Change Management for AI

AI Change Is Different

AI adoption requires deeper change management than most technology deployments. It doesn't just change how work is done — it changes what work is valuable, what skills matter, and sometimes what roles exist.

Treating it like an ERP rollout ("install the software, train the users, done") consistently fails.

The ADKAR Model for AI

Awareness — Does everyone understand why AI is being adopted? Not just "it's a priority" but the specific problems it solves.

Desire — Do people want to participate? Desire comes from understanding personal benefit, not just organizational benefit. Answer: "what's in it for me?"

Knowledge — Do people know how to use it? Training that covers capability but not workflow integration is incomplete.

Ability — Can people actually use it in their daily work? Knowledge ≠ ability. Supervised practice in real work contexts builds ability.

Reinforcement — Are behaviors being reinforced? Without reinforcement, new behaviors revert. Celebrate wins, recognize early adopters, measure and share progress.

Workforce Communication Strategy

Communicate about job impact early, honestly, and repeatedly. Vague reassurances ("AI will augment everyone") breed distrust. Specific commitments ("this implementation will not result in layoffs in the next 24 months") build trust — only make them if you can keep them.

The Skills Transition

Map current roles to future AI-augmented roles. For each role: what changes, what new skills are needed, and what support will the organization provide for the transition?

Investing in retraining is not optional. It's the cost of a managed transition vs an unmanaged one.

Measuring Adoption

Track adoption metrics separately from outcome metrics. You can have excellent outcomes but low adoption (a few power users doing all the work) or high adoption but poor outcomes (everyone using it ineffectively). You need both.