From back office automation to competitive advantage: asset managers shift AI focus to risk and research

By Sean Judge, Softwire Director of Financial Services and Insurance

From back office automation to competitive advantage: asset managers shift AI focus to risk and research, Softwire survey finds

Research among 225 senior technology leaders finds firms are entering a second wave of AI adoption, but poor data, legacy systems and leadership resistance remain major obstacles to transformation

UK asset and wealth management firms are shifting their AI focus from back-office automation towards higher-value use cases such as risk modelling, investment research and software development according to new research from Softwire. Yet many are struggling to build the data, technology and organisational foundations needed to deploy them at scale.

The shift is taking place in an industry responsible for managing a record £10 trillion in assets, making the UK the world’s second-largest asset management centre after the US.

The survey of 225 senior technology leaders in UK asset and wealth management found that firms are already using AI to improve existing workflows. Back-office process automation is the most commonly implemented use case, selected by 49% of respondents, followed by client-facing AI tools at 46%, automated data extraction at 43% and fraud detection at 42%.

However, the next wave of AI adoption is taking a different shape. Looking ahead, AI-assisted software development, AI-assisted investment research, and risk modelling emerged as the leading use cases, each cited by 33% of respondents. Back-office automation fell to 21%, suggesting many asset and wealth managers now see it as work already underway.

Sean Judge, Softwire Director of Financial Services and Insurance, said:

“Asset and wealth managers are moving beyond simply using AI to automate processes and extract data faster, building the trust to tackle more complex use cases. The next phase brings it closer to risk, research, and the decisions that shape performance.

“That switch is significant. Newer models are making more complex knowledge work possible, but higher-value use cases place a much more demanding test on the data, technology, governance and specialist capacity supporting them.”

Softwire’s survey suggests that many in the sector are not yet equipped to meet that test at scale. While 98% have implemented at least one AI proof of concept or pilot into production over the past 12 months, just 12% describe AI as truly transforming their business. As one Chief Digital Officer at a UK hedge fund put it: “The biggest obstacle is the difficulty in expanding the pilot project to a solution that covers the entire enterprise.”

Data and legacy architecture remain major constraints. 81% of leaders say poor data and legacy systems are limiting their AI progress, while 44% say their data does not have the quality, lineage and traceability needed for regulatory confidence. One CTO at an asset management firm described the problem bluntly: “Data governance is a nightmare, everyone owns data, nobody cleans it, and auditors panic when we try to use it creatively.”

A further 42% said improved legacy modernisation would do most to help them realise value from AI. Yet the survey shows why progress is difficult: 41% cited the risk of disrupting critical operations as the leading barrier, with 40% mentioning the challenge of integrating new technology with existing systems and data. Skills and capacity were cited by 38%, cost and budget constraints by 35%, and regulatory or compliance concerns by 34%.

According to the COO at an asset management firm: “Fragmented data, legacy systems, and limited specialist capacity to deliver change alongside BAU, these issues make it harder to turn technology investment into measurable business value.”

The barriers are organisational as well as technical. More than a third of respondents, 37%, identified leadership buy-in or cultural resistance as a top barrier to scaling AI. As the head of change and transformation at a UK pension fund said:  “Executive buy-in is weak, so projects stall and lose momentum.”

This helps explain why 86% of firms expect to work with external partners in some form and a further 7% approach each project differently depending on its scale, while only 8% plan to build entirely in-house. The demand is not simply for additional capacity, but for support that can connect technology delivery with business outcomes while strengthening internal capability.

The findings also point to a possible slowdown in client-facing AI. While client-facing tools are currently the second most implemented AI use case, selected by 46% of respondents, only 28% identify them as the next focus. Softwire suggests this may reflect the challenge of applying generative AI in areas where consumer trust, regulatory oversight and reliability are especially important.

Sean said: “Client-facing AI is where the confidence test becomes much harder. Back-office automation can often be kept within a narrower process, but client-facing AI depends on more of the organisation being ready at the same time.

“In a regulated sector, it is not enough for the technology to be impressive. It has to be reliable, explainable and built on data the organisation can trust. That means strong data foundations, modern architecture and organisational buy-in all have to come together.

“That is why client-facing AI may take longer to scale, even as the models themselves become more capable. Firms will also need to build consumer trust by being transparent about when and how AI is being used. ”

Sean concluded: “The survey shows a sector with no shortage of AI ambition, but a clear gap between experimentation and transformation. Firms are targeting more valuable use cases, yet fragmented data, legacy systems, operational risk and limited delivery capacity are making them harder to scale.

“The priority now should be to focus on a small number of high-value use cases, modernise the data and systems they depend on, and build clear business ownership around delivery. That is how firms can reduce risk, prove value and move AI beyond the pilot stage.”

The findings are published in Softwire’s new report, Beyond the Pilot: How Asset and Wealth Management Is Navigating the Data and AI Transition, based on independent research conducted by Vitreous World among senior IT and technology leaders in UK asset and wealth management firms.