My Advice to Banks on AI: Ian Murrin of Digiterre

I spoke with Ian Murrin, CEO of Digiterre, an entrepreneur and technology executive with over 30 years' experience founding, scaling, and exiting high-impact businesses who has led technology-led transformation programmes for more than 130 major organisations including Shell, BP, Glencore, Deutsche Bank, Nomura, and Morgan Stanley. Ian shares his practical advice for bank executives navigating AI and data strategy in an era where generative AI promises much but delivers inconsistently without the right foundations.

Over to you Ian - my questions are in bold:

Can you give us an introduction to you and an overview of your organisation?

I'm Ian Murrin, an entrepreneur and technology executive with over 30 years' experience founding, scaling, and exiting high-impact businesses. I've led technology-led transformation programs for more than 130 major organisations, including Shell, BP, Glencore, Deutsche Bank, Nomura, and Morgan Stanley.

In 2000, I founded Digiterre to bridge the gap between business ambition and technology execution. Over the years, we've built mission-critical trading applications and data platforms covering the entire financial markets life cycle, earning more than 20 industry awards along the way. After Digiterre was acquired by Ascendion in 2023, I continued as CEO of the Digiterre division, helping deliver specialist engineering solutions globally.

Digiterre serves major financial institutions, hedge funds, energy companies, and commodities traders, providing high-performance, secure systems for complex, data-driven operations. Over the years, the firm has earned 20+ industry awards for innovation and excellence.

If you were advising a bank CEO today, what would you say is the single biggest mistake they're making with data and AI?

The biggest mistake is assuming that AI, particularly generative AI, will work despite the state of their data. GenAI is extremely good at producing human-readable, conversational outputs, but it is not good at being precise unless the data behind it is accurate, well-structured and well-governed. In many banks, the data simply isn't there yet. As a result, AI outputs can look convincing whilst being wrong or inconsistent. In a sector built on trust, that's dangerous. AI doesn't fix data problems, it exposes them.

What's one AI or data capability banks should prioritise in the next 12–18 months, and why?

They should prioritise building a governed data foundation that focuses on accuracy, validity and lineage. This isn't glamorous work, but it's what separates meaningful progress from stalled pilots. If a bank can't trust its data, it can't trust its AI. Getting this right makes everything else easier, from regulatory compliance to rolling out AI tools that staff actually rely on.

Where do you see banks overestimating AI, and where are they underestimating it?

Banks often overestimate GenAI's ability to deliver reliable outcomes at scale. It frequently hits an accuracy ceiling that's fine for exploration but unacceptable for decision-making. Where they underestimate AI is in how powerful it can be when used correctly. Combining traditional, highly accurate models with GenAI as a conversational interface creates a much more useful system. The AI helps people explore, explain and interrogate data rather than pretending to replace judgement.

What does "good" actually look like when AI and data are working well inside a bank?

Good looks like precision and trust. Systems behave in predictable ways, outcomes are measurable, and it's clear when something hasn't worked. People know when to rely on AI and when to challenge it. Humans design the architecture and remain accountable, with AI acting as a powerful support rather than an autonomous decision-maker.

What's the hardest AI or data decision bank executives are avoiding right now, and why?

Modernising legacy systems. Legacy platforms are expensive, difficult to change and often poorly documented, but they sit at the heart of the data estate. Modernising them isn't easy and it carries risk, but it also unlocks the ability for organisations to transform what they do and how they do it, to remain competitive and keep costs under control. AI can be used to knit existing data sources together and to surface value to users, but without doing the hard work of transformation around data and governance architectures, AI tooling will hit a ceiling that limits the value it can add.

Thank you Ian! You can connect with Ian on his LinkedIn Profile and find out more about the company at www.digiterre.com.