Artificial intelligence (AI) is proving to be more than just a trendy investment theme. AI has real-world implications that are disrupting all sectors and their associated operations, including financial advisory business models.
Financial advisory firms are continuously adding AI into their practices to foster automation for streamlined business operations. Lauren Wilkinson, CIO of Vanguard Financial Advisor Services, met with TMX VettaFi’s Ben Hernandez to outline a strategic roadmap for integrating AI into the advisory profession. Read on for the insightful interview.
Daily AI for Advisors
Ben: We at TMX VettaFi operate in the ETF world. There, AI is generating excitement from investors regarding speculation as an investment theme. But at the advisor level, it’s all about application. For advisors who haven’t already integrated AI into their daily tasks, how can they get started using it?
Lauren: I’ve been having a lot of conversations with registered investment advisors (RIAs) of all sizes, and that’s a common question. I would say that where we are now, there’s a wide range of adoption within advisory firms. Some are just getting started. On the other end of the spectrum, there’s some that are fully integrated with AI and rethinking their whole business with AI. For the most part, the majority are somewhere in the middle.
In hearing stories from a lot of different firms, the best way to get started is to consider your North Star. AI is really an enabler of an overall business strategy. The firms that have been most successful with AI have started from the top at the C-suite level. This involves really thinking through the enterprise strategy and the company strategy, and how AI becomes a part of those strategies. For example, at Vanguard, our AI North Star is using it responsibly to deliver better investor outcomes — simple as that. This aligns with Vanguard’s core purpose to improve investment outcomes for all investors. That said, the firms that are best with AI integration start from the top with a very clear North Star on how they rethink their overall strategy with AI.
Second, once you have your strategy, you can look at tactics to get started. The first place to get started is looking for practical productivity wins. At Vanguard, we say there are three “As” of AI maturity: assist, augment, and action. The first “A” means looking for practical productivity wins where AI can assist in daily workflows.
At the advisory level, something I feel very passionately about is Advisor’s Alpha — how much emotional trust and relationship building matters in an advisory engagement. Given that those client touch points are a key part of an advisor’s value add, AI can play a role in each touch point. AI technology can summarize the recent interactions with a client or help prepare customized content that interests the client. During an engagement, AI can take notes so that the advisor can stay squarely focused on the client. And then after an engagement, AI can perform tasks such as summarizing notes and drafting follow-up emails to the client.
So those are some examples of practical productivity wins to help get advisors started. At Vanguard, one example of a productivity win is our Client-Ready AI Summaries, which allows advisors to create personalized AI summaries of market updates and perspectives published by Vanguard’s research team, tailored to fit their client’s financial acumen.
My third tip for getting started is learning. It sounds basic, but again, this begins in the C-suite — organizational leaders spending a lot of time learning about AI. This includes hands-on practice and experimentation, then cascading it all the way through the organization to ensure their employees know how to use AI, build their AI fluency, and perform hands-on AI application. The best way to reach an understanding of AI’s potential is to use it.
Data and AI
Ben: A lot of good information you’ve just given, and that’s actually a nice segue into the second question. As it is often said, information is power. These days, advisors have access to a variety of data. How can they use this to build a data foundation that maximizes the aforementioned AI tools?
Lauren: One way is to harness the capabilities of generative AI, which can learn from the data that it’s given. There is a caveat — the AI output is only as good as the data you have. As you’ve probably heard before: garbage in, garbage out. That being said, I have some suggestions here based on talking to a lot of firms and hearing what works best.
The first step is to get started with the basics — get your data organized. Most advisory firms have all the data they need, but it’s not completely organized. It’s spread across the customer relationship management (CRM) spreadsheet, portfolio management system, email, and unstructured notes found on your desktop. So it’s really important to organize the data and identify the systems that serve as your source of truth. For example, your CRM is the source of truth for contact and client information or the portfolio management system is the source of truth for your positions. Take stock of all the data you have and start organizing it.
The second step is what I call data classification or data privacy. Advisors have access to a lot of very sensitive data, such as social security numbers, addresses, and other confidential information. It’s really important that enterprise systems are able to classify data into various categories. For example, categories like public information — marketing material could fall into this category. You might also have information that’s for internal, firm-specific purposes only. Then, as mentioned, the most sensitive information would be client-specific or personally identifiable information. So it’s important to make sure that you have a system that lets you label your data. Then when you are implementing an AI solution, the AI needs to be able to identify those classifications and use them appropriately.
My last suggestion is to hire a data engineer. I certainly recommend this for large firms, but also smaller and midsize firms. Large firms with high levels of AI integration are already bringing data engineers on as full-time staff. For a smaller firm, even having someone come in for a short-term consulting engagement — looking at the data in the firm and making recommendations on how to make it more AI-ready — can provide a lot of value from a cost-benefit standpoint.
Finding the Right AI for Your Practice
Ben: There’s a lot of vendors out there for AI products that are starting to flood the marketplace. How do advisors sift through this mass of products and find the right ones for them?
Lauren: When considering a vendor, have everything rooted in the North Star. What’s the business problem you’re trying to solve? It’s also important to evaluate at least two vendors. I think sometimes it’s easy to just choose the first one that comes along. But it’s important to assess at least two, and score them objectively using specific criteria that’s pertinent to your business. There’s a lot of activity in the advisory fintech space. A lot of vendors [are]coming online with point solutions that might solve an immediate need, but may not provide benefits in the long term.
The second consideration is integration with other systems. Most firms have already invested in some of the technology I mentioned earlier — a document suite, a CRM, a portfolio management system, as well as a trading and rebalancing system. A very important consideration when considering new AI technology from a vendor is to ask yourself: How is it going to integrate with the other systems that you already have? A lot of the systems that firms have already adopted are starting to build AI use cases into their existing systems.
The last criteria that I would recommend is responsible AI. For any AI vendor, you really want to make sure that they are implementing AI responsibly. So what does that ultimately mean? One thing to look for is data privacy. You want to make sure that your data isn’t going to leave the walls of your firm, and that it stays within your enterprise. Most vendors will say that they implement AI responsibly, but it’s best to perform your own pressure tests.
Some other things to look for in responsible AI would be explainability — how is the model making its decisions? In terms of the outputs that the AI is producing — are they explainable? Do they have some traceability into what source documents they’re extracting data from? How are the AI models trained? What controls are in place to reduce bias, hallucination, and other data that could be viewed as misleading? We always encourage firms to ensure that there’s [a]human in the loop to ensure accuracy and safety.
Ben: Are there certain vendors or products that are becoming the default technology among advisors?
Lauren: There are vendors that have been getting a lot of traction in this space. Here at Vanguard, we’ve been hosting several study groups where RIAs can get together and share best practices with each other. They always love hearing from each other to discuss what’s working and identify these best practices. A topical theme a lot of advisors are discussing right now is point solution vendors. There’s a lot of upstart companies that are providing point solutions in certain areas, but the systems that they’ve already installed are starting to add AI features. Because of this, it’s sometimes hard to decide whether to stick with the current system and wait for the buildout of those AI features, or to invest in a point solution. If they do choose the point solution, they need to determine how it fits into their long-term business strategy.
The Evolution of AI
Ben: Given the speed of technological advancement these days, whatever comes out today might not be relevant tomorrow. That said, with tech rapidly advancing, what’s next on the horizon for AI?
Lauren: I’ll anchor to the three “As” framework that I mentioned earlier regarding AI maturity. The first “A” is assist — AI is essentially functioning as a productivity assistant. This includes technology such as AI chat bots, writing emails faster, creating documents faster, and other basic administrative tasks. Beyond these basic tasks, there are also advancements in generative AI. Here at Vanguard, we provide several algorithmic models for advisors to do things like estimate social security income or healthcare costs in retirement. So those are some examples of how AI can assist in this first stage. Most advisors have already adopted AI in this capacity.
The next stage of AI maturity is augment. This entails using AI as a thought partner to provide insights to help formulate a decision. A great example of this is portfolio analysis, where high-order thinking is required. AI can analyze a portfolio and provide insights that can deliver better outcomes for an investor. To that note, AI has moved beyond just being a productivity tool. Another example is financial planning. As a use case scenario, AI can assess complex estate and trust documents to make recommendations during the estate planning process. Again, I would say, most advisors have fully adopted the assist stage and are starting to move into this augment stage.
By the end of this year and into next year, we’ll fully embrace this third stage: action. This is where AI moves beyond insights and begins performing tasks on your behalf — a stage that requires a high level of trust in the technology. For instance, in the portfolio example I mentioned, rather than the AI simply providing insights for you to act upon, it could go one step further — it can actually execute a trade for you. Afterwards, it can automatically rebalance a portfolio based on AI-driven data. Another example involves advisor-client meetings.
We’ve already seen significant productivity wins in note-taking. However, the “action” evolution means that instead of just creating a summary, an AI agent could automatically populate that data into a CRM. If an advisor is discussing a new investment opportunity with a client, the AI could automatically generate a lead in the sales pipeline. You could even trust the AI to send follow-up emails without you having to manually check them later. An additional example is client outreach. AI technology can automatically draft personalized client messages, and then book the meetings directly onto the client’s calendar. The core idea is that instead of the AI simply presenting information for you to copy and paste, it autonomously handles those administrative actions for you.
Human Relationships Power Practices
Ben: We’re talking about AI and all the incredible things it can do, but some advisors are worried: “Will this replace me one day?” I recall a Vanguard survey showing that most clients still prefer a human advisor over anything else. What can you tell advisors for reassurance that AI isn’t going to replace them one day?
Lauren: It’s a great question. I want to anchor the answer in our Advisors’ Alpha research. We’ve conducted numerous studies on advisors and their clients regarding financial outcomes, and all of our research suggests that relationship-oriented services lead to greater client satisfaction.
The highest value of an advisor isn’t actually investment selection or portfolio management; it’s at the other end of the spectrum. The high-value activities are relationship-building, emotional trust, financial planning, behavioral coaching, and guidance. The true value of advice comes from having a trustworthy partner you can rely on to help you stay the course when the market gets tough, and you might be tempted to make a decision that conflicts with your long-term objectives.
Because emotional trust is arguably the most important piece of the puzzle, the role of AI is really to streamline and automate administrative as well as operational tasks. This frees up advisors to focus on the human elements of the job — those higher-value services. My vision is for AI to allow advisors to spend the vast majority of their time building client trust and providing behavioral guidance. AI augments the advisor; it does not replace them.
Ben: Thank you, Lauren. I appreciate the time you’ve taken to discuss AI today.
Originally published on Advisor Perspectives
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