Center of Excellence for Artificial Intelligence: Developing Intelligence into a Core Business Capability

AI Center of Excellence - CoE

As artificial intelligence evolves from a series of experiments into a core component of business, companies need to organize their AI capabilities more deliberately every day. This enables them to continue delivering value, keep up with ever-changing regulations, and foster innovation while maintaining ethical standards.

Today, there is a genuine sense of urgency: more organizations are joining the generative AI trend, AI systems are becoming more complex and interconnected, and regulatory scrutiny is increasing especially with the EU AI Act and similar frameworks emerging globally.

As a result, AI Centers of Excellence (CoEs) have begun to emerge as critical structures for connecting the dots in AI implementation. In fact, the CoE model is not new; such structures are already used in agile transformation processes to bridge strategic governance and operational execution. For instance, many large organizations implementing SAFe (Scaled Agile Framework) establish a Lean-Agile Center of Excellence (LACE) to standardize and scale agile practices across the enterprise. These units act as guidance and capability hubs that support the transformation vision and help align various teams.

Similarly, AI CoEs support the shift of AI from a purely technical tool into a strategic value-generation enabler. While aligning technical capabilities with business objectives, they also define governance standards and foster cross-functional collaboration.

These centralized (or semi-centralized) entities operate in a multifunctional manner—not only focused on technology, but also serving as strategic engines that bring together talent, infrastructure, governance, and ethical AI practices under a unified framework. An effective AI CoE helps create a shared understanding of AI across the organization, promotes experimentation, and facilitates knowledge sharing between departments. In this way, AI becomes not just a separate project but an integral part of business operations.

Choosing the Right Operating Model for Your AI Center of Excellence

When it comes to building a successful AI Center of Excellence (AI CoE), good intentions are not enough; having a defined architecture is essential. Across industries, five primary CoE models have emerged: centralized, federated, distributed, hybrid, and specialized. Each offers distinct advantages depending on the company’s size, governance preferences, and innovation culture. Regardless of the model, the core objective remains the same: to transform AI into a reliable source of competitive advantage.

1. Centralized AI CoE: Ensuring Consistency, Compliance, and Control

In a centralized CoE, all AI initiatives are consolidated within a single dedicated unit. This strategy is particularly effective for organizations that are early in their AI journey or operate in heavily regulated sectors such as healthcare, finance, or government. It ensures adherence to policies, proper risk management, and streamlined tools—building a strong foundation for future growth.

Companies like Walmart and IBM have adopted centralized models to establish AI standards, governance protocols, and reusable assets, serving as reference points for best practices. They demonstrated success through executive sponsorship, robust data infrastructure, investments in training, and collaboration with external partners.

A centralized CoE provides a solid starting point for companies aiming to scale AI usage while maintaining consistency and control. However, as business units grow, centralization may turn into a bottleneck. If domain-specific needs aren’t taken into account, centrally developed models might fall short of meeting localized business requirements.

2. Federated AI CoE: Empowering Local Execution through Central Strategy

The federated model combines centralized standards with distributed execution. While a central CoE sets the enterprise-wide AI strategy and governance, individual business units are responsible for implementing their own plans.

Companies like Roche have adopted this model to support diverse functions such as R&D, supply chain, and marketing. As a result, over 90 AI projects have been executed simultaneously under a shared governance framework. Microsoft’s Azure Cloud Adoption Framework describes this as the “hub-and-spoke” model—bringing direction and innovation together through coordinated systems.

One critical success factor in this model is enabling alignment across the organization. This requires rituals like AI councils, shared KPIs and OKRs, and clear decision-making charters. When implemented well, the federated model strikes a balance between agility and consistency, aligning complex organizations that span multiple geographies.

3. Distributed AI CoE: Flexibility through Decentralization

Unlike the previous models, the distributed CoE integrates AI directly into various business units with minimal central coordination. This structure is often seen in digital-native companies like Booking.com or Airbnb, where speed, experimentation, and ownership are core values.

These organizations empower local teams to take the lead in AI development and tailor models to their unique needs. While this accelerates delivery and innovation, it may also lead to duplicated efforts, fragmented governance, and ethical oversights—especially in the absence of shared infrastructure or standardized data dictionaries.

That said, distributed CoEs can reach their full potential in business units with strong technical capabilities and informal communication cultures. Even in such setups, maintaining lightweight governance layers remains a smart approach.

4. Hybrid AI CoE: Strategic Backbone, Agile Edges

The hybrid model preserves the benefits of centralized oversight while enabling distributed execution. It delivers a global AI strategy while simultaneously supporting localized innovation tailored to regional or domain-specific needs.

Google is a great example of how the hybrid model can be implemented. While centralizing its core AI tools and ethical guidelines, it also embeds AI developers directly within product teams—ensuring that solutions remain relevant, practical, and closely aligned with user needs.

When roles are clearly defined, leadership is engaged, and protocols are transparent, hybrid AI CoEs function efficiently. This model is increasingly favored by organizations that are scaling pilot initiatives or transitioning into AI-native operations, as it offers an optimal balance between consistency and agility.

You might think federated and hybrid models sound very similar—and indeed, they share many traits. However, there is a key difference: in the federated model, a central CoE defines strategy and standards, while execution is left to local business units under a shared governance umbrella. In contrast, the hybrid model manages both strategy and execution in tandem; AI developers are embedded directly into product teams, allowing central control and local innovation to operate simultaneously. In short, federated models work on a “central strategy, local implementation” logic, whereas hybrid models adopt a “central intelligence, embedded execution” philosophy, offering a more integrated approach.

5. Specialized AI CoE: Deep Expertise in Focused Domains

A specialized AI CoE focuses on advancing specific AI fields such as generative AI, responsible AI, or computer vision. These centers often function like internal R&D hubs staffed by senior researchers, typically collaborating with external labs or academic institutions.

A clear example of this model is the Generative AI CoE formed by Accenture and Google Cloud. Its primary goal is to help companies evolve using Google’s generative AI tools. By combining deep technical expertise, industry insights, and robust partnerships, such CoEs make it easier for organizations to develop and scale advanced AI capabilities.

Core Functions of an Effective AI CoE

An effective Artificial Intelligence Center of Excellence (AI CoE) is not merely a technical support unit; it plays a strategic guiding role. It ensures that AI initiatives are fully aligned with corporate goals. Strategic alignment emphasizes not the number of models developed, but the tangible business impact those models create. To steer projects in this direction, tools such as KPIs (Key Performance Indicators), OKRs (Objectives and Key Results), solid business cases, and value realization frameworks must be utilized. These tools shift the focus from technical success to strategic value creation.

At the same time, the responsibility of an AI CoE is not limited to measuring success; it also aims to embed ethics and regulatory compliance into the design process, ensuring fair, safe, and legally compliant solutions. To achieve this, a cultural shift is necessary within the organization. Promoting data literacy, embracing an experimental mindset, and cultivating an environment open to continuous learning are all critical enablers. Agile approaches strengthen the influence of the CoE in this context. In addition, establishing model libraries, APIs, and governance playbooks helps build organizational memory and ensure long-term scalability and reusability. An AI CoE is not just a team—it is an ecosystem.

Regardless of the model, an effective AI CoE must bring together individuals with varying levels of organizational expertise and engage a wide range of stakeholders in the process. It is a constantly evolving system that spans ethics, infrastructure, business alignment, and user feedback. Of course, to build such a dynamic ecosystem, the following elements need to come together:

• Data scientists, software developers, and machine learning engineers should collectively build the technical foundation.

• Ethics and regulatory teams must ensure that AI is used safely and fairly.

• Business unit or domain experts should help identify problems and assess their impacts.

• End users must provide feedback and signal real-world adoption and market fit.

• Senior executives should prioritize AI investments and provide strategic direction for the future.

• Partners and vendors should collaborate to accelerate growth, foster innovation, and support the development of new products.

No One-Size-Fits-All for CoE Design

When choosing the "right" CoE model, the issue is not architectural complexity—it's organizational alignment. Many companies evolve over time: they start with a centralized approach, transition into a hybrid model as they mature, and eventually adopt specialized structures to capitalize on emerging technologies. In fact, some organizations even operate multiple models simultaneously: one for compliance, another for scaling, and others for deep technical research.

At the core, success relies on a few critical elements:

• A clear strategy and strong support from executive leadership

• Multidisciplinary teams that integrate financial, data, and governance expertise

• Infrastructure and tools that enable organizational agility

• Continuous alignment between AI initiatives and business objectives

If we want to stay ahead in the AI era, we must accelerate our learning pace. AI CoEs are not static departments—they are living, adaptive systems that unify an organization’s people, decisions, data, and ethical frameworks into a cohesive operational model.