AI adoption
How widely an organization's people use AI tools — licences, active users, use cases. It is rising faster than enterprise value, because adoption is not the same as changing how the work gets done.
AI adoption describes how far AI has spread through an organization: how many people have licences, how many use the tools weekly, how many functions have a use case, how many pilots are running. It is easy to count, and so it often becomes the headline measure of AI progress.
The difficulty is that adoption measures activity, not value. Surveys regularly find large majorities who say AI improves their individual productivity, and far smaller shares who can attribute any profit impact to it. That gap does not mean the productivity is fake. It means productivity is upstream of value, and an organization still has to decide what to do with the freed capacity.
Adoption is the wrong unit of change
Choosing the tool or the task as the unit of change is the common mistake. The economically meaningful unit is usually larger: the workflow, the decision, the operating model. Faster steps inside an unchanged operating model mostly make yesterday's company run faster.
Use adoption as an input, not a result. The more important program is AI transformation, and the practical building block is a well-run AI pilot that another team can reproduce.
Read more in Stop adopting AI.
Related terms
AI transformation
Changing how a company works — its workflows, decisions and operating model — around what AI makes possible, rather than adding AI tools to existing processes.
Operating model
How an organization actually runs: its workflows, decisions, roles, governance and handoffs. AI creates value when it changes the operating model, not when it is bolted onto the old one.
AI pilot
A limited trial of an AI use case to test whether it works in practice. A pilot proves one team can succeed; capability starts when others can reproduce the result.
Used in these essays
Stop adopting AI
AI adoption is rising faster than enterprise value because companies keep installing new intelligence inside old operating models.
The wrong questions about AI right now
Many of the questions that helped us orient ourselves around generative AI are now too blunt to be useful. The harder work is no longer asking what AI is in the abstract, but specifying where it works, where it fails, what authority it should have, and what the whole system costs.