This article was originally published by Microsoft on its UK AI Hub. 

The pursuit of rapid growth is a delicate balancing act. Grow revenue or acquisition numbers too slowly, and you fall behind the competition. Grow too fast and you risk compromising the employee and customer experience.  

In the banking sector, business leaders are balancing the need to deliver seamless, real-time digital services with the opportunity to harness AI agents to drive growth. The complexity of the environment – from strict regulatory demands to sophisticated cybersecurity threats and the presence of legacy systems – heightens the strategic value of getting this right.  

Rather than slowing innovation, these factors are encouraging many financial organisations to take a more deliberate, phased approach to deploying AI. By starting with targeted use cases and building on early successes, they’re laying the foundation for trusted, scalable transformation. 

ClearBank – the first new clearing bank to launch in the United Kingdom in 250 years – is one such company, offering valuable examples and insights into how the financial sector can simultaneously accelerate and de-risk AI-powered growth. 

Running its operations in the cloud has enabled ClearBank to process more than 30 million payments a month and supported revenue growth of 30% in 2024. Investing heavily in shipping new client-facing products and features has helped drive this revenue and customer acquisition. Yet ClearBank recognises the equal importance of backend infrastructure for supporting day-to-day operations. 

In every sector – innovation cannot come at the expense of customer trust. Every new system, service, or AI model must be secure by design and compliant with stringent regulatory standards.  

It often makes sense to build new AI solutions on the same trusted platform that core systems are already running on. This way, security and compliance are built in from the start, and it’s easy to apply consistent policies across both operational systems and experimental AI environments. For organisations already running core systems on Azure, this creates a natural advantage. But you don’t need to move everything to Azure to benefit. 

Thanks to Microsoft’s unique hybrid and multi-cloud capabilities, those benefits can also extend across diverse IT environments – enabling financial services organisations to govern, scale and secure AI innovation wherever their data and applications live. 

“Data protection is absolutely essential in our industry,” says Altieri. “So, as we have been a Microsoft house and using Azure for some time, it was a straightforward choice to use Azure OpenAI Service to build and support our bespoke AI models. That decision has been integral to expediting our AI use cases as quickly as they have.” 

When infrastructure and AI strategy are aligned, financial organisations don’t have to slow down to stay secure – because they’ve already solved for trust. Microservices architecture is another key example of this. 

Using microservices architecture means that, instead of building one large, monolithic AI platform, each AI application is developed in its own dedicated environment – within Azure OpenAI. This allows teams to test and deploy one use case at a time without worrying that an issue in one area will impact critical operations elsewhere. When AI applications prove effective, they can be scaled quickly and independently without re-engineering other systems. 

This modular approach reduces dependencies and complexity while dramatically accelerating time to value – turning AI development into a series of manageable, low-risk experiments that can be scaled as needed. 

Rather than trying to boil the ocean and transform the organisation all at once, one effective approach is to identify the processes creating the most friction – the ones dragging down employee time and energy. 

For example, ClearBank identified Credit Payment Recovery, Fraud Inquiry Management and Beneficiary Claiming Non-Receipt as smaller, high-value use cases where custom agentic AI capabilities could support Ben’s primary objective: helping colleagues manage enquiries more efficiently while delivering a seamless experience for clients. 

“Rather than pursuing bigger, more complex and riskier applications that would require a lot of manual oversight, these are smaller but significant improvements that keep humans in the loop,” says Ben. “Focused, practical AI deployments still add up over time to significantly reshape how we work and operate as a fast-growing organisation.” 

No AI transformation succeeds in isolation. It requires teams across the organisation to work together with speed and clarity. Yet often the biggest blockers aren’t technical – they’re organisational.  

ClearBank addresses this challenge by using Azure DevOps to provide a unified environment where development, operations, and product teams can collaborate effectively. Ben describes it as “the glue that holds the bank together.” 

Secure collaboration platforms like this shorten feedback loops, reduce misalignment, and create space for innovation to happen safely – even in a tightly regulated environment. It’s a capability that helps financial sector organisations respond more quickly to shifting customer demands and emerging market opportunities. This culture of collaboration shows up in the working relationship between Microsoft and ClearBank, too. 

ClearBank’s journey with Azure demonstrates the transformative impact of innovative AI solutions and robust infrastructure – for driving growth, enhancing client and employee experiences, and ensuring operational excellence.