Our editorial approach to enterprise AI coverage has evolved in ways worth stating plainly, reflecting lessons learned from coverage that, in retrospect, didn't hold up as well as it should have.
We stopped treating vendor case studies as reliable evidence on their own
Early coverage sometimes cited vendor-published case studies as evidence of a deployment's success without enough independent verification. We now treat these as a starting point for further reporting rather than as evidence in themselves.
We became more specific about what "deployed" means
We used to use "deployed" fairly loosely, without much distinction between a pilot and a full rollout. We now try to specify deployment scope explicitly in every piece, since the difference matters enormously to how readers should interpret a claim.
We started following up on our own past coverage more systematically
We didn't consistently revisit enterprise AI deployments we'd covered to see how they actually played out over time. We now do this more deliberately, since a deployment's early framing and its eventual outcome sometimes diverge significantly.
Why this evolution matters to state directly
We think readers deserve to know that our standards for enterprise AI coverage have tightened specifically in response to gaps we noticed in our own earlier work, not just as an abstract commitment to improvement.