AI Change Management and Training Plan
Plan the training, workflow changes, review habits, communications, and feedback loops that adoption needs.
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The exact sections inside the download.
Frame the operating decision
Which new behavior should users adopt? Which manual workaround, copy-paste habit, or shadow process should end? Use the combined view to name the business outcome, source of truth, and owner who will decide whether to proceed.
Set the control boundary
How should users inspect, approve, correct, or escalate AI output? What does each role need to understand before launch? State the risk to avoid, review or escalation route, and record needed to keep the workflow controlled.
Commit to the first move
How do users report weak outputs, missing context, or workflow friction? What should people understand about purpose, limits, and support? Record the smallest safe scope, baseline, owner, evidence, decision date, and next action.
Decision summary
What should people understand about purpose, limits, and support? Who delivers and updates training? Summarize the proposed move, operating benefit, and the evidence that supports it.
Evidence and control
Which behavior proves the workflow is being used correctly? State the source, review boundary, accountable owner, and condition that would require revision or pause. Keep the decision receipt with the workflow record.
Next review
Record the decision owner, evidence source, review date, and the explicit condition to scale, revise, defer, or stop. Confirm how the resulting decision will be communicated, implemented, and retained in the operating record.
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Frame the decision
Name the real operating need before designing a solution.
Name what people will do differently after launch.
Name teams, roles, reviewers, managers, or customers affected.
Name who manages training and feedback.
How to use it
Start with one real decision.
Complete the canvas with the workflow owner, then use the blank areas to expose missing context and controls.