The AI Centre of Excellence sits at the intersection of strategy, governance, enablement, and assurance. Its remit is deliberately broad but focused on creating repeatable, safe, and value-driven AI outcomes across the enterprise.
Core Responsibilities
1. Define Strategy and Standards
The CoE owns the enterprise AI strategy—translating business objectives into a prioritised roadmap of AI initiatives. This includes:
- Strategic roadmap aligned to business OKRs and investment priorities
- Non-negotiables — The policies, architectural patterns, and controls that every AI solution must follow (identity management, data governance, security baselines, compliance requirements)
- Technology standards — Approved tools, platforms, and vendor partnerships
Why it matters: Without a unified strategy, AI becomes a collection of disconnected experiments. Standards prevent tool sprawl, reduce technical debt, and ensure consistency.
2. Govern Intake, Prioritisation, and Funding
Every AI initiative—whether a new use case, model deployment, or vendor evaluation—flows through the CoE’s governance process:
- Intake and triage — Assess business value, feasibility, risk, and strategic fit
- Prioritisation framework — Score and rank initiatives based on ROI, urgency, and resource availability
- Stage-gate funding — Release funding in phases (PoC → pilot → production) with go/no-go decisions at each gate
Why it matters: Prioritisation prevents the organisation from chasing too many low-value initiatives. Stage-gates reduce the risk of sunk costs on failing projects.
3. Provide Enablement
The CoE doesn’t build every AI solution—business domains do. But the CoE makes it easier by providing:
- Golden paths — Pre-approved reference architectures, pipelines, and patterns
- Playbooks and templates — Standardised approaches for common tasks (data ingestion, model training, deployment, monitoring)
- Training and certification — Upskilling teams on tools, practices, and responsible AI principles
- Communities of practice — Forums for sharing lessons learned, demos, and reusable assets
Why it matters: Enablement accelerates delivery, reduces rework, and democratises AI capability across the organisation.
4. Assure Lifecycle Quality
Before any AI solution goes live—and continuously after launch—the CoE ensures it meets enterprise standards:
- Pre-production reviews — Architecture, security, privacy, and model risk assessments
- Evaluation gates — Models must pass bias, accuracy, and robustness tests before deployment
- Monitoring and observability — Track model performance, drift, data quality, and incidents
- Post-mortems and continuous improvement — When things go wrong, the CoE facilitates root-cause analysis and corrective actions
Why it matters: Assurance reduces the risk of production failures, regulatory breaches, and reputational damage.
5. Measure Outcomes
The CoE tracks the AI portfolio as a strategic investment:
- Value metrics — ROI, time-to-value, cost-to-serve, business outcomes delivered
- Adoption and reuse — How many teams are using golden paths, shared components, and approved tools
- Risk and reliability KPIs — Incident frequency, drift detection, SLA adherence, audit readiness
Why it matters: Measurement demonstrates the value of AI investment and identifies underperforming initiatives early.
What the CoE Does NOT Do
To remain effective, the CoE must maintain boundaries:
- Does not build every solution — Business domains own delivery; CoE provides patterns and reviews
- Does not micromanage — Golden paths are opt-in defaults, not bureaucratic hurdles
- Does not replace domain expertise — CoE governs standards, but domain teams understand business context
- Does not “own” all AI models — Models belong to the business; CoE owns the registry, governance, and lifecycle standards
Key Success Factors
A high-performing AI CoE exhibits:
- Clear decision rights — RACI clarity on who decides what
- Transparent gates — Predictable review criteria and timelines
- Fast enablement — Golden paths that genuinely speed up delivery, not slow it down
- Continuous learning — Regular retrospectives and pattern evolution
Next Steps
Once the remit is defined, the next step is building the right team with the right roles and skills.