This ensures systems don't fail after launch due to confusion or resistance.
You're defining how AI changes will be owned, adopted, and sustained. This focuses on people and ownership, not technology.
Step 1: Define Clear Ownership
For each AI initiative, identify:
- One accountable owner
- One backup owner
- Who approves changes
AI without ownership fails.
Step 2: Set Governance Rules
Document:
- Who can change prompts, logic, or rules
- How changes are reviewed
- How issues are escalated
Keep governance light but explicit.
Step 3: Plan Adoption Support
Identify:
- Who needs training
- What level of training is required
- How users will ask for help
Training should match role and exposure.
Step 4: Address Resistance Early
Call out:
- Roles likely to resist change
- Fears around job impact
- Trust concerns with AI output
Address these directly, not quietly.
Step 5: Define Success Signals
Specify:
- Adoption metrics
- Usage frequency
- Quality or speed improvements
If usage drops, intervene early.
What You Should Have Now
✅ Change Management Summary
✅ Ownership and governance plan
✅ Adoption and training notes
✅ Success signals
Quality Check
- Ownership is unambiguous
- Governance is practical
- Adoption risks are acknowledged
- Success is measurable
Next Step: With change management planned, you're ready to think about long-term AI transformation.
This playbook is the map. We're the execution team → OpsSystem.ai