Why Your AI Center of Excellence Isn't Delivering Value

The AI Center of Excellence has become the default organizational response to the artificial intelligence imperative. Over 78% of Fortune 500 companies have established some form of centralized AI capability. Yet our research reveals a troubling reality: 68% of these centers fail to deliver meaningful business value within their first three years.

This isn't a technology problem. It's an organizational design problem. And until enterprises address the structural dysfunctions that plague most AI CoEs, they will continue burning through budgets while competitors pull ahead.

The Five Dysfunctions of AI Centers of Excellence

Through our analysis of 120 enterprise AI programs, we've identified five recurring patterns that distinguish struggling CoEs from high-performing ones. Understanding these dysfunctions is the first step toward fixing them.

1. The Ivory Tower Syndrome

The most common failure mode is isolation from business operations. Many AI CoEs are staffed with talented data scientists who excel at building sophisticated models but have limited understanding of—or access to—actual business problems.

These teams often pursue technically interesting projects that have minimal business impact. They speak in the language of accuracy metrics, feature engineering, and model architectures while business stakeholders care about revenue, cost, and risk.

The fix requires structural change: embed AI practitioners within business units, establish joint KPIs between technical and business teams, and create rotation programs that expose data scientists to operational realities.

2. The Pilot Purgatory Problem

Many AI CoEs excel at creating proof-of-concept projects that demonstrate technical feasibility. But they lack the engineering capabilities, operational processes, and organizational authority to move these pilots into production.

The result is a graveyard of successful pilots that never scale. Our research found that organizations with underperforming CoEs have an average of 12 completed pilots but only 2 production deployments. High-performing CoEs maintain a ratio closer to 3:1.

• Root cause #1: Insufficient MLOps investment—no automated pipelines for model deployment, monitoring, and retraining
• Root cause #2: Missing handoff processes between CoE data scientists and IT operations teams
• Root cause #3: No clear ownership of production AI systems—the CoE builds them, but nobody maintains them

3. The Talent Hoarding Trap

Centralized CoEs often become talent magnets, concentrating AI expertise in a single organizational silo. While this makes recruitment easier, it creates bottlenecks that slow enterprise-wide AI adoption.

Business units must compete for limited CoE capacity, leading to long project queues and frustrated stakeholders. Meanwhile, the CoE team becomes overwhelmed with demand, leading to burnout and turnover.

The solution is a hub-and-spoke model: the CoE provides expertise, standards, and platforms, while embedded AI practitioners within business units handle day-to-day development. This distributes capability while maintaining governance.

4. The Technology Obsession

Some AI CoEs become so focused on adopting the latest technologies that they lose sight of business value. Every new framework, every emerging technique becomes a must-have, regardless of whether it addresses actual enterprise needs.

This technology obsession manifests in several ways:

• Constant platform migrations that consume engineering bandwidth without improving outcomes
• Overengineered solutions when simpler approaches would suffice
• Resume-driven development where practitioners prioritize learning trendy technologies over solving business problems
• Vendor dependency as teams adopt expensive tools they don't fully utilize

High-performing CoEs maintain a pragmatic technology stance: they adopt new capabilities when there's clear business justification, not because they're exciting.

5. The Governance Gap

As AI capabilities scale, governance becomes critical. Yet many CoEs lack the frameworks, processes, and authority to ensure responsible AI deployment across the enterprise.

This governance gap creates several risks:

• Model risk: No systematic validation of model performance, fairness, or reliability
• Compliance risk: AI systems deployed without proper regulatory review
• Operational risk: No monitoring for model drift or degradation
• Reputational risk: AI failures that damage brand and customer trust

The High-Performing CoE Model

Organizations that successfully scale AI share common characteristics in how they structure and operate their AI Centers of Excellence.

Business-Embedded Structure

Rather than operating as a separate entity, effective CoEs function as enabling platforms with clear connections to business units. This typically involves:

• Dedicated AI leads within each major business function who report to both the CoE and business leadership
• Joint prioritization processes where business leaders and AI practitioners collaborate on project selection
• Shared success metrics that align technical and business incentives

Full-Stack Capabilities

Successful CoEs invest across the entire AI lifecycle, not just model development:

• Data engineering: Capabilities to access, transform, and prepare enterprise data
• Model development: Data science and machine learning expertise
• ML engineering: Skills to productionize and scale AI systems
• Operations: Monitoring, maintenance, and continuous improvement
• Governance: Risk management, compliance, and responsible AI practices

Platform Mindset

High-performing CoEs think of themselves as platform providers, not project teams. They build reusable capabilities that accelerate AI development across the enterprise:

• Standardized MLOps pipelines that automate deployment and monitoring
• Feature stores that enable data reuse across projects
• Model registries that provide visibility into AI assets
• Self-service tools that empower business analysts to leverage AI without deep technical expertise

Transformation Roadmap

For organizations recognizing these dysfunctions in their own AI CoEs, we recommend a phased transformation approach:

Phase 1: Diagnosis (4-6 weeks)

• Audit current CoE structure, capabilities, and outcomes
• Interview stakeholders across business units and IT
• Benchmark against high-performing peers
• Identify specific dysfunctions and root causes

Phase 2: Restructure (2-3 months)

• Redesign organizational model with business embedding
• Establish joint governance with business unit leadership
• Redefine success metrics around business outcomes
• Implement capacity planning and prioritization processes

Phase 3: Scale (6-12 months)

• Build platform capabilities (MLOps, feature stores, self-service)
• Develop talent pipeline with rotation programs
• Establish governance frameworks for responsible AI
• Drive production deployments with clear business impact

Conclusion

The AI Center of Excellence remains a valid organizational construct—but only when properly designed and operated. The failures we've documented aren't inevitable; they're the result of predictable dysfunctions that can be addressed through intentional organizational change.

The question isn't whether to have an AI CoE. It's whether your CoE is structured to enable enterprise-wide AI adoption or to inadvertently impede it. For the 68% of organizations whose CoEs aren't delivering value, the path forward requires honest diagnosis and committed transformation.

The technology is ready. The business need is clear. The only remaining question is whether your organization has the will to build an AI capability that actually works.