My Advice to Banks on AI: Matt Ryan of Origina

I spoke with Matt Ryan, Chief Customer Officer at Origina, an independent software maintenance provider that helps enterprises retain control over their existing technology estates. Matt shares practical advice for bank executives navigating AI and data strategy, with a focus on stabilising the foundations before transforming at scale.

Over to you Matt - my questions are in bold:

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

I'm Chief Customer Officer at Origina. My role is focused on how customers realise real outcomes from their existing technology, not just promises tied to future upgrades.

Origina is an independent software maintenance provider that helps enterprises get more value from the technology they already own. We work with organisations that rely on large, complex software estates and are under pressure from vendors to upgrade, migrate, or re-licence systems that are still doing the job they were designed to do.

What we do is help customers step out of that cycle by providing independent support and maintenance for the software they already run. This allows organisations to keep systems stable, reduce ongoing support costs, and make decisions about change based on business need rather than vendor timelines.

A good example of this is our work with Capital One. The bank needed additional functionality from its IBM OpenPages platform. The vendor-led path would have required a full software upgrade, with associated cost and disruption. Instead, Capital One worked with Origina to meet those requirements within the version it was already running, avoiding an upgrade altogether.

That engagement reflects how Origina operates more broadly. We help enterprises retain control over their technology roadmap and invest in change when it makes sense for the business, not because they're forced into it.

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

The single biggest mistake bank CEOs are making with AI and data is assuming the latter can compensate for the structural complexity buried inside their technology estate. Many believe AI belongs in the 'transform' bucket — but they haven't stabilised or even understood what's happening in their 'run' and 'change' environments first. The result is that AI deployed sits on top of fragmented data, rigid architectures, and vendor-driven upgrade cycles — which AI doesn't fix, it exposes.

Banks often rush into AI, thinking it will deliver instant transformation. However, if you don't know what systems you rely on, what data you control, or where risks sit, AI simply becomes another layer of uncertainty. This is why so many AI projects stall: they are trying to transform on top of a change estate that is already too large, too complex, and too vendor dependent.

The banks that will succeed with AI will be those who step back first. They get clarity on what's running well, what genuinely needs to change, and what doesn't. Only then can AI be deployed deliberately — reducing the noise rather than adding to it and expanding the 'transform' bucket by shrinking unnecessary change activity.

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

For banks, the most important capability is having clear visibility into how existing systems and data are being used, and whether proposed changes are genuinely necessary. Many organisations are under constant pressure to upgrade or migrate software, even when current platforms continue to meet operational and regulatory requirements.

What we consistently see is that better decisions come from understanding what value existing systems are already delivering. In the case of Capital One OpenPages, the bank needed additional functionality but did not want to take on a full software upgrade simply to access it. By focusing on the specific outcome required, rather than defaulting to the vendor's upgrade path, Capital One was able to extend the value of its existing environment and avoid unnecessary costs or disruption.

Over the next 12–18 months, banks that get the most from data and AI will be those that use insight to support these kinds of decisions: when to stay put, when to change, and where the investment actually delivers value. That approach allows organisations to retain control over their technology roadmap and avoid change driven purely by external timelines.

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

Banks often overestimate AI's ability to work around foundational issues. No amount of advanced tooling can compensate for unclear ownership, brittle architectures, or excessive dependency on external vendors.

At the same time, AI is underestimated as a tool for reducing internal complexity. Used well, it can help teams prioritise more effectively, surface meaningful insight from existing data, and reduce the constant pressure to react.

The real value of AI lies in clarity. When organisations use it to better understand their environment and challenge assumptions about change, it becomes a stabilising force rather than a source of disruption.

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

Good looks like banks using data and AI to regain control over their existing technology, rather than accelerating change for its own sake. Instead of being driven by vendor roadmaps, audits or end-of-support deadlines, organisations have the insight they need to understand what is running, what it delivers and where risk genuinely sits. That clarity allows leaders to separate necessary change from unnecessary disruption.

Across large enterprises we work with the most effective use of data is not about replacing systems wholesale, but about optimising what already exists. When organisations have visibility into urgency, costs and dependency, they are better placed to extend the life of existing platforms, manage technical debt and avoid upgrades that add complexity without delivering proportional benefit. This approach frees up budget, time and people to focus on areas that genuinely support growth and resilience.

When AI and data are working well, they reduce pressure rather than add to it. Leaders are not reacting to outages, audits, or vendor-driven agendas, but making measured decisions based on evidence. "Good" means fewer forced upgrades, more predictable outcomes and a tech roadmap shaped by business need rather than external timelines.

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

The hardest decision many bank executives are avoiding is whether they actually need to change as much of their technology estate as they are being told to by megavendors. AI has increased pressure to modernise quickly, but that pressure is often driven by vendor roadmaps, audits, or end-of-support deadlines rather than clear business or regulatory need.

Challenging those assumptions is difficult. It requires leaders to slow down, assess what systems they already rely on, and decide where change is genuinely necessary versus where optimisation is the better option. Many avoid this because questioning vendor-driven narratives and inherited roadmaps can feel risky, even when existing systems continue to perform.

However, banks that confront this decision directly are better positioned to use AI and data deliberately, rather than allowing external pressure to dictate their technology strategy.

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