What is AI Center of Excellence?
An AI Center of Excellence is a dedicated cross-functional team or organizational unit that centralizes AI expertise, establishes best practices, governs AI initiatives, and supports business units across the company in identifying, developing, and deploying AI solutions effectively.
What Is an AI Center of Excellence?
An AI Center of Excellence (AI CoE) is a centralized team or organizational function that serves as the hub for all AI-related activities within a company. It brings together AI expertise, establishes standards and best practices, and supports business units across the organization in adopting AI successfully.
Think of an AI CoE as an internal AI consulting firm. Business units come to the CoE with problems and opportunities, and the CoE helps them evaluate, develop, and deploy AI solutions using proven methodologies and shared resources.
Why Companies Create AI Centers of Excellence
Without a CoE, AI efforts tend to become fragmented. Different departments buy different tools, hire different vendors, and develop different approaches. This leads to:
- Duplicated effort — Multiple teams solving similar problems independently
- Inconsistent quality — No shared standards for data, models, or deployment
- Wasted resources — Each team building infrastructure and capabilities from scratch
- Governance gaps — No coordinated approach to ethics, bias, privacy, or compliance
- Knowledge silos — Lessons learned in one department never reach others
An AI CoE solves these problems by providing centralized expertise, shared infrastructure, and consistent governance across the organization.
Core Functions of an AI CoE
Strategy and Governance
- Define the organization's overall AI strategy and roadmap
- Establish policies for responsible AI use, data privacy, and compliance
- Prioritize AI initiatives based on business impact and feasibility
- Manage the AI project portfolio and allocate resources
Technical Platform and Tools
- Build and maintain shared AI infrastructure and development platforms
- Evaluate and select AI tools, frameworks, and vendor solutions
- Create reusable components, templates, and model libraries
- Ensure technical standards for data engineering, model development, and deployment
Talent and Education
- Recruit, develop, and retain AI talent for the organization
- Deliver AI literacy training for leadership and business teams
- Provide technical training and mentoring for practitioners
- Build a community of practice for knowledge sharing
Use Case Development
- Work with business units to identify and prioritize AI use cases
- Lead proof of concept and pilot projects
- Provide hands-on support for AI development and deployment
- Transfer knowledge and capabilities to business units over time
Performance and Optimization
- Monitor deployed AI systems for accuracy, fairness, and performance
- Conduct regular model reviews and retraining
- Track and report AI ROI across the organization
- Identify opportunities for optimization and improvement
AI CoE Operating Models
There are several common structures for an AI CoE:
Centralized Model
All AI talent and projects sit within the CoE. Business units request support from the CoE team.
- Best for: Organizations early in their AI journey that need to build concentrated expertise
- Risk: Can become a bottleneck if demand exceeds CoE capacity
Hub-and-Spoke Model
The CoE provides central standards, tools, and governance while embedded AI professionals work within business units.
- Best for: Larger organizations with multiple business units that need both standardization and local responsiveness
- Risk: Requires strong coordination to prevent fragmentation
Federated Model
Business units have their own AI teams, and the CoE serves primarily as a governance and knowledge-sharing body.
- Best for: Mature AI organizations where AI capabilities are widely distributed
- Risk: Can lose coherence if governance is not enforced consistently
Building an AI CoE: A Practical Approach
Phase 1: Foundation (Months 1-3)
- Appoint an AI CoE leader with both technical and business credentials
- Define the CoE's mission, scope, and operating model
- Establish initial governance policies and standards
- Identify the first 2-3 high-priority AI use cases to pursue
Phase 2: Capability Building (Months 3-9)
- Hire or assign core team members (data engineers, ML engineers, AI product managers)
- Build or deploy shared AI infrastructure and development platforms
- Launch initial AI projects to deliver early value and build credibility
- Begin AI literacy training across the organization
Phase 3: Scaling (Months 9-18)
- Expand the CoE to support more business units and use cases
- Develop reusable frameworks and tools that accelerate new projects
- Establish formal knowledge-sharing processes and communities of practice
- Measure and communicate AI ROI to build continued support
AI CoE for Southeast Asian SMBs
Not every SMB needs a full-scale AI Center of Excellence. For smaller companies in ASEAN markets, consider these practical alternatives:
- Virtual CoE — A part-time group of internal champions plus an external AI consulting partner who provides expertise on demand
- Shared CoE — Partner with other companies in your industry or region to share AI resources and expertise
- Fractional AI leader — Hire a part-time Chief AI Officer or AI Director to guide strategy while the team grows
- Consulting-augmented CoE — Maintain a small internal team supplemented by consulting partners for specialized projects
An AI Center of Excellence is the organizational structure that makes the difference between scattered AI experiments and a systematic, scalable AI program. For CEOs, the CoE provides confidence that AI investments are coordinated, governed, and delivering returns. It eliminates the chaos of multiple departments pursuing AI independently with no shared standards or learning.
The financial case for a CoE is strong. By centralizing AI infrastructure, avoiding duplicated vendor contracts, and sharing expertise across departments, a CoE typically reduces the per-project cost of AI by 30 to 50 percent compared to a fragmented approach. It also accelerates time to value because each new project benefits from the lessons and tools developed in previous ones.
For CTOs, the CoE solves the talent challenge. Rather than every department trying to hire scarce AI specialists, the CoE builds a concentrated team of experts who serve the entire organization. This is especially important in ASEAN markets where AI talent is limited. The CoE also ensures technical consistency, proper governance, and model quality standards that protect the organization from AI-related risks.
- Start small — a CoE of 3 to 5 people can serve an SMB effectively in its early stages
- Choose a CoE leader who combines technical AI knowledge with strong business acumen and communication skills
- Define the CoE operating model based on your organization size and AI maturity level
- Focus on delivering quick wins in the first 6 months to build credibility and organizational support
- Invest in shared infrastructure and reusable tools that make each new AI project faster and cheaper
- Include governance and ethics responsibilities from the beginning, not as an afterthought
- For smaller companies, consider a virtual or consulting-augmented CoE model rather than a full-time dedicated team
Frequently Asked Questions
How big should an AI Center of Excellence be?
The size depends on your organization and ambitions. A small SMB might start with a virtual CoE of 2 to 3 part-time internal champions plus an external consulting partner. A mid-sized company might build a team of 5 to 10 dedicated professionals. The key is to start lean, demonstrate value, and grow based on demand. Overstaffing a CoE before it has proven its value is a common and expensive mistake.
What roles are most important for an AI CoE?
The three most critical roles are an AI CoE leader who can bridge business and technology, a data engineer who can build and maintain data pipelines, and an ML engineer who can develop and deploy models. As the CoE grows, add an AI product manager to manage use case prioritization and stakeholder relationships, and a data governance specialist. Business analysts who understand the operational context are also valuable additions.
More Questions
The right time is when your organization has completed at least one successful AI pilot and is ready to scale AI across multiple use cases or business units. If you are still in the exploration phase with a single AI project, a CoE may be premature. However, even before forming a formal CoE, you can benefit from designating an AI champion and establishing basic governance standards.
Need help implementing AI Center of Excellence?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how ai center of excellence fits into your AI roadmap.