Business & Deployment
AI Adoption
Between “we have licenses” and “it changed the work” lies the stretch that matters. Adoption is not measured in accounts but in whether processes were rebuilt — otherwise AI remains a text-snippet generator next to the real process. The experience so far: technology is rarely the bottleneck; time to learn, clear use cases and role models in management are.
An example
A management team reports 80 percent adoption because 80 percent of the workforce has access. Usage data shows that one fifth works with it daily, another fifth occasionally, and the rest logged in once. And the number that actually matters is missing entirely — in which workflow anything really changed through use. Counting accounts is easy and says nothing.
A closer look
Between “tried it” and “embedded in the workflow” lie the decisive stages. Only metrics that measure the second state are meaningful: share of cases that run through, repeat rate, share without rework.
Why it matters
The questions that hold up are less comfortable: Which process runs differently today than six months ago? By how much? And who decided it would stay that way? Adoption that does not show up in a changed workflow is private productivity of individuals — nice, but neither scalable nor transferable nor reliable. The transition from individual use to an institutionalized workflow is the real leap, and it is an organizational achievement, not a technical one.
Commonly misunderstood
Usage figures from surveys measure curiosity. Anyone who wants to know whether AI has arrived should check which work step would no longer take place without it.