An AI Centre of Excellence can accelerate value delivery—or become a bureaucratic bottleneck. The difference lies in how you measure success and which anti-patterns you avoid. This page outlines the metrics that matter and the traps to sidestep.
Success Metrics
1. Time-to-Value Metrics
These measure how quickly the CoE helps AI initiatives progress from idea to production.
Key metrics:
- Intake to PoC: Days from use case submission to proof-of-concept approval
- Target: <30 days
- PoC to production: Days from successful PoC to live deployment
- Target: <90 days
- Architecture review cycle time: Days from submission to approval/conditions/decline
- Target: <5 business days
- Stage-gate approval time: Days from gate submission to decision
- Target: <10 business days
Why they matter: Slow approvals kill momentum and drive teams to bypass the CoE entirely.
2. Quality and Reliability Metrics
These measure whether the CoE’s standards actually improve outcomes.
Key metrics:
- Evaluation pass rate: % of models passing bias, accuracy, and robustness thresholds before production
- Target: >90%
- Incident frequency: Number of production incidents (model failures, security breaches, data quality issues) per month
- Target: <2 per month (reducing over time)
- Mean time to detection (MTTD): Hours from incident start to detection
- Target: <1 hour
- Mean time to recovery (MTTR): Hours from detection to rollback/fix
- Target: <2 hours
- Drift detection rate: % of models with drift monitoring enabled
- Target: 100%
- SLA adherence: % uptime for production AI services
- Target: >99.5%
Why they matter: Quality metrics show whether guardrails are working and whether the platform is reliable.
3. Adoption and Reuse Metrics
These measure whether teams are actually using golden paths and shared components.
Key metrics:
- Golden path adoption rate: % of new AI initiatives using pre-approved reference architectures
- Target: >80%
- Reuse rate: % of solutions reusing shared components (pipelines, feature stores, evaluation frameworks)
- Target: >60%
- Community engagement: Attendance at Community of Practice meetings; contributions to shared libraries
- Target: >50% of AI practitioners attending regularly
- Platform utilisation: % of AI workloads running on the shared platform (vs. shadow IT)
- Target: >90%
Why they matter: Adoption metrics reveal whether the CoE is genuinely enabling teams or being bypassed.
4. Value and ROI Metrics
These measure whether AI investments are delivering business outcomes.
Key metrics:
- Portfolio ROI: Total value delivered (revenue uplift, cost savings) vs. AI investment
- Target: >3:1 within 18 months
- Cost per outcome: Total AI spend divided by outcomes delivered (e.g., £100K per £1M revenue uplift)
- Benchmark: Compare to industry peers
- Active use cases: Number of AI solutions in production delivering measurable value
- Target: 5+ within first year; 20+ by year two
- Benefits realisation rate: % of predicted benefits actually achieved
- Target: >70%
Why they matter: Executives fund CoEs to deliver value, not to create process overhead.
5. Risk and Compliance Metrics
These measure whether the CoE is managing AI risk effectively.
Key metrics:
- Audit findings: Number of critical/high/medium findings in internal or external audits
- Target: Zero critical, <3 medium
- Compliance adherence: % of AI solutions with complete DPIA, threat model, and bias testing
- Target: 100% for high-risk use cases
- Vendor risk: % of vendors with fair contract terms (exit clauses, IP rights, data portability)
- Target: 100%
- Policy violations: Number of teams bypassing governance or ignoring non-negotiables
- Target: <2 per quarter (declining over time)
Why they matter: Risk metrics demonstrate the CoE’s value to Legal, Compliance, and Risk leaders.
Leading Indicators vs. Lagging Indicators
Leading indicators (predict future success):
- Golden path adoption rate
- Community engagement
- Architecture review cycle time
- Evaluation pass rate
Lagging indicators (measure past performance):
- ROI and benefits realisation
- Production incidents
- Audit findings
Focus on leading indicators early — they give you time to course-correct before lagging indicators reveal problems.
Anti-Patterns to Avoid
Even well-intentioned CoEs can fall into these traps. Here’s how to recognise and avoid them.
1. Shadow IT and Tool Sprawl
What it looks like:
- Teams bypass the CoE and use unapproved tools (e.g., personal OpenAI accounts, unlicensed cloud services)
- Inconsistent technology stacks across initiatives
- No visibility into AI spend or risk
Root causes:
- CoE approval process too slow or bureaucratic
- Approved tools don’t meet team needs
- Lack of enforcement or consequences
How to avoid:
- Make golden paths faster and easier than shadow IT
- Continuously gather feedback on tool gaps
- Implement spend visibility (Finance + Procurement)
- Enforce policy with guardrails (network controls, IAM)
2. Over-Centralisation (Ivory Tower)
What it looks like:
- CoE tries to build every AI solution instead of enabling domain teams
- Long approval queues; teams wait weeks for decisions
- CoE becomes a bottleneck, not an accelerator
Root causes:
- Centralised delivery model instead of federated enablement
- Micromanagement of domain teams
- Fear of losing control
How to avoid:
- Adopt federated model: CoE sets guardrails, domains deliver
- Empower domain teams with golden paths and self-service tools
- Measure success by enablement, not control
3. Process Over Outcomes
What it looks like:
- Governance meetings focus on process compliance, not value delivery
- Stage-gates become tick-box exercises without real risk assessment
- Teams spend more time documenting than building
Root causes:
- Risk-averse culture
- Lack of clear decision criteria
- Misaligned incentives (rewarded for compliance, not outcomes)
How to avoid:
- Define clear go/no-go criteria for stage-gates
- Streamline documentation requirements (templates, automation)
- Celebrate value delivery, not just process adherence
- Regularly review and simplify governance cadence
4. Vendor Lock-In Without Exit Plan
What it looks like:
- Heavy reliance on a single vendor (e.g., AWS, Azure, Databricks) with no portability
- Proprietary formats and APIs make switching painful
- Vendor holds organisation hostage on pricing or terms
Root causes:
- Convenience over long-term flexibility
- Lack of contract negotiation expertise
- Ignoring open-source alternatives
How to avoid:
- Negotiate exit clauses, data portability, and fair terms upfront
- Prefer open-source tools where possible (MLflow, Kubernetes, dbt)
- Maintain abstraction layers (don’t hardcode vendor-specific APIs)
- Regularly review vendor health and alternatives
5. Unfunded Operations
What it looks like:
- Platform and golden paths exist but aren’t maintained
- No budget for upgrades, security patches, or tool evolution
- CoE team stretched thin; high burnout
Root causes:
- CapEx funding for initial build, but no OpEx for ongoing support
- Underestimating operational overhead
- “Set it and forget it” mentality
How to avoid:
- Budget 20–30% of initial build cost for annual operations
- Secure multi-year funding commitments from executives
- Build sustainability into the roadmap (automation, self-service)
6. Skipping Evaluations and Red-Teaming
What it looks like:
- Models deployed without bias, robustness, or safety testing
- Prompt injection vulnerabilities in production LLM apps
- Incidents caused by preventable risks
Root causes:
- Pressure to “move fast”
- Lack of evaluation expertise
- Treating evaluations as optional
How to avoid:
- Make evaluations mandatory stage-gates (no exceptions)
- Build evaluation frameworks into golden paths
- Red-team all high-risk use cases before launch
- Track evaluation pass rates as a CoE KPI
7. Weak Post-Mortems and Learning
What it looks like:
- Incidents happen, but root causes aren’t addressed
- Same mistakes repeated across initiatives
- No culture of continuous improvement
Root causes:
- Fear of blame
- Lack of time for retrospectives
- No mechanism to close the loop on learnings
How to avoid:
- Conduct blameless post-mortems within 48 hours of incidents
- Track corrective actions to completion
- Share lessons learned via Community of Practice
- Update golden paths and runbooks based on incidents
Measuring CoE Maturity
Over time, track your CoE’s maturity across these dimensions:
| Dimension | Basic | Standard | Advanced | Leading |
|---|---|---|---|---|
| Time-to-Value | >6 months idea→prod | 3–6 months | 1–3 months | <1 month |
| Golden Path Adoption | <30% | 30–60% | 60–80% | >80% |
| Incident Frequency | >5/month | 2–5/month | <2/month | <1/quarter |
| Portfolio ROI | <1:1 | 1–2:1 | 2–3:1 | >3:1 |
| Audit Findings | Multiple critical | 1–2 critical | Zero critical | Leading practice |
| Community Engagement | <20% participation | 20–40% | 40–60% | >60% |
Goal: Reach Advanced maturity within 12–18 months of CoE launch.
Quarterly Health Check
Every quarter, the CoE should assess:
- Are we delivering value? (ROI, time-to-value, adoption)
- Are we managing risk? (incidents, audit findings, compliance)
- Are we enabling teams? (golden path adoption, community engagement)
- Are we evolving? (lessons learned implemented, standards updated)
Output: Health check dashboard shared with Executive Steering.
Final Thoughts
A successful AI CoE is measured not by the size of its team or the complexity of its governance, but by whether it accelerates value delivery while managing risk. If teams bypass your CoE, you’re doing it wrong. If they actively seek out your golden paths and celebrate your support, you’re on the right track.
Next Steps
You’ve now completed the AI Centre of Excellence guide. Ready to put these principles into action?
- Download the AI CoE Terms of Reference pack for governance templates
- Read the companion article: Why Your Business Needs an AI CoE
- Explore the AI Readiness Assessment to baseline your starting point
- Get in touch if you’d like support designing, launching, or optimising your CoE