Competing with the Big Boys & Girls: Building an AI Centre of Excellence

“Companies with at least $500 million in annual revenue are changing more quickly than smaller organizations.” — McKinsey

For years small companies have been fast and nimble whilst larger firms are slow to adopt new approaches, but an uncomfortable truth about AI adoption is that large enterprises have the resources and dedicated teams to drive rapid transformation. The scale of benefit they can achieve is huge and has therefore made it a real focus for them. Smaller firms, by contrast, often struggle to give AI innovation the sustained focus and bandwidth it will need if they want to stay ahead.

Yet as the maelstrom of the AI technology wave continues, many smaller firms are experimenting. Across these smaller organisations, pockets of innovation are emerging with individuals, hobbyists, and ambitious teams are using new tools in creative ways. But without structure, these efforts often remain siloed. Good work is happening in isolation, without alignment or momentum.

If smaller firms truly want to ride the AI wave and not be swept aside by it, there are two important things you’ll need to do to grow and support grassroot innovation and give it direction. The key lies in establishing Machine Learning Operations (MLOps) and an AI Centre of Excellence (AICoE).

So what is MLOps?

It’s getting insights from your data. Think of MLOps as the evolution of your traditional MI/BI teams. Instead of managing data warehouses, these teams know how to feed, train, and tune your AI models. They understand how to turn data into insight and insight into strategy, to power the next generation of decision-making.

How MLops benefits you/your company:

• Reliable data pipelines that keeps your AI initiatives running smoothly

• Scalable infrastructure that supports your experimentation and growth

• Operational governance ensuring your models are ethical, explainable, and compliant

  
  

In short, MLOps is the bridge between ambition and execution.

The AI Centre of Excellence – Aligning your approach

While MLOps delivers the “how,” the AI Centre of Excellence (AICoE or however you wish to abbreviate it) defines the “why” and the “where.” This is where your internal experts, emerging talent, and external partners collaborate to identify the biggest opportunities for AI-driven improvement.

An AICoE’s role isn’t about central control, it’s about guidance, alignment, and prioritisation. Like an enterprise architecture or innovation team, it ensures every AI initiative contributes to organisational goals and delivers measurable value.

The best AICoEs focus on:

• Knowledge sharing: turning isolated experiments into reusable best practices

• Strategic alignment: focusing resources on projects that drive impact

• Rapid learning: testing, validating, and scaling what works

• Cultural change: helping teams understand the art of the possible with AI

How we can help

Building this capability requires both technical know-how and organisational finesse. That’s where we come in.

We help organisations:

• Design and stand up effective MLOps and AICoE functions

• Assess and optimise delivery flow, ensuring innovation moves from idea to impact

• Bridge the gap between business ambition and technical reality

• Build internal capability, so your teams can sustain the transformation long-term

  
  

Practical First Steps

If you’re just starting out, don’t over-engineer it. Begin with a lightweight forum, a space where like-minded people can share ideas, discuss what’s working, and explore opportunities together. You can seed this with a few outside experts to accelerate learning and inject new perspectives.

From there, evolve toward a more structured AICoE as your needs grow. The goal is to remain fleet of foot and nimble enough to innovate quickly, but coordinated enough to scale the right successes across the business.

In Summary

Smaller firms can compete with the big players not by mimicking them, but by nurturing existing fledgling expertise, being more focused, and deliberate in how they deliver change. With the right foundations, you can turn your early AI experiments into a strategic advantage.