Your AI center of excellence should work itself out of a job

Everyone building an AI center of excellence assumes it will be permanent.

Wrong move. The best ones are designed to disappear.

Here’s what I’ve learned building workflow automation at Tallyfy: when we see customers create centralized process teams that eventually dissolve, those implementations succeed. When the team becomes permanent, process thinking stays trapped in one department instead of spreading throughout the company.

Same pattern with AI.

Why CoEs become bureaucratic dead ends

I came across research on center of excellence effectiveness that hit me hard. CoEs aren’t viewed as adding value. They’re seen as bureaucratic auditors policing the organization under the guise of promoting best practices.

That’s the trap. You start with good intentions. Centralize AI expertise. Share knowledge. Establish standards. Then something shifts.

The CoE becomes a bottleneck. Teams need approval to experiment. The approval process gets longer. Politics creep in. The structure designed to accelerate AI adoption now slows it down.

Mid-size companies can’t afford this. You don’t have the overhead budget for a permanent AI coordination layer that doesn’t generate direct value. Every dollar and every person needs to count.

The data backs this up. Organizations report 54% of their AI tools don’t talk to each other because of fragmentation. Adding a permanent AI center of excellence often makes this worse, not better. You create another silo trying to coordinate the silos.

What temporary CoEs actually do

Think of an AI center of excellence as scaffolding, not foundation.

Scaffolding supports construction. Once the building stands, you remove it. Same with CoEs. They support AI capability development. Once that capability exists throughout your organization, the CoE should go away.

What does this look like practically? The CoE focuses on four things:

Knowledge transfer, not knowledge hoarding. Every project includes training for the business team. Documentation happens in their systems, not yours. They own the capability when you’re done.

Standard development without enforcement. Create templates, frameworks, guidelines. Make them available. Don’t make teams ask permission to deviate. Research shows centralized decision making driven by politics impacts organizational growth.

Problem-solving support, not problem solving. When teams hit walls, you help them find solutions. You don’t take over and solve it for them. Big difference.

Success pattern identification. You see what works across multiple teams and share it. But you let each team adapt patterns to their context.

Notice what’s missing? Control. Approval. Gatekeeping. Those emerge when CoEs become permanent.

Designing for planned obsolescence

Here’s where most organizations mess up. They never define what success looks like for their AI center of excellence.

Success isn’t the number of AI projects launched. It’s not models deployed or cost savings generated. Those are project metrics.

Success is: we don’t need this team anymore.

Set a timeline. 18 months works for most mid-size companies. That’s enough time to run multiple AI initiatives, build capability in several departments, and establish working patterns.

Then measure capability transfer. Can business teams now identify AI opportunities without the CoE? Can they evaluate vendors independently? Do they know how to structure pilots? Can they measure results properly?

Small and mid-sized organizations stand to gain significantly from establishing an AI CoE, but only if that CoE builds capability rather than dependency.

Track the inverse metric: how often do teams come to you for help? At the start, high dependency is fine. Six months in, it should drop. By month 12, teams should only escalate complex problems. By month 18, they shouldn’t need you at all.

That’s when you dissolve the CoE.

The practical structure for mid-size companies

You don’t need a big team. Three to five people max.

One person who understands AI technology deeply. Not someone who just reads about it. Someone who has built things, debugged models, knows where implementations typically fail.

One person who understands your business operations. They know the processes, the pain points, the politics. They can translate AI capabilities into business value.

One person focused on knowledge management. Documentation, training materials, playbooks. Everything the CoE learns gets captured in a form others can use.

That’s the core. Add specialists temporarily as needed. Data engineer for a specific project. Change management support for a major rollout. But keep the core small.

Where does this team sit? Not in IT. Not in a business unit. Directly under the COO or CEO for mid-size companies. The AI center of excellence needs organizational authority to work across departments without getting trapped in one silo’s priorities.

But here’s the critical part: rotate people through the CoE. Six-month rotations. Business people come in, learn AI. AI people go back to business units with context.

This prevents the knowledge concentration that kills capability transfer. Research on knowledge management shows effective knowledge transfer can increase productivity by 25% and decrease turnover by 35%. But only if knowledge actually spreads.

Building capability that lasts

The activities matter more than the structure.

Every AI project should include embedded training. The business team learns by doing, with CoE members coaching. Not the CoE doing the work while business watches.

Create templates and frameworks, but make them forkable. Teams should be able to copy, modify, and make them their own. You want proliferation, not standardization.

Run regular knowledge sharing sessions, but make them peer-to-peer. The CoE facilitates, but business teams present their learnings to other business teams. This builds the muscle for ongoing knowledge transfer after the CoE dissolves.

Document everything in the business team’s tools. Not in the CoE’s repository. If they keep coming back to your documentation system after you’re gone, you’ve failed.

Build a network, not a hierarchy. Connect people working on similar problems across departments. They’ll support each other after the CoE ends.

I’ve seen this pattern work repeatedly at Tallyfy. Customers who build process thinking into their teams rather than centralizing it in a permanent department scale faster and sustain improvements longer.

There’s one exception to the temporary model: if AI is your core business. If you’re building AI products, running AI services, or competing primarily on AI capabilities, then a permanent AI center of excellence makes sense. You need ongoing coordination of a strategic capability.

But for most mid-size companies, AI is a tool. A powerful one, but still a tool for running your actual business better. Tools shouldn’t require permanent coordination committees.

The sign you’ve succeeded? Two years after launching your AI center of excellence, nobody remembers it existed. AI projects just happen. Teams evaluate and implement AI tools as part of normal work. Knowledge spreads organically through the networks you built.

That’s what knowledge transfer actually looks like. Not a permanent team maintaining the knowledge. But knowledge so well distributed that the team becomes unnecessary.

Start planning for that from day one.