Wealth management firms have invested heavily in digital platforms to boost productivity. Yet, wealth managers still spend more time on administrative tasks.

This is because traditional software often just moved the paperwork to a screen rather than eliminating the underlying manual work. As AI adoption accelerates, firms are shifting their focus away from technology novelty and toward finally reducing this operational drag.

The immediate opportunity lies in automating the repetitive analytical work that consumes a relationship manager’s day. AI can scan large volumes of portfolio data to identify underperforming assets, surface tax-loss-harvesting opportunities, and flag high-expense financial products far more quickly than manual reviews. Rather than replacing advisor judgment, it helps streamline the analysis that supports it.

This distinction also matters from a regulatory perspective. The Financial Industry Regulatory Authority’s (FINRA) June 2024 Regulatory Notice reminded firms that rules are technology-neutral. Whether insights arise from a spreadsheet or a large language model, firms remain responsible for appropriate supervision, validation, and documentation.

Reclaiming time for client growth

Platform-level insights, such as identifying rebalancing or tax-loss-harvesting opportunities, already inform outreach and review cycles. Firms are now looking beyond portfolio intelligence toward operational efficiency. But how effective is AI at saving time?

McKinsey estimates that asset managers spend 25% to 40% of their time on distribution functions. Cerulli’s 2023 Advisor Metrics report finds that advisors spend about 42% of their time on non-client tasks. Firms can use AI to automate compliance and client operations, reducing this burden and allowing advisors to focus on other high-value activities.

Wealth managers gain the most when AI prioritizes high-impact alerts, such as concentrated positions, Investment Policy Statement band drifts, liquidity issues, or tax opportunities, and agentic solutions route relevant client and account signals to them for timely action.

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. And every single one aligns with the same goal: Reducing the operational load surrounding advisors.

Addressing implementation friction

Despite its benefits, implementing AI requires caution. Teams can be overwhelmed by fragmented workbenches and poorly integrated data leading to frustration. Industry surveys confirm that bad data and integration gaps are ongoing pain points for wealth managers.

The team that develops Virtusa’s digital wealth personalization solution continually evaluates the advisor experience to ensure the platform offers an intuitive, efficient interface that delivers the right amount of relevant information. Recognizing that no two experts are alike, the platform provides customization to match individual workflow and customer interactions rather than a one-size-fits-all approach.

Measuring operational impact

What does effective AI implementation in wealth management actually look like? We have had a number of successful engagements where we have met or exceeded expectations by delivering measurable outcomes.

For example, we partnered with a leading bank to implement AI for analyzing complex legal documents, such as trusts and estates. Our genAI-powered system automated secure ingestion, intelligent extraction, and document summarization. As a result, client meetings were shorter by 20 minutes, and annual legal review costs fell by 67%.

Another example is our AI copilot proof of concept (POC). The firm encountered a common industry hurdle: Intensive client-meeting preparation. Deploying an agentic AI ecosystem cut data-gathering time from 4 hours to about 90 seconds. The solution also improved the retrieval of historical client context for interactions through a centralized client memory engine. Anyone familiar with Regulation Best Interest (Reg BI) knows this shift is a significant advantage for professionals.

Creating room for growth

AI delivers the most value when it eases daily friction for wealth managers. Firms looking to scale team productivity are increasingly focusing on reliable, well-governed systems that simplify analytical work, streamline compliance activities, and improve responsiveness to client needs. With stronger integration and data governance, these systems help professionals spend less time navigating administrative complexity and more time strengthening client relationships and growing assets under management.

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