This article is based on a webinar delivered by Matthew Rawlins, Michael Lafferty, and Chiraag Swaly.

AI adoption is moving quickly. Across the UK, organisations are investing in new tools, rolling out AI licences, and experimenting with ways to improve productivity.

But there's a problem - buying the technology is often the easy part. The real challenge is turning that investment into meaningful business impact.

That means answering some important questions, including:

• Who sets the strategy?
• Who builds the technical foundations
• Who helps employees actually use AI in their day-to-day work?

Without clear answers to those questions, AI adoption can quickly become disjointed.

Different departments start experimenting with different tools, employees find their own ways of using AI and technical teams are left trying to connect systems that were never designed to work together.

This is what's sometimes referred to as 'Shadow AI', where AI is used across an organisation without a coordinated strategy, governance or oversight.

We recently explored how organisations can move beyond isolated AI training and build a connected approach to developing AI capability.

The answer is a three-tier model:

• Tier 1: Leaders who set the strategic direction
• Tier 2: Practitioners who build the technical foundations
• Tier 3: Champions who drive adoption within teams

Each has a different role to play. And, importantly, none of them works effectively in isolation.

Why one-off AI training isn't enough

It can be tempting to think AI adoption starts with giving employees access to a new tool or sending them on a training course. But access doesn't necessarily lead to adoption.

As Michael Lafferty explained during our recent webinar, many organisations have fallen into a 'fragmentation trap', where individual departments purchase software, run isolated experiments, or send employees on generic AI courses.

The result can be a collection of disconnected initiatives rather than a coherent AI strategy. There are practical consequences to this. For example:

• Data may be spread across different systems.
• Teams may duplicate work.
• Employees may use tools without fully understanding the security or governance implications.
• Leaders can struggle to demonstrate what return they're actually getting from their investment.
• AI adoption may become inconsistent across teams, making it harder to scale successful ways of working.

That's why sustainable AI adoption requires people across the organisation to understand how AI fits into the bigger picture.

Tier 1: Leaders set the direction

Every successful AI strategy needs strong leadership.

While senior leaders don't need to become AI specialists, they do need enough understanding to make informed decisions about where AI can add value and how success should be measured.

Leaders are responsible for turning AI from an area of experimentation into a clear business priority. That means deciding where investment should be focused, what outcomes the organisation wants to achieve, and how progress will be measured.

During our webinar, Chiraag Swaly highlighted the importance of leaders looking beyond individual AI tools and considering the bigger picture.

Rather than asking, 'what can this technology do', leaders need to consider, 'what are we trying to achieve, and where can AI help us get there?'

This shift in perspective can help organisations make better decisions about where to invest their time and resources. It also gives technical teams and AI practitioners a clearer understanding of what they are working towards.

That's why Kaplan's Level 5 AI Leadership units focus on helping leaders turn AI ambitions into clear plans for their organisation. Learners can develop practical outputs, including an AI roadmap and workforce resilience plan tailored to their business.

The aim is to move from 'we should be doing something with AI' to 'here's what we're trying to achieve, and here's where AI can help.'

Tier 2: Practitioners build the technical foundations

Strategy is essential, but it still needs to be put into practice.

AI and Automation Practitioners are responsible for designing, developing, and implementing the systems that enable AI to deliver real business value.

A key part of that role is understanding data. AI is only as effective as the information it has access to. As Michael explained during our webinar, the familiar principle of 'garbage in, garbage out' applies: if AI is built on fragmented, poorly structured, or disconnected data, the quality of its outputs will inevitably suffer.

Practitioners therefore need to understand how data is organised, how systems connect, and how information flows across the organisation before introducing AI solutions.

From there, they can identify opportunities to improve existing processes through automation. This could involve developing proof-of-concepts, centralising data, integrating large language models, creating automated workflows, or deploying AI agents.

The Level 4 AI and Automation Practitioner programme is designed to develop these technical skills while also recognising that successful implementation isn't just about technology.

Practitioners need to consider how new solutions will be adopted, how they'll affect the people using them, and whether they're delivering meaningful improvements for the organisation.

Their role is to bridge the gap between strategy and delivery, turning AI ambitions into real-world solutions that solve real business problems.

Tier 3: Champions make AI adoption happen

With the strategy in place and the technical foundations built, the final step is helping employees use AI confidently in their day-to-day work. This is where AI Champions play an important role.

AI Champions sit within individual teams rather than central IT. No matter whether they work in finance, HR, sales, or operations, they understand the challenges and priorities of the colleagues around them.

That makes them well placed to demonstrate tangible uses for AI, answer questions, and encourage colleagues to use AI safely and effectively.

As Michael explained during the webinar, providing access to AI tools is only part of the picture.

Employees also need the confidence and support to use them in ways that deliver real value. AI Champions help embed AI into everyday ways of working, making adoption more consistent across the organisation.

Kaplan's Level 3 AI Champion programme is designed to prepare learners for this role.

It builds a strong understanding of generative AI, including its capabilities, limitations, ethics, security, and the risks associated with Shadow AI, before moving on to practical applications such as improving workflows and identifying opportunities for automation.

Instead of focusing on a single tool, the programme equips learners with the skills to support colleagues, encourage good practice, and adapt as AI continues to evolve. The result is a network of AI Champions who can help drive long-term AI adoption across the organisation.

The three tiers work together

The real value comes when these three tiers work together.

An organisation might have a clear AI strategy, but without the people to turn it into real-world solutions, progress can quickly stall.

Equally, even the best technical solutions won't deliver lasting value if employees don't have the confidence to adopt them.

Rather than operating independently, each tier strengthens the others. As Matthew Rawlins noted during the webinar, they're like three cogs in the same machine. Remove one, and the whole system becomes less effective.

Together, they create a joined-up approach to AI adoption across the organisation.

This was reflected in a poll during the webinar, where 42% of attendees said their biggest challenge involved all three levels of AI training. The results suggest many organisations aren't facing a single skills gap, but the need to build AI capability across every level of the business.

Don't get distracted by the pace of AI

One of the questions raised during the webinar was how organisations can keep their people up to date when AI is changing so quickly. It's a fair question.

New models and features are appearing all the time, and it can feel as though organisations are constantly playing catch-up. But there's a danger in focusing too heavily on what's new.

If employees haven't mastered the fundamentals, the latest technology is unlikely to deliver meaningful value.

As Chiraag explained, organisations don't need to jump straight to the most advanced AI capabilities if people aren't yet confident using the tools already available to them. Building strong foundations makes it much easier to adapt as new technologies emerge.

That's why the training programmes focus on practical application rather than individual tools. Learners are encouraged to experiment, research new developments, and apply what they learn to real challenges within their organisation.

While the technology will continue to change, the ability to evaluate, adopt, and apply it effectively is what will have the greatest long-term impact. As Michael Lafferty noted, quoting OpenAI CEO Sam Altman: “AI won't replace humans, but humans who use AI will replace those who don't.”

AI capability isn't just an IT responsibility

Perhaps the biggest takeaway is that AI transformation can't sit with one department. It's not simply an IT project, it's not purely an HR initiative, and it's not something that can be delegated to an external specialist and forgotten about.

AI has implications for every part of an organisation, influencing decisions, processes, and the way people work.

That means responsibility for successful adoption needs to be shared across the business, rather than owned by a single team.

The organisations that realise the greatest value from AI won't necessarily be those investing in the most technology. They'll be the ones that create the conditions for AI to be adopted consistently, responsibly and with a clear purpose.

Building AI capability across your organisation

Developing AI capability doesn't have to mean starting from scratch.

Kaplan's Data and Technology apprenticeship programmes help organisations build practical AI skills that can be applied directly in the workplace. Designed for different roles and levels of experience, they help organisations turn AI ambitions into measurable business outcomes.

The three-tier framework provides a practical way to assess your organisation's AI capability today and identify where to invest in skills for the future.

Explore Kaplan's Data and Technology apprenticeship solutions to discover how you can develop AI capability across your organisation.