A few months ago, I was preparing for a meeting with a potential partner. I had done my usual preparation – notes, context, a few questions mapped out. But this time, I also ran everything through an Artificial Intelligence (AI) tool. 

I asked it to build a brief, flag risks, and suggest what to lead with. The output was clean, structured, and confident. I walked in feeling prepared. The meeting went well. But halfway through, something surfaced that the AI had missed. Not factually wrong, but contextually off. A shift in the company’s direction that had not yet made its way into the data it was drawing from. 

I adjusted, we recovered, and things moved forward. But on the way back, one question stayed with me: how much of that brief had I actually checked before walking in? The honest answer was simple, not enough.

This is not really a story about AI getting something wrong. That part is expected. Tools will always have limits. This is a story about something more subtle, and far more important: when do you actually trust it? 

We tend to ask the wrong question. We ask, “Is this accurate?” when the better question is, “When do I hand this the wheel?” Because working with AI today feels less like using a tool and more like working with a new colleague, one who is fast, capable, and always available, but still unfamiliar with your context.

Think about the last time you worked with someone new. Impressive on paper. Quick to respond. Strong output. You did not immediately hand them your most critical work. You started small. You observed. You noticed where their thinking aligned with yours, and where it didn’t. Over time, you built a map. Where you trust them fully. Where you review. Where you still own the decision. We do this instinctively with people. We have not yet learnt to do it with AI.

Instead, most of us operate at extremes. We either treat AI like a basic search engine; useful, but always double-checked, or we treat it like a seasoned colleague, trusting it too quickly without understanding its edges. Both approaches cost us. One keeps us doing everything manually. The other quietly introduces risk. The skill we actually need sits somewhere in between. It is calibration. Not a technical skill, but a judgement skill.

Calibration is knowing how much trust to place, in what situation, at what moment. It is like driving a car with advanced assistance systems. On an open road, you can ease off slightly. In a crowded street, you stay fully engaged. The system may be capable, but the environment determines how much you rely on it. 

The same applies here. For well-documented, stable information, trust can be higher. For anything involving recent changes, local nuance, human dynamics, or shifting context, trust needs to be lower, with more verification. And for decisions where the cost of being wrong is high, a client pitch, a public statement, a strategic move, you stay in control.

Calibration is not built overnight. It develops over time, through attention. You notice where AI surprises you, not just when it is wrong, but when it is unexpectedly right. You begin to understand its patterns, its strengths, and its blind spots. It is less like flipping a switch and more like tuning an instrument. Too much tension, and it snaps. Too little, and it loses clarity. The balance is where it works best.

I will be honest. This is something I am still learning. When things move fast, the temptation to accept AI output and move on is very real. The friction of checking feels like a delay. But unchecked output, like unchecked advice from anyone, carries assumptions you may not immediately see. And those assumptions can follow you into rooms where clarity matters most. The responsibility does not disappear because the work is assisted. It simply shifts.

There is something reassuring in this as well. Calibration is not new to us. We have been building it our entire lives. We know how to read people. We know how to sense reliability. We know when to trust and when to question. AI is not different in nature. It is different in scale.

This is why the future of working with AI is not about whether you use it. Most of us already do. The real question is whether you are building a working relationship with it, or simply relying on it without understanding it. Because trust, with anything or anyone, is not something you switch on. It is something you build, one decision at a time.

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