Matt Reiner: Practical AI Use Cases for High-Growth RIAs

This podcast is brought to you by Wells Fargo Advisors. Looking for a firm your clients can't outgrow? Wells Fargo Advisors offers financial advisors full balance sheet solutions with wealth planning, investment products, banking, and lending that meet the needs of affluent to ultra-high net worth clients. Build your practice your way and on your schedule with independent and employee model options designed to meet your needs today and tomorrow. Learn more at joinwfadvisors.com. AI is reshaping every industry, and for advisors, the question isn't whether to adopt AI, but how to use it in ways that deepen client trust, enhance efficiency, and future-proof your practice. Hi everyone, I'm business coach Steve Sandusky for Barron's Advisor, The Way Forward podcast. My guest today is Matt Reiner. Matt is a CFP, CFA, a managing partner for Capital Investment Advisors, which is a $7 billion AUM firm with more than 100 team members. In today's conversation, Matt and I dive deep into the current state of AI in wealth management. We talk about the rise of agentic AI and how advisors can experiment with these tools to gain efficiency without losing the human edge. Matt explains where firms are already seeing value. From AI note-taking and CRM integration to meeting prep and client personalization, and where he thinks the next breakthroughs are likely to come. We also explore compliance challenges, cultural adoption, and the mindset shifts advisors need to embrace so they can scale smarter and stay ahead of the curve. With that, here's my conversation with Matt Reiner. I want to start by grounding this conversation in reality. Before we get too futuristic. So give me a sense for what are you seeing are the most realistic use cases for AI right now in advisor practices?

First and foremost that a lot of firms are using is the AI note-takers, I think is a big step up in organizing and staying up to date. You know, you have those companies like Jump AI and Fireflies and Zoom even has some AI features in it as well. I think that that's the most adopted because it seems the simplest and the lowest barrier to entry for advisors. And of course, I think you see a lot of advisory firms now starting to adopt large language models or LLMs like ChatGPT or Claude or Perplexity. But I think that those are still way far behind just the AI note-takers these days. I think that whether the firm is adopting it, what is being seen and what I've been hearing as I talk to other advisors and team members is that whether their firm allows them to or not, team members are using one of those LLMs within their day-to-day, and they have familiarity with them because they're using it in their personal life and they, they find value in it and they're bringing it into their professional life, which then raises the question. I think that firms should move forward on trying to create an infrastructure to allow it to be within the organization. But I think that I would say that the LLMs are probably the number 2 behind the AI note-takers as being the most commonly used today. And you've got a lot of these ancillary ones, but those are the ones that everybody tends to use.

Well, I want to talk about the note-takers for a second. So one could argue that that's sort of table stakes. It's the low-hanging fruit. Give me a little more specifics in terms of how people are actually using the note-takers. Are they using them for prospect calls? Are they using them for client calls? Are they using them in-person meetings and recording the conversations? And then what are they doing with the output or the transcript from those conversations, and how is it being incorporated into workflows?

When you think about the workflows, is that I have a meeting, I have my AI note-taker in the meeting, the AI note-taker creates a summary of the meeting, creates a list of action items for both me and the client or prospect, and then it also can create that final follow-up email to anybody on the team or the client. And some of these note-takers allow you to also push it into the CRM. So as opposed to having to go into the CRM to put in notes for the meeting, you just have that summary go in. And some of these also create the task items inside of the CRM as well. So you basically have the meeting and there's nothing you have to do outside of that meeting other than just move to the next meeting because everything else is being taken care of. Now I'm also seeing it within internal meetings and the internal meetings are using the transcripts and the action items to help do more project management of internal meetings. But I'm also seeing companies use these as knowledge bases to where you can then take the summarization and either do it within the AI note-taker app or take it over to an LLM like ChatGPT or Claude and upload those notes every time you have those meetings. And you now can have a conversation with ChatGPT about what did we talk about a couple of months ago? And how does that relate to something that we talked about recently? And so I'm starting to see teams do that for team efficiency and organization. And then the final way that I do see some people use it is that if it is an in-person meeting or they don't feel comfortable having it on a Zoom, is they're using it as a transcription tool. So they'll go in and kind of like the old days with doctors, with the recorders, they'll record in there. And then that will then allow them just to have train of thought. Talk as much as they want and then create the to-dos and the follow-up email and then push the notes into the CRM as well. So there's really 3 areas that I see it based on the mode of the meeting, not necessarily the type of client, etc., from that standpoint.

So 3 questions from that. One is, how does compliance feel about this? Let's talk both from broker-dealer reps and from RIA-only reps. Yeah.

So most of my focus is on the RIA side and not necessarily the rep side. And within our, the RIA side, it's more of the SEC regulations. Regulation side. And I think that this is the challenge just overall with AI is that there's a lot of uncertainty about what can be used. How do I go about it? We're trying to fit this new solution inside of old regulation. And I think that that's causing a lot of the tepidness of firms using these tools. Because for instance, I've talked to many firms and we all do it differently. For instance, our firm, we get the client to sign a consent form that we're recording the meeting so that there's a consent form. Other firms, you know, have something that is set up on the screen showing that you are being recorded. Some firms don't do anything, and they say that the AI tool saying that we're recording the meeting and the client being in the meeting is associating with it. And so they're taking that, but there is no clear legislation around this. And I think that, you know, and we may get to this over time, and it's one of the reasons I started something called the AI Wealth Forum, is we need a voice within independent wealth management for AI regulation, because I think that that uncertainty is causing a lot of uneasiness. And I think that how FINRA focuses on it and how the SEC focuses on it is different right now, and every state focuses on it differently. So me talking to you in Oregon may be different than me talking to a family in Colorado versus someone in Arizona, and that becomes a compliance nightmare. And so I think that right now it's a matter of I'm trying my best to get as much as I can But there's probably going to be a lot more fallout from it over time as companies get more insight from regulators on how to do it. But right now it's, I would say it's the wild west from that standpoint.

Well, you answered my second question, which was, do you have to get consent from clients on this? And how are people going about doing that? Are you finding many people, many clients are saying, no, I don't want this recorded. Has that ever been an issue?

I think as we look at it on our side with the families that have been offered the AI recording, I would say it's probably about 10 to 15% are saying no. So it's a very low percentage still. And I think that those are the families that are just very worried about data privacy and what does it mean and everything of that nature. But you have a 10 to 15% no rate right now. I think that that's pretty good. So we haven't seen that too highly as of now.

The third question is the human in the loop. So you mentioned we got the note-taker, it's summarizing things, it can send emails out. Where is the human in that loop, or is the human in the loop?

Yeah, I think human in the loop, or HILP as they call it in like the AI world, is a really big necessity within our industry. And I think that within these AI note-takers, you have in certain different note-takers the ability to review the transcript or review the action items before they take the action. So within the platform itself, you can review what action items have been said in the summary to make sure that some things are going the right way. And like a lot of the team members on our side, they'll review that summary before they push it over into the CRM. So they'll just take a quick scan of it. And so where is the time savings? It's not the maximum time savings, but we're moving along the time savings spectrum to getting a little bit where they don't have to type them. They're reading it and editing it. So they're now the editor as opposed to the author of the notes, and that's a time saver. And I think that as we gain more confidence in it, you'll start to just push it over. But human in the loop, I think is completely necessary. And you have the ability to do that before sending a follow-up email, before pushing it to the CRM in many of these tools as well.

If we go back 15, 20, 25 years ago, most of the technology that was being developed for financial advisors, whether it's financial planning software, rebalancing software, most of that was actually built by advisors who had a technical bent, who were in the profession, who understood the problem that needed to be solved. And so they figured out, how can I create some technology to solve this problem? What I'm seeing, though, with AI and with, with sort of the latest technology is it seems like most of this really new cutting-edge technology is being created by technologists who really never had any foot in the financial industry. Are you seeing any disconnect between what the technology is looking like that's being built by people who were not in the industry to begin with versus what the actual needs of the advisor are with the technology?

Yeah, I see it all the time. And I think that there is a different mentality when you build technology as a technology company. And then when you build technology to serve a professional services industry, like wealth management, or you can say healthcare. Or somewhere else where the intricacies of the processes and the intricacies of the relationship are unique and it's not universal. I had recently on someone on our podcast, the CEO of Savvy Wealth, and he had sold two technology companies and then came into wealth management. And I asked him, you know, what is the difference? And it was the intricacies of the processes that, that people don't know from a technology standpoint that are trying to build technology for universal usage, as opposed to specific utilization like wealth management. And I think that what you're seeing is that we have to remember that we are in the very early stages. And you think about like ChatGPT, or you think about some of these other specialized tools like Fireflies meeting AI notetaker, etc. They are built for the mass usage. They're not built for specific financial services, but we're in the early innings. And what I think is going to happen is, is that AI is making it easier for you to build layers on top of tools and to specify it And people don't necessarily have to be senior engineers to do that. And I think that the next stage of this is more specified AI that's built with the profession in mind. And you think about like what Jason Wenck is doing at Altruist and some of these other, you know, what Aaron Klein is now doing with his new startup is they are taking the wealth management mentality, leveraging the benefits and the value of AI and a lot of the LLMs that were built for the masses and now bringing it back to wealth management. And I think that you're going to see a lot more of that where you're going to see that disconnect really shrink and minimize over time as more wealth management-focused individuals start gaining a better understanding of AI and how to apply it within this space, which means that the Kitces landscape map could either consolidate or you have to have to create a whole new one because I think there's going to be a lot of new different tools that come out over the time.

In some respects, that's part of the problem in that as technology continues to advance, we can never put a stake in the ground and say, I got my tech stack and it's good for the next 10 years. It's like we're always in a state of developing. And gosh, I mean, you know, I'm old enough to go back to the PC generation where everything was on your local computer, and then we went to the internet, and so, well, let's put stuff on the internet. And then we went to the cloud. Oh, well, it's in the cloud now. And, and now we're going to AI. And so it's like every new platform, every new major development, it's going to completely redesign the software that we're using, and we have to continue to learn these new things. And so I think that's one of the key things really to take away from our conversation, even though we've just started it, is we're always in a state of learning here. And, you know, any advisor that thinks that they've sort of cracked the nut here, you're going to fall behind pretty quickly because, you know, this technology and this world is changing faster than most people can keep up. So, Maybe that probably leads to another good question here, which is if we think about the continuum of technology, you know, some people would say, well, there's the bleeding edge of technology, there's the leading edge of technology, there's like right on the edge, maybe a little bit behind. Where do you fall? Like what makes sense for a financial advisory practice in terms of how early of an adopter should they be in some of this technology?

When I think about technology utilization, I think about where someone falls, I go back to my roots in wealth management and say, it's a, it's a portfolio, it's a diversification aspect. And so I think that you have to have exposure to all different levels, right? We think about technology on our side within this RISE framework, where it's, you have risk-averse innovation, you have speculative innovation, and then you have exponential innovation. And I tell our team and I work with our team that we have to be in each of those levels. Risk-averse is that technology that there's no, there's very low level of failure rate because it's been adopted by other firms. So think about like making a change from, you know, one CRM to another. There's not really much risk there because there's a lot of people that use it. Then you have speculative innovation, which is something that may have been used in other industries, but not necessarily in our industry where you know it works, but you don't know if it works here. Then exponential is like, this could be game-changing to change the way that we are. And I think that you have to have a portfolio approach to these types of innovations and technologies. And when you think about it from a wealth management standpoint is the technologies that you show and use in front of your clients that you serve should be more on that risk-averse, that are proven, are stable, and have shown a value add in a way to be used. But I think internally you can use some of these speculative new tools that are changing all the time. And maybe that's helping you better serve families behind the scenes, or you're testing it out, or you're doing it on a small segment of families or clients that you serve that you know are willing to do it. And then you should be spending some time in the exponential side. That is like the stuff that is changing all the time, whether it's virtual reality, which I'm a big proponent of, or it's avatar usage or voice recognition or whatever it may be. You should be testing it out. And when you think about an allocation, I would say like, yeah, 70% of your time should be focused on that. Core stuff that's already baked, but then you should be spending 20% of your time on the stuff that is like out in the sphere, and then 10% should be like really thinking differently. And you can think about that of how you expose that to clients as well. And I would always say that when you're using these new technologies, I think it's a very bad idea to expose all of your families at once to a new technology. It's never worked for us. You should roll them out. And even internally on teams, you should roll it out on a small team by team and you roll it out. You learn, you iterate it, you roll it out again, you learn, you iterate, and you keep building that way. I do think that companies, wealth management firms need to take more of an approach of lessons learned from technology companies on how we use technology in our sphere. And if we can get that mentality, I think it's really successful for firms going forward.

Well, there's two things in particular that you just said there that I thought were really insightful. One is that as you experiment with this exponential technology, for the most part, experiment internally with it because you don't want to come out with some brand spanking new technology with your clients that's not been proven and it's going to break. You know, the last thing you want to do is have something that breaks and makes you look stupid in front of your clients. So, I like that idea of really doing a lot of the experimental stuff internally. And then the second thing is just the gradual rollout. I've advised advisors on that idea as well. Like, if they're making a big change and there's a couple of different ways they could do it, you know, it's like, well, send out the letter to a small subset of the clients and see what kind of reaction you get and feedback. And then based on that feedback, you can iterate before you do a full rollout. So, I think those are two important things to think about here. So let's talk about exponential for a second. We've got LLMs. We're all familiar with those— ChatGPT, Claude, Perplexity. And now we've got agents. And so that's sort of the big thing that everybody's talking about right now is the agentic side of artificial intelligence. So first of all, what is agent AI and how does that differ from the large language models that we're all familiar with right now?

I think the easiest way of thinking about agentic AI is it is executing on your behalf. It's executing specific tasks that are needed. I'll share two different ways that we can think about this. One is there's this one area of agentic AI where you can have multiple agents that are focused on specific aspects. So call them worker bees. Maybe I have one agent that is an AI that's only understanding of our investment committee research, and then another agent that is only understanding of how to read our portfolio statements, and another agent that is only understanding of our Salesforce environment. And those agents specifically only understand that. And on top of that, you have what we call is like a queen bee or an, an agentic leader. You can call it, I guess. And that agentic leader gets a task from me and I say, I want to do meeting preparation for Steve Sandusky. He's coming in. I want to do some media preparation. That bee will then go to each of the other agents and work on your behalf. They will just be like a human going to their team of operations team members or associates or whatever it may be and say, I need you to go get that. As opposed to going to humans, you are now going to AI and they're pulling all the necessary information because they're experts in those domains. Deliver it back up to the Worker B, and the Worker B then delivers it to you on your behalf. The other way that you can think about agentic AI, and I'll just use this by an example, is that there's a, there's a tool out there called Perplexity Comet, which is a new browser that is built on top of Chrome, but it's utilizing the Perplexity LLM. And so the other day I utilized it with an email. I highlighted the email, which was saying, hey, can you research some AI ETFs for me. And I highlighted the email and asked Perplexity to do the research, draft an email, and send it off to the client, which it was just me in that situation because I was doing it as a test case. And it went through and analyzed all the different AI ETFs. It gave some information, put it into bullet points, drafted up an email, and sent it from my Gmail without me doing anything. That is agentic AI, where the AI is now taking actions on your behalf, and it's working without being told exactly what needs to be done. You don't have to tell it, go click send. It knows what to do. And I believe that that is the future where teams are going to have human employees and we're going to have agentic employees where we're going to have lots of them from that standpoint. McKinsey recently just launched, I think, 12,000 agents that they're managing to help with every different aspect of that business right now.

I was listening to one of your podcasts and you mentioned a firm that's in Y Combinator and they were posting for 3 positions and they said, we're looking for 3 agents, humans don't apply. So tell me a little bit more about that.

Yeah, I think it was a little bit of a PR play by the company, but it worked really well. But the idea was a lot of these companies are now able to build teams and businesses at a lot less of the cost than it used to be. Because what they were asking for in that specific post was they wanted someone to go build the agent and send it over to them, and they would buy the agent from them and pay the agent, right? So the agent has a specific task. So when you think about job descriptions nowadays, there are job descriptions that we always had. Now we can divide those job descriptions into two. What is the human-only elements? The servicing, the communication, the, emotional connection standpoint needs. And then what are the other tasks that are there? Like, I remember in job descriptions or in resumes, it was like, I need Microsoft Excel skills or whatever it may be. Like, you don't need that anymore. Whatever they need to do in that job description, you can build an agent to do that. And companies aren't necessarily hiring agents. That was a PR play, but they are building agents to do all these tasks. And then you have to have someone that manages them just like humans necessarily, but these run 24/7 and you need to monitor to make sure that they're doing the work the right way in the right time, touching the right places. And you're going to have to manage those types of swarms of agents.

What type of firm do you think is going to be on the bleeding edge of this, implementing this kind of agent technology? Is it going to be the super large multi-billion dollar firms like your firm? I think you're what, over $7 billion at Capital Investment Advisors. Is it going to be a firm like yours? Is it going to be the small 1 or 2 person shop where they love technology and they're going to fiddle with it and do this stuff? Is it going to be some combination? Who do you think is going to be really on the leading edge of rolling this kind of stuff out?

When you think about wealth management, I think it's going to start with the bigger organizations, though. And I think that, you know, one company that I've been super impressed with is Morgan Stanley and their AI approach. And I think that like a company like Morgan Stanley, that's a really AI-forward company. They've already made a ton of strides on the AI front. That would be a company that would be a leader, I think, in the agentic framework if I were to have to put a prediction on it. I think that that then bleeds into mid-market firms like, like I would say us maybe from that standpoint, like the $7 billion to $50 billion type of firm, because I think what you're starting to see and we've all probably read Welcome to the Jungle paper, that's wealth management. As firms start, especially PE-backed firms, as they start getting compressed on how to juice out more bottom line margin, they're going to have to create some efficiencies. And the best way to invest in that is through AI and to create these agentics to start to create more margin as it becomes more difficult to create that through inorganic growth or acquisitions from that standpoint. So I would say you start with the Morgan Stanleys of the world. You then move to these kind of these aggregator firms, and then you get down to the mid-market firms. Unfortunately, I think that the smaller firms, let's call it not smaller, but sub-$500 million firms could really add benefit from these. But I think that they're going to be delayed in this approach from a time and knowledge standpoint, and also an infrastructure standpoint, because I think they're going to wait for some of these organizations that come out that allow them to use different agents inside of their organization before they start adopting it. From that standpoint, but I hope that those firms move faster because I think there's a ton of value for them in that standpoint.

Yeah, the only thing that I might quibble with there is that last piece about the, some of the smaller firms. I definitely think there are very forward-thinking advisors with 1, 2, 3 person shops that just love this technology and they're trying to figure out how can I use technology to scale my business without adding a whole bunch of employees. And I think Agentic AI is ideally suited for that. You've got, again, you got firms like Altruist you mentioned earlier, you know, they're totally set up to work with, with advisors like that who are very tech forward and who want to outsource a lot of this stuff without hiring a bunch of employees. So yeah, I definitely think there's going to be a certain mix of those folks that are going to be really on the leading edge of this.

I don't disagree with that. I think that there's going to be a, there's a, a swath of firms, especially like the altruist companies that work with Altruist, because those are forward-thinking, innovative type companies that will be able to go and experiment with it because there's a lot of tools out there like Lindy and N8N and these types of tools that will help create the infrastructure for that to allow them to grow on that side. I don't necessarily disagree with that. I think that the, the challenge will always come back to an infrastructure standpoint, and that's what Jason's trying to create and solve for at Altruist, but If you don't have the infrastructure, because the agents are only as good as the data that goes into them and the data flow that has there. And so the tool is not going to solve that problem. And I think that data infrastructure still has a lot of space and it takes a lot of time and some money as well to get that really good. But I do think that there are a large number of, you know, sub-$200 million firms, 1, 2, 3 person shops that can do this. And that will lead with it. But I don't know if the infrastructure necessarily will be there yet, but I hope that it gets there because I would love for that to happen.

You touched on this a little bit earlier about some of the applications for this right now. So I, I wanna explore that a little bit further. So let's talk about the agents here. What part of the advisor practice do you think is most ripe to be utilized with AI agents? And is that happening today, or are we still 6 months, 12 months, 3 years away from this being adopted from a mainstream standpoint?

Yeah, the one that comes to mind the easiest is meeting preparation, right? Because there's so many different avenues that, so many areas that you have to go to. I don't think that that's there yet. I think that there's some AI meeting note-takers that are trying to help with that and some other companies that are trying to do that. The challenge is if the technologies aren't connected to each other, it makes it very difficult to get everything that you need. But I think that that is there. I do believe that portfolio monitoring is there as well on an Agentic side. I think that client communication and personalization has a ton of opportunity within the Agentic framework. The ability to understand how someone is feeling through tone of email, connect that through Salesforce CRM, connect that to how their portfolio is doing, connect that with the markets themselves and create a response based on that, based on who they are, where they came from, and people like them. Is an opportunity for an agentic standpoint as well. I would say that agentic AI is still in its very early infancy and maybe really non-exposed right now within wealth management because wealth management is still trying to figure out how to use the core functions of AI and in a compliant way to then now layer on agentic, which uses the underlying tools I think is jumping the gun for a lot of these tools. To your point, that is where maybe some of the sub-$500 million firms could really excel because they may have the ability to be nimbler than others on the Agentic side. But I think Agentic AI is what a lot of firms are using. But this has only really come up over the past 12 months or so, and I think our industry is still wrapping our head around AI in general and how to use it within the firm. So I think it would be cart before the horse if we start talking about agentic side from that standpoint.

Well, but you and I are a couple of guys that just love to push things forward here. So I'm going to continue. We're going to continue to challenge the industry to look at these things. And just picking up on your point earlier about some of this really leading-edge technology, like experiment with this stuff internally first before we do the client stuff. So with that idea in mind, I heard you talk about this idea of the way that we oftentimes are using AI today. Like if we want to try and automate a process, we're going to say, okay, we want to like really dial in on our manual process today and we want to identify what are the specific steps to like open up a new account, let's just say. And so we want to know here's sort of the flowchart for that. And then we ask the AI, take a look at this process that we have for opening up an account. Now tell me how you can, like, optimize this and what parts can AI do versus what parts does a human in the loop need to do. But what I heard you say about that as it relates to agents, and I think this is a really important point, which is with agents, you sort of reverse the process in that rather than telling the AI, this is what we're doing, now you optimize it, you flip the script and basically say, here's the ultimate outcome that we want. Now I want you, Mr. or Mrs. AI, to sort of work backwards and figure out what is the best way to get to this outcome. So did I state that clearly?

I think that that is a masterful way of how to use AI. And I think that too many people go and ask it for the answer to the question as opposed to having them understand what your ultimate outcome is and having AI work backwards. I think that that's the strategic way of using AI. I've been telling a lot of people lately that AI hasn't created more time for me because I go so deep into it and I have such a depth conversation and people are like, well, AI is making people dumber. And I say that AI has actually made me smarter because it's given me so many different perspectives and I've had so many detailed, in-depth conversations around strategy planning, growth opportunities, whatever it may be. And I ask it to solve this problem from a different way. And so if I say, here's what my landscape looks like, I'm working with this custodian, this custodian, this CRM, this is my type of client, this is how many people I have, this is my team structure. And you have to write a lot and commit a lot. I always love the aspect of using LLMs to say, interview me to get enough information that you can then solve this. This is the outcome I want. I want to make the most efficient onboarding process possible, and I want it to take under 5 minutes, and I want you to work within these constraints and then interview me to get everything else. Let AI then provide that solution, and then you can go and say, okay, you have this solution, this is how I'm doing it. Help me understand the gaps to get from where I am to where you say you want is the ideal solution, which I agree with now, and help me create a roadmap to get there. And how can I use different AI tools to help me solve for this problem to get to that solution?

This is fun for me. I could talk to you for many hours on this topic. So let me pull on a couple of threads here. So, so one is this idea of AI is not making you dumber, it's making you smarter. And I've got a, a hypothesis that the people who are going to benefit the most are the ones who are already an expert in that subject matter, because they're going to know what are the prompts that I should be asking, what are the different angles that I can approach this question or this problem. And then when they're getting feedback from the AI, they'll be able to tell whether this thing is just hallucinating a bunch of BS or whether it's actually come up with some kind of novel idea that is just— I would have never thought of that without the AI. So what's your take on that? Do you think the people who really are subject matter experts are the ones who are really going to get the most value out of this technology?

I think that subject matter expertise will drive better insights and better value from the AI. I'm 100% a believer of that. And I think that the best advisors, in my opinion, will be and have the opportunity to be the best AI users there are. And the reason I say that is because the best advisors anywhere are great at asking questions and continuing to dive deeper. And the art of prompt and the art of using AI tools is asking phenomenal questions, being really good at delegating. The best delegators in the world are gonna be good at this because to be a good delegator, you have to give an understanding and a frame of reference of what are my barriers? What am I looking to accomplish? What are my aspects and my tools that I can use? What's the information I need to accomplish? And then telling you to go accomplish it. And I think that the idea of understanding whether it hallucinates or not, I also believe is also in the art of how you ask the question and in the art of how you prompt the AI itself. Too many people prompt it for just like they give very little information and the AI is going to go make up information. So how you overcome that is that you create really great prompts that give enough information that then give it to go and use that information to create outcomes and solutions. Not that you want it to do, but gives you the perspectives using the information you want it to use in that instance. And so I do think that having understanding of the topic will allow you to be a better user and a better— you'll gain more value from AI than that person that's the novice that has no topic, because they'll take the first answer and they'll never ask more questions. And it's a matter of questions with AI.

I think something else that you said there was AI is not saving you time. Right now, and it does save me time on some of the work that I do. But to your point, it's like I'm just so intrigued by all of this that I continue to spend a lot of time with it. So maybe at the end of the day, it's not saving me time. But my question is, there are a lot of people who are worried about AI technology specifically, and maybe technology in general. And this is not a new worry, but anytime new technology comes out, everybody's worried that this is going to eliminate jobs. And clearly, historically, it's technology— new technology has eliminated jobs, but it's also added new jobs as well. The big rub is in that transition period when the people who lose their jobs are not able to retrain fast enough to move to where the new jobs are. So, you know, that causes a lot of social problems, clearly. But as it relates to AI specifically right now in an advisor practice, what's your take on what it's going to mean for advisor practice efficiency? Is it going to enable advisory firms to serve more people with fewer staff or serve more people with the same amount of staff or serve more people with more staff? I mean, where are we going to land with AI, let's say, over the next 3 years or so?

It's always a question of are people going to lose their jobs because of AI? And I think that the best protection from not losing a job because of AI is to really become really great at AI. I think that that's the best protection. I think for wealth management firms, yes, I think that you can take many of these paths, right? And I think that that's the beauty of this. I think that there will be some firms that take the path of using AI to allow for us to just have a more volume business where we're just going to be able to serve more people with the same amount of people and just create a great, you know, value add there. I believe that the true value though lies in, in the, what we're trying to solve every single day is how do we serve more people, more families, more clients with the same number of team members that we have, but go deeper with each family that we serve. And I think that AI allows us to go deeper with our families, allows us to serve them in a more unique way, allows for us to have more people on our team be serving that family and go deeper. Because going deep doesn't mean that that's solely on the advisor. With AI now, I am empowering and upskilling my associate or my operations team member to have these different types of conversations with families that only in the back in the day the advisor was able to have because all the knowledge stood in their mind. Now I can have this consistent tone of conversation with them and I can go deeper with 3 different touchpoints. And so now I've created this scale. And so I believe that we are going to get a combination of serving more people and being able to go deeper with our families that we serve. So we're going to be able to serve more people with the same number of team members while also providing a higher level of service is how I believe that the end game happens.. And I think you start to see some of that trickle-down effect of that impact over the next 3 and 5 years from that standpoint.

Specifically, how does AI translate into serving more people, more clients with the same amount of people? Like, what are the specific activities of the staff that AI is now gonna do in 30 seconds that it might have taken a human an hour to do? What are some examples there?

Yeah, so I mean, let's just start with what takes up a lot of people's days, email responses, right? Email management. And so if you think about email management, and I don't think that this is far off, I think that you can already do this with Microsoft Copilot right now, is that an email comes in. If I have my email connected to my Salesforce and my Black Diamond and everything of that nature, right when the email comes in, I don't have to type anything. I don't have to go to any area. There is a response ready for me to edit right from there. So now something that takes 5 minutes takes 30 seconds. It's deeper, it's more thoughtful likely, and it's more comprehensive than anybody would do because we're just trying to get through it. And I can now have that done in 30 seconds. So that right there cuts a lot of time from that standpoint. You then think about like all these one-off requests that may be happening, you know, whether it's the client that calls in like, how's my portfolio? Or I need to change my beneficiaries or anything of that nature. I do think that you find this more self-serving mentality on some of these tasks that are really repeatable and they're just very similar, right? I have a change of address. Well, now you can just handle that. You take the operations team member out of it so they don't have to waste their time on that— not waste their time, but they don't have to dedicate their time on that. And that can be done in self-service in a way with AI, very personal because of where AI is moving in the sense of avatar usage or voice usage or whatever it may be. That can be taken care of on that side. And then you think about content creation, right? If I need to do preparation or content creation for families, that's now a fraction of the cost and it's more detailed and more with more depth than ever before. And so you start taking those minutes and now I can potentially get 8 meetings a day as opposed to having to have, you know, 2 hours of email responses and financial planning and everything of that nature. You push that to AI and now you have more time in front of the client and you also now use AI note-takers. You align that with LLMs, and I don't have to sit and have anything after a meeting because it's all connected from that meeting right there. Is that done in 3 years, Steve? That's not done in 3 years then. Okay.

So this business has been and will continue to be based on trust between the advisor, the client, the firm. And we put AI in there. We've got something very new into the mix here. So as you think about the years ahead, how does the role or the skill set required of the advisor to be effective in the future change when AI is doing a lot of the technical stuff that an advisor or someone else on the team used to do? What are the skills that they're going to need to have to be an effective advisor where they've got AI and they've got agents as their support worker?

I think it comes down to EQ and it comes down to understanding emotions and helping to navigate difficult conversations with people. We have to be better conversationalists, better at empathy, you know, emotional awareness. And I think that the technical skills, a lot of those will be done by AI. But we have to be able to translate that information into a way that is relevant in that point in time, based on where that client is mentally, what the situation is that they have. And so there is this communication skill that has to be there at a highest level, along with EQ and emotional awareness from that standpoint. And we are becoming more of this relationship builder that's going deeper. If you think about it though, in general, even in the sense is that We are already doing that, but we're hinging our benefit on this technical skills that we have of the financial portfolio that we build or the financial plan that we create. And we show that, but people don't sign up with us because we have a beautiful financial plan or because of our performance numbers. They sign up because they trust us. And then you have to ask, well, why do they trust us? And it's because it's just natural human nature. There's something about how they feel. When they talk to us, there's something about how they view us from that standpoint and how we present ourselves, not to present the paper or anything of that nature. And we have to double down on that and start just getting to know and understand our families and our clients on a deeper level. And if we can do that, that is where trust is built. When you can have an emotional connection outside of the technical skillset that is there.

I want to spin a scenario here and get your take on it. So I had a conversation this week with an advisor. They're going to be hiring a couple of new financial advisors, and I just threw out this idea, and I said, what if you record your interviews with them? You use an AI to record the interview, whether it's remote or the in-person interview. Then you take that transcript and you prompt AI to analyze the transcript and see if it can identify the emotion of the candidate, the tone, what parts of the interview did they seem confident when they were answering a question, which parts of the interview did they not seem confident or seem hesitant, what are some additional follow-up questions that you should be asking as the hiring advisor based on AI's interpretation of the transcript of that. Now, connected to that, I heard you talk about some technology. It may be from Meta, where they've got these new glasses that are coming out. You mentioned that you're a big fan of VR and AR. And you were talking about a scenario where you're wearing these glasses, and through facial recognition, it recognizes the person that you're approaching. It's whispering in your ear, like, all the facts about this. And I think the example you gave was, you have a client that is outside in the front area of the office, you're ready to meet with them, and as you're, you know, going from your office to meet with them, the AI is whispering in your ear, bringing up all the facts of their situation and all the questions that you should ask. Could you envision a scenario where, just like the AI is analyzing the tone and the emotion of the candidate that you're interviewing, that in real time you're sitting with the client having this conversation, things are getting emotional, is AI going to be listening to that and be whispering in your ear how you should be responding or what questions you should be asking? Is this getting a little dystopian here, or is that something that we should look forward to?

So I am a big fan of AR and VR, and I think that there's so many use cases for it. It just hasn't had its AI moment yet. I do see that happening, yes. And I don't think that that's far off. Because you think about these glasses have a small little speaker right by your ear and it's whispering some of these things. And a lot of people will say that that's creepy. And I don't disagree with them that it's creepy or it's weird or it's fake on the relationship side. But what I will say is that actors still have to perform the script that a writer writes. And you still have to be human. You still have to have empathy. AI can tell you what you should do, but you can't be robotic in how you do it or it falls flat. And I think that that is the real thing. You still have to be human because what is not going to change, at least I don't think in my lifetime, maybe towards the end of my lifetime, is that people are going to be able to have this emotional connection with machines and robots. And so you have to still be human. In that nature. And so you have to take what they're doing, you have to process that and then connect it to the moment in time for that human. But I don't see that being that far off. And to be honest, I think if that will allow people to be better at connecting with individuals, I think that that is better for everybody because trust and connection allows people to feel more confident in a plan. And feel more confident in taking action in their situation. And I think that that can lead to better outcomes. Now, everybody's going to be like, well, there's these bad outliers. And of course there is with any technology there is. But I think if you look at it in the good lens, in the positive lens, I think it can create a lot of positive outcomes for clients and for advisors in that sense.

I want to go a little deeper on something that you said there that I think is really important, which is in our industry, we talk a lot about, oh, the 10 best questions to ask in the discovery process. And we're all about, oh, these are the best questions to ask in this scenario. And yeah, that's good. Okay, we've got to ask good questions. But there's also this concept in psychology where if you're a psychologist or a therapist, that you can only take someone as far as you yourself have gone. So you've got to have self-awareness. You've got to have a clear understanding of what the heck it is that you're talking about. And experience in that, rather than, oh, I've been trained to ask these 5 questions, and when they respond this way, oh, I'm going to ask that question, I'm going to sort of pull this out of my question bank. But if you don't have the experience and the depth and breadth of personal understanding and consciousness, it's just going to be rote. It's just going to be like that machine that's asking the question, and people can pick up on that. I mean, just like a dog can sense if you're fearful, of the dog, humans can sense that as well. And so this idea of we really need to have the EQ, we need, really need to have the empathy, I think that is very true. And as we're hiring people going forward, we've always had that need, but I think with technology, we're going to need it even more. And I think what we're also going to see is that as much as technology is going to make our practices efficient, and it may cause prices to drop a little bit as firms say, hey, I want to use AI to run an efficient practice. I can lower my price and I can grab more business because I'm the low-cost provider. Probably will happen. But I think you'll see the opposite happen as well, that the firms that put more emphasis on human first and maybe AI in the background for the operational internal efficiency are going to be able to charge a premium price because there's still going to be a lot of clients out there who say, I want a real human being that understands me as a person. We can have a conversation, we can develop trust, and sure, they're going to use technology to make my life convenient and easy, but not so much that they're using technology as a way to not spend time talking to me.

So what's your thought? A few points on that. I couldn't agree with you more. The Future Proof Advisor framework and method that I've created, which is on my website, I talk about the first pillar is mind. It's mind, leverage, and innovation. And I say that you have to go in that order because if you don't have the mindset and that, that mindset is like self-awareness, you have to go deep on yourself and understand your own challenges and who you are and go through that side. And if you don't do that, nothing can help you get the value out of the, any innovation or technology. And so you have to have this experience and this understanding of self, and you have to have done it yourself, and you have to have deep awareness and skills in knowing that in order to gain any value from these tools, in my opinion. Otherwise, you're just going to become robotic without any sense of connection.

I want to read a quote here from Kyla Scanlan. You may know, Kyla, she's a financial content creator, and what she wrote here recently, she said the conventional narrative about AI and work focuses on displacement and efficiency— which jobs will be automated, how much productivity will increase, whether humans will become obsolete. But I think the real story happening is about people figuring out how to maintain their sense of purpose and agency. Too many organizations are treating AI implementation as a purely technical challenge rather than a cultural transition. So end quote there. I think this is another really interesting point about technology's role in your culture. So you've got a big multibillion-dollar RIA firm. You guys are very much tech-forward and people-forward as well. How do you think about technology's role in your culture? How do you support your team members as you try and implement more and more of this technology? How do you ensure in their minds that we're not doing this technology to replace you? You know, it's really to help equip you to be more effective in your job, give you more agency, give you more purpose. So how do you think of this technology from a cultural transition standpoint?

I just had this conversation with our team this morning, and a national study that we just did across affluent Americans with over $100,000 of investable assets showed that The human is necessary, right? 89% of affluent Americans that have over $100,000 in investable assets say that they believe that a human adds more value within financial advice than technology alone. But the caveat is, is that more than 70% of people that have more than $1 million in investable assets say that they believe that to be a great financial advisor, that you need to understand and utilize good technology. If you don't, they'll leave you. And so that is to us, like the ethos of who we are is that we share with people to say that we want you to have purpose in your life. We want you to have purpose in what you do. We want you to have fulfillment in your role. And what our goal with technology is not to eliminate the role itself, but to elevate the way that you deliver on what gives you the most fulfillment. I don't find many people in our organization finding fulfillment in processing paperwork or doing digging on note-taking or whatever it may be. They find fulfillment on connecting with families. And the whole root of what we are doing is putting our people at the, at the center of every technology decision that we make and asking ourselves, is this technology going to allow us to put our best people, which is every single one of our team members, in front of more families, because if we can do that, they're going to be satisfied and they're going to shine in front of them. And that is how we share it with our team members. And we, you know, there is always this fear, no matter what I say or what my business partners say in front of a town hall about that, it's not going to eliminate jobs. They hear it, but they still are fearful of it because it's their income. And so what we take from a leadership standpoint is that We're going to show by investing in them and upskilling them, not firing them. And we're going to show by our actions as opposed to our words. And that is the action that we take, is that we're going to continue to invest in them. And the way that we invest in them is by giving them the best upskilling possible, the best enablement tools possible, and the best technology tools possible. And also, we have to connect with our team. And so we've rebuilt our entire structure of our organization to where our team leaders are connecting with all of their peers on a one-to-one basis more frequently and understanding what it is that they want to do that gives them joy and ensuring that that bubbles up to our initiatives and our focuses from a strategic standpoint. And if you do that, that's also how I think you do it with clients is that understand what is their purpose and gives them joy. And if you help get them there, they don't care what tools you're using to get them there, as long as you're understanding who they are and putting them at the center of their story every single day.

Well, Matt, as we wrap up here, I could take this transcript, I could throw it into AI and ask it to give me the 3 most important insights that Matt shared in our conversation. But short of me doing that, I'm going to give you the opportunity as the human in the loop here. So based on what we've talked about here today, what are 2 or 3 insights that you would like to leave us with?

Yeah, I think that first and foremost would be stay curious and always be curious about what's going on in AI. I think that this is a paradigm shift in the world of how we, we work and how we live. And, you know, just because it gives you the wrong answer or it seems weird or it seems wrong, continue to stay curious about what is possible from it. And I think that that will be there. I think as an organization, I would challenge organizations, and I've talked about this in some of my keynotes, is I would challenge you to start thinking more like Google than Goldman. And what I mean by that is start thinking about how you, how do you brainstorm? How do you create the psychological safety within your organization for a new employee to come in and say, I think you're doing this wrong and this tool could really help, and accepting that, and also accepting that things may not work, but still moving forward with them and not dismissing them. And I think if we can do that, that'd be incredible. And I think that I would ask everybody to experiment, right? That's part of staying curious, but experiment. And what I mean by that is I would experiment with that 10% of technologies, experiment with those things like VR goggles or Ray-Ban Meta glasses or whatever it is, and learn something new because every new innovation that's going to happen within your organization is built on lessons that you'll learn in every one of those failures. Or those experiments that you do. And it may not be applicable today, but I will tell you there will be an applicability to that information you gain in the future. And so those would be the 3 takeaways I would give.

Great way to wrap up, Matt. If folks want to stay connected with you, what's the best way for them to catch your, your videos, your podcast, and other work that you're doing?

Yeah, follow me on LinkedIn, MattReiner.com. You can sign up for my weekly newsletter. I also send out AI I find tools and get practical use cases there. Go to YouTube and check me out. And of course, subscribe to my podcast, The Future Proof Advisor Podcast, where we talk about everything under the sun for wealth managers from there.

All right. That's all for today. Make sure you like and share this podcast through your favorite social platforms. And for more great podcasts, visit us at barons.com/podcast. Take care and be safe. This podcast was brought to you by Wells Fargo Advisors. Looking for a firm your clients can't outgrow? Wells Fargo Advisors offers financial advisors full balance sheet solutions with wealth planning, investment products, banking, and lending that meet the needs of affluent to ultra-high net worth clients. Build your practice your way and on your schedule with independent and employee model options designed to meet your needs today and tomorrow. Learn more at joinwfadvisors.com.