How to Build an AI Center of Excellence

Introduction: Why Your Organisation Needs an AI Center of Excellence

As AI moves from experimentation to enterprise-wide deployment, organisations face a critical challenge: how do you scale AI responsibly? Isolated AI projects in individual departments lead to duplicated effort, inconsistent quality, ungoverned risk, and missed opportunities for cross-functional impact.

An AI Center of Excellence (CoE) solves this by providing a centralised hub for AI strategy, governance, talent, and best practices. It is the organisational structure that transforms scattered AI experiments into a coordinated, governed, and scalable AI capability. This guide provides a practical blueprint for building an AI CoE — from structure and roles to governance integration, success metrics, and a maturity model for continuous improvement.

What Is an AI Center of Excellence?

An AI Center of Excellence is a cross-functional organisational unit responsible for driving AI adoption, establishing standards, building shared capabilities, and ensuring governance across the enterprise. It acts as both a service centre (providing expertise and resources to business units) and a governance body (ensuring AI is developed and deployed responsibly).

The CoE model is proven in other technology domains — cloud, data, DevOps — and is now essential for AI. Without it, organisations end up with fragmented AI initiatives, duplicated infrastructure, inconsistent quality, and governance blind spots.

CoE vs. Decentralised AI Teams

A CoE does not mean centralising all AI development. The most effective model is a hub-and-spoke structure: the CoE provides the central hub of expertise, standards, and governance, while embedded AI practitioners in business units (the spokes) execute projects aligned with those standards.

This hybrid approach balances the benefits of centralised governance with the speed and domain knowledge of decentralised execution.

Organisational Structure and Roles

The structure of your AI CoE depends on your organisation size, AI maturity, and strategic ambition. Here is a recommended structure that scales from small teams to large enterprises.

Core Leadership

• Head of AI / Chief AI Officer — Leads the CoE, reports to CTO, CDO, or CEO. Sets AI strategy, manages the CoE budget, and represents AI at the executive level.
• AI Governance Lead — Owns the AI governance framework, policies, and compliance. Works closely with legal, compliance, and risk management.
• AI Engineering Lead — Manages the technical platform, MLOps infrastructure, shared tools, and engineering standards.

Functional Teams

Within the CoE, several functional areas work together.

1. AI Strategy and Innovation — Identifies high-value AI use cases, conducts feasibility assessments, and manages the AI portfolio pipeline.
2. AI Engineering and Platform — Builds and maintains shared AI infrastructure, MLOps pipelines, model registries, and deployment platforms.
3. AI Governance and Risk — Develops and enforces governance policies, conducts AI risk assessments, manages regulatory compliance, and oversees the AI ethics review process.
4. AI Enablement and Training — Upskills the organisation through training programmes, creates best practice guides, and provides consultative support to business units.
5. Data Engineering for AI — Ensures data readiness for AI initiatives including data quality, feature stores, data pipelines, and data governance alignment.

Embedded Practitioners (The Spokes)

In addition to the central team, embed AI practitioners within business units. These individuals report functionally to the CoE (for standards and governance) and operationally to their business unit (for project delivery). This dual reporting structure ensures consistency while maintaining business relevance.

For a detailed breakdown of AI security roles, see our CISO's Guide to AI in 2026.

Integrating Governance into the CoE

Governance is not a separate activity bolted onto the CoE — it is woven into every function. The most effective AI CoEs make governance a natural part of the AI lifecycle, not a gate that slows projects down.

Governance Integration Points

• Use case intake — Every AI initiative begins with a standardised intake process that includes risk classification and governance requirements.
• Development standards — The CoE establishes coding standards, documentation requirements, testing protocols, and peer review processes for AI development.
• Model validation — Independent model validation before production deployment, covering performance, bias, robustness, and security.
• Deployment approval — Tiered approval workflow based on risk classification — automated for low risk, committee review for high risk.
• Production monitoring — Continuous monitoring of deployed models for drift, performance degradation, bias, and security vulnerabilities.
• Audit and compliance — Regular internal audits of AI systems against governance policies and external regulations.

The AI Governance Platform

A centralised AI governance platform is the technical backbone that makes governance scalable. It provides the AI registry (catalogue of all AI systems), risk assessment workflows, policy management, monitoring dashboards, and compliance reporting.

Explore our AI Governance Framework Template for a detailed governance structure you can integrate into your CoE.

Building the Business Case for an AI CoE

Securing executive sponsorship and budget for an AI CoE requires a compelling business case built on quantifiable value.

Value Drivers

1. Reduced duplication — Shared infrastructure, models, and tools eliminate redundant spending across departments. Organisations typically save 30-40% on AI infrastructure costs.
2. Accelerated time to value — Standardised processes, reusable components, and expert support reduce AI project timelines by 40-60%.
3. Risk reduction — Centralised governance reduces the likelihood and impact of AI-related incidents, compliance failures, and reputational damage.
4. Talent efficiency — A CoE attracts and retains top AI talent more effectively than scattered departmental roles, and maximises the impact of scarce expertise.
5. Strategic alignment — Ensures AI investments align with business priorities rather than being driven by departmental interests.

Investment Requirements

A mid-size enterprise AI CoE typically requires 8-15 dedicated staff and an annual budget of $2-5 million (including personnel, infrastructure, and tools). Large enterprises may invest $10-20 million or more. The ROI typically becomes positive within 12-18 months through cost avoidance, accelerated projects, and risk reduction.

AI CoE Maturity Model

AI CoE capabilities develop over time. This maturity model helps you assess your current state and plan your growth trajectory.

Level 1: Ad Hoc

• No formal CoE structure.
• AI projects are isolated and departmental.
• No governance framework.
• Inconsistent tools and practices.

Level 2: Foundational

• CoE team established with core leadership.
• Basic AI governance policies in place.
• AI inventory and risk classification started.
• Shared infrastructure being deployed.

Level 3: Operational

• Full CoE team with functional areas defined.
• Governance integrated into the AI lifecycle.
• Hub-and-spoke model with embedded practitioners.
• Standardised MLOps and deployment pipelines.
• Regular training and enablement programmes.

Level 4: Optimised

• AI governance fully automated and scalable.
• Advanced metrics and continuous improvement.
• AI innovation embedded in business strategy.
• External benchmarking and industry leadership.
• Proactive regulatory compliance.

Success Metrics for Your AI CoE

Track these metrics to demonstrate value and guide continuous improvement.

Operational Metrics

• Number of AI projects supported and delivered.
• Average time from use case identification to production deployment.
• Infrastructure utilisation and cost efficiency.
• Reuse rate of shared models, features, and components.

Governance Metrics

• Percentage of AI systems under formal governance.
• AI risk assessment completion rate.
• Policy compliance rate across all AI initiatives.
• Audit findings and remediation time.

Business Impact Metrics

• Revenue impact of AI initiatives.
• Cost savings from AI-driven automation.
• Employee productivity improvements attributable to AI.
• Customer satisfaction improvements from AI-powered services.

For more on measuring AI governance ROI, explore our AI Governance Guides.

Start Building Your AI Center of Excellence

An AI Center of Excellence is the organisational foundation for scaling AI responsibly. Whether you are starting from scratch or formalising existing capabilities, the key is to begin with clear objectives, the right structure, and governance built in from day one.

• Download our AI Governance Framework Template for the governance backbone of your CoE.
• Explore the AI Governance Glossary for clear definitions of key terms.
• Compare AI governance platforms in our Solution Comparison Guide.

Ready to accelerate your AI CoE? Book a demo with Aona and see how our platform provides the governance foundation your Center of Excellence needs — from AI discovery to policy enforcement to compliance reporting.