A few weeks ago I wrapped up another sprint with a company in the ~500+ employees range (can’t disclose details due to NDA).
What I can share though is the big lesson I learned.
I realized that 1 concrete AI opportunity can beat a map of 7 clusters worth $1M.
Finding AI ideas is no longer the hard part.
Figuring out the right starting point is.
But let’s start at the beginning.
How it started
I came in with the goal to get an AI opportunity map for the organization. See where the AI profit pockets are. It’s a classic deliverable for me, so four weeks later, they had it.
We ran a week of AI Opportunity Mapping workshop where the goal was not to see "where can we use AI", but "where does this business have real pain points or bottlenecks," and then, "which of these are actually solvable with AI."
That surfaced around 350 problems out of which 270 looked like a good fit for AI support.
What people often get wrong about these kinds of workshops is that they are less about brainstorming and more of a selection exercise.
Example AI Opportunity Map
Having a long list of AI ideas isn't an asset any more (has it ever been?). Anyone can produce 270 "AI use cases" in an afternoon with ChatGPT. But figuring out which ones are actually worth doing is the hard part.
Do the test yourself: Of all the AI ideas floating around your team right now, how many have an actual number attached? Which one is clearly the best?
More options aren’t always better. On the contrary, more opportunities cost you more. More attention. More alignment. More overhead.
In short: more work.
So we consolidated the 270 by doing three things:
Filter by value. Which ones survive the $10K threshold? This exercise alone brought us down to ~110.
Skip the org chart. Most ideas are really just repeated expressions of the same phenomenon or underlying problem. "We need better insights for marketing" and "our onboarding funnel leaks users" aren’t two use cases. They’re essentially the same ask for actionable insights.
Stop calling it "use cases". At least on a high-level. What you want to leave with is a portfolio of opportunities. Sometimes I call these "case packages", sometimes "opportunity clusters", sometimes just "projects". What matters is that discussions stay focused on outcomes, not "what LLM works best for this" at this point.
In our case, we ended up with 7 clusters that together had $1M+ a year in untapped potential.
A clean map with a real number.
And that's where it got hard.
The problem with a good map
A good map shows you the opportunity but it does not automatically create ownership.
These 7 clusters we identified weren't "put an FAQ chatbot on the website" types.
They were things nobody walked in asking for. One of them: use AI coding to accelerate in-house development capabilities. This opportunity alone touches so many things. That sounds fine until someone has to fund it. Seven clusters means (at least) seven business cases, seven owners, and seven budget discussions. Who owns a cluster that spans three departments?
My usual instinct in those cases is to bundle the opportunity portfolio, bring it to the executive team, and ask for sponsorship from the top.
Sometimes, this is exactly what’s needed. But it also creates a risk: The discussion becomes bigger. The stakes become bigger. And the timeline becomes longer.
The company can end up debating the roadmap instead of building the first profitable thing.
Last time I checked, the budget discussion was still going on.
The first step
Having a good map is never wrong. But the way you present it matters even more.
Instead of asking:
“Here are 7 major AI opportunities. Which ones do we want to fund?”
I should have said:
“Here is the full opportunity map. And here is the one opportunity we should start with.”
That distinction matters a lot because a map creates confidence, but it does note give you momentum. Working on a First AI project does.
A roadmap tells you the staircase is there. A first opportunity gets your foot on the first step.
And in AI, that first opportunity should meet a few conditions:
It should be valuable enough to matter. Don’t necessarily chase the biggest opportunity on the map, but big enough that success gets actually noticed.
It should be narrow enough to ship. If the first iteration requires three VPs, ten approvals, and a 6-month data platform evaluation project, it is probably not the first step.
It should have a clear owner. Someone must be able to say: “This is mine and I’m standing up for it.” Not “this is strategically relevant to our function.” Someone with budget authority.
It should be close to production. The goal is not to impress people in a demo but to improve the way work gets done. If you can’t see an implementation roadmap clearly for whatever reason, it is a bad first step.
It should compound. A good first AI opportunity is not just a “quick win”. Quick wins give you something once. But a compounding step gives you a new asset – a capability, workflow, or operating muscle that makes the next step easier.
A well-executed opportunity that hits these criteria beats a roadmap slide every time.
What’s next
The bottom line is this:
You don’t always need to see the whole staircase before you take the first step. You just need to know the staircase is there.
That is what the map is for. It shows you whether there is something worth climbing toward. But once you know the staircase is real, staring at the whole thing for too long can become its own problem. It can make the opportunity feel too big, too political, and too hard to start. That’s how companies get paralyzed. “Let’s align everyone first and revisit next quarter.”
In staircase terms:
“Let’s wait until someone builds an elevator.”
(Which likely never happens.)
So pick a single opportunity that offers more than an isolated quick win, let it become the step into the next. Repeat.
Often, you don't need more options. You need fewer.
Because one profitable AI solution you actually ship beats a $1M roadmap you never touch.
Now go find yours.
See you next Saturday,
Tobias