AI can dramatically expand an advisor’s capacity to deliver personalized, tax-efficient strategies, but it cannot substitute for the licensure, experience, and professional skepticism that define high-quality advice. Identifying underperforming assets, optimizing tax-loss harvesting, and pinpointing financial products with high expense ratios are just a few of the ways AI can serve as another set of eyes when reviewing client portfolios. Even with the most robust AI platforms, there is still a need to rely on the judgment of licensed and experienced advisors to act in each client's best interest.

Regulators echo the “augment, don’t abdicate” message. FINRA’s June 2024 Regulatory Notice reminded firms that rules are technology neutral: Whether insights arise from a spreadsheet or a large language model, firms must supervise, validate, and document appropriately.

Platform-level insights (e.g., identifying candidates for rebalancing or tax loss harvesting) are already informing outreach and review cycles, particularly when they flag items of immediate concern rather than routine portfolio maintenance.

But how effective is AI at saving time? Research from McKinsey estimates that 25% – 40% of the average asset manager's budget is allocated to distribution functions. AI has the potential to offset that time value loss by automating various compliance and client operations tasks. Cerulli’s 2023 Advisor Metrics note that advisors spend roughly 42% of their time on non-client tasks. Which means Wealth management firms can deploy AI to reduce that burden and redirect time toward client relationships and growth.

Advisors benefit most when AI prioritizes high-impact alerts, such as concentrated positions, drift outside Investment Policy Statement bands, liquidity flags, or tax opportunities. Virtusa’s Helio Advisor exemplifies this approach by routing specific, relevant client and account signals to advisors, triggering timely outreach and decisions.

Aside from portfolio management, AI helps automate meeting preparation and follow-ups, craft tailored communications, and distill complex market or policy changes for clients. There are already several industry-level use cases spanning onboarding, plan generation, next-best actions, suitability checks, and compliance. Every one of these use cases aligns with a single goal: to give advisors more time to focus on clients.

Even with the benefits of AI clearly laid out, caution is recommended with each implementation. Advisors can find fragmented workbenches and poorly integrated data overwhelming. Industry surveys confirm that bad data and integration gaps are top pain points for advisors. The team behind Virtusa’s Digital Wealth Personalization Solution continues to evaluate the advisor experience to ensure platform enhancements provide an intuitive and expedient interface; an interface that not only surfaces relevant information but the right amount of it. Because no two advisors are the same, that experience will include a level of customization that aligns with how each advisor works through their book of business Even with the benefits of AI clearly laid out, caution is recommended with each implementation. Advisors can find fragmented workbenches and poorly integrated data overwhelming. Industry surveys confirm that bad data and integration gaps are top pain points for advisors. The team behind Virtusa’s Digital Wealth Personalization Solution continues to evaluate the advisor experience to ensure platform enhancements provide an intuitive and expedient interface; an interface that not only surfaces relevant information but the right amount of it. Because no two advisors are the same, that experience will include a level of customization that aligns with how each advisor works through their book of business and interacts with their clients. Too many firms create a “one-size-fits-most” solution and expect the advisor to adapt to their solution instead.

What does effective implementation actually look like? We’ve had a number of successful engagements where we’ve met or exceeded our clients’ expectations by delivering measurable outcomes. For instance, we worked with an industry-leading bank and implemented AI to analyze volumes of complex legal documents such as trusts and estates. We deployed a genAI-powered system for automating, secure ingestion, intelligent extraction, and document summarization. The results? 20 fewer minutes of client friction during meetings and a 67% reduction in annual legal review costs ($1.68 million).

Building on that success, another great example is our AI Copilot Proof of Concept (POC). An advisory firm experienced what most advisors across the industry do: client meeting preparation. Our deployment of an agentic AI ecosystem not only reduced the time it took for advisors to aggregate pre-meeting client data from 4 hours to about 90 seconds but also ensured 100% historical accuracy and context for every client interaction through AI retrieval via our Centralized Client Memory engine. Anyone who understands the importance of Regulation Best Interest (Reg BI) knows it is a game-changer for advisors.

AI is a powerful tool when it elevates but doesn’t replace advisor judgment. The winning model for the future of wealth management is human expertise amplified by trustworthy, well-governed AI. By delivering the right insights at the right time and adhering to regulatory controls, AI can scale and streamline practice without sacrificing the human touch, and give advisors back the time they need to grow assets under management.

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