How to Find $30K in 60 Minutes

"Finding good AI opportunities" used to be treated as quarter-long projects.

I've been there too: innovation workshops, planning meetings, prep sessions. A quarter later you're sitting on a pile of "opportunities," unclear what to do next. So you build a matrix, score everything, and another quarter later still nothing has been decided.

Guess what – that whole timeline just collapsed.

Last week I ran a live workshop to demo something I couldn't have done a year ago: sitting down with a specialized AI Copilot I trained on my own methodology, and walking it through a real business to surface at least one profitable AI opportunity – live, on the call, in under an hour.

By the end, the Copilot and I had found a $30,000-a-year opportunity. In about 40 minutes.

Want to see how we got there?

Let's dive in!

The company I picked was from an industry I know too well – trade shows, exhibitions, and events (yes, that's a whole industry!). An exhibition organizer, ~500 employees, running about 20 trade shows a year. I put myself in the shoes of the Director of Exhibitor Support.

We started with two basic assumptions:

Now, most workshops would have started here with a process mapping.

But we didn’t.

And I’ll be brief, because I wrote about the reason why last week in Outcome-Driven AI Discovery.

The instinct here is to document the current process. How do support emails get handled today? Map the steps, then sprinkle AI on each one.

This works — and that's the problem. You get a dozen tidy "quick wins" that each save a few percent and quietly anchor every idea to the way you already operate. But your current operating model is often the very thing holding AI back.

So instead of asking what happens here, we asked what is this part of the business supposed to produce?

The answer wasn't a process, but an outcome: Exhibitor request resolved.

Here's where the Copilot came into play. I handed it that one outcome, and it broke it down into 5–8 sub-outcomes for us to review – the kind of decomposition that normally takes a room full of people and a whiteboard:

With that map of outcomes, the Copilot extracted the current and future problems tied to each outcome from us – a.k.a. the pain points and bottlenecks. Not all of them are AI-shaped, so it also mapped my AI Skills framework against each problem, flagging which ones are actually a good fit for AI and how.

And that reveals the real issue: these days, AI can seemingly do everything. That abundance is exactly what paralyzes people. So we need a filter – the $10K Threshold from up top.

It’s one step where the Copilot can’t help us because this one relies on our own judgement.

For each AI solution in front of us, we ask:

There could be 4 possible answers:

This tells us whether a solution cleared the Threshold.

A few clustering steps later (again, the Copilot did the grunt work) we ended up with a handful of ideas, each one a collection of similar AI capabilities aimed at related, prioritized problems.

And this is where most discovery sessions go painfully wrong.

Say you land on something like the Email Reply Copilot – an AI that drafts source-backed replies inside people's inboxes.

The wrong question is: how much does this cost? Because the same solution could run anywhere from $50 a month to $15,000+, depending on how you build it and what you want it to do exactly.

So the better move is to ask two different questions:

This is the shift most leaders have never been shown — and it's the one that matters most. You declare the value first, and let that define the ceiling.

An Email Reply Copilot might save the team ~1,000 working hours a year, call it a rough €50,000 in value. If that's your top estimate, you'd be insane to spend anywhere near it. The cost has to be a fraction. So you cap it at, say, $10,000 a year – and that becomes the basis for scoping the idea.

For our case, the Copilot followed exactly this logic and produced two fundable bets:

The Exhibitor Reply Copilot — source-backed replies in people’s inbox.

The Policy Knowledge Copilot – relevant document or previous case suggestions for complex requests

In AI, following this discipline isn’t optional. Unlike classical IT projects, AI costs don't spike at the beginning and stay relatively flat over time – AI costs climb as the system scales and also forces recurring quality checks and validations. If you can't name the recurring value on day one, you won’t have a case for keeping the project alive.

Now, none of this is new. But having AI guide you through this process is just 10x faster (and also more fun). It allows you to cut through the hype and surface ideas that really matter.

So in about half an hour, a vague "let's do AI in support" became two specific bets, each with a number, a clear ceiling, and a clear value hypothesis.

But of course, this is still an idea, not a solution.

The worst move now is to run off and build it. At this stage the idea still has a real chance of being wrong. (Maybe a Copilot-type solution isn't even the right fit here, and the cost cap might not be even realistic.) We need another step to find out.

I call this next step a “Solution Concept” – a short document that spells out exactly:

And guess what — there's a Copilot for that one too.

But that's a story for another day.

See you next Saturday,
Tobias