Building an effective AI strategy for your practice, with Aneet Deshpande

Shaun Tucker:

Hello, and welcome to our PracticeLab podcast, where we talk with advisors about what makes them successful so that you can apply those lessons in your business. I’m Shaun Tucker, the Director of Practice Management here at Capital Group.

You’ve probably used AI before, but do you have an AI strategy?

Nearly nine out of 10 advisors report using AI in their work, according to our Capital Group “AI Adoption in Financial Advisory” study conducted in February of this year. But there’s a difference between just using AI and using AI strategically.

How do you identify all the areas in your practice where AI can help you? How do you prioritize the solutions that can have the most impact on your practice? And how do you pick the right tools to use among an ever-growing crowd of AI applications?

To help you answer those questions, Aneet Deshpande recently spoke with my colleague Winston Chang. Aneet is the chief investment officer at Clearstead Advisors, which has $50 billion in assets under advisement, and is a member of Capital Group’s RIA advisory board. Let’s dive in.

Winston Chang: Hey Aneet, thanks so much for taking the time to join us today on the PracticeLab podcast. Uh, why don't you give us a quick introduction for our audience members who don't know you?

Aneet Deshpande: Yeah, Winston, thank you. And it's a pleasure to be here with you. Yeah, my name is Aneet Deshpande. I'm the chief investment officer for Clearstead Advisors.

Clearstead is a $50 billion investment advisory firm focusing on ultra high net worth and endowments and foundations. We have about a dozen offices, and, um, headquartered in Cleveland, Ohio, 280 employees. And so we touch a wide variety of client types and focus holistically on those clients from tax to planning all the way down to investments.

Winston Chang: Great. Thanks Aneet. We're talking about AI today. And I guess the question is why? What got you interested in this? Why is Clearstead interested in this? Why are you guys spending time on exploring a technology that comes rife with its own healthy dose of skepticism and negative headlines and things like that?

Like why is it important for advisors, RIAs to be exploring this technology for themselves?

Aneet Deshpande: It's a multifaceted answer I think, but it really all starts with, I think where most people started, which is, how do you make yourself more efficient around your own practice or your own business?

It's really important to understand technology and where technology's going. 'Cause the reality is the industry's changing much quicker than most tech platforms and companies are able to adapt to. So AI, it seems like is one of those areas where we can immerse ourselves, invest in and use to our advantage to be able to scale the business across all facets, whether that's back office, front office, middle office.

I think that's where, a lot of the interest really started, there was always a curiosity associated with, you know, once ChatGPT launched and just generative AI in general and how you could use that. And moving from there to where we are today, which is a number of different technologies that have come out that are AI based and already on.

Or being implemented in firms is pretty remarkable. I mean, that's happened in two short years. So our general view is that firms and advisors that embrace AI are more than likely to outlast advisors and firms that don't embrace it. So we think there's some table stakes there and potentially, uh, competitive or comparative advantage.

Winston Chang: Great. Thanks Aneet. Any specific examples or proof points that you would bring up to kind of convince people like, hey, this, this really is a competitive advantage, now and durably in the future?

Aneet Deshpande: Yeah, I mean, I think some of that's gonna come through by way of efficiency. So, I'll use one example for us and whether or not it's a competitive advantage, I think is to be debated, but it's something that's given us more efficiency. We use a RFP platform. It's a request for proposal for, in many cases, I would say mostly for the endowments and foundations practice, but, um, in some cases also ultra net worth and family office.

And, you know, historically used to have data banks with answers and an answer library. You'd crawl through questions and manually try to match answers together, and then you'd try to piece together written responses by going to the various areas of the firm that were required to answer those questions.

You know, in this day and age now, there's software out there, one of which we're using, that has embedded AI into it. And so the ability to curate full responses by simply providing a handful of bullet points, is there now. And so that's a pretty real palatable efficiency generator for us and allows us to be more efficient about and quicker to market, which is very important, to be able to respond as quickly and, and thoughtfully and thoroughly as possible. So it's, that's just one sort of small example of that. Again, I don't know that that's a differentiator necessarily. More is, adding, an efficiency to an otherwise, pretty manually intensive process that, had existed in the industry.

I mean, that's one of those things that changed virtually overnight. So I think there's a lot of different instances of that where you could, whether it's note takers, whether it's, you know, software on the tax side or document readers, things like that, that are helping the processes throughout the firm.

And so, differentiation I think is gonna be a little bit more nuanced for firms. And our sense is, and who knows where this all goes, but the sense given today's information is if you're using it to get more efficient and to drive better client outcomes, whether that's through the onboarding process for clients and being able to ingest documents more quickly, with less errors than would've been done if with a, you know, somebody just manually typing and entering data versus using a PDF reader for example.

You know, those are the kinds of things that become inherently, I think valuable in differentiating and where that differentiation shows up is probably in higher margins. And so, you know, the better businesses down the road are gonna learn how to use that efficiency towards driving better profitability.

Winston Chang: That's super helpful. Thanks, Aneet. So I want to get a little tactical in terms of identifying the areas in your practice where there's potential to drive greater efficiency. And then as you said, down the line, that should translate to better margins. So putting on your, you know, CTO, your chief technology officer hat for a moment, or your the other type of CIO, chief information officer.

How do you guys think through identifying the areas of the practice where you should consider making investments in AI? I mean, you could be more efficient everywhere, right? And in some cases you actually might not even know what's possible. So, without trying to boil the ocean, how do you go about identifying those areas?

Aneet Deshpande: Yeah, and this is, I think, um, I appreciate the question. It's coming from a CIO on the other side as a chief investment officer. A lot of it is born out of just simply seeing a lot of things come through that are venture cap backed and or otherwise fintech that just show up. And they show, in my seat or to my seat from an investment point of view. And so, I'll answer that a couple ways. One, I think you have to have strategy. And so for firms or practices, it's probably incumbent to either formalize or create some such some sort of AI strategy or policy, some working document that's living that allows you to incorporate the strategy of the business and see how that intersects with AI.

Because I think without that, what ends up happening is you end up looking for problems that may not actually be problems, but trying to solve problems that don't necessarily move the needle for the firm or for your practice or what have you. So I think a lot of it is really focusing on where are the areas of the firm where we could benefit from getting more efficient.

So, you know, I, a couple of examples, you know, just sort of on the investment side, using tools like, you know, the obvious candidates are note takers to help on the due diligence process. As a firm, we are generally looking for third party managers. We don't manage stock portfolios, bond portfolios, so we're looking for third party managers.

Part of that due diligence process is very manual intensive. So that's from investment due diligence to operational due diligence, asking questions, being able to suss out answers on questionnaires and just basic diligence where gen AI is able to help automate that process and those systems.

And that's one just sort of basic example in research with respect to due diligence, in, you know, areas like tax and financial planning. Clearly there's already technology out there that's helping pull out data, extract data. Trust documents, being able to simplify, summarize documents like that.

Those are all things that are out there that, you know, while it may not do the job, but it's aiding advisors, um, and or practitioners to be able to get more efficient around their core deliverables or competencies. So that's technology to get kind of smarter and faster in that sense.

And then, you know, I think there is the, you know, how do you use it to, you know, say one it's front office, back office, middle office, and those two things are generally, or things I just described are mostly back office, middle office, you know, front office on, you know, in terms of organic growth, for example, being able to use platforms to help drive organic growth. And so whether that's lead generation, whether that's helping parse through your own client data lakes, or CRM systems to be able to draw conclusions on that data in terms of how to approach certain clients during certain environments or how to just better articulate a communication process or strategy out to your clients, you know, specific to things that they may be interested in. Those are the kinds of things I think are very interesting. Because it's solving problems instead of finding problems to, you know, essentially solve. You know, these are things that are just generally in the face of most advisory firms.

'Cause you know, I think one last part, and I'll stop talking on this question, is you're also not only by function front mill back, you also have different client segments that have very different needs. And generally speaking, for us, for example, we may have various client segments, ultra high net worth being one of 'em in ultra high net worth.

That's a pretty manually intensive set of clients, highly customized. A lot of work on the front end, both in tax and planning. Investment portfolios tend to look very different, and so in that world of otherwise appearing to be inefficient, you're trying to find ways to get efficient and that's where some of this technology can help.

And that is again, like using whether it's PDF readers at its very simplest form, or pay, you know, and, and we don't do this, but on the tax side, if you're using software to help drive simplified tax, maybe that's something that you could, you look to, to help. Again, not necessarily get a hundred percent completion, but get 70% of the way there.

And I think that's really where the rubber hits the road is understanding how to use it and creating a set of expectations on what it's gonna drive, because the reality is between hallucinations, between where we are and the evolution of technology, it's just not there yet. And so how do you use it within a confidence interval that works for you, as a practitioner or advisor or researcher or whatever role you may be filling in a company.

Winston Chang: Thanks, Aneet. You mentioned something there using PDF readers as the example. About the tasks that AI's good at tackling, being really manual, right? Like that to you is a flag that this is a candidate for something that we could use AI for. What else? Like what makes something a good candidate to be done with the help of AI?

Where do you even before you've started applying the tool, kind of have a sense of, hey, we could significantly gain on the efficiency front. 'Cause that'll teach us, right? How to prioritize all these different areas where we could apply up AI, what we should start with.

Aneet Deshpande: Yeah. I think that goes back to strategy, Winston. It's a, it's a great question. So there's. You know, you don't just blindly apply AI, which I think it would be easy to do, by just simply saying, Hey, here's, here's a tool. I'm gonna go apply that to something in the firm, whether it's a problem or not.

Versus really isolating out the strategy of your firm or practice and, and really isolating in the pain points from how you operate. And I think that's really where it comes from. And just sitting down and formulating the strategy and doing it from a top down point of view, not a bottoms up.

And so that could manifest itself a couple of different ways, whether it's how much time do we spend, you know, completing taxes or valuing taxes or reading trust documents. How much time do we spend looking at investment portfolios? How much time do we spend diligencing managers?

It should cause a lot of introspection so that you understand where you're spending your time and energy, because that should then inform where you apply technology and or make investments to drive the efficiencies on the platform. I think the reality is there is so much fintech out there that it looks like a problem looking for a solution, or a solution looking for a problem rather.

And, if you don't know where you're going, is one of our, our head of sales has this saying, he says, if you don't know, know where you're going, any road will take you there. I think it's the same sort of thing. You need to know what your problems are, and then you have to have a defined strategy to solve those.

Otherwise, you're just gonna have a, without calling it a chaotic tech stack, a chaotic series of solutions that may not be speaking to what you're trying to actually solve for from a corporate objective point of view.

Winston Chang: That's great, Aneet. I love that quote. If you dunno where you're going, any road will take you there. And to your point about it causing a lot of introspection, I mean, ideally firms are regularly documenting their procedures, doing assessments of, you know, where they're spending time and, and energy.

And so if you're doing that, then uh, you should be able to pretty clearly see where you stand to gain the most, because that's where your, all your time, your hours, your energy, your, your team's resources are going. So I think that's really valuable advice.

You said top down and not bottom up. I'd like to dig into that a little bit. 'Cause I'm sure it's a little bit of both too, right? Like you, you mentioned earlier, you're seeing things pop up on your radar from an investment point of view, right? But you have a lot of different functions in the office. So you mentioned front office, back office, marketing, uh, in addition to investments, right?

There's all kinds of different teams doing different things. So how do you incorporate the bottom up view where you are getting the best use cases, you know, that are popping up on everyone else's different radars? Do you have like an AI steering committee that's got representation from all different functions, both front and back office?

Like how do you put together a system where you've got enough boots on the ground to see what the best, in class is for, for different functions?

Aneet Deshpande: Yeah, that's a great question. And just very quickly to go back to the prior point too, every firm, depending on where you are, in your journey, you know, the amount of, I'd say clarity that a firm or practice or an advisor may have on data and how they're using data and how things are being articulated are, are, they're just different. I think, it's hopeful that we all are on the same place or at the same, on the same plane or at the same place. But I think the reality is everybody's at different junctures along the way. Some are building businesses, some are building books, some are building corporate enterprises.

And so depending on where you are in that sort of life cycle, that's gonna dictate how much clarity you have in terms of your operational business, how much time you're spending doing task A versus task B, and that could be split across the firm. So that's an important thing.

And one of the byproducts of that is encouraging people to spend time to really understand what that looks like within their own practice and firm. Um, it's a very important thing because that's how you get efficiency out and in a world where you're both advisor and relationship manager and portfolio manager and researcher, and you know, time is stretched a lot of times. There just isn't that corporate function in firms dedicated or otherwise that can help drive those questions. And so it does take a minute to just pause, which is very hard to do when you're managing money and trying to grow the business to just take a moment and create that strategy.

And just to kind of pivot, I guess segue into your question, you know, I think the idea for Clearstead, we are in the process of formulating and formalizing an AI steer co, or steering committee, comprised of various individuals. One of the things that we have found and I think is pretty interesting is a lot of our staunchest AI users and more interested people have tended to be more seasoned advisors and consultants and professionals, and it's partly because everybody's busy and they're simply just trying to figure out how to make their lives more efficient. And that's part of the bottoms up. And so what we recognized is there's so much of this bottoms up going on across functions that we have to tie this together from the top down. From a corporate point of view, as I said, I get a lot of things on the investment side, whether that's TAMP related or portal related, or due diligence related, or just investment related.

May be saying their own things. Our marketing team may be saying their own things and, and so on and so forth. So, it is incumbent to pull that up into a body of the firm that can make those decisions and create those priorities. Otherwise, as I said before, it becomes chaos of a dozen tools potentially competing against each other and also all fighting for the same internal resources.

So it's important to have a top-down perspective with us and focus on the highest of priorities.

Winston Chang: Great. Thanks Aneet. Staying with the top down thing a bit, you mentioned AI strategy and policy a couple of times. Could you give us a little bit of a look under the hood as to like how you go about building that?

Should I go to Claude or, you know, o3 or whatever and say like, Hey, you're a chief AI officer with 30 years of experience,  generate for me, uh, you know, a comprehensive AI strategy for my financial advisory firm. Like, do I, should I use an LLM for that? Or how do you go about putting together, uh, a good AI strategy policy?

Aneet Deshpande: We'll know when it's done. I don't have the answer to that, but I would tell you, I, um, you know, it's probably gonna encompass a lot of things, which is first, best practices from an LLM point of view. What should we be using? Is it a Grok, is it a ChatGPT? Is it Anthropic? Is it, you know, Perplexity?

Like where, where do we go for that? And they're all good at solving different things. And, and so like, there's a literal component of where do we want to be best practice wise. And then there's the natural compliance piece, which, you know, our chief compliance officer would say, we have to be very careful here because we're talking about client data.

We have to be very mindful of public large language models. We have to be mindful of how we deploy and think about this. Which I think is very real. I think there are a lot of, potentially the firms, advisors, teams, practices, however you wanna call it, may be putting themselves at risk of something unintentionally by the use of public large language models. Now, I think that's pretty well documented publicly that I would assume that isn't something. But I think the reality is there's always somebody flying too close to the sun. So I think the first starting point is what are the dos, what are the don'ts?

And then best practices. And then also beyond that, what are the ways to use it to drive, again, corporate efficiency and use it to create advantage advantages for the firm. A multifaceted plan should likely include all of those things. And I think that's why you have to have a lot of people around the table with different experiences.

And it's an opportunity candidly also to get people in the firm involved in something that they may not have otherwise had a chance to be involved in. So, you know, it creates a tool to highlight that Clearstead as a firm we're, are thinking about the future and making sure we're, we're creating a 25, 50, a hundred year firm, not a 10-year-old firm three years from now.

Which I think is a real risk for a lot of firms that aren't thinking this through.

Winston Chang: That's great. Thanks Aneet. How individualized firm specific do you think this AI strategy and policy has to be? Like a lot of people have been making drawing comparisons between AI and, uh, like fundamental foundational technologies like the internet, right?

There's no firm out there, or advisor out there worrying about like who their internet provider is and you know, like if they're on 500 megs or a gig or you know, like, so they don't need an internet strategy and policy. So why can't an advisor turn to, you know, just go to a study group or go to a conference and be like, Hey, what works for you?

What works for you? What works for you? Okay. I'm just gonna copy all that. Right? Uh, how individualized does it have to be? What do you, what's your perspective on that?

Aneet Deshpande: This is a really good point. Down the road, it's probably gonna feel very individualized today.

You know, this is the same way that you were using Internet Explorer or Netscape at some point, everybody was using Internet Explorer. And I have, you know, three browsers on my machine, I think. I don't, I use one. I would expect that down the road it probably looks pretty similar.

It's hard for me to imagine just kind of putting the investment hat on, the number of, you know, venture cap backed unicorns that we have that are mostly AI focused, a lot of which are the financial services, can continue to exist in this marketplace. I just think competition says that should not happen.

So, if five firms are doing the same thing, probably one or two will win out. That would be, I think, a reasonable guess if using internet analogy is the right one. And it feels right to us, to me. So I think it's very individualized upfront, but where we all end is could look pretty homogenous.

I don't know how much differentiation there's gonna be. PDF readers are, how many of those do you need? You know, note takers, how many I've gotten, you know, a half a dozen different, emails on note takers. How different can those be? So I think there's a fight to get shelf space and down the road there's just gonna be few winners and losers.

Or a few winners and a lot of losers, I guess, for those core competencies. Then I think there's gonna be some, you know, differentiators and the differentiation for certain firms out there that figure out how to create more end-to-end based solutions. That's where I think this is gonna get more interesting, whether that's on the investment side or otherwise.

Winston Chang: Yeah, that's a great segue Aneet into our next topic of conversation, which is about how you go about finding the tools, due diligencing them, making the selections, making the investments. Because to your point, there's gonna be a lot of churn in the marketplace, right? We're not gonna have a million note takers, PDF readers.

Though do you wait it out or do you get in now? And then if you get in now, how do you pick. Should you pick the one with the most, with the biggest, you know, capitalization, like you, you don't pick the, the little startup that just got its series A funding. You pick the wealth box integration, right? Like you pick the CRM that's been around for a while, or you pick Zoom AI companion.

'Cause you know that that's like, how do you think through those questions when you're going about selecting tools?

Aneet Deshpande: Some of that's gonna be related to enterprise risk, and some of it's gonna be related to how well a firm has engineered its data lake, if that's something that they're doing. Because I think the reality is the only way you can turn over technology is by having a stable and articulate data lake so that you can move software over top of it with ease. Otherwise you're gonna be stuck with vendors over time. And we've seen that, we've observed that with legacy and technology that is hard to move off because you're trapped because of data, because that you just don't have control of your data. So I think flexibility is key there, to be able to drive that home.

You know, the whole vendor, this is a really important point too, the enterprise risk piece, is how comfortable are you doing business with a startup with a series A company? Where do you want to be in terms of who you're partnering with? And I think that's something that we think about a lot. I know I think about it a lot.

Because the, what I would expect to be future consolidation and probably rampant consolidation. Now you're talking about disruption. From a service point of view, from an ownership point of view, all those things come into fore, so if you're gonna be on a leading edge, I think it's hard to not be in vendors and with vendors that are gonna have a higher degree of turnover.

Whether that means going concern or whether it means M&A or whatever the case may be. If you want to be sort of just moving along with the industry so you're not falling behind, I think there's a better way to do it. So you're not getting stuck with vendors that have the potential of disrupting your own business practice if something were to happen to them.

So I think this goes into a lot of that vendor due diligence and having a codified way of evaluating vendors. Whether that's from an operational point of view, from a funding point of view, all of those things I think really matter. So, uh, yeah, I mean, I would suggest having a robust vendor process. And again, and that's hard for firms and advisors or teams that don't have, you know, let alone dedicated marketing now to ask to have dedicated staff. So I think that's where it really matters who you're partnering with, and that's why firms like Clearstead are, I think, attractive M&A partners because we can offer those solutions for smaller firms and teams and advisors that see that lift as too heavy of a burden for themselves.

Yeah, I think that's a long way of saying you have to have very well curated, codified vendor evaluation processes.

Winston Chang: You said you've been getting a ton of emails from note takers among others. Do you have a preferred method of determining which meetings you're gonna take? Like how much desk aside research do you do?

Or are you still at the stage where like it's so fast moving and you want to be at least smart on the bleeding edge? So you're taking most of those meetings. Are you asking for referrals first? What's your approach to identifying which vendors to be talking to?

Aneet Deshpande: Yeah, I typically take meetings. Now, note takers may be a little bit more, that's probably not gonna happen, but I tend to take meetings just to make sure, one, I'm aware, two, the firm's aware, and then three, that I can pass those along to our information technology team, to evaluate, you know, to see if it's something that we wanna push through.

Eventually, that'll become something that we have a natural funnel or pipeline into our AI steering committee, that we can move these things through organically through as a process instead of more whack-a-mole-ish. And so I think that's gonna be an important part of the process for us.

But I generally take the meetings because I think there's just a lot of, you know, it's a fat tail experience, much like internet was. And so, in the spirit of, I think we know what our problems are and take meetings of, from solutions or providers that appear to be solving one of those problems.

It is not about taking meetings for the sake of taking them. It's about taking meetings so that, um, you know, a around a known set of problems or areas where we have a deeper desire to get more efficient.

Winston Chang: All things being equal. Do you like the bigger end-to-end integrated solutions or those really specific purpose standalone applications?

Aneet Deshpande: I think it depends on the use case. On the investment side, I've seen a few things that are end-to-end and I think those are great. I think, you know, in the world of, hey, you're trying to simplify document reading, I think that's just a very blunt instrument approach of attacking, you know, very precision solution instead of a little bit more abstract and open-ended, like an end-to-end solution. So I think it just depends on the problem that you're trying to solve for. But I don't think it's either or. It's probably both.

Winston Chang: I was gonna ask you about industry specific versus generic providers too.

Similar answer, it just depends on-

Aneet Deshpande: Yeah. I think that's fair. Yeah. I, I think it's, I think it, it would, it'll depend, yeah, holistically. I think you're gonna see other, you know, you're gonna see non-industry players get into financial services. I mean, it is just kind of happening. We have tech companies that realize they have technology that has an application in financial services and, and they're migrating it.

So it's always good to work with people that understand your industry because it just makes the conversations a little bit more apples to apples instead of translation errors and things like that.

Winston Chang: Yeah, that makes a lot of sense. Aneet, I wanna close out our conversation with, um, just one last bit about implementation.

Earlier when we were talking, you mentioned something about having expectations about what the AI's gonna deliver, right? Once you adopt the tool, I think that's really important. So let's say you've got a strategy and within the context of that strategy, you've decided, okay, we're gonna try a few tools.

Then there's the whole implementation bit, right? Getting your team onboarded. Identifying the right people who are gonna use that tool, teaching them how to use that tool, giving them the ongoing support that they're gonna need, and then measuring the output. Like especially if you're going after efficiency gains, better margins, whatever, then you gotta measure that somehow.

Right? So, talk to us a little bit about how you put that together on the implementation front.

Aneet Deshpande: Yeah, and also don't forget, I think this is a probably one of the more important points, managing change is very important. And so, more technology, more tools means disrupting people's days.

One, you gotta train people up. You have to pull 'em away from their day jobs, which is, obviously the core competency of what we're doing, so there's an element of managing change and having a process for that. So, you know, while you may find five things that are extremely important and interesting, you have to prioritize and you have to timescale those so you're not shoving four and five new technologies on, on the firm.

That becomes onerous. And this is a legacy industry that had been, say, generally used to having one login. You know, you log into one system, you're just kind of going, now we're talking about multiple layers of technology. And so that's I think critical. Like how do you manage change and how do you roll out and implement is, is also very important part of that process.

And that change control, change management point of view. So I think it's part of that whole strategy for probably for anything is, what does the project look? Scoping out projects, making sure you have explicit KPIs or deliverables against it so you can measure success so that we're not doing things that aren't helping the firm and continuing to pull on things that don't help.

You want to make sure your, the whole purpose of this endeavor is to make everybody's lives easier, presumably, and to drive efficiencies, which has a direct tie into margins and profitability. So if you're not accomplishing that, I think it's pretty simple. The numbers tell the story. And whether that's after a year, two years, or three years, you'll have your answer.

But generally speaking, people tell you when implementations go wrong. The first person in your office is the person that has to experience the bad outcome of a bad implementation. So, so much of this is just understanding how to implement, and creating an implementation strategy along with everything else.

And then, you know, having users and for firms that aren't experienced in having things like user acceptance testing for example, or beta testing, what are the case with pilot programs? Things like that I think are also, we're thinking through. We've done a couple of things where we pilot with smaller subsets, and that tends to work well. And we learn from, we have a couple of advisors and folks that are very willing to immerse themselves in new technology and whatnot. And so we are able to, without saying the word test, we're able to pilot, and use that as a litmus test for efficacy.

And then you kind of go from there. Um, so I think it's, I think it's multifaceted also.

Winston Chang: Absolutely. Hey, Aneet, thank you so much. This has been incredibly helpful. Any last piece of especially actionable advice, like next step for the person who's listening to this podcast episode and like, okay, I'm interested, what do I do next?

You've thrown a lot of information at me, like, what's the next thing that I should do right after I turn off this episode?

Aneet Deshpande: Yeah, I, I think formulating a internal strategy now, if you're an individual advisor, it's essentially, how do I Immerse myself in AI so that I don't lose to the advisor that is using AI.

And so that could be a little bit more abstract based on their own practices and, and how they wanna optimize their business, or their books. You know, there's that, there's a component of if you're a, a small team or a, a small company, RIA or practice that you're evaluating the tools that are at your disposal.

That could be something that, at a corporate level, they're either not giving you or giving you. I think that's very important. And if you're an enterprise or, or even a small company or whatever, you're probably thinking about AI policy, AI rollout, building that around your own business strategy.

I would highly suggest to not jump right into something like an AI strategy without having a business strategy. It goes back to the, any road will take you somewhere if you don't know where you're going. So the starting point is having a business plan, a business strategy, and then backing into some of these things.

So, that would be my biggest, I think, action item, if nothing else. I do, our view is, depending on how you look at the math, GDP growth in sum over the next decade is likely to get a pretty significant boost that academically most people say is gonna come through the lens of productivity, more productivity, more GDP growth.

And so how do you get that productivity is through using things that make you more efficient, that drive productivity. So that's a very abstract way, Winston, of measuring how if all of us do this, we should all get more productive and we should all be able to manage more clients and become more efficient and drive better businesses, and ultimately drive better outcomes for clients.

And so that'll be the decade-long measuring stick. But I don't think it's going anywhere. There's a ton of investment going on. I mean, I'm sure everybody's seen this, but half of venture cap dollars raised are in AI, there's just, you have to get immersed. And the easiest starting place is to download Grok or ChatGPT or something and just figure out how useful those things are.

Um, and then you can start to expand from there.

Winston Chang: Aneet, thank you so much. Really appreciate your, uh, I feel like we could have had this conversation for hours and hours. I'll just create a voice clone of you and then, keep going with the, with the clone. Appreciate it. Thank you. Yeah. But, uh, yeah, thanks again, uh, for, for all of your insights and spending some time with us today.

Aneet Deshpande: Thank you Winston for having me and uh, look forward to speaking soon.

Shaun Tucker: That’s it for this episode. Thank you for listening.

What I appreciate about Aneet’s insights is that they’re applicable to everyone, everywhere in your practice. Share this episode with team members who might find it relevant. If you haven’t already started thinking about your AI strategy, now’s the time.

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Winston Chang: During this episode, we used several terms that need to be defined. Claude, o3 and Grok all refer to large language models, or LLMs, a type of AI built to process and generate human language. They are offered by companies Anthropic, OpenAI and xAI respectively. Perplexity is another AI company with an LLM-powered search tool. A Series A company refers to a startup that has moved beyond its initial seed funding stage and sought more substantial investment. CRM stands for customer relationship management. M&A stands for mergers and acquisitions. KPI stands for key performance indicator. TAMP stands for turnkey asset management platform. GDP refers to gross domestic product. Fat tail refers to probability distributions in which the extremes are more common than normal.

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