Case study 01 of 15
From AI Experiments to an Enterprise Operating Model
AI work appeared across teams before a durable operating model existed.
Business challenge
The organization needed to move from enthusiasm to repeatability without building a heavy approval culture — otherwise every new request required its own negotiation from scratch.
Architecture approach
Shared platform services, an intake and prioritization process, reference patterns, and a feedback loop between practitioners and governance groups — enough structure to support reuse without freezing teams out of adapting it.
How the work moved from request to production
- Business need
- Intake & risk classification
- Select reusable pattern
- Build / configure
- Review & validate
- Deploy / enable
- Monitor usage, risk, cost, value
Governance considerations
Decision rights and ownership were defined explicitly, with governance expressed as clear expectations — what data is involved, who owns it, who can access it, what happens if it fails — rather than a vague approval process.
Results
Better visibility into AI work, clearer prioritization, and a foundation strong enough to scale, plus a reusable lesson that shaped how the next request was handled.
Lesson learned
AI maturity isn't the number of experiments running. It's the ability to repeat value creation safely.
Discussion questions
- Where should AI portfolio decisions live?
- How do you prevent a center of excellence from becoming a bottleneck?
- Which metrics actually prove an operating model is working?