Banks in Britain are racing to adopt AI, yet despite rising investment, only 61% of banking professionals say AI is delivering on its potential. An MIT study found that just 5% of companies investing in AI actually profit from it. The gap between AI spend and measurable ROI remains stubbornly wide at a time when nearly 78.3% of banking professionals face increasing pressure to demonstrate clear value from AI and automation investments.
AI is now a central component for digital transformation efforts across the sector; however, achieving returns on this investment still remains elusive.
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The missing link? Process Intelligence.
AI cannot simply be plugged into existing operations and deliver results. Successful AI adoption works when business leaders have full visibility of how work in their organisation flows between systems. Otherwise, AI operates on top of broken processes, making it difficult to scale, govern or deliver effective transformation. This is where Process Intelligence becomes the trick up your sleeve. It provides a consolidated, end-to-end view banks need to identify, prioritise, and orchestrate their most impactful AI implementations, while giving AI the real-time context to make fine-tuned decisions and trigger the right agents at exactly the right time.
Why AI projects struggle
The root problem is complexity.
Currently, the industry is facing an “execution gap” with less than half of banking AI projects considered fully successful and meeting ROI expectations. In retail and business banking, core banking systems (CBS), CRM platforms, and business rules management (BRM) tools have become deeply fragmented over time. These fragmented systems fail to provide the proper view and insight necessary for effective AI deployment.
Research by Boston Consulting Group found that as much as 60% of banking technology spend is consumed by maintaining existing systems. Banks often can’t see how their processes actually operate and the AI built on top of that fragmentation inherits its blind spots. This lack of clarity is a major roadblock causing poor data quality preventing the successful roll out of AI at scale.
The silo problem
AI is fundamentally a decision engine: give it the right data and operational context, and it makes smart decisions. But in banking, processes run across dozens of disconnected systems, and without unified data, AI makes poor decisions.
Banks are struggling in siloed environments where systems are disconnected and teams work toward different goals. This is why bolting AI onto siloed or legacy infrastructure consistently underdelivers. Without sufficient operational context, AI lacks the understanding needed to make effective decisions. This creates a ‘context gap’ identified by nearly half of banking leaders as a major barrier contributing to the inability of AI pilots to achieve measurable financial impact.
End-to-end visibility
When AI has total visibility over how operations really work, it delivers. End-to-end operational visibility is the ‘missing link’ required to ensure AI projects drive results, and this is where Process Intelligence comes in. It brings together data from the banks Central Banking System (CBS) plus other systems such as CRM, Rules Management and Anti-Money Laundering (AML) and creates a living ‘digital twin’ of the entire banking operation.
Examining this digital twin, banking leaders can finally have true insight into how processes run end-to-end across different technologies, systems and departments. Process Intelligence helps banks to embrace change, evolve their operations and harness the true power of AI, helping to streamline operations and power data-driven transformation.
Moving to enterprise-scale impact
For banks, mastering AI depends on understanding not just business processes but also how they interact. This foundation is critical as the industry shifts toward the next phase of AI adoption. Organisations that get this right will be best positioned to unlock enterprise-scale impact.
Banks are already using AI to pass low-stakes, high-volume tasks to Robotic Process Automation software robots (RPAs), trained to carry out actions such as transferring files or entering financial data. Unlike humans, RPA bots do not lose focus, so they reduce errors and cut out bottlenecks. AI is also finding its place in Intelligent Document Processing tools which are simplifying Know Your Customer (KYC) processes by automatically extracting customer data from identity documents, while AI applications are helping to detect discrepancies in transactions and customer behaviour, helping banks to zoom in on fraud and other threats.
Delivering ROI
For banks, understanding their own processes is a foundational step towards AI success. Process Intelligence helps to unravel the complexity of financial service organisations, highlighting the highest-value opportunities for automation, and giving AI the business context it needs to make accurate, important decisions. This technology doesn’t just complement AI, rather one is the prerequisite for the other.
Chris Johnston, SVP, Head of Global Banking at Celonis
