AI in Action: Paul Bradley on building AI capability at AIB

“Over the past couple of years, AI has moved from a niche capability to something central to our transformation agenda.” 

In this first article in a new series profiling financial services professionals leading and adopting AI, we speak with Paul Bradley, Head of Retail Automation and Continuous Improvement, AIB, about how AI is supporting innovation at AIB.

With extensive experience across retail banking, operations and change, Paul has recently moved into roles focused on transformation, leading initiatives in AI, automation and continuous improvement.

He shares how AI-driven initiatives are helping to improve customer and colleague experiences, why data quality remains fundamental to success, and how financial services professionals can engage with AI as it becomes a bigger part of the industry.

Hi Paul, thanks for speaking with us. Can you tell us a little about your career journey in financial services? 

I’ve been in banking for over 22 years across 2 organisations, I started out in more traditional retail banking roles, working close to customers and frontline operations. That grounding shaped how I think about everything—understanding what customers actually need. Over time, I moved into roles focused on delivery and change. In recent years, my focus has shifted fully into transformation—leading initiatives across automation, AI, and continuous improvement. The common thread has been solving real problems at scale, whether that’s improving customer experience or making work easier for colleagues.

You moved into roles focused on transformation in recent years – what have you enjoyed most about this work?

The pace and the impact. Transformation work forces you to challenge how things have always been done and to focus on outcomes rather than process. What I’ve enjoyed most is building capability—not just delivering projects, but helping teams think differently, adopt new tools, and take ownership of change themselves. When it works well, you move from isolated initiatives to a more embedded culture of continuous improvement.

When did you first begin working on projects that leveraged AI?

Around 2021 when ChatGPT was everywhere and AI was becoming more mainstream and accessible. My early exposure was through automation and decisioning tools, which were precursors to the kind of AI we’re talking about now. As AI capabilities matured—particularly with natural language and generative models—it became clear that this was different. It wasn’t just about efficiency; it was about fundamentally changing how we interact with customers and how colleagues access information. Over the past couple of years, AI has moved from a niche capability to something central to our transformation agenda.

When you look at the initiatives you’re involved with – where is AI making the biggest difference for customers or colleagues right now?

The biggest impact is in simplifying interactions. For customers, that means faster, clearer answers and less friction when they need help. For colleagues, it’s about not only simplifying their job—whether that’s finding information, summarising conversations, or handling routine tasks. But it also frees our people and allows them to focus on the more complex cases our customers really need support with. AI is particularly strong where there is high volume, repetition, and a need for consistency. It allows people to focus on judgement and empathy, rather than process.

You led the introduction of Abi – AIB’s AI-driven digital assistant – can you tell us a little about this initiative and how it’s making a positive impact?

Abi was a cross bank collaboration between a number of teams which worked together to deploy Abi. Abi was designed to improve how we support customers in real time. At its core, it uses AI to understand customer queries and respond in a natural, conversational way. The focus wasn’t just on launching a digital assistant, but on making sure the experience was genuinely useful—accurate, timely, and aligned with how customers actually ask questions. The impact has been in availability and speed—customers can get support quickly without waiting, and colleagues benefit from reduced demand on more routine queries. It also sets a foundation for continuous improvement.

Data is often discussed as important foundation for AI – what has been your experience of how data quality can impact results on the ground?

Data quality is critical. AI will amplify whatever it is given—good or bad. In practice, that means if your data is inconsistent, outdated, or poorly structured, the outputs will reflect that. A lot of the real work in AI programmes is not the model itself, but getting the data into a state where it can be trusted. That includes governance, ownership, and ongoing maintenance. When that’s done well, the difference in performance is very visible.

What would be your advice to organisations seeking to identify use cases for AI – what challenges are a good fit for AI?

Start with the problem, not the technology. Good use cases tend to have a few characteristics: high volume, repeatability, and a clear pain point for either customers or colleagues. Areas where people are spending time searching, summarising, or handling routine queries are strong candidates. It’s also important to be realistic about complexity—simple, well-defined use cases are often where you see value fastest.

What kinds of AI skills do you think will matter most for financial services professionals over the next few years?

A baseline understanding of how AI works and its limitations will be important for everyone. Beyond that, the key skills are less about coding and more about application—being able to identify opportunities, interpret outputs, and apply judgement. Critical thinking, data literacy, and an understanding of risk and governance will remain essential, particularly in a regulated industry.

What excites you about AI’s potential in financial services?

The ability to improve both sides of the experience at the same time. Done properly, AI can make services more accessible, more responsive, and more personalised for customers, while also making work more efficient and meaningful for colleagues. The opportunity is not just in individual tools, but in how these capabilities come together to reshape our interactions.