Experimentation is the many things that people are trying to do using AI. AI adoption is the AI use that has become part of how work reliably gets done.
This is not about usage and pilots. It is about evidence of repeated use, workflow integration, measurable value, and organizational commitment.
Evidence of meaningful adoption includes:
- people returning to the AI tool because it improves their work,
- AI embedded into recurring workflows rather than one-off tasks,
- clear ownership, standards, and use cases,
- measurable improvements in speed, quality, cost, capacity, or customer outcomes,
- training and support for users,
- integration with organizational data and systems,
- defined review, security, and escalation processes,
- and continued use after the novelty of the pilot wears off.
Experimentation often looks like random individual use, isolated pilots and proofs of concept, with little to no evidence that work has changed.
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