The Opportunity You Can Serve Beats the One You Can Describe

Every company I have worked with has had more ambition than resources. For founders and early-stage teams the gap is widest: few people, one product (or maybe not even a product yet), and a 'target market' that actually contains multiple segments with different requirements and buying behaviors.

So the first real strategic choice is usually not what to build. It is who to build it for first.

Being right for one segment beats being plausible for five

Segments are not variations on the same customer. They differ in what hurts enough to pay for, what they will pay, who signs, how long they take to decide, where you can reach them, and what they compare you to. Meet one segment's requirements thoroughly and you have a business that works. Try to satisfy them all at once and you have a product that is everyone's second choice.

This can feel counter-intuitive, especially to a founder. Narrowing feels like giving up revenue, and the segment you set aside always seems to have a staunch supporter in the room.

The more expensive mistake is reaching past what you have

There is another pitfall that is harder to see, because it looks like ambition rather than indecision.

People get fixated on a bigger prize: an adjacent market, an integrated offering, a platform the business 'could become.' The potential is clear, but the path to reach it is not (or worse, it's ignored).

The larger market may require products the company hasn't built yet, capabilities it does not have, or a sales motion it is not structured to support. These constraints can be overcome with time or extra resources, but that can be a dangerous distraction for founders and other leaders.

I have encountered this exact issue numerous times in my career. One startup leader developed a plan to forward-integrate in order to deliver a complex solution further down in the value chain. The problem was that they were still in the early stages of commercializing their existing technology. The market hadn't even validated them yet, and the proposed strategy required significant capital, development resources, and Go-To-Market infrastructure they did not have.

The decision deserves more structure than it usually gets

For such a consequential decision, target selection is made with remarkably little structure.

The typical exercise produces candidates. Someone writes up four or five segments, each with a page of supporting reasoning, and the research behind them varies from genuine market evidence to a confident paragraph. Different people arrive with different assumptions about what matters. Nothing in the process makes the quality of evidence visible, so the candidates cannot really be compared. The decision then goes to whoever argues most persuasively, or ranks highest.

More candidates alone do not fix that. The missing piece is a way to evaluate them on the same basis.

What I have been building

So I have been building the evaluation half.

In a nutshell, my AI-based tool evaluates your product or service, generates potential ICPs and submits them to an LLM Council (OpenAI, Anthropic, and Gemini) to independently analyze each potential ICP. Finally, those analyses are scored blind against a custom rubric defined at the start of the project. Most importantly, the claims the recommendation depends on are checked against live sources.

The last part turned out to matter more than I expected.

What testing taught me

In one run, 39 cited sources carried the weight of the arguments. Only fifteen held up. Thirteen were contradicted by the source they named, and eleven could not be found at all. A confidently titled consumer survey did not exist, and had been cited in two separate analyses. A very specific statistic was wrong by more than half, and it had fed a market-size calculation that looked perfectly reasonable in the write-up.

None of that was visible before the checking existed. The analyses that cited most specifically initially seemed to be the most rigorous, which is exactly the problem: a citation that sounds precise and a citation that is true look identical on the page.

The rubric is where the judgment goes. Define it loosely and you get a system that rewards whichever analysis is longest and most confident, which is the same mistake a room full of people makes.

Where that leaves the decision

This tool enables better decision-making. It leverages technology to run analyses and comparisons, and to make recommendations. The quality of the output matters. But, in the end, that decision rests with the founder/leader/executive.