AI Operating Models (Centralised, Distributed, Hybrid)

How you organise your AI capability within your business matters as much as the technology you choose. A centralised model puts all AI expertise in a single team - a centre of excellence - that serves the entire organisation. This ensures quality and consistency but can create bottlenecks and disconnect from business realities. A distributed model embeds AI expertise within individual business units, giving them autonomy to pursue their own initiatives. This is more responsive to business needs but risks duplication, inconsistency, and an inability to share learnings across the organisation. Most mature organisations settle on a hybrid model: a central team that sets standards, manages shared infrastructure, develops reusable tools, and maintains governance frameworks, combined with embedded practitioners in business units who understand the domain context and can move quickly. The central team provides the platform and the guardrails; the embedded teams provide the business knowledge and agility. There is no single right answer - the best model depends on your organisation's size, culture, the maturity of your AI capability, and the diversity of your use cases. What matters most is making a deliberate choice rather than letting your operating model emerge by accident, which is what happens in most organisations and typically results in the worst of all worlds.