The first wave of AI adoption has made businesses more productive, but also increasingly alike. Most organisations have concentrated on similar use cases, from summarising meetings and accelerating coding to automating admin tasks and streamlining workflows. The results have been compelling enough that 92% of companies plan to increase AI investment over the next three years.
Yet despite widespread adoption, just 1% describe themselves as mature in AI deployment, and more than half of business and IT leaders report that fewer than 50% of their AI projects have delivered measurable improvements. AI may be widely available, but relatively few businesses have embedded it deeply enough to reshape how they work.
One reason is that most AI initiatives have focused on improving existing processes rather than reimagining them. Organisations are layering AI onto established workflows to increase efficiency, automate routine tasks and reduce manual effort. These projects are often relatively straightforward to implement, produce measurable productivity gains and create quick wins. The challenge is that competitors can do exactly the same thing.
Productivity improvements are valuable, but they rarely create distinction on their own. The bigger opportunity lies in using AI to rethink how work gets done and design entirely new ways of operating.
Where AI provides real value
The companies seeing the most value from AI are nearly three times more likely than others to have fundamentally redesigned workflows, while leading companies allocate more than 80% of AI investment towards reshaping functions and creating new offerings rather than incremental productivity projects.
These organisations view AI as more than another technology layer. They are using it to change how decisions are made, how services are delivered and how value is created. That can mean moving beyond faster ticket responses towards proactive issue resolution, replacing periodic reviews with real-time decision making, or building products and services with intelligence at their core rather than layering it on afterwards.
In practice, organisations can begin by identifying where decisions are still made manually, where employees spend significant time processing information, and where services rely on reacting to events rather than anticipating them.
A manufacturer might use AI to predict equipment failures and automatically schedule maintenance before problems occur. Ecommerce businesses could use real-time data or AI agents to recommend products and adjust pricing to directly improve customer experiences. Financial services firms may program AI to handle routine analysis and monitoring, allowing employees to focus more on customer support, risk management and strategic decision-making.
This approach is harder than simply adding AI into current processes. It requires investment, organisational change and a willingness to challenge long-held assumptions, but it also offers a genuine opportunity to create advantages that are difficult for competitors to replicate.
The barriers to business redesign
Success will depend on more than access to AI models. Effectively redesigning a business around AI requires the right foundations, including the right data, processes and governance to translate intelligence into action. This means improving visibility across data sources, establishing clear ownership of critical business information and putting frameworks in place to monitor how AI systems are used and how decisions are made.
For many organisations, the biggest barrier to AI transformation is not the technology itself, but the quality of the data behind it. Data is often fragmented across systems, ownership is unclear and visibility across customer journeys and business processes remains limited. As a result, businesses can find themselves attempting to implement AI on top of disconnected information, creating faster processes but not necessarily better decisions. While general-purpose tools such as ChatGPT and Claude are becoming increasingly accessible, real differentiation comes from applying AI to the organisation enterprise data, customer knowledge and operational insight that competitors cannot easily reproduce.
This is why trust and governance must sit at the centre of AI transformation. Firms should start by identifying the data that powers their most important decisions, addressing gaps in quality and accessibility, and assigning clear accountability for how that data is managed. They also need processes to monitor AI outputs, test for accuracy and ensure decisions can be understood and challenged when necessary.
Without these foundations, AI can scale poor decision-making just as easily as good decision-making. Establishing trust therefore requires collaboration across business leaders, data teams, technology specialists and governance functions. With clear ownership and accountability across the organisation, companies are better positioned to move beyond productivity gains and use AI to support meaningful transformation.
The next phase of AI adoption
The companies that create lasting advantage will be those using AI to redesign parts of the business rather than simply accelerating existing processes. Closing the gap between adoption and transformation requires strong data foundations, effective governance and collaboration across business, technology and data teams.
Perhaps the most important question business leaders should be asking is not how quickly they can deploy AI, but what they want their organisation to look like in three years’ time. The answer to that question will determine whether AI becomes just a tool for optimisation, or a catalyst for transformation.
