Five Reasons Leaders Need to Use AI Personally Before They Lead On It

Most leaders I speak to now have a firm opinion on AI. Far fewer have sat down, opened a tool and used it on a real piece of work, and that gap matters more than it looks.

It matters now because decisions about budgets, policies and training are being made today, often on the strength of someone else's summary. If you're signing those off without any hands-on experience, you're deciding with borrowed eyes. Here are five reasons to change that.

1. You can't judge what you haven't tried

Reading about AI is a bit like reading about swimming. You'll pick up the vocabulary, but you won't know what it feels like when it works brilliantly, or when it confidently gets something wrong. Both experiences shape how much you trust it, and where you'd be comfortable letting it near your business.

2. Your people can tell

Teams notice quickly whether a leader is speaking from experience or repeating a headline. If you ask people to adopt AI but never mention using it yourself, the message is that it's something for other people. When you say "I used it to prepare for this meeting, and here's what it got wrong", you give everyone permission to experiment and to be honest about the results. That honesty is a big part of psychological safety, because people only admit what didn't work when they see the boss doing it first.

3. You'll ask better questions

Once you've struggled with a vague prompt and watched the output improve as you add context, constraints and examples, you start asking sharper questions of your team and your suppliers. What data is it using? Who checks the output? What happens when it's wrong? Those questions can't be bluffed, and they're what separate real adoption from theatre. It's also the quickest way to understand that good AI use is less about clever prompts and more about giving it the right context.

4. You'll see the real risks, not just the imagined ones

Leaders without hands-on experience tend to swing between banning everything and approving everything. Using AI yourself shows you where the genuine risks sit, such as sensitive data, accuracy and accountability, and where you've been worrying about nothing. That's the heart of sensible governance. Govern what matters, don't slow what works. You can't tell the difference between the two from a distance.

5. You'll find out what it's actually for

The biggest value usually isn't in the headline use case. It's in the small, repeated jobs, like drafting a difficult email, testing an argument, summarising a long report or preparing for a board meeting. You only find those by trying things, and the leaders who find them tend to free up real thinking time, which is the one thing most of us never have enough of.

Your challenge this week

Pick one task you'd normally do alone, whether that's a briefing, a plan or a tricky message, and do it with AI as your thinking partner. Notice what surprises you, then tell your team what you learned, including the bits that didn't work.

If you're not willing to try it yourself, why would you expect anyone else to?

Frequently asked questions

Why is hands-on experience with AI important for leaders making strategic decisions?

Reading about AI is like reading about swimming, you learn vocabulary but miss how it actually feels. Leaders who sign off budgets, policies and training without hands-on experience are deciding with borrowed eyes. Trying AI yourself reveals when it works brilliantly and when it confidently gets things wrong, helping you judge where to trust it.

How does a leader using AI personally impact their team?

Teams quickly notice if a leader is speaking from experience or repeating headlines. When leaders share their own AI use, including what went wrong, they give everyone permission to experiment and be honest about results. This openness builds psychological safety, as staff only admit what failed when they see their boss doing it first.

How does personal AI use help leaders ask better questions of teams and suppliers?

Working through vague prompts and adding context teaches leaders how AI output actually improves. This hands-on practice enables leaders to ask sharper questions about data sources, output checks and error handling. These practical questions cannot be bluffed, separating genuine organisational adoption from mere theatre and showing that context matters more than clever prompts.

How does using AI directly improve a leader's approach to governance and risk management?

Leaders without hands-on experience often swing between banning everything and approving everything. Using AI directly helps leaders identify genuine risks, such as sensitive data, accuracy and accountability, while eliminating unneeded worries. This practical view allows leaders to establish sensible governance that protects what matters without slowing down work that actually functions well.

Where do leaders typically find the greatest value when using AI themselves?

The biggest value is rarely in headline use cases. It usually comes from small, repeated tasks like drafting difficult emails, testing arguments, summarising reports or preparing for meetings. By trying AI as a thinking partner on routine solitary tasks, leaders discover where it works best and free up valuable thinking time for themselves.