is uncomfortable.

AI offers an escape. It feels productive. You're getting things done. Your to-do list shrinks. Your inbox empties.

But productivity without understanding is just motion. And motion without direction is just waste with metrics.

The smartest people are often the most vulnerable. They're used to being the ones with answers. AI gives them answers faster than they've ever had answers before. The seduction is overwhelming.

And organizations reward output, not understanding. Nobody gets promoted for saying "I spent three hours really thinking about this." They get promoted for shipping. For delivering. For producing.

So the incentives push everyone toward cognitive offloading. And the disease spreads.

Our research confirms this. When we asked participants why they skipped verification, the top three responses were: time pressure (67%), confidence in AI accuracy (41%), and lack of clear review processes (38%). The system is designed to produce this outcome.

The companies that will dominate the AI era are the ones that think the best, not automate the most.

They use AI to handle the parts of work that don't require specialized or complex judgment calls so humans can focus on the parts that do. They use AI to surface information faster so humans have more time to analyze it. They use AI to generate options so humans can make better choices.

The human stays in the loop, not as a rubber stamp, as the actual decision-maker.

These organizations build "augmented intelligence" instead of "artificial replacement." The AI makes the human better. The human makes the AI useful.

It's slower than full automation. but less risky than letting AI run unsupervised.

But it's the only approach that builds durable competitive advantage. Because your competitors have access to the same AI tools you do. The only differentiator left is how well your people think.

1. The Vulnerability Scan Audit where your organization is using AI to skip thinking. Look for the places where AI outputs go directly into decisions without meaningful human engagement. Map every workflow where AI touches critical decisions. Those are your exposure points.

2. The Incentive Flip Change what gets rewarded. Stop celebrating volume. Start celebrating quality of reasoning. Ask people to explain their logic, not just their output. Make "I used AI to help me think through this" a badge of honor. Make "I just accepted what AI gave me" a red flag.

3. The Contribution Clarity Test Build this question into every role: can each person on your team articulate exactly where their judgment added value versus where they let AI handle execution? If they can't answer clearly, they're either over-relying on AI or under-utilizing it. Both are problems.

4. The Thinking Firewall Protect your organization's ability to think. This sounds abstract but it's concrete. Give people time to analyze, not just produce. Have meetings where you discuss what something means, not just what to do next. Value the employee who asks hard questions over the one who ships fastest.

5. The Leadership Mirror Model the behavior yourself. If you lead a team, show them what it looks like to use AI as a thinking partner rather than a thinking replacement. Narrate your process. Show them when you pushed back on AI output. Show them when you dug deeper instead of accepting the easy answer.

AI is the most powerful thinking tool humans have ever built. Used well, it amplifies human intelligence in ways that were impossible two years ago.

But a tool is only as good as the hand that wields it. And a thinking tool used to avoid thinking is worse than useless. It's actively destructive.

Nearly half of your workforce has already crossed that line at least once. One in ten is crossing it multiple times a week.

The dumbest AI decision you can make is letting the tool do the one thing only you can do.

Don't outsource your brain.

The findings cited in this article are derived from the AI Cognitive Offloading Study (2025), conducted by the Section 9 AI Research Lab between September and December 2025.

Study Design: Cross-sectional survey employing a stratified random sampling methodology to ensure representative distribution across organizational demographics.

Sample: N=372 professionals employed at medium to large enterprises (500+ employees). Participants were recruited through industry partnerships and professional networks across twelve sectors including financial services, healthcare, technology, manufacturing, retail, professional services, telecommunications, energy, logistics, education, media, and government.

Stratification: The sample was stratified 80/20 between medium enterprises (500-4,999 employees) and large enterprises (5,000+ employees) to capture behavioral patterns across organizational scale. Gender distribution was balanced (51% female, 48% male, 1% non-binary/preferred not to say). Role seniority was distributed across individual contributors (34%), managers (28%), senior managers (22%), and director-level and above (16%).

Data Collection: Anonymous self-report questionnaire administered via secure online platform. Anonymity was guaranteed to minimize social desirability bias and encourage candid disclosure of AI usage behaviors. No personally identifiable information was collected. IP addresses were not logged.

Instrument: 47-item questionnaire measuring AI tool usage frequency, verification behaviors, decision-making processes, and organizational factors influencing AI adoption. Items were developed through expert consultation and pilot-tested with a subset of 40 participants for clarity and reliability (Cronbach's α = 0.84).

Analysis: Descriptive statistics were calculated for primary outcome measures. Chi-square tests and logistic regression were employed to examine relationships between verification behaviors and demographic variables. Confidence intervals are reported at 95%.

Limitations: Self-report data may underestimate actual frequency of unverified AI usage due to residual social desirability effects despite anonymity guarantees. Sample was weighted toward North American and Caribbean enterprises; generalizability to other regions should be interpreted with caution.

Ethics: Study protocol was reviewed and approved by Section 9’s Research Ethics Board. All participants provided informed consent prior to participation.

For inquiries regarding the full study findings or methodology, contact: [email protected]

Adrian Dunkley is the Founder and CEO of StarApple AI, the Caribbean's first AI company. An award winning innovator, scientist and serial AI entrepreneur, EY Startup Founder of the Year, university lecturer, and AI researcher, he writes about what actually works when the hype fades and the real work begins.