Leaders spend enormous energy choosing models and building systems, then treat rollout as an afterthought. It is backwards. Every ROI figure for AI is really a figure about people using AI well — and that is a change-management problem, not a technical one.
The 10-20-70 pattern
A widely cited pattern in successful AI programs puts roughly 10% of the effort on algorithms, 20% on technology and data, and 70% on people and process. The systems are necessary; the adoption is decisive.
What good enablement includes
- Training for teams new to AI — practical, role-specific, not abstract.
- A usage policy — what staff may put in, and when human review is required.
- A shared prompt library so good practice is reused, not reinvented.
- A playbook that survives turnover, so the firm keeps its knowledge when people leave.
Adoption is the multiplier on every other investment. A great system at 10% adoption loses to a good system at 90%.
Design for it from the start
Adoption is not a phase you bolt on at the end — it is designed in from prioritization onward. That is the thinking behind AI Enablement, and why so many pilots die without it — see pilot to production.
Start with an AI Audit.
Make AI stick
Celadon’s enablement work turns AI from a pilot into daily practice — training, policy, and a playbook that survives turnover.
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