The question we hear most often about our enterprise AI coverage is some version of: how do you tell the difference between a company that's genuinely deployed AI at scale and one that's deployed a pilot and is describing it as transformation? It's a good question, because the two get described almost identically in press materials.
Why this distinction is genuinely difficult to make from the outside
Companies have real incentive to describe early-stage pilots in the same confident, transformation-oriented language as mature, scaled deployments, and the press materials available to outside observers rarely distinguish the two honestly.
What we actually look for
Specific, checkable operational detail - how many employees actually use a deployed system daily, what percentage of a relevant workflow it covers, what happened when it scaled past the initial pilot group - tends to separate genuine deployment from pilot dressed as deployment more reliably than the company's own framing.
Why we don't always get this distinction right
Companies aren't always forthcoming with this level of operational detail, and sometimes we have to make an editorial judgment call with incomplete information, erring toward describing a deployment's actual scope conservatively rather than accepting an ambitious framing at face value.
Why we think this distinction matters for readers
The gap between pilot-stage and scaled enterprise AI deployment is large, and readers making their own adoption decisions are poorly served by coverage that doesn't distinguish the two carefully.