HCLTech Report Reveals 84% of Wealth Firms Need AI Redesign, But Only 7% Build Agentic Capabilities

HCLTech Releases Groundbreaking Synthetic Research on AI in Wealth Management

HCLTech has released Hidden In Pl(AI)n Sight, an AI-based synthetic research study that surveyed 1,066 AI personas modeled on senior wealth management decision-makers across 17 global markets1

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. Conducted in partnership with Evidenza, this pioneering study represents one of the global wealth management industry's most comprehensive applications of synthetic research to date, combining AI's scale and speed with rigorous human expertise throughout the research journey1

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The HCLTech report uncovers a critical disconnect: 84% of wealth management firms believe their operating models require fundamental redesign to fully realize AI's promise, yet fewer than 10% are prepared for that transition2

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. This gap reveals that AI transformation extends beyond mere technology adoption—it demands reimagining how businesses operate, compete and grow1

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Agentic AI Capabilities Remain Underdeveloped Despite Widespread AI Adoption Plans

While 98% of leadership teams in the global wealth management industry are actively pursuing an AI agenda, only slightly more than 7% are building agentic AI capabilities1

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. These advanced systems can complete a series of tasks toward a goal with limited human direction, rather than merely responding to individual requests2

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. The figures expose how AI adoption plans significantly outpace the development of truly autonomous capabilities that could deliver transformative business outcomes.

Srinivasan Seshadri, Chief Growth Officer and Global Head of Financial Services at HCLTech, emphasized the core challenge: "The industry doesn't have an investment problem. It has a choices problem. 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"1

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Three Critical Blind Spots Prevent Firms from Converting AI Enthusiasm into Revenue

The synthetic research identifies three blind spots blocking wealth management firms from converting AI enthusiasm into measurable business outcomes. First, the ambition gap: firms recognize the need for operating model redesign but continue funding AI primarily for efficiency gains rather than revenue-generating changes1

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Second, the execution gap: significant technology investments are not mirrored by investments in proprietary client data and insights that create real competitive advantage1

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. Executives actually ranked first-party and behavioral data as a more valuable differentiator than technology infrastructure, cloud platforms or AI partnerships1

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Third, the strategy gap: firms track AI adoption but fail to measure its impact on growth, revenue and clients. Although 84% of leaders want fundamental redesign, just 12% are measuring the new revenue that such redesign should produce1

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. Seshadri noted, "That's the blind spot the winners will close first"1

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Regional Readiness Varies Dramatically Across Global Markets

AI readiness and transformation confidence varies significantly across regions. Asia-Pacific leads with 89% confidence, followed by North America at 84%, while Europe lags considerably at 38.3%1

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. This disparity underscores a markedly different pace of readiness across markets, suggesting Europe faces unique challenges in AI transformation that could impact competitive positioning.

Nearly 80% of senior wealth management decision-makers believe future industry leaders will be those that best orchestrate AI, human expertise and ecosystem partners1

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. This suggests the winning formula extends beyond technology deployment to encompass how firms combine AI with their most unique assets: decades of proprietary client knowledge and human judgment.

Synthetic Research Methodology Demonstrates Responsible AI in Action

The methodology behind this AI-based synthetic research study reflects the approach HCLTech recommends to clients: AI at scale, human judgment at critical moments1

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. Industry practitioners, researchers and subject matter experts played active roles throughout, challenging assumptions, enriching context and rigorously validating findings to ensure outcomes were both innovative and grounded in real-world industry experience1

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Jill Kouri, Global Chief Marketing Officer at HCLTech, explained: "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. It's a demonstration of what's possible when AI and human expertise work together"1

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. The distinction matters: the 1,066 AI personas were modeled on decision-makers rather than representing actual executives interviewed for a conventional survey2

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. This approach demonstrates how organizations can scale intelligence while maintaining trust, rigor and transparency in an era where efficiency alone won't secure competitive advantage.