The AI Adoption Curve

The AI adoption curve describes how successful organisations actually adopt: a small, bounded pilot with success criteria written in advance; an honest proof phase measured against a baseline; then disciplined scaling, where the playbook — training, prompts, guardrails — is templated and rolled to the next teams. It is deliberately boring, and it works.

Executives should care because the failure modes are symmetric and common. Some organisations pilot forever: fourteen experiments, nothing in production, enthusiasm curdling into scepticism. Others skip proof entirely, scaling a demo that collapses under real workload and poisoning the well for years. The curve avoids both by making evidence — not enthusiasm or fear — the fuel for each stage transition.

Concrete example: a bank caps every AI pilot at eight weeks with a go/no-go review against pre-agreed metrics. Most pilots are honourably closed; the two that pass are scaled to hundreds of staff within two quarters, with playbooks the next team copies rather than reinvents.

The executive habit: for every AI initiative, ask which stage it is in and what evidence earned the transition.