Most every wealth management firm is now using AI in one way or another. But almost none of them are actually leveraging meaningful lift from their AI tools.

This disconnect isn’t exactly a technology problem (though it can be). More likely, it’s a problem with clarity. Integrating AI is different from knowing which AI solutions to integrate and how to integrate them into a firm’s workflow.

We’ve witnessed an industry-wide execution gap. There’s a rush to adopt AI tech without a matching understanding of which type of AI does what for wealth managers. That means too many firms are investing in the wrong place, at the wrong time.

Sitting back to wait for AI to sort itself out isn’t exactly an option: 60% of wealthy clients expect their advisors to use AI for more than mere processing tasks, and we can expect that number to climb. At the same time, the industry is facing a looming shortage of wealth managers (and thus increased demands on their time and attention): McKinsey projects a shortfall of about 100,000 advisors by 2034.

Wealth management firms are being asked to serve higher client expectations with fewer wealth managers. It’s no wonder that nearly every firm is turning toward the promises of AI tools.

But the best practice for leading wealth management firms is not to throw AI at the wall and see what sticks. It’s to execute more efficiently and more comprehensively, with the intelligence they already have. Understanding when to apply generative AI, automation, and agentic AI might be the most strategic edge in a firm’s tech stack.

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The distinction between three types of AI

Automation is for rules-based execution

Workflow automation is nothing new to wealth management tech stacks. These tools follow prescribed sets of rules: if this, then that. A wealth manager can update a field in the CRM that triggers the assignment of a task and the sending of a form email.

These sorts of automation improve efficiency for highly predictable processes, especially those at high volume. You experience these every time you receive a marketing email cadence after purchasing a product. They might have your first name in the [firstname] field, but they are not concerned with nuance or able to adapt to your input.

Automation works the same for wealth management firms: these tools cannot make reasoned decisions based on context. An action might trigger a client follow-up email, but the automation tool cannot know if the follow-up is routine or urgent. And when a workflow doesn’t fit the tidy parameters set for it, the tool leaves the advisor to pick up the slack.

Without the ability to examine context, automated tools can handle only what they’re pre-programmed to do. They are not constructive tools for the dynamic complexity behind most client relationships.

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Generative AI is for creation

Generative AI is what most people think of when talking about AI. These are the tools that take a prompt and produce content, whether written, visual, video, or audio. These are tools like ChatGPT, Claude, and Gemini. Wealth managers use these generative AI tools to draft client emails, summarize and organize notes, create transcripts, or produce templates.

These generative AI tools can be genuinely useful and help save time and effort. They also have fundamental limitations for wealth managers. As stand-alone tools, they are disconnected from the clients' context. They don’t know or incorporate knowledge gleaned from client meetings or the CRM, such as major life events, shifts in risk tolerance, and new goals. Every prompt to a generative AI tool is a blank slate.

Without deeper context, the result might look smooth, but the substance risks being generic.

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Agentic AI is for context-aware execution

Agentic AI is categorically a step above these other types of tools. It doesn’t require prompts or fulfill prescribed functions. Agentic AI understands context and makes reasoned decisions on taking the next best steps.

Understands is the key word for agentic AI tools. An AI agent synthesizes relevant information from across a tech stack (including CRM records, meeting transcripts, advisor notes, email history, and client documents) in order to create a comprehensive picture of a client’s relationship at a given moment in time.

It then determines what needs to happen to best serve that client. It can identify outstanding tasks, flag risks, and indicate time-sensitive opportunities. It will prompt advisors to complete certain tasks and raise discussion points for client meetings. The tasks it can execute itself will be, with the wealth manager in the loop for final oversight.

With the ability to understand context, agentic AI represents the shift from AI as a piece of software to AI as a system of action.

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Why agentic AI matters in wealth management

Wealth management is a context-dependent, relationship-driven business. A single client relationship can span several accounts, multiple entities, decades of history, and uncounted conversations. That history weaves through the CRM, different email inboxes, reams of documents, and the living memory of the wealth managers involved.

That data, while comprehensive, is too fragmented to be meaningfully accessible.

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That complexity creates a high likelihood of an execution gap

A wealth manager’s knowledge is invaluable to a client relationship. But wealth managers are human. Follow-through, when done manually, can become inconsistent and delayed. Connecting the dots, updating records, communicating with clients, and routing tasks within the firm all detract from a wealth manager’s most valuable focus (and risk causing a firm to inadvertently fall short of regulatory requirements).

That tracks with what we’ve found, that while 60% of meaningful client information is surfaced in meetings and calls, less than a quarter of that information is ever logged in a CRM. The effort is there, but traditional infrastructure can’t back it up with reliable execution.

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Agentic AI closes that execution gap

An AI agent pulls together the disparate systems within a wealth management firm. It connects the data between them, not by rote, but with discernment—the AI agent understands the intent behind the data to identify client needs and advisor requirements. And it executes the required steps automatically, so the advisor can serve as the final arbiter rather than the manual laborer.

Agentic AI systems may also incorporate generative AI and automated tools, but they outpace them. It can use a generative AI tool to draft a client email—but with the knowledge of why the client needs it. It can trigger an automated system to update the CRM—because it understands why the initial input is needed.

McKinsey reports 20-30% time savings for advisors, emphasizing AI tools to improve task-based efficiency. They’re talking about generative and automated tools—and they acknowledge that the power that agentic AI can bring to bear on wealth management will be an entire step beyond.

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That future is already here

Today’s best AI agents bridge the gulf between the fragmented data across a firm’s knowledge base and the executable actions that need to flow from that data at any given point.

For wealth managers, the best of these AI agents minimize the amount of necessary-but-tedious busywork inherent in the role, and maximize the focus dedicated to improving clients’ lives.

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‍Ready to see what the leading AI agent in wealth management can do for you? Book a demo for Zeplyn’s Agent Nexus today.