Why setting up an AI Centre of Excellence holds the key to moving from pilots to production and from effort to execution
One of the major challenges with AI is how to move beyond the pilot project and deliver a real, working system that delivers real benefits. While many organisations have successfully launched AI pilots and proofs of concept, far fewer have managed to scale those initiatives into production environments that deliver measurable business value.
The problems are not so much to do with having access to the right technology, but a lack of structure, operating model, and integration expertise, and concerns over management of applications, reliability, governance and security.
In other words, it’s not just one problem. Rather, it is a set of interconnected challenges that organisations need to address before they can expect to see ROI on their AI investments.
One way of addressing this set of challenges is to create an AI Centre of Excellence (CoE) to act as a point of focus for AI projects, develop knowledge and understanding of AI, and ensure that the work being carried out is aligned with business strategy and values.
What is an AI Centre of Excellence?
An AI Centre of Excellence (AI CoE) provides governance, expertise, standards and strategic oversight needed to turn AI initiatives into scalable, business-focused outcomes. By bringing together people, processes and technology, it helps organisations move from experimentation to execution.
An AI Centre of Excellence is the place in which all the previously fragmented aspects of AI development – tools and skills, governance and guardrails, compliance and standards, security and data protection, and strategic objectives – are brought together.
As well as being a centre for AI knowledge and learning, a CoE will be tasked with building trust and credibility with employees. It will act as a hub for the sharing of information and experience and as a point of connection between individuals, business functions, and stakeholders – both internally and externally. As knowledge accumulates within the CoE, it will establish the frameworks and protections needed to support successful AI adoption across the organisation.
Ultimately, the CoE will create an operating model for assessing viability, building projects and finally taking them into production and a live environment. It will ensure investments in AI are aligned with business objectives and will deliver real returns. It will also act as the driver of AI innovation, ensuring that projects are being driven forward and can be scaled across the organisation effectively.
When all this is considered, it starts to look like an AI CoE is essential to any organisation that plans to derive significant benefits from the technology. But all too often, it’s only after they have tried and failed with several projects that organisations realise that they need an AI CoE.
How to build an AI Centre of Excellence (AI CoE)
How do you set up an AI CoE? You need to work with your customer to appoint a leader and start assembling a team.
Getting the right people involved right at the start is crucial. In particular, the leader of the CoE will play a crucial role in its success. They will need to be an AI champion within the business, a creative and visionary individual with real energy and enthusiasm for the subject. Someone who genuinely believes that AI can deliver significant benefits for the organisation. They, and the whole CoE, will need to generate excitement and belief around AI within the business and having people with good communications skills on the team is important.
Other members of the team need to be just as enthusiastic and should be drawn from different areas of the organisation to ensure that every aspect of AI projects and their potential implications are considered. Individuals with AI-relevant skills, who understand large language models (LLMs) and how to manage and structure data for AI will be crucial. Specific skills such as software coding and IT project management will also be required. The cybersecurity team and those responsible for business-critical apps and processes, will need to be consulted.
The CoE will also need input from other business functions – HR, legal, quality control, operations, finance, customer services and sales - and the CoE leader will need to have strong organisational and delegation skills to ensure efforts are co-ordinated and always moving in the right direction.
Key success factors for a CoE
Why do some AI Centres of Excellence succeed whilst other struggle? The answer usually comes down to executive sponsorship, investment and organisational commitment.
Senior management backing is the most important factor in making an AI CoE work. The leader and senior members of the CoE team need to have the ear and the support of the board. The CoE needs to be properly resourced, which may mean providing additional support for areas of the organisation from which the members of the CoE team are drawn, to give them more time to focus on AI.
The CoE team also needs to be brought together as a real team, with a shared sense of purpose and belief. This underlines the importance of appointing the right person to be the CoE leader. Each team member needs to be bright, enthusiastic, open and eager to learn, and always ready to share their thoughts and ideas. It should be a team that both fosters and thrives upon innovation, that will not flinch when challenges seem insurmountable and able to analyse problems and find solutions.
Most importantly, the COE must remain focused on delivering business outcomes. Success should be measured not by the number of AI projects launched, but by the value those projects create once deployed into production.
How can partners help with AI CoEs?
Partners have a significant opportunity to help customers overcome one of the biggest barriers to AI adoption: moving from isolated experiments to scalable, governed and business-aligned deployments.
As trusted advisors to their customers, partners can play a vital role in helping customers establish an AI CoE. A partner’s own experts in AI and data modelling can make a valuable contribution to the CoE team, both during the initial set-up and as the CoE starts to make its presence felt.
One major requirement at the start will be enablement and training of team members, many of whom may need to fill gaps in their knowledge and understanding of AI. From that point on, the partner will be able to add value by providing support on the more technical aspects of AI projects and providing practical help when required, with application development, or with infrastructure and cybersecurity.
Partners will of course, need to have sufficient proficiency in AI themselves before they can offer expert assistance to their customers.
TD SYNNEX’s Destination AI programme provides access to a tailored enablement journey and extensive resources that enable partners to build the knowledge, capabilities and confidence needed to develop their own AI business practice and support customers on their AI adoption journey.
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