A busy AI Center of Excellence can point to training sessions, policies, and pilots. An effective one can show what changed because of them.
Microsoft's Cloud Adoption Framework for an AI Center of Excellence recommends tracking AI adoption rates, compliance levels, project cycle times, and other measures of business impact. Those metrics show what changed, while training sessions, published policies, and tools evaluated show what the CoE did. Both matter, but effectiveness comes down to whether that activity produced a measurable result.
CoE activity: Training employees, approving tools, and launching pilots.
Measure instead: Whether the people and teams targeted by those programs actually use the approved tools.
Useful measures include:
CoE activity: Running training and skills-development programs.
Measure instead: Whether people become more capable AI users afterward.
Microsoft recommends skills assessments as part of building organizational AI capabilities. Compare proficiency before and after enablement rather than relying on attendance or satisfaction surveys.
Larridin's AI Fluency provides one way to measure that change.
CoE activity: Publishing policies and establishing a use-case review process.
Measure instead: Whether people follow those policies and whether the review process works.
Useful measures can include:
A policy count tells you the CoE created rules. These measures tell you whether those rules changed behavior.
CoE activity: Reviewing AI purchases, budgets, and use cases.
Measure instead: Whether the organization gets better control of AI costs as the program scales.
Track measures such as:
Microsoft's named CoE KPI list doesn't include cost control, so this is an additional scorecard dimension for enterprise AI programs.
CoE activity: Launching and supporting strategic pilots.
Measure instead: Whether those initiatives produce a documented business result.
Microsoft recommends pilots that demonstrate business value. Tie each major initiative to a baseline and an outcome such as:
The strongest evidence is a before-and-after result from your own deployment, not an industry productivity estimate applied later.
A simple scorecard should make the relationship visible:
If the CoE can report the activity but not what changed afterward, that’s a program status update, not evidence of effectiveness.
For ROI claims specifically, look for a documented baseline and a specific deployment. Without those, the number is an estimate rather than measured ROI.
The CoE will naturally have its own program data: training completion, policy publication, review queues, pilot status, and budgets.
Pair that with data showing what happened afterward.
Larridin AI Adoption can show whether usage changes after CoE programs. AI Fluency can show whether proficiency improves. Cost and business-outcome data can show whether the organization is getting more value as the program matures.
That combination lets leadership see both what the CoE did and whether it worked.
Measure both program activity and results. A useful effectiveness scorecard covers adoption, proficiency, governance, cost control, and business outcomes, with each area tied to measurable change.
Start with a baseline for a specific initiative, then measure what changes after deployment. Depending on the use case, that could include cycle time, cost, revenue, errors, or throughput.
Larridin brings together the data an AI CoE needs to evaluate adoption, proficiency, spend, and business impact. AI Adoption measures usage, AI Fluency measures proficiency, Token Spend & Insights provides cost visibility, and AI Impact connects AI activity to business outcomes.