Why AI Roadmaps beat AI Projects

I used to have AI budget conversations like this:

"The prototype will cost about $20K. Ok to go ahead?"

Naturally, people asked: "What am I getting for this?" and "How do I know this works?" The honest answer was always "confidence" – and "there's no guarantee, that's why it's a prototype."

Confidence is what the first AI investment actually buys. Not ROI. Just enough conviction to keep going.

But getting that first $20K was never the biggest problem. The problem came when the first prototype didn't work as expected. Leaders scratched their heads: "Should we really continue with this?" And by the time they're asking that question, the AI initiative is already dead.

I found a way to make that question irrelevant.

Let’s dive into how.

The Problem With One AI Project at a Time

AI projects have a high rate of failure because of the baked-in complexity and risk. Failure rates sit somewhere between 70-95%, especially for first-time AI projects. Put bluntly: your first AI project will probably fail.

Now, when companies evaluate AI projects individually, a predictable pattern unfolds.

Every single project has to justify itself from scratch. The sponsor builds a business case. Someone pokes holes in it. The team defends. And it trains your entire organization to treat AI investments with suspicion.

Worse: if that first project underwhelms, the narrative becomes "We tried AI, it didn't work." There's no second attempt, no iteration, no learning curve. One shot, one result, done.

At this point your AI initiative is either dead or you're just using the "safe" solutions — like buying a Copilot license for some employees and calling it a day.

You never get to see the total picture – what AI could actually be worth to your business across operations, not just in one corner or for marginal productivity gains.

This is why fewer than 1 in 10 companies have successfully scaled AI across their organization. Most never built the map. They funded a lighthouse project, watched the beam flicker, and walked away.

There's a better way to set this up.

Map First, Build Second

That’s why before you approve, budget, or build anything, I recommend to map the total AI opportunity in your business.

Not "where could AI be cool?" or "where are others using AI?" – but where are your business's real pain points? The bottlenecks that are blocking growth? Where is competition leaving you behind?

Most companies never assess their AI potential holistically. They jump from "AI is exciting" straight to "let's build a chatbot." Don’t get me wrong. There's nothing that speaks against building a chatbot which saves money.

But there's a difference between deploying one in isolation and deploying one as the first step of larger AI roadmap. A, truly, First AI initiative. An isolated chatbot is a small project experiment. Lose it and you'll lose a small efficiency gain.

An AI roadmap, however, is a strategic move that answers two questions:

1) Where are the big AI potentials in our organization?
2) How do we plan to realize them?

This is the difference between motion and progress.

Without a map, every project has to fight for its life in isolation – and most of them lose that fight, not because they're bad ideas, but because there's no bigger picture to anchor them to.

So how do you find your total AI Opportunity, and – more importantly – how do you give it a number?

Creating the Map

Here's the method I use with every client, whether it's a 15-person agency or a 1,000+ finance firm.

1) Define your threshold.

What's the minimum recurring payoff that justifies action in your organization? Every AI project needs budget, attention, and someone to own it. What's the minimum result that justifies all three? I often use $10K/month. We'll use that as our example.

2) Ignore AI. Focus on problems.

Don't ask "what could AI do?" Ask "where does it hurt?" and "where will it hurt?" Pain points and bottlenecks from actual departments, actual processes, actual people. This works best when you talk to the people doing the work, not only the people managing the work. The manager says "it's fine." The person doing it says "I spend two days a week on this."

3) Apply the $10K filter.

For each pain point, ask one question: is this problem bigger than your threshold? If not, deprioritize for now.

Here's a concrete example. A consulting firm had senior consultants reviewing marketing content before it went out. Quality assurance — sounds reasonable. But 5-6 consultants spending roughly one day per month each on reviews is about six person-days. At $2,000 per day, that's $12,000 per month in billable capacity that isn't billing. Above the threshold.

Key test: if you free up those six days, where do they go? If the answer is "billable client work," the number holds. If it's "people will be less stressed" – that's real, but it's not $10K.

Some pain points won't clear the bar. That's fine. They're not gone forever. They're just not roadmap material yet.

4) Count what survives.

If 8 problems pass the filter, your roadmap floor is 8 × $10K/month = $80K/month = nearly $1M/year. And that's the minimum – because $10K was the threshold, not the estimate. Many of those problems will be worth significantly more.

That number is your anchor for every AI conversation going forward.

Without the map you’d be having budget conversations like "Is this project worth a $20K?”

With the map you’ll be asking “Are these $20K the best first step we can take to unlock our AI potential of $1M+?

This reframe changes everything.

If the first project underwhelms, you don't lose momentum. Because you already have the next case on your map and move on. Failure of single projects does not lead to failure of the entire roadmap.

Which is why a good roadmap beats a good project every time.

Executing the Roadmap

Let’s say you have a map with a number. Now what?

I work with three implementation principles:

Feasibility first, not impact. Every case on your map already clears the $10K threshold, which means impact is a given. The question that matters is: which one can you ship fastest? The first win builds the credibility and the budget for everything that comes after it.

No single failure kills the initiative. That's the whole point of the map. If case #1 doesn't work, you haven't failed at AI. You've learned something and you move to case #2. The map gives you options. Companies that bet on one project are fragile. Companies that work from a map are resilient.

Big opportunities get sequenced. A $100K/year opportunity doesn't start as a $100K project. It starts as a small, self-contained first step — maybe a $10K prototype that proves the concept. Then a second increment that gets it ready for production. Then a third that expands automation or integration. Each stage pays for the next. Each stage has its own business case.

The map tells you where to look. The sequencing tells you how much to bet. Getting both right is the difference between an AI initiative that stalls after one project and one that compounds into real operational value.

See you next Saturday,
Tobias