THE METHOD

I've Sat Through Hundreds of Vendor Pitches. Here's How to Read an AI Tool's Case Studies Like a Buyer

2026-09-20

Years selling enterprise software, and later managing the partnerships behind solutions being resold, meant writing case studies for a living and, just as often, picking apart a competitor's case study in the middle of a live deal. It's a skill that transfers directly to reading what AI tool vendors publish now, because the same tricks show up in the same places.

The first tell: a percentage with no baseline. 40% faster means nothing without knowing what it was 40% faster than, and the vendors who leave that number out usually left it out on purpose, because the honest starting point wasn't impressive.

The second tell: no named company, or a company so small the case study reads more like a favor than a customer relationship. Real case studies name the customer, because the relationship is worth more to the customer's own reputation than it is to the vendor's marketing page. An anonymous logo, or a suspiciously generic one, usually means the results didn't survive attribution.

The third, and the oldest trick in the book applied to a new category: measuring activity instead of outcome. Posts published, emails sent, hours saved on a task nobody would have spent that many hours on anyway. None of that is revenue, retention, or a decision that actually got better. It's the easiest number to make look good, which is exactly why it gets used so often.

This is a real part of how we vet what goes on this site's Tool Directory: an honest take is supposed to mean we looked past the press-release language to what the numbers actually support. If a case study can't survive these three questions, it goes in as a claim to verify, not a reason to recommend.