Transform Your AI COE Into a Strategic Value Enabler

11 November 2025 - ID G00839661 - 13 min read
By Dongbae Park, Neil Osmond,  and 1 more
Many enterprises have launched AI COEs to build initial technical expertise and pilot use cases. But as AI adoption grows, few have evolved the COE’s mandate to align with enterprise AI maturity and strategic business priorities. This research will help CIOs define and execute four imperatives to transform AI COEs into strategic enablers of business outcomes.

Insights at a Glance

The AI center of excellence (COE) often begins as a technical execution hub, but its mandate must evolve into strategically enabling and orchestrating AI initiatives across the enterprise. This expanded role requires CIOs to embed four strategic imperatives into the COE’s operating model.
4 Value-Enabling AI COE Imperatives
  • Evolve the AI COE Mission: The mandate of the AI COE must shift from a narrow technology-centric focus to a comprehensive value enablement approach. This is especially important when organizations are moving from experimentation to stabilization. The evolution should prioritize business enablement, strategic alignment and measurable value realization.
  • Establish Structural Clarity: It is critical to clearly differentiate the COE’s identity, and its relationship with AI communities of practices (CoPs), AI platforms and labs, enterprise IT and the data team. This structural clarity ensures the COE’s unique role in driving enterprisewide AI adoption and value creation.
  • Implement Actionable, Adaptive Governance: Effective AI COEs require robust governance frameworks that extend beyond technical oversight to encompass risk management, compliance, ethics and accountability. Establishing cross-functional decision-making bodies, such as an AI council comprising finance, HR, legal and procurement, is essential for securing executive sponsorship and enhancing organizational accountability.
  • Build and Promote AI Capabilities and Literacy: The AI COE must actively foster AI capability development and promote tailored AI literacy for employees, managers and business leaders across the organization. Collaboration with HR and business leaders is vital for connecting the goals of learning programs with the earnings aligned with strategic business outcomes.

Why This Is Important

AI COEs have been established by many organizations to centralize technical expertise and accelerate AI adoption. Initially, they can serve this purpose, but as AI initiatives scale, organizations face new challenges that technical teams alone cannot solve — such as stakeholder alignment, governance and measurable business value realization across different business units. Further, structural ambiguities regarding the COE’s identity, leadership and stakeholder engagement can hinder its effectiveness. Transforming the AI COE with the above four strategic imperatives will ensure that AI initiatives deliver enterprisewide value, align with business strategy and support long-term growth or mission success.

Actions and Cautions

Actions

Execute these four imperatives to ensure your AI COE delivers maximum business value:
  • Align AI Investments With the Business Strategy: Direct AI initiatives toward business priorities and measure success by relevant outcomes.
  • Clarify the AI COE’s Organizational Role: Define the COE’s unique function to orchestrate and complement existing AI teams, avoiding duplication.
  • Establish Responsible Governance and Stakeholder Engagement: Create inclusive structures for decision making and communication to ensure accountability and alignment.
  • Drive Continuous AI Literacy and Adoption: Promote broad understanding and support for AI across the enterprise.

Cautions

Avoid these common pitfalls to ensure your AI COE achieves maximum business impact:
  • Misalignment of AI and Business Strategy: Failing to closely link AI efforts with business objectives or senior leadership guidance reduces impact.
  • Overly Large, Undifferentiated COE: Consolidating all AI resources blurs the COE’s distinct value and diminishes strategic focus.
  • Narrow Technical Focus: Overemphasizing technical talent while neglecting business functions limits cross-functional collaboration and buy-in.
  • Solely Technical Performance Metrics: Focusing only on operational metrics undermines enterprisewide value by overlooking business impact and adoption.

How To Execute

The following imperatives and actions provide a practical framework for executing this transformation. This research is intended for organizations transitioning from the experimentation to the stabilization stage of AI maturity. For those at higher maturity levels, the role of the AI COE must be redefined to reflect more advanced enterprise capabilities and operating models. See A Strategic Guide to Maturing AI, How AI Maturity Should Shape AI Organizational Design and How to Design an AI Organization for more information.

1. Align AI Investments With the Business Strategy

Many organizations struggle to realize the full value of their AI investments when initiatives are not closely linked to business strategy. A narrow, technical focus often limits cross-functional collaboration and reduces organizational buy-in, thereby failing to translate efforts into meaningful business outcomes. This misalignment prevents organizations from achieving top performance, as organizations that achieve high AI maturity are significantly more likely to have their AI strategy deeply integrated or as a core component of the business strategy.
The core mission of the AI COE must, therefore, be expanded to encompass strategic alignment and value realization across the enterprise. CIOs must ensure that every AI project is prioritized and evaluated based on its potential to drive strategic business outcomes, and establish executive oversight to maintain focus on high-impact initiatives.

Key Actions for CIOs

  • Redesign the COE Mandate: Broaden the AI COE’s focus to prioritize business enablement and strategic value, incorporating nontechnical experts and allowing for flexible, nondedicated member participation.
  • Establish Formal Alignment and Oversight: Implement regular alignment processes (e.g., quarterly reviews) and leverage executive oversight to ensure AI initiatives consistently support enterprise objectives and deliver high strategic impact.
  • Define Business-Focused Metrics and Accountability: Set clear business outcome KPIs and hold AI leadership responsible for executing an AI strategy that drives measurable business results.

To scale its distributed GenAI efforts in a secure manner, Verizon built a GenAI center of excellence (COE) to operationalize its enterprisewide AI strategy, which is set by its cross-functional AI council. The AI council (comprising the global CIO, CDO, CISO and business area leaders) steers the broad AI strategy and sets risk guardrails, ensuring GenAI execution aligns with the overarching business goals.
The COE helps business-led product teams and foundational AI platform teams articulate business outcomes from their use cases, and advises them on defining value metrics and leading indicators (e.g., click rates and conversion) to track quantifiable business value. This focus shifted Verizon from “aggressive experimentation” to “thoughtful implementation” by ensuring rigor in defining and measuring business value. See Case Study: Enable and Scale GenAI Experiments With Verizon’s Strategy for more information.

2. Clarify the AI COE’s Position Within the Organization

Defining an optimal structure for the AI COE, specifically distinguishing its role from the existing enterprise IT and data analytics teams, AI platforms or dedicated AI labs, is often a challenge as use cases scale. Integrating all AI-related resources into one broad entity often blurs the distinction and diminishes the COE’s unique value proposition.
As AI adoption expands, their mandate must evolve to emphasize value realization and orchestrate nontechnical capabilities, such as strategy alignment, governance and literacy enhancement, to scale AI across the enterprise.

Key Actions for CIOs

  • Clearly Differentiate the COE’s Position: Articulate the COE’s unique value proposition as the central orchestrator focused on strategy alignment, adaptive governance and literacy building, clearly distinguishing it from other AI-related technical teams such as AI platform teams and/or AI labs.
  • Enable Strategic and Operational Integration Across Execution and Governance Layers: Establish structured linkages between the COE and governance bodies, such as the AI council, to ensure strategic alignment, while also supporting execution teams (e.g., AI fusion teams or product teams) and AI CoPs.

Verizon established a clear distinction between its centralized GenAI COE (focused on orchestration, governance and organizational capability building) and its technical platform — the Verizon Enterprise GenAI Services (VEGAS) Platform. The COE coordinates GenAI experiments and deployments, and works closely with foundational platform teams to build reusable services.
The VEGAS platform provides developers with tools and reusable components, such as a GenAI Development Workbench and Capability Building Blocks, enabling product teams to rapidly build solutions in a secure and cost-effective manner. This separation ensures the COE maintains its focus on value enablement while simplifying technical complexity for distributed product teams through reusable design patterns (like the content generation pattern) and shared technology. See Case Study: Enable and Scale GenAI Experiments With Verizon’s Strategy for more information.
Please note that the COE’s role needs to be shifted again as organizational AI maturity evolves. The COE’s temporary structure at the stabilization stage is designed to support the mainstreaming of AI capabilities. It will ultimately empower teams to leverage self-service tools and frameworks, and prevent the COE from becoming a bottleneck to enterprisewide AI democratization.

3. Establish Adaptive Governance for Responsible AI and Stakeholder Engagement

A one-size-fits-all approach to AI governance cannot deliver the value, scale and speed required for modern AI initiatives. As organizations mature, they must establish adaptive governance frameworks that extend significantly beyond technical management to address evolving risks, compliance, ethics and accountability.
Adaptive governance is crucial for accelerating responsible AI development, particularly during idea vetting and prototyping phases. Structural ambiguities regarding leadership and stakeholder engagement can severely hinder the effectiveness of AI COEs, making it essential to embed responsible AI practices organizationwide.
Adaptive governance is an organizational capability that determines the appropriate governance styles and mechanisms that deliver required business outcomes in a given context. For more information, see Reference Guide for AI Governance and Executive AI Governance Playbook.

Key Actions for CIOs

  • Establish Strategic Governance Structures: Establish formal decision-making bodies, such as an AI council, and embed business leaders and representatives from nontechnical corporate functions, including finance, HR, legal and procurement, into the COE’s operating model for holistic value and responsible adoption.
  • Ensure Early Governance Involvement: Drive governance processes that involve legal, compliance and ethics experts across all stages of the AI life cycle, particularly in the critical idea vetting and prototyping phases, to manage risk preemptively. Develop robust communication channels among the COE, implementation teams and senior executives to ensure ongoing alignment and secure organizational buy-in.

To anchor governance in responsible AI practices, Clifford Chance established an AI and innovation board involving C-suite leaders, including the CTO (chair), CFO, CHRO and General Counsel, to establish the AI vision, strategy and governance. The firm’s AI transformation team (a dedicated cross-functional standing team of about 35 employees) acts as a conduit, enabling transformative efforts and securing ethical use enterprisewide. See Case Study: Executive Alignment for AI Transformation for more information.

4. Drive Continuous AI Literacy and Adoption

Sustainable AI transformation and value realization is reliant on the ability to continuously build and maintain organizational AI literacy. This effort must extend beyond implementation teams to the broader business units, as fostering capability uplift and promoting literacy are key functions of the AI COEs.
Collaboration with HR and business leaders is necessary to design learning programs that effectively connect technical skills to demonstrate business value. Importantly, executives and managers must also be included in these efforts because many lack the knowledge to effectively lead AI fusion teams.

Key Actions for CIOs

MinterEllison, through a partnership between its chief digital officer/CIO and HR leaders, implemented a “Dedicated GenAI Time” program. This program protected 12 hours over 12 weeks for employees to build digital fluency, with the time credited toward performance targets (like billable hours) for client-facing staff. To sustain engagement beyond the initial spike, they utilized a “social-media-style marketing” approach, offering incentives (MintCoin internal cryptocurrency, digital badges for prompt engineering) and fostering visibility via leaderboards. This led to a 50% rise in employees using AI weekly. See Case Study: Building an AI-Ready Enterprise Workforce for more information.

Success Measures

The AI COE acts as an orchestrator and enabler of business outcomes, and success must be measured across four dimensions:
1. Strategic Alignment and Business Value Realization: Expand the AI COE’s mandate to ensure AI investments deliver measurable business results and are closely mapped to the enterprise’s overarching business strategy. Success is measured by the COE’s ability to connect AI investment to tangible business results (see Table 1).

Strategic Alignment and Business Value Realization

Business Goal Alignment Rate
Measures the extent to which AI initiatives are tied to the core strategic business goal and objectives
Alignment Rate = (Number of AI Initiatives Aligned With Strategic Business Goals/Total Number of AI Initiatives)×100%
AI Investment Value Realization Scorecard*
Measures quantifiable outcomes and value creation, focusing on financial and business benefits
Value Score = Quantifiable Business Benefit Achieved via AI Project (e.g., ROI, Cost Reduction)/Total Investment Cost of AI Initiative​
Executive Accountability & Sponsorship Index
Measures the level of active guidance and engagement from senior leadership
Sponsorship Index = Qualitative Score based on (AI Council/Governance Body) Executive Attendance and Decision Involvement
* See Ten AI Value Metrics for Cost Reduction, Revenue Growth and Productivity for more practical value metrics examples for value realization.
Source: Gartner (November 2025)
2. Operational Efficiency and Clarity of COE: The COE must eliminate structural ambiguities, and ensure efficient operation and integration within the organization (see Table 2).

Operational Efficiency and Clarity of COE

Role & Mandate Clarity Score
Measures the elimination of structural ambiguity
Clarity Score = Qualitative Index derived From Stakeholder Surveys Assessing Perceived Role and Mandate Ambiguity
AI Initiative Time to Value (TTV)
Tracks the end-to-end duration required for AI projects
TTV = Time (Idea Initiation) → Time (Production Deployment)
Source: Gartner (November 2025)
3. Responsible AI and Governance Adherence: The AI COE acts as an orchestrator, establishing robust governance to ensure AI is developed and deployed responsibly and ethically across the organization. To ensure governance supports — but does not dominate — value realization, it is essential to balance compliance with metrics that directly measure business impact and stakeholder enablement (see Table 3).

Responsible AI and Governance Adherence

Responsible AI Compliance Rate
Tracks adherence to the established governance framework and ethical principles
Compliance Rate = (Number of AI Projects Adhering to Governance Standards and Ethical Principles/Total Number of AI Projects)​ × 100%
Cross-Functional Governance Participation
Quantifies the integration of nontechnical corporate functions (e.g., finance, HR, legal and procurement) into governance structures to drive holistic value
Participation Rate = (Number of Nontechnical Stakeholders Participating in Governance Decision Bodies/Total Required Nontechnical Stakeholders)​ × 100%
Source: Gartner (November 2025)
4. Organizational AI Capability Uplift: The COE’s key function is to enhance the organization’s overall AI maturity by fostering skills, promoting literacy and enabling distributed teams (see Table 4).

Organizational AI Capability Uplift

Enterprise AI Maturity Index
Measures the organization’s overall AI capability across several fundamental maturity pillars: AI organization, AI people and culture
Maturity Index: Gartner AI Maturity Model
Workforce AI Adoption Rate
Measures the velocity and frequency of AI tool adoption across business units and among individual employees
Adoption Rate = (Number of Employees Using AI Tools Daily or Weekly/Total Target Employees) × 100%
See Case Study: Executive Alignment for AI Transformation for real-life examples, i.e., % of AI nonusers, frequency of AI use in daily work and employee AI attitudes.
Source: Gartner (November 2025)
Disclaimer: The organization (or organizations) profiled in this research is (or are) provided for illustrative purposes only, and does (or do) not constitute an exhaustive list of examples in this field nor an endorsement by Gartner of the organization or its offerings.