Artificial intelligence has become a priority for global wealth management firms, but the industry is still in the early stages of adopting advanced agentic AI, according to a recent HCLTech study. While an overwhelming 98% of leadership teams are actively pursuing an AI agenda, a mere 7% are actively developing agentic AI capabilities.
The study, titled Hidden In Pl(AI)n Sight, was conducted by HCLTech in partnership with Evidenza. It leveraged synthetic research, modeling 1,066 AI personas based on senior wealth management decision-makers across 17 global markets, with human experts validating the findings.
Wealth Management & the Agentic AI Gap
The significant disparity between broad AI pursuit and agentic AI development underscores a critical challenge for the sector. Firms are struggling to transition from merely experimenting with AI as an efficiency tool to fundamentally redesigning their wealth management operations to unlock new growth, revenue, and client value.
What is Agentic AI?
Agentic AI refers to systems designed to autonomously execute complex tasks, make decisions, and interact with environments to achieve specific goals, often by chaining together multiple AI models. In wealth management, this could mean AI systems that not only analyze data but also proactively manage portfolios, personalize client interactions, and even identify new investment opportunities with minimal human intervention, effectively transforming operating models.
Key Challenges: Three "Blind Spots"
HCLTech identified three primary "blind spots" that are hindering firms from fully realizing AI's potential:
- Ambition Blind Spot: While firms acknowledge the need for transformation, their funding for AI initiatives largely remains focused on achieving efficiency gains rather than driving strategic growth.
- Execution Blind Spot: Investment in advanced technology is often not adequately matched by corresponding investment in proprietary client data and actionable insights, which are crucial for effective AI deployment.
- Strategy Blind Spot: Firms are tracking AI adoption metrics but are failing to sufficiently measure whether these investments are translating into tangible growth, increased revenue, or enhanced client value.
Revenue as a Missing Metric
The study revealed a stark disconnect in how firms measure the success of their AI investments. Despite 84% of leaders believing their operating models require fundamental redesign, only 12% are actually measuring the new revenue generated by such redesigns. This suggests that many AI programs are progressing without a clear commercial framework to determine their true impact.
Interestingly, executives rated first-party and behavioral data as more valuable competitive differentiators than technology infrastructure, cloud platforms, or AI partnerships. Nearly 80% believe future industry leaders will be those adept at combining AI, human expertise, and ecosystem partners.
Regional Readiness Varies
The research also highlighted significant differences in AI readiness across regions. The Asia-Pacific (APAC) region showed the highest confidence at 89%, followed by North America at 84%. Europe, however, demonstrated a substantially lower level of confidence in AI readiness and transformation, at 38.3%.
The Path Forward
HCLTech concludes that the wealth management industry is entering a new phase where competitive advantage will depend less on simply deploying AI, and more on strategically combining AI capabilities with deep proprietary client knowledge and invaluable human expertise to drive meaningful business transformation and generate new value.