AI Center of Excellence (AI CoEs): Real-Life Examples ['26]
Across industries, organizations use AI Center of Excellence (AI CoEs) to solve practical problems such as scaling AI initiatives, enforcing governance, reducing duplication, and connecting AI work to measurable business outcomes.
Explore what an AI Center of Excellence is, why organizations set one up, and how it operates in practice. Learn about the structure, responsibilities, operating models, and real-life examples of AI CoEs, and how organizations use them to scale AI initiatives, enforce governance, reduce duplication, and connect AI work to measurable business outcomes.
What is an AI center of excellence?
An AI center of excellence (AI CoE) is an organizational structure that coordinates the development, governance, and use of artificial intelligence across an enterprise. It brings together AI expertise, tools, data, and decision-making authority to ensure that AI initiatives align with business goals and deliver measurable business value.
Rather than operating as a standalone innovation unit, an AI CoE serves the wider organization. Its role is to guide AI adoption, reduce duplication, manage risk, and help teams move from experimentation to production-level AI applications.
In practice, an AI center of excellence acts as a shared capability that supports business leaders, product teams, and technical experts working on AI projects.
How an AI center of excellence creates business value
An AI CoE is expected to justify its existence through outcomes, not experimentation. Most organizations evaluate success based on clear links to business results.
Ways AI CoEs contribute to business value include:
- Prioritizing AI projects with clear business use cases.
- Setting key performance indicators tied to operational or financial impact.
- Reducing duplicated AI efforts across teams.
- Shortening the time from idea to implementation.
Many organizations aim to demonstrate measurable outcomes within the first 60–90 days of a new AI initiative to avoid long-running pilots with limited impact.
Operating models for an AI center of excellence
There is no single structure that fits every organization. However, most AI CoEs follow a hub-and-spoke model.
- The central AI CoE defines standards, governance, and shared services.
- Business units and product teams implement AI solutions locally.
- Feedback flows back to the center to support continuous improvement.
As AI adoption matures, some organizations shift the AI CoE from a control-focused role to a more advisory function.
Skills and roles within an AI center of excellence
Role | Function | Key responsibility |
|---|---|---|
Executive sponsor | Strategic leadership and oversight | Secures budget, sets priorities, and ensures alignment with business goals |
AI CoE lead / head of AI | Central coordination and accountability | Defines AI strategy and oversees delivery of AI initiatives |
Data scientists | Model development and analysis | Build and improve AI models for business use cases |
Machine learning specialists | Model deployment and optimization | Deploy, scale, and monitor machine learning models |
AI architects | Technical design and integration | Design AI architectures and ensure system integration |
Technical experts | Tooling and infrastructure support | Maintain AI tools, platforms, and development standards |
Product managers / product teams | Use case ownership and delivery | Define and prioritize AI use cases |
Business stakeholders | Strategic alignment and adoption | Connect AI projects to business needs and adoption |
Security specialists | Risk and security management | Protect AI systems and manage security risks |
Legal and compliance experts | Governance and regulatory compliance | Ensure responsible use and regulatory compliance |
Why organizations set up an AI center of excellence
Many organizations begin their AI efforts with isolated pilots or department-led initiatives. Over time, this approach often creates recurring problems:
- Multiple teams solving the same technical problems independently.
- Inconsistent use of AI tools and models.
- Limited visibility into risk, cost, and performance.
- Difficulty scaling successful AI solutions across business functions.
An AI CoE exists to address these issues. It provides a coordinated framework for AI adoption that balances innovation with accountability. Common reasons organizations establish an AI CoE include:
- The need for strategic alignment between AI initiatives and enterprise priorities.
- Growing regulatory compliance and data privacy requirements.
- Increased use of machine learning and AI applications in core business processes.
- Pressure from executive leadership to demonstrate business impact.
Core responsibilities of an AI center of excellence
1. Defining AI strategy and priorities
The AI CoE translates enterprise strategy into an actionable AI strategy. This includes:
- Identifying high-value use cases tied to business goals.
- Evaluating AI opportunities based on technical feasibility and business impact.
- Creating roadmaps that connect AI initiatives to measurable outcomes.
This work helps business leaders understand where AI can realistically support decision-making, automation, or product development.
2. Governing AI initiatives and risk
Governance is a central function of any AI CoE. The goal is not to slow down development, but to create clarity and consistency. Typical governance responsibilities include:
- Establishing policies for data privacy, security, and responsible use.
- Defining accountability for AI models across their lifecycle.
- Ensuring transparency in how AI solutions are built and used.
- Supporting regulatory compliance through documentation and controls.
Many AI CoEs use lightweight governance frameworks to avoid bottlenecks while still managing risk.
3. Enabling teams with shared capabilities
An AI CoE supports enabling teams rather than replacing them. It provides shared assets that product teams and business units can reuse. These assets often include:
- Standardized AI tools and development environments.
- Reusable assets such as templates, pipelines, and evaluation methods.
- Access to shared infrastructure for data, training, and deployment.
- Guidance from data scientists and technical experts.
This approach helps accelerate AI adoption while keeping costs under control.
4. Supporting data management and quality
AI projects depend heavily on data quality and availability. AI CoEs often work closely with data teams to:
- Define standards for data management and documentation.
- Ensure data used in AI applications is traceable and auditable.
- Address data quality issues early in the development process.
Strong data practices reduce downstream risk and improve model performance.
Real-life examples
Financial services: Federated AI CoEs with a central GenAI foundation from PwC
In large financial institutions, AI use cases vary significantly across business functions, including retail banking, wealth management, asset management, and corporate banking. To address this complexity, some banks adopt a federated AI center-of-excellence model.
In this setup, divisional AI CoEs operate within individual business units. Each divisional CoE focuses on AI solutions tailored to its domain, such as portfolio optimization in asset management or customer support automation in retail banking. These teams combine technical expertise and domain knowledge with business needs.
At the same time, divisional CoEs are connected through a federated network that enables:
- Shared governance and ethical standards.
- Reuse of AI capabilities across divisions.
- Reduced duplication of AI efforts.
- Cross-functional collaboration.
To support scale, banks often combine this structure with a central GenAI foundation, sometimes referred to as a “GenAI factory.” This central layer provides shared AI capabilities, common tools, and standardized evaluation methods. Governance remains centralized, covering data privacy, model quality, regulatory compliance, and risk management, while execution remains distributed.
This hybrid approach allows financial institutions to scale AI solutions efficiently while preserving flexibility for business-specific requirements.1
How Microsoft partners apply AI CoE guidance
Microsoft provides structured guidance for an AI Center of Excellence as part of its cloud and AI ecosystem. Several global partners use this guidance as a foundation, then adapt it to their own delivery models, industry focus, and technical expertise. The following examples show different ways an AI CoE can be applied in practice.2
NTT DATA: Scaling agentic AI through a dedicated AI CoE
NTT DATA used Microsoft’s AI CoE guidance to build an Agentic AI Center of Excellence focused on helping customers scale AI in cloud environments.
The CoE supports both low-code and pro-code development of agentic AI systems. Its role is not limited to model development. Instead, it provides a structured environment where customers can design, deploy, and operate AI agents across hyperscale cloud platforms.
Key aspects of this approach include:
- Centralized AI governance aligned with cloud architecture.
- Shared infrastructure for building and running agent-based AI applications.
- Close coordination with Microsoft experts to follow consistent standards.
In practice, the AI CoE acts as a delivery backbone. It allows customers to move from experimentation to production while maintaining control over scalability, security, and operational consistency.
Capgemini: Applying AI CoE guidance to client-facing AI transformations
Capgemini uses Microsoft’s AI CoE approach as a reference framework across its AI services and offerings. Rather than creating isolated AI solutions, Capgemini integrates CoE principles into its approach to helping customers adopt generative and agentic AI.
The focus is on:
- Applying consistent governance and delivery practices across projects.
- Reusing AI assets, patterns, and tools across industries.
- Ensuring AI initiatives are tied to measurable business impact.
Capgemini’s AI CoE approach emphasizes execution at scale. By standardizing how AI projects are implemented, the firm reduces variability across customer engagements and helps organizations move faster while maintaining alignment with enterprise requirements.
EY: Internal AI CoE built around secure experimentation and scaling
EY established a global AI Center of Excellence, with Microsoft’s guidance, to scale generative AI use cases internally.
A core element of this AI CoE is a secure sandbox environment. This sandbox allows teams to:
- Rapidly test AI use cases in a controlled setting.
- Validate technical feasibility and risk before deployment.
- Move promising ideas into production more quickly.
The AI CoE manages a pipeline of AI ideas and use cases, with central oversight to ensure consistency, security, and compliance. By standardizing how AI experiments are evaluated and scaled, EY reduced the time between ideation and implementation and avoided fragmented AI adoption across regions and teams.
Technology enterprise: Internal AI CoE as an operating model from Microsoft Digital
Within large technology organizations, AI CoEs are often used to guide internal AI adoption rather than customer-facing delivery. In this model, the AI center of excellence:
- Collects AI ideas from across the organization.
- Evaluates initiatives based on business value and implementation effort.
- Prioritizes projects using a transparent pipeline.
- Provides shared infrastructure, tools, and architectural standards.
The CoE typically operates across several pillars, such as:
- Strategy: aligning AI initiatives with business goals.
- Architecture: ensuring scalable infrastructure, security, and data readiness.
- Roadmap: coordinating implementation plans and user experience.
- Culture: promoting responsible use, training, and collaboration.
Governance and responsible AI practices are embedded throughout the process. Employees are encouraged to experiment within approved environments, while central oversight ensures consistency, safety, and compliance. This approach helps AI become part of daily work rather than a series of isolated projects.3
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Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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