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
Showing level 1, Exploring
Key decisions
- Who owns AI directionGrassroots energy versus a named owner
- Upskill or hireCulture and speed versus scarce expertise
- Central center of excellence or federated domainsControl versus scale
- Where the CDAIO reportsBusiness pull versus technology depth
- What AI fluency is expected of every roleA requirement in the role versus an optional skill
Anti-patterns
- Mandates without executive role models
- Training without changed roles
- A center of excellence that becomes the bottleneck
- Raising fluency without redesigning jobs
Measure progress
- Active AI use by function
- Share of leaders with AI objectives
- Time from use-case idea to a funded team
- Share of roles redesigned around AI
Builds on
The starting level. Nothing precedes it.
Nothing in another pillar. It builds on the level before it.
Enables
Only the next level in this pillar.
Top of this pillar. Nothing else depends on it.
Plays to run here
No play for this level yet.
No play for this level yet.
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 |
Proven. Use it by default for this purpose.
Production-ready. Use it with a clear scope.
Promising. Explore with a small team.
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.