UK banking sector faces stark disconnect between AI investment and measurable returns

Banks across the United Kingdom are racing to adopt artificial intelligence, yet a significant chasm remains between heavy capital expenditure and tangible financial returns. Despite rising investment, data indicates that only 5% of companies deploying AI technology actually generate a profit from their spending. This stark reality stands in contrast to the high pressure felt by the industry, where nearly 78.3% of banking professionals face increasing demands to demonstrate clear value from automation initiatives.

The core of the problem appears to be an execution gap, where AI initiatives are frequently bolted onto disconnected infrastructure without sufficient operational context. Chris Johnston, SVP and Head of Global Banking at Celonis, warns that AI cannot be successfully implemented atop fragmented systems. When business leaders lack full visibility into how work flows between different platforms, AI operates on broken processes, making it difficult to scale, govern, or deliver the transformation required to meet investor expectations.

This fragmentation is a historical issue within the sector. Core banking systems, customer relationship management platforms, and business rules management tools have become deeply siloed over time. Research by Boston Consulting Group suggests that up to 60% of banking technology spend is consumed merely by maintaining these existing systems rather than driving innovation. Consequently, banks often cannot see how their processes actually operate, meaning any AI built on top of this fragmentation inherits its blind spots and struggles with poor data quality.

To resolve this, experts are proposing process intelligence as a prerequisite solution to successful AI deployment. This approach aims to unify data from core banking systems, CRM tools, and anti-money laundering applications into a comprehensive digital twin of the entire banking operation. By creating a living, end-to-end view of how operations function, banks can provide AI with the real-time context needed to make fine-tuned decisions and trigger the right agents at the correct time.

Currently, the application of AI in banking is largely limited to low-stakes, high-volume tasks such as robotic process automation for file transfers and intelligent document processing for know your customer procedures. While these tools reduce errors and cut bottlenecks, they do not address the deeper strategic integration required for enterprise-scale impact. Without understanding the interaction between business processes and technology, organisations risk underdelivering on their digital transformation goals.

Industry leaders argue that mastering AI depends on understanding not just the technology itself, but how it interacts with underlying business processes. As the sector shifts toward the next phase of AI adoption, the focus is moving from simple automation to ensuring that AI has total visibility over operations. Organisations that prioritise this foundational step of unifying data and closing the context gap will be best positioned to unlock the true power of artificial intelligence and achieve measurable financial results.