Technology evaluation for an established owner‑led business deciding where AI belongs in its business model.
The owners were going about this the right way. They knew AI had a place in the business and wanted a more forward‑thinking model built around it. What they wanted to avoid was starting in the wrong place, spending first and learning second. So rather than move on the first credible option in front of them, they slowed down.
The option on the table
A data activation platform: unify the data, then let agents act on it inside existing workflows. A serious product, from a company with a substantial client list, including names anyone would recognise.
Why a client list is persuasive, and what it does not tell you
That client list is a real asset, and it is exactly why companies publish them. If they use it, why shouldn’t we is a legitimate argument and an effective one, because it usually is a signal of quality.
But a roster proves a product works for the companies on it. It does not tell you whether it works for you. Size, risk tolerance, goals, starting point, fit is relative to every one of them.
What we actually built
So we did not evaluate the product. We built a way to evaluate any product: what overhead it genuinely removes, what it truly costs once the unlisted costs surface, and what the business would be able to do that it cannot do today.
Then the owners took a set of questions into the demonstration.
The vendor’s team could not answer them.
The decision
That was the decision, and the owners made it themselves. Nobody had to be talked out of anything. Once the right questions were on the table, the distance between what had been presented and what could be substantiated was simply visible. Underneath the headline price sat a handful of costs the pitch had not outlined, and no confirmed path to added revenue.
They are still actively evaluating AI tools. What changed is that they now have a clear read on their own risk, and a way to tell a real partner from a good pitch.