Agentic AI in wealth management
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Agentic AI is transforming wealth management by blending human expertise with intelligent automation, boosting advisor efficiency, client trust, and personalized financial outcomes

The wealth management industry faces a dilemma. On one hand, a major demographic shift comes as a large percentage of advisors near retirement. On the other hand, the rise of advanced self-service generative AI (Gen AI) agents raises questions about future relevance of human advisors. Investment firms must now consider whether to invest in a new generation of professionals or transition toward AI financial advisors and hybrid advisory models that promise operational efficiency, despite lingering concerns over client trust in non-human financial interactions.

Advisors in the U.S. from aging to training 

The financial advisor industry is experiencing a notable aging trend which has various known and un-known impacts on the industry. Given that 48% of relationship managers (a role synonymous with financial advisor) are expected to retire by 2040,¹ it can have a significant impact on investor relationship and trust, leading to a decline in efficiency and diminishing AUM.  

While aging remains to be a significant concern, it is accompanied with other worrying trends, a major wealth transfer arising from doubling of population aged 65 and above² and an army of digital advisors (at times dubious) promising returns. 

To address the impending shortage and ensure that investors do not get alluded by new age investment advisors, the industry needs to attract and train a significant number of new advisors. Over the next decade, over 100 thousand advisors are expected to retire and the replacement from new advisors falls short with a 72% failure rate to perform the job well.³  

Even when investment firms decide to invest in the above success areas, there remain notable differences in knowledge and experience between aging advisors and new entrants. Older advisors tend to possess what is often called “intelligence to sell,” the ability to understand client needs, build trust, and craft persuasive, tailored advice based on years of relationship management. In contrast, younger advisors often excel in “fluid intelligence,” which refers to the capacity to learn quickly, adapt to new technologies, and apply innovative approaches to problem-solving.⁴ Bridging this gap requires embedding mentorship into day-to-day work, supported by a willingness to adopt fluid intelligence or offer it directly to tech-savvy clients. In both cases, leveraging digital tools across the advisory journey can capture and document the “intelligence to sell” before retirement and make it usable through advanced agentic AI in finance tools. 

Enhancing efficiency in financial advisory with specialized gen AI workflow agents

We are witnessing the emergence of AI systems capable of autonomous decision-making, designed to specialize in specific tasks and take action. This is achieved by implementation of agentic AI, a specialized AI bot for a specific task, in our case performing various steps in the financial advisory journey.⁵ 

These agents learn from past behavioral data, including client interactions and transactions, and deliver personalized, automated services. They help advisors maintain trust through genuine human-to-human connection while introducing what we call augmented advisory: a model that combines human judgement with AI support. Agents can provide real time dialogue assistance, generate financial analysis, execute routine tasks, and produce clear summaries. Together, these capabilities enable proactive, intelligent actions that improve client outcomes. 

Time is a scarce resource for advisors and it takes immense effort to personalize each report, manage client asks, while being effectively able to summarize and persuade clients. Especially for new advisors, it is difficult for them to cope with multiple client requests which requires experience and knowledge in handling clients, here specialized bots come to aid offering support with real-time operations, dialogue, and research. We provide you with a glimpse of few emerging industry examples.

Categorization of AI agents for financial services 

Marketing Agent: Identifies prospective clients by life stage and preference. Delivers timely, personalized messages across channels. Tracks return on investment, analyzes click throughs and conversions, and helps lower acquisition cost while increasing retention through data driven interactions.

Master Agent: Provides a single dashboard for the advisor to monitor client activity and agent output. Orchestrates specialized agents, allocates work, and measures value. Prompts the advisor for manual steps and proposes automation as it learns the workflow.

Operations Agent: Collects client details, validates documents, and triggers the steps to open or amend accounts. Supports compliance reporting, captures minutes, and handles routine administration. Improves accuracy and reduces cycle time.

Service Agent: Tracks prior actions and conversations, then sends timely updates to clients and advisors. Triggers other agents to prepare documents and surfaces priority items such as scheduled portfolio reviews. Manages task lists so critical work is handled on time.

Sales Advisory Agent: Supports advisors during client calls with prompts on planning topics, product fit, and required compliance language. Shares clear next actions with the client, including documents to gather, portfolio details, and recommendations. Flags market linked changes and helps the relationship manager act promptly.

Research Agent: Generates research based on client goals and recent discussions. Provides market and portfolio analysis on demand for advisors and, where appropriate, for clients. Pulls from approved data sources to support recommendations.

Call Agent: Understands caller intent and routes calls to the right agent or to the advisor. Reduces call time and improves first contact resolution. Sends real time nudges to the advisor and produces a short summary with key actions and follow up dates.

Snapshot of AI use cases in financial services

AI in finance services – specifically agentic AI – is changing advisory work. They deliver advanced analytics, personalized recommendations, automation of routine tasks, support for real time conversations, and help with content creation. AI agents can generate client reports, answer client queries, and surface market insights. They can also be tailored to niche tasks such as portfolio rebalancing. Together, these capabilities improve advisor efficiency and the client experience.⁶

Investing in future advisors 

The financial advisory industry in the U.S. is at a crossroads. To sustain growth and meet rising demand, firms should adopt agentic AI and related tools in ways that augment human judgment. This approach can bring experienced and new advisors together, raise service quality, and help clients achieve financial goals.

Ready to reimagine client engagement in wealth management?

Capgemini can be your end-to-end partner in harnessing Agentic AI to transform advisor-client interactions. We invite you to schedule a discussion to explore how our suite of Gen AI innovations and robust partner ecosystem can help elevate engagement and deliver intelligent, outcome-driven experiences.