A business can employ someone who understands AI and still have nobody with the time and remit to change how the organisation works.

In a recent conversation about a business's AI plans, its internal specialist was focused on developing capabilities customers would see and value. There was also an ambition to improve internal operations. The difficulty was finding the time and bandwidth to do both.

That is a management problem worth recognising early. Building customer-facing products and improving internal operations compete for attention and budget, even when they use some of the same technology.

Expertise Needs a Clear Remit

An internal AI specialist may be an excellent developer or product innovator. Their existing responsibilities may already fill the working week. Asking them to cover business strategy, process redesign, data readiness, governance and staff adoption creates additional work that needs to be planned and resourced.

This does not diminish their expertise. It makes the limits of an individual role visible.

The broader capability gap is well established. DSIT research published in 2026, based on a 2025 survey of 3,500 UK businesses with at least five employees, found that 60% cited limited AI skills, expertise or knowledge as a factor preventing or having previously prevented adoption.[1] That finding concerns skills; the client conversation above illustrates the separate question of available capacity.

Leaders should therefore look beyond whether somebody in the business knows about AI. They need to establish who owns each outcome, what that person is already committed to, and which responsibilities remain uncovered.

Customer-facing development needs a clear connection to customer demand and commercial value. Internal adoption needs people who can work across departments, change processes and help colleagues use new tools effectively. Both also require appropriate technical and governance support.

Use Specialist External Advice to Strengthen the Team

Some CEOs are exceptionally well read on AI. Others recognise that they need help assessing opportunities and risks. Embrace ‘outside in’ thinking or inviting ‘rebel’ ideas to the table.  Implementing AI and the game changing transformational opportunities it brings is unchartered territory and few organisations will have this in their own ranks.  In either case, outside expertise is useful when it comes to the big picture planning, technical architecture and ‘plumbing’ (AI does not sit quietly inside existing systems), seeing the unforeseen potholes in the road, and resolving decisions the business is struggling to make.

As a Professor of AI advising business leaders, I see value in connecting technical possibilities with commercial priorities and the realities of implementation. That can include challenging the business case, setting a practical roadmap or helping an internal specialist focus on the work where their contribution matters most.

A focused engagement should have a defined output. A prioritised investment plan, an agreed operating approach or an evaluation of build and buy options. This will help the leadership team to better understand, what workflow efficiencies can be achieved - how much time and cost will be saved, what better, faster decisions can be made and how administratively or strategically those may reshape a business. It will also set out a road map for successful implementation to ensure AI in the business becomes a reality - saying and doing are two different things.  Internal AI expertise, capacity and capabilities are key.  Everyone is an expert nowadays, but time and again, we see overreach, which makes it difficult for a leadership team to know where to draw the line.  

Support can also be proportionate. A business may need specialist input at key decisions rather than another permanent senior hire. Equally, an adviser cannot compensate indefinitely for an organisation that assigns nobody the time or authority to implement the decisions.

The aim should be to leave the internal team better equipped and give leadership a clearer view of delivery. Internal knowledge and external experience can work together when responsibilities are explicit.

If your AI ambitions repeatedly lose out to day-to-day priorities, examine the capacity behind the plan. Informed.AI can help you identify the gaps and decide where focused support would make the greatest difference.

Discuss your AI capability and delivery priorities at informed.ai.

Sources

[1] Department for Science, Innovation and Technology. AI Adoption Research. Published January 2026 and updated February 2026; survey fieldwork February to May 2025.

About the author

Professor Andy Pardoe is a Professor of AI and founder of Informed.AI. He brings more than 30 years of technology, business and management experience to his work with CEOs and boards on AI strategy, governance and adoption.