I Met With 100-Plus Advisers to Develop This AI Road Map

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I love my job. As part of Vanguard's Financial Advisor Services team, I meet with financial advisers across the country to discuss their fast-growing technology stacks and determine how they can use AI to deliver better investment outcomes to their clients in a responsible way.

Our advice is to always focus on improving outcomes for investors, and that may mean using AI to help free up time that can be dedicated to supporting clients. Vanguard research shows the real benefit for clients lies in behavioral coaching.

But for advisers to use AI in ways that truly lead to better outcomes, they need to understand and trust it. In our 100-plus meetings with advisers in 2025 alone, these are their most common AI-related questions.

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How can I get started using AI?

As you get started using AI, consider your north star. If you haven't developed a north star — what you want to accomplish with AI — you should. The north star should be established at the C-suite level, be mission-aligned and include a governance framework.

Vanguard's north star, for example, is to use AI to deliver better investor outcomes in a responsible way.

About Adviser Intel

The author of this article is a participant in Kiplinger's Adviser Intel program, a curated network of trusted financial professionals who share expert insights on wealth building and preservation. Contributors, including fiduciary financial planners, wealth managers, CEOs and attorneys, provide actionable advice about retirement planning, estate planning, tax strategies and more. Experts are invited to contribute and do not pay to be included, so you can trust their advice is honest and valuable.

At the more tactical level, advisers who are newer to AI adoption are looking for quick, practical productivity wins. For advisers, the easiest wins involve using AI to support client interactions, summarize information and draft everyday content.

Every step of a client touchpoint can, and should, be enhanced with AI:

Before a meeting, a GenAI application can create prep materials summarizing email activity and previous engagements logged in your customer relationship management (CRM) system
During the meeting, AI can transcribe and take notes, allowing you to be more engaged
After the meeting, AI can create customized follow-ups to keep the conversation going

As you continue using AI to help with client engagements, it will learn from your feedback and build its database of client communications, providing stronger drafts in the future.

Between client meetings, advisers spend much of their time reading and analyzing complex documentation — market perspectives, forecasts, economic news and policies, and so on.

At Vanguard, we leverage GenAI tools to summarize our market updates and perspectives to help advisers create personalized insights for clients based on their financial acumen, allowing advisers to more quickly get actionable information in their clients' hands.

Speed is of the essence when building trust with clients, and AI can help.

How can I build a data foundation that maximizes my AI tools?

You may have heard the phrase "garbage in, garbage out." Your AI tools are only as good as the data you have. In our conversations, many advisers have expressed that inconsistent data is a top constraint on AI value.

Beyond simple data collection from a CRM system or related tool, data classification and architecture are critical components to any enterprise AI strategy.

As advisers collect data for an AI tool to leverage, classification is critical. Advisers must ensure they have a system that designates access levels for all information, from simple emails to personal client data.

Most companies have established policies to designate data as being confidential, public and in-between. Those companies must ensure their AI tools — and their team members — understand and adhere to them.

For larger firms, investing in data engineers can be a great first step to create accountability in data classification and metadata development. By cleanly organizing data, AI tools can work more efficiently.

How do I find the right vendors?

When sourcing vendors, your north star and current tech stack and data infrastructure must be considered. Vendors that can stitch together existing tools such as CRM platforms, email platforms, content repositories and more can help avoid some of the "swivel chair" work that comes from platforms not being truly integrated.

Enterprise data privacy is a critical safeguard. It ensures your data remains within your organization's boundaries. Your vendor's technology must clearly distinguish what data it can and cannot use, preventing any information from being fed back into the LLM during employee interactions.

While most providers claim to offer this protection, we recommend validating it through a pilot period.

We also recommend that companies have multiple lines of "human-in-the-loop" governance reviews with quality control checks before any AI use case is made widely available. This can address potential hallucinations or biases and ensure any generated content is compliance-approved and aligned with your brand.

Even after these checkpoints, employees should still be trained on responsible use cases with any new tool.

What's next with AI?

When discussing AI adoption with advisers, I tend to define adoption in three stages, or the three As: assist, augment, and action. The industry is well into the "assist" stage, as advisers are already using algorithmic models to estimate Social Security income and health care costs in retirement.

In the near future, we are likely to see GenAI take these tools to the next level, supporting advisers with portfolio health checks, analysis and recommendations, moving us into the "augment" stage.

Further out, we will enter the "action" stage, where AI will move beyond helping to taking actions on the adviser's behalf. Advisory firms will leverage AI agents to execute tasks such as portfolio monitoring, rebalancing and routine client service, allowing advisers to focus fully on strategic planning and relationship building with their clients.

From quick productivity wins to building a robust data foundation and selecting the right partners, success with AI starts with clarity of purpose and responsible governance.

Advisers who embrace these principles will not only streamline operations but also free up time for what matters most: Guiding clients through complex financial decisions with confidence and care.

Disclaimer

This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

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Lauren is Head of Financial Advisor Services (FAS) Technology. She leads a global team of technologists who are driving exceptional outcomes for advisors and the clients they serve through digital solutions. Prior to Vanguard, she led digital experiences for investors and financial advisors for 15 years at Charles Schwab and also held technology and product roles at a couple startups. She has a strong track record of setting strategy, building high performing teams, delivering business results and scaling organizations through change.