How to Turn AI From a Risky Expense Into a Real Product Advantage

Tomorrow at noon BST, Mike and I are running a webinar with Barclays Eagle Labs on how to integrate AI into your product without lighting cash on fire. It's a question that comes up in nearly every conversation we have with founders right now, and the honest answer is that most teams are getting it wrong in one of three predictable ways.

They bolt AI onto the wrong feature. They pay for an enterprise model when a small one would do. Or they freeze, waiting for the perfect moment that never comes. Each one is expensive, in different ways.

This post is the short version of what we'll be walking through. Sign up for the webinar if you want the long version with Q&A, or grab the free playbook if you'd rather just read it.

AI is a competitive advantage. It's also a strategic risk.

That's the line Mike and I keep coming back to. Of the 125+ products we've helped build, almost every one of them now has some AI inside it — and the ones doing well aren't necessarily the ones with the most AI. They're the ones that picked the right place to put it, in the right way, at the right stage.

A pre-seed founder treating AI like a Series-B scale problem will burn months on infrastructure they don't need yet. A scale-up bolting on a chat interface because their competitor did will end up with a feature nobody asked for. The cost isn't always money — sometimes it's six months of focus.

We're not consultants who tell people not to build with AI. We've been picked by Figma as a global exemplar of AI development, and we just won Lovable's international SheBuilds hackathon — we use AI in nearly every project. But we've also seen plenty of teams use it badly enough that the right answer was less AI, not more.

The four-stage framework

The webinar walks through the framework we use to figure out what to build, where, and when. The headlines:

1. Choose the right AI strategy for your stage. Pre-seed, seed, Series A, scale-up, enterprise — each has a different set of sensible bets. Most failures we see are people copying the wrong stage's playbook.

2. Prioritise the right places to use it. Not every feature needs AI. Some absolutely do. The 80/20 of where AI actually creates value is narrower than most people think, and the easiest way to waste money is to ignore it.

3. Build with governance, guardrails, and scalability in mind. This is the bit founders skip until something embarrassing happens in production. A small amount of effort early — evals, observability, cost ceilings — saves a lot of pain later.

4. Turn AI into a strategic asset, not a budget drain. Caching, smaller models, batch and async patterns, in-house vs API. The teams that compound an advantage from AI aren't the ones using the biggest model. They're the ones with discipline about cost and clarity about what they're measuring.

Want the detail?

There's a free six-page playbook that walks through all four stages with the questions we actually ask and the patterns we use. Download it here — no sales pitch, just the framework.

If you'd rather hear it live with the chance to ask Mike and me anything, register for tomorrow's webinar. It's free, it's online, it's an hour, and there's a recording for anyone who registers but can't make it live.

And if you're already building something AI-powered and want a second opinion before you ship, get in touch. It's the kind of thing we help with.