HCLTech study says wealth managers lag on AI readiness

HCLTech has released a synthetic research report on AI readiness in the global wealth management industry. The study found that 84% of firms believe their operating models need fundamental redesign.

Based on 1,066 AI personas modelled on senior decision-makers across 17 markets, the report found that although 98% of leadership teams have an AI agenda, only just over 7% are actively building agentic AI systems.

The findings point to a gap between investment intentions and organisational readiness. Many firms still treat AI chiefly as a tool for efficiency rather than using it to reshape business models, client service and revenue generation.

The research identified three main blind spots. The first, an ambition blind spot, reflects how executives accept the need for broader change but continue to fund projects aimed mainly at efficiency.

The second, an execution blind spot, shows firms spending on technology without matching that investment with proprietary client data and insights, which the report argued are more likely to create durable differentiation.

The third, a strategy blind spot, is that firms often track adoption levels but do not measure whether AI is contributing to growth, revenue or client outcomes.

That pattern appears in one of the report's central figures. While 84% of respondents said they wanted a fundamental redesign of the operating model, only 12% said they were measuring the new revenue such a redesign should produce.

Regional differences were also marked. Asia-Pacific recorded the highest confidence levels at 89%, while North America stood at 84% and Europe lagged at 38.3%, indicating uneven readiness across the industry.

Data focus

The report argued that competitive advantage is likely to depend less on access to general AI tools than on how firms combine those tools with data they already hold on clients. Executives ranked first-party and behavioural data as more valuable differentiators than technology infrastructure, cloud platforms or AI partnerships.

Nearly 80% of respondents also said future leaders in wealth management would be the firms that best coordinate AI, human expertise and ecosystem partners. That suggests the industry sees AI deployment as part of a broader operating model question rather than a stand-alone technology decision.

The research was carried out with Evidenza and used synthetic personas rather than direct polling of executives. Industry practitioners, researchers and subject matter experts were involved in designing the personas, shaping the research and validating the conclusions.

The use of synthetic research reflects growing interest in AI-generated modelling as a way to test market views at scale. Supporters argue it can broaden the range of scenarios studied and reduce the time needed to gather results, though its reliability depends heavily on the quality of source assumptions and expert oversight.

Industry choices

For wealth managers, the findings come at a time when firms are under pressure to modernise client engagement while keeping costs under control. Many groups have been experimenting with AI in internal operations, but fewer appear ready to change how advisers, data teams and product groups work together.

HCLTech's financial services leadership said the issue was not a lack of spending but a lack of clear decisions about where AI should be applied and what business outcomes should follow.

"The industry doesn't have an investment problem. It has a choices problem," said Srinivasan Seshadri, Chief Growth Officer and Global Head of Financial Services, HCLTech.

"Nearly every wealth management firm is spending on AI. Far fewer can say which programs they are funding, how far AI actually reaches into the operating model, or whether they're measuring the outcomes that matter - new client value, growth and revenue models. Our research found that 84% of leaders want a fundamental redesign, yet just 12% are measuring the new revenue that the redesign should produce. That's the blind spot the winners will close first," Seshadri said.

HCLTech also presented the study as an example of how AI-led research can be combined with human review. It said that approach allowed it to work at scale while keeping findings grounded in industry input.

Jill Kouri, Global Chief Marketing Officer, said the model was intended to show how AI and human specialists can work together in internal research.

"This study represents a new model for how we generate insights, one where AI gives us scale and speed while human expertise ensures every finding is credible and trustworthy," said Jill Kouri, Global Chief Marketing Officer, HCLTech.

"It's a demonstration of what's possible when AI and human expertise work together and exactly the kind of capability we intend to build more in-house," Kouri said.