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Most large enterprises today have or are building an AI Center of Excellence (CoE). It sounds like a best practice. A central team of experts that can standardize frameworks, develop accelerators, and guide the rest of the business on AI usage.

But the truth? Many CoEs are failing to drive impact. They become bottlenecks. They get stuck doing demos. They push templates that teams ignore. And they often operate in isolation from real business goals.

If you are serious about AI transformation, it is time to rethink the CoE. Not as a gatekeeper, but as an enabler. Not as a lab, but as a multiplier.

This post explores how to turn your AI CoE into an engine of measurable outcomes.

The Problem With Traditional CoEs

AI CoEs were originally modeled after older IT structures:

• A central group with experts in data science, modeling, and ML ops
• A mandate to evaluate use cases and standardize tooling
• A focus on proof-of-concepts, prototypes, and research

But this model has three problems:

• It slows down execution. Business teams cannot move without CoE approval, creating a bottleneck.
• It prioritizes perfection over progress. CoEs over-engineer solutions while teams want something usable now.
• It is often misaligned with business impact. CoEs measure model accuracy, not revenue, cost, or customer experience.

The result? AI becomes a science project — not a strategic capability.

The Shift: From Gatekeeping to Product Thinking

A modern AI CoE must shift its mindset from policing to productizing:

• Less control, more enablement
• Less perfection, more iteration
• Less demos, more deployment

It should behave like an internal platform and accelerator team, not an oversight body.

This means:

• Creating reusable components and services (e.g. prompt libraries, fine-tuned models, data access APIs)
• Building toolkits and templates teams can use directly
• Coaching teams to run their own AI projects with support — not permission

Think of the CoE as a builder of roads, not the driver of every vehicle.

Embed CoE into Business Value Streams

Traditional CoEs sit outside the business. They host office hours. They wait for requests. They run pilots.

Modern CoEs must go the other way. They should embed directly into value streams:

• A CoE team member should be part of the marketing AI squad
• Another should work with the operations automation stream
• Another with the customer service augmentation initiative

This ensures that the CoE:

• Feels the pressure of business metrics
• Understands user feedback and blockers
• Co-owns outcomes, not just code

Embedded CoEs do not just suggest. They deliver.

Create a Catalog of AI Accelerators

One powerful way to make the CoE valuable is to productize its outputs. Instead of just publishing guidelines, build a catalog of AI accelerators:

• Pre-trained models for common tasks (e.g. classification, summarization)
• Prompt libraries for different functions
• Fine-tuned LLMs on internal knowledge
• Data connectors to key systems
• Evaluation frameworks
• UX components for integrating AI into workflows

Make these assets easy to discover, consume, and adapt. This turns the CoE into a force multiplier. Teams do not start from scratch — they build on proven foundations.

Define Metrics That Matter

Another reason CoEs lose steam is they measure the wrong things:

• Number of pilots
• Number of models trained
• Number of hours spent on research

These do not move the business. Modern CoEs should be tracked on:

• Revenue influenced by AI features
• Cost savings from automation
• Time saved in decision-making
• Customer satisfaction lift
• Number of reusable components adopted

If you cannot tie your CoE work to an OKR, it is not strategic work.

Operationalize Model Deployment and Monitoring

One silent killer of AI projects is what happens after the model is built:

• Where is it hosted?
• How does it scale?
• Who monitors performance drift?
• Who handles feedback and updates?

The CoE must not just hand off models — it must own the lifecycle. This includes:

• Model versioning and rollback policies
• Monitoring dashboards for accuracy, latency, bias
• Feedback pipelines to retrain or tune
• Uptime and error logs for incident response

Treat models like products — with a roadmap, maintenance plan, and SLAs.

Coach, Do Not Command

One final cultural shift: the CoE must stop acting like the AI police. Instead of blocking teams from using the latest tool or model, coach them:

• Share best practices, not rules
• Offer reviews, not approvals
• Run brown bags, internal demos, and ask-me-anything sessions
• Pair data scientists from CoE with analysts in the business

The goal is not to be the only team that knows how to do AI. The goal is to make everyone in the org AI-fluent.

Real-World Example: A Telco CoE Transformation

One global telecom provider had an AI CoE of 50+ people. It was respected — but feared. Business teams avoided it, saying it took too long to approve anything.

A new AI leader took charge and restructured the CoE:

• Embedded team members into four key business units
• Converted internal assets into a catalog of APIs and components
• Changed performance metrics to business outcomes
• Built a shared dashboard to track AI adoption and value

The result?

• Time to deploy AI projects dropped by 60 percent
• Adoption tripled across business units
• Business units began requesting CoE expansion, not bypassing it

The CoE became a partner — not a barrier.

The Way Forward

If your AI CoE is still structured like a research lab, it is time to evolve. The future of AI in enterprises is not about central control. It is about distributed intelligence — supported by platforms, practices, and partners.

A modern AI CoE does not gatekeep. It guides. It builds assets. It embeds into business streams. It helps everyone do AI better — safely, scalably, and strategically.

The question is not whether you need an AI CoE. The question is: is yours built for outcomes, or for optics?