How long does an AI implementation take?

Most single workflow implementations take 2-6 weeks from kickoff to production. Full AI transformation programmes run 6-12 weeks.

Do you work with specific AI models?

We are model-agnostic and work with all major providers including Anthropic Claude, OpenAI GPT, Google Gemini, Meta Llama, Mistral, and more.

Can you deploy AI on our own servers?

Yes. Our Local & Private AI service deploys models on your own infrastructure or private cloud.

Glossary

AI Centre of Excellence (CoE)

An AI Centre of Excellence is a centralised team or organisational unit that provides AI expertise, best practices, governance, and support to enable consistent, effective AI adoption across an organisation.

What is an AI Centre of Excellence?

An AI Centre of Excellence (CoE) is a cross-functional team that serves as the hub for AI expertise, standards, and governance within an organisation. It centralises AI knowledge and capabilities, providing shared resources that individual business units can leverage for their AI initiatives. Typical CoE functions include developing and maintaining AI standards and best practices, providing technical expertise and consultation to project teams, managing shared AI infrastructure and tools, establishing governance frameworks and ethical guidelines, building and delivering AI training programmes, evaluating and recommending AI technologies, and tracking the portfolio of AI initiatives across the organisation. CoE structures vary. Some are fully centralised teams that build and deploy AI solutions for the entire organisation. Others operate as a federated model, with embedded AI practitioners in business units supported by a central team that provides standards, tools, and governance.

Why an AI CoE Matters for Business

An AI CoE prevents the fragmentation and duplication that occurs when AI adoption happens independently across business units. Without centralised coordination, teams reinvent solutions, make inconsistent technology choices, create governance gaps, and fail to share learnings. The CoE accelerates AI adoption by providing ready-made infrastructure, templates, and expertise that individual teams would otherwise need to build from scratch. A project team can focus on their specific use case rather than solving foundational AI challenges that have already been addressed. A CoE also ensures consistent quality and governance. As AI regulation increases, having a centralised body that maintains standards, conducts audits, and ensures compliance becomes essential. The CoE provides the organisational structure needed for responsible AI at scale.

Related Terms

Explore further

ai maturity model ai champion ai literacy responsible ai ai readiness

FAQ

Frequently asked questions

When the organisation has multiple AI initiatives underway or planned, and coordination, standardisation, and governance become important. This typically happens after initial experimentation proves AI value and the organisation wants to scale adoption.

Start small — 3-5 people covering key roles (AI strategy, engineering, data, governance). Scale as the AI programme grows. The CoE should be large enough to support current initiatives but lean enough to justify its overhead through clear value delivery.

Both models work. A 'builder' CoE directly develops AI solutions for business units. An 'enabler' CoE provides standards, tools, and guidance while business units build their own solutions. Many CoEs evolve from builder to enabler as organisational AI capability grows.

AI Maturity Model

Assessing organisational AI capability.

AI Literacy

Building AI understanding across the organisation.

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