The most common question about our AI adoption coverage is whether adoption statistics we cite actually measure meaningful usage or just nominal access to AI tools that employees rarely use.
Why this distinction matters
A company reporting high AI adoption based on tool access or account provisioning can differ enormously from actual sustained, meaningful usage, and adoption statistics rarely distinguish the two clearly.
Our actual practice
We try to specify what an adoption statistic actually measures whenever we cite one - access, one-time trial, or sustained regular use - rather than treating all adoption figures as equivalent.
Why we don't always have this level of clarity available
Not every adoption statistic we encounter comes with enough methodology detail to make this distinction clearly, and in those cases we try to flag the ambiguity rather than presenting the figure as more precise than it is.
Why this precision matters
Adoption statistics shape how readers understand the AI industry's actual maturity, and imprecise reporting on what's actually being measured risks overstating how far adoption has genuinely progressed.