Just a few short years ago, artificial intelligence seemed far removed from everyday life for many people. Back then, it was considered more speculative than practical. Even those who used AI through platforms like Alexa and Siri probably didn’t give it much thought.
Today, generative AI is both talked about and used in settings ranging from K-12 and college classrooms to organizations of all types around the globe.
According to McKinsey’s 2026 State of AI report, nearly nine in ten respondents say their organizations regularly use AI in at least one business function. AI can now do the tedious work of customer research, performing tasks such as conducting interviews, running analyses, surfacing themes and coding transcripts.
Although AI is extraordinarily complex and continues to evolve, it has undoubtedly become a must-have part of operational infrastructure.
It’s also something of a black box.
The Black Box Paradox
In science circles, a black box is a system that allows users to see inputs and outputs but not necessarily understand how inputs are turned into outputs. That’s exactly the type of system that AI represents.
We can see the information we provide to an AI system and the outputs it generates. What we can’t always determine, with a high degree of confidence, is how those outputs were achieved. We don’t fully understand the reasoning.
An AI that analyzes 500 customer interviews and surfaces three themes and a set of recommended actions has done something useful. But without insights into which specific conversations produced which themes, which quotes supported which conclusions, and what the AI might have weighted heavily or ignored, it’s impossible to reliably verify the outputs.
The black box of AI hands us conclusions without showing how they were produced. That can be perfectly fine for low-stakes activities like planning a vacation. It becomes far riskier, though, when outputs are relied on to drive positioning, pricing, product decisions or marketing strategies.
Efficiency Is Not Understanding
As AI becomes more embedded in operations, we may be outsourcing some of the thinking, but we can’t outsource the understanding.
Receiving a recommendation isn’t the same as understanding why it’s correct. That’s the important caution: If your team simply accepts AI outputs, the AI hasn’t just automated the analysis; it has replaced a big part of your understanding.
The better AI gets, of course, the easier it is to depend on, and the less likely it is for anyone to ask: “What evidence supports this?” “What did the customer actually say?” “What might the AI have missed?”
There’s a new term that’s relevant here: “cognitive surrender.” It’s what happens when individuals start assuming AI outputs as authentically their own instead of exercising their own judgment.
Speed takes the place of confidence. And that can be a slippery slope. AI that produces wrong answers quickly is still producing wrong answers. The speed doesn’t drive accurate understanding. The risk, though, is that it may drive faster organizational reactions based on spurious outputs.
The Stakes Are Higher Than They Appear
AI’s black box problem isn’t theoretical. It surfaces whenever an AI recommendation drives a decision that turns out to be wrong and nobody can explain why the AI said what it said.
Consider a hypothetical example. An AI analyzes customer feedback and flags ‘price’ as the top reason why customers are leaving. The executive team lowers prices. Churn continues. When someone finally goes back and reads the original customer conversations, a different picture emerges. Customers weren’t complaining about price. They were frustrated with how long implementation was taking. The AI saw a pattern, but it saw the wrong one. Prices came up often enough to register, and it was easy to quantify. The deeper frustration about timelines was harder to measure, so it got underweighted.
Executives acted in good faith based on their AI output. But the problem remained invisible until after the damage was done. Nobody caught it because nobody had access to the reasoning behind the conclusion.
The analysis was fast, well-formatted, and confidently presented.
But it was wrong.
A One-Question Test
Here’s a simple, one-question test worth considering whenever delegating a decision to AI: “If the AI were wrong, would I know?”
If the answer is ‘no,’ you aren’t just outsourcing labor. You’re outsourcing your strategy and understanding. That’s where dependency begins, when the organization loses the habit of asking what the evidence actually says, who the customers actually are, and what the customers actually expressed in their own words.
The answer isn’t to use AI less. It’s to use it in a way that keeps the evidence visible. Go ahead and use AI to interview a thousand customers and summarize what they said. Just keep asking the most important question: “What does this actually mean, and how do I know?”
Just because AI is a black box doesn’t mean we don’t have the power to override it. Before making any decisions that represent potential risk to your organization, that’s exactly what you should be doing.
About the Author
Stu Sjouwerman (pronounced “shower-man”) is co-founder and CEO of ReadingMinds.ai. He is the author of “Agent-Powered Growth: Deploy AI Agents that Build Your Marketing Pipeline 24/7” [Wiley, 2026], which became a national bestseller, debuting at No. 17 on the USA Today’s Bestselling Book List within two weeks of its publication.
