Marlon almost didn’t hire her. On paper, she was the wrong choice. Fewer years of experience. No big-name companies on her résumé. The algorithm ranked her third out of five candidates, but something did not sit right.
During the interview, she asked better questions than anyone else. Real ones. The kind that told you she was trying to understand the people behind the work. Marlon went against the data. Six months later, she became the most trusted person on his team.
The problem is we have too much information, and we are starting to trust the wrong parts of it. We are living in a moment where artificial intelligence can scan thousands of data points in seconds, flag patterns, and recommend decisions faster than any human ever could (monday.com, 2026). That speed feels like certainty. It feels like control, but faster decisions are not always better decisions.
AI is built to recognize patterns. Humans are built to recognize meaning, and those are not the same thing. One study in cognitive psychology found that humans are heavily influenced by recent experiences, a bias known as the availability heuristic (Kahneman, 2011). In plain language: what just happened to you feels more important than what is actually true, and that is where AI shines.
If you feed it months of customer feedback, project data, or performance reports, it can spot trends your brain simply cannot hold at once. It can tell you if a big problem is actually affecting two out of 100 cases (that is just 2%) not the crisis it feels like in the moment, but here is what AI cannot see.
It cannot see the silence in a room after a difficult conversation. It cannot measure the weight of someone trying not to fall apart at work. It cannot quantify trust, and that is where things start to break, because some of the most important decisions you will ever make are about people.
AI might tell you an employee’s productivity has dropped by 15%. That’s data. Your instinct might tell you they are going through something they have not said out loud yet. Both can be true. Only one can see the full human story. This is where most leaders (and institutions) get it wrong. They treat data as the final answer instead of one piece of the conversation.
When AI and your instincts disagree, that is the moment you are supposed to slow down. Instead, many people rush. They assume the machine must be right because it is objective, but objective just means measurable, and not everything that matters can be measured.
Consider this: if AI flags one employee as a top performer based on output, but your experience tells you they are quietly eroding team trust, what do you do? If you follow the data blindly, you may scale dysfunction. If you ignore it entirely, you may miss something real. The answer is learning when each deserves the lead.
Use AI where scale overwhelms you. When you are tracking hundreds of interactions, thousands of comments, or patterns across time, it becomes an extension of your awareness. It helps you see what you would otherwise miss. When the decision sits in the realm of human behaviour: motivation, resilience, trust, leadership, your gut is a data set built over years that no system can replicate.
The real risk is outsourcing your judgment, because the moment you stop questioning the recommendation is the moment you stop leading, and leadership, at its core, is about being responsible for the decisions you make, even when they are difficult, even when they are uncertain.
Marlon questioned the data. He asked himself a simple but uncomfortable question, “What might this system not be seeing?” That question changed everything.
The future is whether we have the discipline to consider AI versus human instinct. To let AI sharpen our thinking, but not replace it. To let data inform us, but not define us, and most importantly, to remember that behind every data point is a human story that no algorithm will ever fully understand.
Marlon trusted his gut that day, because it was informed by something deeper than numbers, and sometimes, that is the difference between a good decision, and a transformational one.
References
Kahneman, Daniel. Thinking, Fast and Slow. 2011.
monday.com. “When to Trust AI vs. Your Gut.” 2026.
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