Organization, Skills & Operating Model
Will our people and structures carry the change?
Technology rarely stalls AI programs. Unclear ownership, missing skills and old incentives do.
Maturity
1ExploringExample
Enthusiasts experiment. No mandate, no roles.
2Establishing
A center of excellence and first skilling programs.
3Scaling
A federated model. Business domains own AI outcomes.
4Compounding
AI fluency expected in every role. Incentives reward value.
Key decisions
- Central center of excellence or federated domainsControl versus scale
- Upskill or hireCulture and speed versus scarce expertise
- Where the CDAIO reportsBusiness pull versus technology depth
Anti-patterns
- Training without changed roles
- A center of excellence that becomes the bottleneck
- Mandates without executive role models
Measure progress
- Active AI use by function
- Share of leaders with AI objectives
- Time from use-case idea to a funded team
Builds on
- Domains take ownership when business cases give them a reason.AI Value Realization at Establishing, needed for Scaling
- Incentives can reward value only once value is measured.AI Value Realization at Scaling, needed for Compounding
- AI fluency becomes a role expectation once agents are part of daily work.AI in the Process at Scaling, needed for Compounding
Enables
- Someone has to own benefits across the portfolio.Needed for AI Value Realization at Scaling
- An enterprise ontology is kept alive by the domains that own it.Needed for Enterprise Context Layer at Compounding
- Agent-first processes need business domains that own them.Needed for AI in the Process at Compounding
- Platform controls need a team that defines and runs them.Needed for Governance & Trust at Scaling
Plays
Microsoft-specific guidance, as of September 2026.
Ratings are illustrative and pending author review.
Capabilities
| Capability | Role in this pillar | Rating |
|---|---|---|
| Cloud Adoption Framework for AI | Adoption steps, checklists and operating model guidance | Adopt |
| Microsoft AI Skills Navigator | Role-based AI skilling paths | Trial |
| Copilot adoption resources | Change and champion programs for rollout | Adopt |
Adopt
Proven. Use it by default for this purpose.
Trial
Production-ready. Use it with a clear scope.
Assess
Promising. Explore with a small team.
Hold
Not recommended for new work.
Known gaps
Frameworks give structure. Your operating model still has to fit how power and budget actually work in your organization.