There’s no shortage of ambition around AI in UK businesses. It’s on board agendas, investor decks and product roadmaps. And yet, for all the buzz, not every organization is seeing meaningful value. According to our research, three out of four UK business leaders say they’re falling behind on AI.
It’s not due to a lack of vision. In fact, most businesses know exactly what AI could do – automate manual work, generate insights, scale faster. The challenge often comes down to execution.
Because success with AI isn’t about one tool, one use case or one budget cycle. It’s about the systems, behaviors and product choices that shape how work gets done. And when those foundations aren’t set up for speed, even the smartest AI strategy can stall.
From a product perspective, three recurring patterns emerge: infrastructure that hasn’t kept up, ways of working that resist change and tools that overcomplicate instead of enable. None of these are permanent blockers – but they need to be designed for, not worked around.
Vice President of Sales & Managing Director for EMEA at HubSpot.
Turning legacy systems into launchpads
Most businesses aren’t dealing with broken systems – just ones that were built for a different time. And over years of growth and expansion, those systems can become more tangled than intentional.
45% of UK business leaders say legacy tech stacks are a major barrier to getting real value from AI – often because the systems beneath them can’t keep up. That’s where friction builds: data stored in different formats, tools that don’t integrate, teams working around the tech rather than with it. When AI enters the picture, those gaps matter. It doesn’t just need data – it needs data that moves.
The good news is that you don’t need to start from scratch. Strategic simplification – consolidating systems, integrating platforms, removing duplications – creates the breathing room AI needs to function. It’s about aligning what you already have to work harder, together.
That’s why businesses are moving towards platforms that unify core tools. We see the most progress when customers focus less on overhauling and more on unlocking single sources of truth. When systems are connected and data flows freely, AI becomes less of a bolt-on and more of a multiplier.
Designing change people want to be part of
Our research found that a third of UK business leaders experience pushback when updating legacy systems or introducing new processes. That hesitation is often labelled as resistance – but more often, it’s a call for clarity. People want to understand how AI fits into their day-to-day work.
When AI is introduced without context – or without input from the people expected to use it – it can feel more like disruption than progress. And that’s where adoption often falters.
The real shift happens when leaders approach change like a product rollout – with transparency and feedback built in. That means involving teams early, framing AI as an enabler and showing clear wins that matter to employees: time saved, tasks simplified, better decisions made faster. It also needs commitment from leadership to effective change management and AI empowerment.
Equally important is giving teams the confidence to experiment. AI is an evolving capability. Employees need to feel safe to test, question and shape how these tools work in practice.
It doesn’t always take a huge transformation programme to shift culture. In many teams, the change starts with solving a small, frustrating problem in a better way – and sharing how it’s done.
Keeping it simple enough to scale
Even with modern systems and engaged teams, there is one more barrier that can slow AI adoption: complexity. Not in the concept of AI itself, but in how it shows up in people’s work.
According to our research, 35% of UK business leaders say they’re struggling to bridge this skills gap and give their teams the confidence to use new AI tools effectively. And often, that comes down to how those tools are built – with technical users in mind, not everyday use.
They sit outside established workflows or feel disconnected from the work people are actually trying to do. In resource-conscious organizations, this kind of friction can stall adoption altogether.
Simplicity is all about reducing the time between intention and outcome. The more intuitive a tool is, the faster it delivers value. A well-designed AI system doesn’t just speed up tasks – it helps teams reach clarity faster, with less back-and-forth and fewer dependencies. It also scales better. Tools that are simple to use are easier to roll out, train, and maintain – especially across cross-functional teams.
Creating the right conditions for AI to deliver
UK businesses seeing value from AI aren’t rushing ahead. They’re creating conditions for progress.
That means designing processes that evolve, cultures that stay open to iteration and products that learn alongside the people actually using them. The fact is that AI doesn’t need a perfect environment. It just needs a responsive one – built to both implement change and sustain it.
What matters most isn’t scale on day one, but the ability to keep improving.
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