By Jess Christiansen-Franks, Managing Director, Wade Institute of Entrepreneurship

Whether you’re scrambling to keep up or hoping it’ll pass, it’s hard to get away from talk about AI these days… (and yes, here we are talking about it again). You’ve probably noticed the same thing I have: we all feel a bit behind.

And if your WhatsApp, LinkedIn and email groups are anything like mine, everyone’s scrambling to fill their own blind spots, figure out what’s going on, stay one step ahead of it.

I see it constantly in the rooms I’m in. Leaders who’ve rolled out a tool or two, run a pilot, configured a ‘context window’, and are now trying to work out how to take it across a whole organisation. In any team there’s usually someone with strong opinions about which model is best, and someone else who still insists on a notebook in every meeting. And underneath it all, a familiar fatigue, and a nagging sense that ‘yes, we’re getting more done’, but we’re still not on top of things. That we’re doing AI, but somehow missing the point of it. I’d be lying if I said I hadn’t walked into a few of these traps myself.

And then there’s another group I talk to just as often. Same industries, often the same demographics. But they’re not the type to chase the bleeding edge. They’re a bit sceptical of the hype, they’ve got systems that work, and AI feels like someone else’s problem. Why fix something that isn’t broken? And yet… there’s a quiet nervousness creeping in. Maybe their staff are using it in ways they don’t fully understand. Maybe someone has put it to them that there’s an existential question here they can’t quite dismiss. They’re starting to wonder if not engaging is itself a decision.

Here’s what I’m noticing: both camps are operating from the same foundation. And both are making the same fundamental mistakes.

Mistake 1: Using AI to do the same job faster, instead of asking what job is worth doing

For most of your career, the constraint was throughput. There was always more work than hands to do it, and the job of a good leader was to clear the bottleneck: keep the team productive, keep the work moving, ship the thing. You got very good at removing that friction. Then AI showed up and removed it for you.

Here’s the trap. When you free up that capacity, the trained reflex kicks in: great, now we can do more. More output. More content. More throughput. And so leaders take the single most valuable thing AI has handed them – room to think – and immediately spend it on more doing.

The freed-up capacity isn’t there to help you run faster on the same track. It’s there to let you ask whether you’re on the right track at all. If all you do with AI is the same work, quicker, you’ve pocketed the smallest possible prize on offer.

Mistake 2: Missing the creative opportunity because you’re fixated on productivity

Most business leaders I know actually love being creative – we just haven’t had much chance to do it lately. So when execution comes off your plate, the real question is: what do you want to do with that time?

My answer: use it to think. Use AI as a thinking partner, not just a productivity tool — something that helps you elevate the way you approach a problem, not just clear it faster. Consider what used to be out of reach. As a leader, you’ve probably never been able to commission a wide-scale review of a new process or technique without a fifty-thousand-dollar consulting budget, so you simply didn’t explore those questions. But if AI can produce a credible version of that for you in an afternoon, a whole category of thinking that used to be locked behind a research spend is suddenly available to you. The trap is not noticing the door has opened, because you’re still in the habit of assuming it’s shut.

That’s the opportunity hiding in plain sight. When execution is cheap, curiosity gets cheap too. Don’t waste it making the same decisions slightly faster.

Mistake 3: Forgetting your team already learned AI at home

Here’s something genuinely new. You have probably never had to roll out a business tool that your team is already familiar with. AI arrived as consumer tech first – people are looking up recipes, researching holidays, making images for social media. Almost everyone has formed a personal view of what AI is and what it’s good for, before it ever shows up at work.

That changes the rollout. When you bring in a CRM like HubSpot, nobody has used it at home, so you’re teaching from scratch. With AI, you’re not teaching from zero, you’re reshaping expectations people already hold. And those expectations were built in a low-stakes consumer setting.

Think about it: In personal use, it doesn’t really matter if your AI forgets you’re vegan because you never set up your context properly – you shrug and move on. So people quietly conclude that forgetfulness is just a limitation of the tool. But in a business context, that stuff matters enormously. You need it configured properly, learning over time, working across devices, getting smarter about your organisation. If your team imports their home habits, they’ll never realise how much the set-up actually matters. Rolling out AI well means surfacing those assumptions and rebuilding them for work.

Mistake 4: Debating which tool to use, instead of which function you need

If you’re spending your energy on ‘Claude versus ChatGPT’, you’re asking the wrong question. It’s a bit like bringing your whole team together to agonise over Excel versus Google Sheets when the real question is whether you need a spreadsheet at all, or actually need a workshop. The brand of the tool is the least interesting part.

The conundrum people think they have is which tool to use. The real one is which type of function they’re actually reaching for. AI can do a lot of quite different things, and most people are only using it for one or two of them:

  • Consolidate: turn vast amounts of information into something simple and usable
  • Expand: stretch and challenge your thinking on an idea
  • Organise: take notes, structure a discussion, index a folder or a body of information
  • Create consistently: produce new things in line with your brand, tone of voice, or work you’ve done before
  • Research: give you the lay of the land on a topic, like a fast literature review
  • Execute: follow through on a defined series of actions you’ve taught it to do

I’d bet you’re already doing one or two of these without thinking about it. The opportunity is in the ones you haven’t considered for your business yet. It’s not a question of GPT or Claude. It’s a question of which capability matches the problem in front of you.

Mistake 5: Treating disruption as slow and manageable, when it’s about to move fast

There’s a well-worn theory about how disruption works: it happens slowly, then all at once. There’s a long, quiet build-up where it creeps up on you, and then a sudden tipping point. The mistake is assuming we’re still in the slow part.

We’re not. AI has been around for a couple of years now. Adoption is widespread. It’s already deeply prevalent. Which means the next phase is likely to arrive quickly — close to overnight. Look at what’s already happened: brands no longer necessarily need a photographer to execute a new visual campaign. That disruption isn’t coming; it’s here. Other industries are next, and many of them don’t see it.

Here’s why this matters for everything above. If all you’ve done with AI is work out how to do the same job with fewer hours and fewer people, you’ve optimised for the wrong thing — and you’ll be caught flat-footed when the shift accelerates. The businesses that cut to the bone won’t have the capability or the people to reshape themselves when they suddenly need to. Being poised for this isn’t about cost-cutting. It’s about building the capacity to respond.

So what do you actually do about it?

Whichever camp you’re in – the one racing to keep up, or the one quietly hoping this isn’t your problem – the mistakes are the same, and so is the way through. Disruption is coming for everyone. The leaders who do well won’t be the ones who adopted the most tools or cut the most cost. They’ll be the ones who took the time to think strategically about what AI actually means for their business, while they still had the room to choose.

This is the work I came to the Wade Institute to do. We build professional education programs for business leaders, and our new program (the AI Conundrum) came directly out of the conversations I’ve been describing. The same questions, the same quiet worry, coming up again and again. So we built three days for leaders to step out of the noise and do the thing none of us make time for: create an AI Strategy. Not another tool to roll out. A clear point of view on where AI actually fits in your business, and what it means for where you’re headed. That’s the difference, and it’s the whole point.

You can learn more about the AI Conundrum program and the next intake (winter 2026) on the AI Conundrum program page.