Banks and insurers may be softening their decades-long resistance to core IT renewal, as AI raises the stakes and exposes limitations across every legacy decision. That tension, and why that stance needs to change, was at the heart of a dinner discussion that I recently moderated for SoftServe and Google Cloud at London’s House of Lords.
Conversation among the senior technology and operations attendees from across financial services focused first on why attempts at modernisation have stalled. But the wide-ranging debate also explored what AI now demands of older systems, and whether many underlying issues are technical at all.
From that start it was clear the value of the evening would come from the shared expertise around the table, with the first question posed becoming the core of the discussion: “Is something deemed legacy because it is old, or because it is a problem?”
Legacy paradox
Attendees were quick to defend their existing estates. One said legacy systems provide a competitive advantage, refined over decades to solve real business problems that newer software rarely understands. Another pointed out that systems are never really replaced. Layer is added on top of layer, until no one is quite sure what the original platform does. That uncertainty itself becomes a reason not to touch it.
The arguments against modernising piled up quickly. Customers do not care about the back end, said one attendee, and any project that fails the value-to-client test is hard to defend. Cost is another perpetual worry, especially when modernisation can convert Capex into Opex and disturb established financial models.
Decommissioning is also a brutal proposition, one said. It is harder than building, and harder still to get past sign-off committees while everyone is busy running the day job. Several attendees agreed there is a shortage of success stories. This meant that, until peers can point to a clean migration that paid off, the inertia argument keeps winning.
Nevertheless, it was agreed that replacing systems simply because they are old has no merit. But the cost of doing nothing changes once new technologies enter the picture.
AI catalyst
Some attendees acknowledged that AI might now be the external event catalyst to change that calculation. Several said their organisations were under explicit top-down pressure to deploy AI tools. One participant reported that staff had been told their jobs would be safe if they used AI to improve productivity, and at risk if they didn’t use it.
That pressure has not translated into universal enthusiasm. Banking has not fully bought into AI, one attendee said, because the industry doesn’t yet trust it. A counterpoint came quickly when another participant said their bank had several AI projects running and was eager to do more. A third was more sceptical, citing the hype and the pricing that assumed a perfect technology, but one that still made mistakes. “Has anyone really achieved AI success at scale”, the attendee asked, “or are we still just looking at impressive pilots?”
But some cited examples of concrete wins. One attendee described using AI to process documents related to car-finance mis-selling claims, a paper-heavy problem that AI technology handled well. Another said their call centre had moved from reviewing a random sample of calls to having AI flag every conversation that needed a human ear. Instead of reducing headcount, they delivered a better service.
The discussion showed that while, for some, AI was an excuse to modernise, in many cases, it still required a business case. The harder question, some suggested, is often whether the underlying systems can support it.
Questions of trust
Trust underpinned much of the conversation. Trust in new technology, trust between technologists and the business, and the customer trust that financial firms depend on. It is why, my co-host from Google Cloud noted that firms like his have put such a premium on high security standards for data and other assets.
Regulation always shapes the risk appetite in financial services. UK regulators were described as still fearful of firms even trying AI, partly because they want proof that decisions are correct, which a non-deterministic technology could not easily provide. Other attendees were more sympathetic. The regulator was helping firms modernise properly, one said, holding them to standards they might otherwise duck. Internal audit teams, another added, often set a higher bar than the regulator anyway.
But most agreed that the world is moving faster than corporate governance can manage. That makes the choice of risk frameworks as important as the choice of technology. Can the new system be governed? Can the AI be controlled?
One also noted that younger businesses can sometimes carry a ‘technology premium’ in their valuation, as an investor reward for not being weighed down by mainframes.
Older firms cannot replicate that, but they can take a different route. Incremental change is often more sensible than a big-bang replacement, particularly as the people who have maintained mainframes for decades start to retire from the workforce. But, even here, AI itself may help, one suggested, by writing and refactoring older code, including COBOL.
Google supported that conversation by highlighting how adopting modern Agentic Engineering can be utilised in legacy environments by providing support that maintains existing estates but also uses in tandem a modern, more efficient approach.
Agents, humans and bottlenecks
Agentic AI drew more cautious assessments than headline-grabbing ones. Roles are already collapsing because of AI, one participant said, but the tools haven’t been put into practice yet. Another noted that running an AI agent full-time is currently more expensive than hiring a data analyst.
The longer-term bet, attendees agreed, is that AI will commoditise itself, and humans will become the differentiator. A more human-like interaction generated by AI may improve the customer experience, but trained people will still close the loop. Google added that processes are owned by people, not technology. It is why AI is democratised so that the 'average joe' is aware of the value it can bring and able to benefit.
Cloud resources emerged as a potential bottleneck to progress, as one attendee said their organisation wasn’t scaling cloud fast enough to meet its AI ambitions. Data domicile was another concern, as banks need to know where data is stored, and cloud providers cannot always promise that data will stay within a given jurisdiction. To allay these worries, the Google host said it continues work with customers to support sensitive workloads with Sovereign Cloud solutions. These provide physical equipment loaded with Google’s AI technology to be deployed in client specific premises.
Closing the discussion, we returned to trust as the binding theme, while also drawing in the wider arc. The pace of change is unlike anything the world has seen before, but the industry has an advantage that earlier disruptions did not offer. Miners in the industrial revolution had no warning that the steam engine was coming. Today’s banks and insurers can see what is on the way.
The question was probably unresolved as to what they will choose to do with that foresight. But the discussion showed that many are now giving core modernisation more serious consideration to better deal with those challenges that lie ahead.
To learn more about how banks and insurers can modernise core IT systems safely and read expert perspectives to effectively develop the capabilities AI offers you can find our paper here.
