HCL Tech AI Synthetic Research exposes three wealth management blind spots

AI adoption is moving quickly across wealth management, but investment alone may not be enough to change how firms operate. The HCLTech AI study, based on 1,066 AI personas modeled on senior industry decision-makers across 17 global markets, points to a wider gap between AI ambition and business transformation.

The research found that 84% of global wealth management firms believe their operating models need fundamental redesign to fully realise AI’s potential. At the same time, 98% of leadership teams are pursuing an AI agenda, while only slightly more than 7% are actively building agentic AI capabilities.

HCLTech wealth management research finds an ambition gap

The first blind spot is around ambition. Firms recognise that AI could require major changes, yet much of the funding continues to focus on efficiency gains.

That creates a mismatch between what leaders say AI could change and where investment is actually going. The Synthetic research study suggests the next stage of transformation will depend on treating AI as a way to rethink operating models rather than simply automate existing work.

AI in wealth management still lacks proprietary data focus

The second blind spot is execution. The research points to a gap between technology spending and investment in proprietary client data and insights.

Executives ranked first-party and behavioural data as a more valuable differentiator than technology infrastructure, cloud platforms or AI partnerships. That puts decades of client knowledge at the centre of the AI opportunity for wealth management firms.

HCLTech AI study highlights a measurement problem

The third blind spot is strategy. Firms are tracking AI adoption, but fewer are connecting those efforts to growth, revenue and client outcomes.

According to the research, 84% of leaders want fundamental redesign, yet only 12% are measuring the new revenue that such redesign should generate. Nearly 80% also believe future industry leaders will be those that best combine AI, human expertise and ecosystem partners.

The research itself follows a similar model, combining AI-generated scale and speed with human expertise, industry practitioners and subject matter experts to validate its findings.

For HCLTech wealth management, the findings point to a shift in how AI progress should be viewed. Adoption may show that AI is being used, but operating-model change, proprietary data and measurable business outcomes show whether it is changing the business. That distinction could shape the next phase of AI investment across the industry.

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