There is no shortage of ambition around AI in British companies. It is on board the agendas, investor decks and product roadmaps. And yet, for the whole buzz, not all organizations see significant value. According to our research, three in four British business leaders say they are late on AI.
It is not due to a lack of vision. In fact, most companies know exactly what AI could do – automate manual work, generate information, evolve more quickly. The challenge often comes down to execution.
Because success with AI does not concern a single tool, use cases or a budget cycle. These are the systems, behaviors and choices of products that shape the way the work is done. And when these foundations are not configured for speed, even the most intelligent AI strategy can stall.
From the point of view of the product, three recurring models emerge: an infrastructure that has not followed, working means that resist change and tools that are over -the overlap instead of activating. None of them are permanent blockers – but they must be designed not to work.
Vice-president of sales and general manager of EMEA at HubSpot.
Transform systems inherited into launch cobblestones
Most companies do not deal with broken systems – only those designed for another period. And during the years of growth and expansion, these systems can become more tangled than intentional.
45% of British business leaders claim that the inherited technological batteries are a major obstacle to obtaining the real value of the AI - often because the systems below cannot follow. This is where the friction is built: the data stored in different formats, tools that do not integrate, the teams working around technology rather than with it. When AI enters the image, these gaps count. He doesn’t just need data – he needs data that moves.
The good news is that you don’t need to start from scratch. Strategic simplification – Consolidating systems, integrating platforms, deleting duplications – creates the breathing room that AI must operate. It’s about aligning what you need to work harder, together.
This is why companies are moving towards platforms that unify the basic tools. We see the most progress when customers focus less on revision and more on unlocking unique sources of truth. When the systems are connected and the data circulates freely, the AI becomes less bolted and more a multiplier.
Design changes people want to be part of
Our research has revealed that a third of British business leaders have a decline when updating inherited systems or the introduction of new processes. This hesitation is often labeled as a resistance – but more often than not, it is a call for clarity. People want to understand how AI integrates into their daily work.
When AI is introduced without context – or without contribution of people who expect to use it – this may look more like a disturbance than to progress. And this is where adoption often vacillates.
The real change occurs when leaders are approaching change as a product deployment – with transparency and integrated comments. He also needs leadership commitment to effective change management and AI empowerment.
It is just as important to give teams the trust necessary to experiment. AI is an evolving capacity. Employees must feel safe to test, question and shape how these tools work in practice.
You don’t always need a huge transformation program to change their culture. In many teams, the change begins with the resolution of a little frustrating problem in a better way – and to share how it is done.
Keep things simple enough to evolve
Even with modern systems and committed teams, there is one more barrier which can slow the adoption of AI: complexity. Not in the concept of AI itself, but in the way it appears in people’s work.
According to our research, 35% of British business leaders say they are struggling to fill this skills deficit and give their teams the confidence necessary to effectively use new AI tools. And often, this comes down to the way these tools are built – with technical users in mind, not daily use.
They sit outside the workflows established or feel disconnected from the work that people really try. In organizations concerned with resources, this type of friction can completely stall adoption.
Simplicity is to reduce time between intention and results. The more intuitive a tool, the more rapid value it offers. A well-designed AI system is not content to accelerate tasks-it helps teams reach clarity faster, with less back and forth and less outbuildings. It also evolves better. The easy -to -use tools are easier to deploy, train and maintain – in particular in interfunctional teams.
Create the right conditions for AI to deliver
British companies seeing the value of AI do not rush. They create conditions of progress.
This means to design evolving processes, cultures that remain open to iteration and products that learn alongside people who really use them. The fact is that AI does not need a perfect environment. He just needs a reagent – built both to implement the change and maintain it.
What matters most is not a scale on the first day, but the ability to continue to improve.
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