Asset managers are being forced to rethink not just their technology stacks but their entire organisational mindset as data becomes central to competitive strategy in the age of artificial intelligence.
During a panel discussion at FundsTech 2026 senior executives and advisers argued that firms must treat data as a core asset — on par with capital or talent — if they are to unlock the full potential of AI. Yet the biggest barrier to progress is not technical capability, but human behaviour.
“Every job is a data job,” said Jeremy Hunt, senior partner at Alpha FMC, noting that employees across functions — from sales to operations — generate and rely on data, often without recognising it. The shift to AI-driven business models, he argued, requires a fundamental rethink of roles and responsibilities.
Rob Middleton, chief data officer at Royal London Asset Management, echoed this view, warning that past attempts at transformation have failed when firms focused solely on technology. “If you don’t tackle the cultural and behavioural side alongside the data, you don’t get the transformation you’re aiming for,” he said.
Instead, successful strategies bring “data to people and people to data”, combining investment in platforms with training, incentives and leadership support. Treating data as an asset — something to be curated, maintained and governed — marks a significant departure from legacy approaches where it was seen as a by-product of operations.
Despite heavy investment in AI tools, many firms are discovering that poor data quality remains a critical bottleneck. Early experiments have often faltered because systems lack the context or structure needed to deliver reliable outputs. In some cases, firms have even scaled back deployments after disappointing results.
“The answers are just awful,” Hunt said of poorly configured systems, attributing failures not to the technology itself but to inadequate data and context.
Governance and trust are also emerging as key themes. Simon Bernard, a solutions architect at Ninety One, stressed the importance of transparency and traceability in AI-driven processes. Users may not need to understand the underlying models, but they must be able to trust and interrogate the outputs.
Looking ahead, panelists suggested that the implications of AI extend far beyond efficiency gains. Data-driven strategies are expected to reshape client engagement, product distribution and even the structure of the industry itself. Some envisaged a future where traditional fund structures give way to personalised, data-driven investment “wallets”, curated by AI agents.
Yet for all the technological disruption, the consensus was clear: success will depend on aligning data strategy with broader business objectives and ensuring organisations bring their people along on the journey.
As Middleton put it, “You can’t just spray a new process over the top and hope for the best.”
