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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.
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.
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.