Why Time Has Become the Most Valuable Asset in Wealth Management with Addepar

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What if the biggest competitive advantage in wealth management was no longer access to capital, but access to time? Well, my guest today is the CTO at a company called Adipar, and their platform tracks more than $9,000,000,000,000 in assets globally. And in a world where financial advisers are drowning in fragmented systems, disconnected data, and rising client expectations, my guest believes AI and modern infrastructure are becoming the difference between firms that lead and firms that are falling behind. So today, we'll explore why legacy technology is holding back innovation across wealth management, but how AI is helping advisers surface insights faster, and why predictive intelligence is something that's changing financial advice from reactive to proactive. And we'll also talk about that growing divide between firms embracing modernization and those trapped in the analog era.

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But enough seed setting for me. Let me introduce you to my guest right now. So thank you for joining me on the podcast today. Can you tell everyone listening a little about who you are and what you do?

[00:01:44] - [Speaker 1]
That's great to be here, Neil. So my name is Bob Pisani. I'm the CTO of Atapar. I've been with the company about five and a half years. So I run our R and D organization.

[00:01:53] - [Speaker 1]
So this includes our product development, engineering, design, data, and increasingly focus on AI. For Atapar, we we we've been in business for over seventeen years, and ultimately, we are a data and AI platform used by financial services professionals around the world.

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And for anybody hearing about Adapal for the very first time, can you possibly paint a picture of the the problems that you set out to solve in wealth management, what, going back seventeen years ago, and why managing data across modern platforms now, of course, has become such a difficult challenge for advisers today?

[00:02:31] - [Speaker 1]
It has. So many of the problems still exist. Yeah. You know, Adapar certainly I think solved many. It's predominantly around transparency, data aggregation, and eliminating the just fragmentation of data that exists across so many different sources, banks and and fund administrators and the like.

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And so in the wake of the financial crisis, many clients that we started working with didn't understand what their portfolios contained, they didn't understand the value of those portfolios and where it was located, and ultimately how timely or accurate that data was. So Atapar has fundamentally worked with family offices, wealth managers, private banks and institutions to solve data aggregation. So solving data across hundreds and hundreds of different custodians globally, as well as not only ensuring that data is of high quality, enriching and standardizing that data using a very sophisticated financial, call it, ontology or schema to ensure that the data can not only be normalized, but it can also be analyzed incredibly flexibly in a way that brings the kind of not only discovery, but also power to create insights as well as make decisions with that data. And that's really where it started and that's really the problems that are being solved by Atapar, but many clients that we encounter still face because you're right, many of the systems that are out there don't talk to one another. The data is hard to get access to.

[00:04:05] - [Speaker 1]
It's not well structured, not well understood.

[00:04:08] - [Speaker 0]
And just listening to you there, it feels like time and technology are now a wealth adviser's possibly most valuable asset. So what what does that actually mean in practice when markets are moving in real time and clients are expecting immediate answers and personalized guidance, etcetera? What what does that look like on the ground floor?

[00:04:26] - [Speaker 1]
Yeah. So clients that we end up talk talking to and bringing on the out of our platform are faced with a plethora of systems. You know, they have to, suffer what we call the swivel chair effect, where they're moving between five, ten, 20 different systems just to answer questions. And they often end up taking data, putting in spreadsheets and trying to cobble together enough data to answer what their clients are demanding of them. And to your point Neil, this is a question of how fast can they do that?

[00:04:57] - [Speaker 1]
How responsive can they be? Invariably, years ago, the answer was good enough if you could maybe get something out on a monthly basis, if not quarterly basis. Now, clients are expecting that you not only have complete transparency on their portfolios daily, but also that you can answer questions that are top of mind, driven by market news, and global, and and economic events that require deeper understanding of the data and ultimately what clients intent is, and that's where their time should be spent. They should be spent understanding their clients, they should be understanding their investors and stakeholders, so that they can, to your point, personalize and really ground the advice and ultimately the investment choices in real life and with real goals. And I think that's where the time is most valuable spent most valuably spent.

[00:05:52] - [Speaker 1]
But what ends up happening is they spend more too much time wrangling data and trying to understand where things are and connecting dots. And that's what Atapar seeks to solve, by unifying that data, but also now increasingly applying that personalization through AI and through all the capabilities of the Atopro platform.

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And I suspect we will have a lot of people listening that working in financial firms that still rely on some of those fragmented legacy systems that were built for a completely different era. But from your perspective as a CTO here in this space, what where do these older systems create the biggest bottlenecks for innovation, decision making, and and the overall client experience that that's expected now?

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It it's it comes back to where is your source of truth? And invariably, many of these systems don't provide a single source of truth. So Atapar seeks to provide that. These systems tend to be not only fragmented, they tend to be slow changing or hard to change. And I think that's where innovation gets stifled, because you're now faced with trying to coordinate changes across many systems.

[00:07:02] - [Speaker 1]
These systems tend to be brittle. They sometimes are unstable. They often didn't have strong governance, or they didn't have a strong change management, set of policies around them. So now the systems have, almost in a hidden way, evolved over time to where interpretation of data changes over time. And what and the meaning of data even the meaning of a particular security in one system is not the same as in another system or transaction or how a particular entity or or ownership structure was represented.

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And so Atapar seeks to not only solve the data problem more broadly, which is how do we unify that, create a single source of truth, and abstract away the complexities that exist in these underlying systems and allow you to more easily and seamlessly connect them. But, also, once you have that, now you can iterate more rapidly. Now you are no longer gated by a system's inability to change or fragility or just brittleness in the ability to apply change because the data ultimately is what matters most. And if you have that data in good shape and well aligned and centralized, that allows you to then move much more rapidly and unlock a lot of the new technology and innovations that have been frankly created over years, but certainly now in the AI era.

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And as this is a tech podcast, we do have to mention AI, and AI conversations in finance can sometimes feel very theoretical. But what are some very real examples where AI is already helping advisors reduce friction, surface insights, first of all, make better decisions for clients? Because there's a big focus on ROI when we're including any big tech project now, but what what difference are you seeing AI making here beyond the hype?

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Yeah. I that's a great way to ask that because there is a lot of hype, and there's a lot of bold statements being made. But to make it real, where Adapar has invested and seen real results is in a few areas. So certainly, I think portfolio insights and ultimately leaning in on having an AI that understands financial data and investment data, at depth. And I we see that when clients are using our Addison AI both in terms of typical chat based Q and A, as well as in the idea of surfacing insights.

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And this is where clients save so much time rather than going and looking at individual reports or running lots of analyses and combing through data. AI can ultimately summarize that and provide them client ready insights effectively to push you a button. But that's only part of I think where we're seeing huge success. We're also seeing massive success when you talk about alternative investments in terms of the complexity of alternative documents. And not only in terms of collecting those documents, but also processing those alternative investments, taking what is otherwise trapped in PDFs and spreadsheets and turning them into online transactions and positions and and ultimately actionable statements, it be capital calls, distributions, account statements, and ultimately bringing that to life and marrying that back into the portfolio, so now you can have a total portfolio view.

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And we're also seeing massive, interest in, I would say agents, and that's we're we're rolling out our first agent this quarter to actually take a lot of the operational burden off of advisors so that now whether you're responding to a client inquiry or you're ultimately dealing with data quality of the data that's in the portfolio, you can now have an agent assist you with ultimately resolving issues and explaining why there might be a problem with a return or there might be a problem with a valuation. And I think that's super powerful because now you have an assistant at your fingertips that ultimately was otherwise requiring human beings and people to do that work, but now they can be augmented and accelerated to focus on frankly the problems where their expertise really has the most value. And that's true of an operations team, that's true of an advisor, it's true of an investor.

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And I think one of the biggest challenges in financial services is often turning huge volumes of disconnected data into something genuinely actionable. So how do you approach that problem differently, and and why is data quality becoming so important in the AI era? It's something we hear more and more, isn't it? It is because, you know, some the some of

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the problems with the hype around AI is that, the assumption is you can just take AI, bolt it on, or effectively outsource it. And I think ultimately the problem is that without a deep understanding of data and data that's of high quality, AI is only as good as its underlying information. And you see that problem surface immediately when you take it and try and apply it to random spreadsheets or PDFs or you try and apply it to large data sets that are disjointed and are not quality controlled and frankly, I would say well aligned and governed. And what ends up happening is AI will then start to make mistakes because it's only as good as I think a combination of the data it has it has access to as well as certainly the prompting and governance that's put around it. So what we're seeing is that as clients bring data into Atapar, we go through an extensive quality control conversion process integration where we are ensuring highest quality, verifying results and returns, and ultimately ensuring that the data is well aligned, well governed, connected to our security master, and ultimately ensuring that we can have different assets from different institutions, different banks, well aligned.

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And so now, once we have that, now you can start moving to the higher level and ultimately higher value types of problems that AI helps unlock. And now what's really interesting is that AI helps you to do things that you probably couldn't even do before, which is where you start to move from ultimately high quality data to this idea of clear insights and actionable intelligence because that's what matters. Is you're trying to move from surfacing information to finding out what you don't know and being told what you don't know to actually turning that into a decision. And that's where AI starts to become so so powerful because you're either helping support advice, you're helping provide options around insights, and investment decisions. And that's where, frankly, advisers and wealth managers wanna spend their time because that's the highest value activity you can do versus, frankly, all the unseen parts of this which clients and investors shouldn't see, which is the data wrangling and the data quality, and that's where we think the most value comes from, and that's where we want advisors and wealth managers to spend their time.

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And, wealth management has arguably traditionally been reactive, and by that, I mean responding to market events after they happen. But do you think predictive analytics and adaptive intelligence are these things shifting the industry towards a more proactive model where in an ideal world, advisers can anticipate some of those risks and opportunities early? I know it seems a bit idealistic, but can do you think the tech will will get to that place?

[00:14:22] - [Speaker 1]
I I I you're right. I I I think it not only will get to that place. I think it's not even idealistic. I think it's the expectation increasingly. Where now, we we were just demonstrating at our our user conference last week.

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This idea that when you come into either a a laptop or mobile desktop experience or you're coming to a mobile app, the expectation is that through AI, through predictive analytics, we've already surfaced things that you need to know. We marry that with market news and and news from Internet trusted Internet sources, and we're actually surfacing information about the portfolio and about your clients and about investors that you didn't even think to ask. And so suddenly now, you are becoming proactive, and now we ultimately wanna turn that into, alright. Well, would you like to contact your client? Would you like to draft a letter and send it to them?

[00:15:16] - [Speaker 1]
Would you ultimately like to send a a secure message through a portal or through a mobile experience to contact your client? And I think that is a game changer because that's where I think you're right. It seems idealistic. It seems so far fetched, yet it's real. It's it's becoming reality.

[00:15:32] - [Speaker 1]
And Atapar is so excited that we've invested in our data infrastructure to make this happen. We've invested in our AI infrastructure to make this happen. And this is where the power of the platform comes to bear. Because with high quality data, with a powerful AI ecosystem that's built into that platform, it becomes so much easier to do exactly what you're talking about, which is if you can respond to the types of questions that advisers or investors are asking. You can suddenly turn that around and ask those questions frequently or constantly.

[00:16:03] - [Speaker 1]
And that now turn becomes a source of insights that we then surface to our clients. And that's where you change the game. And that's where suddenly it seems like, wow, a level of service, a level of engagement that you can never get before. And I think that's so powerful because that's what I think will become the norm. I don't know if you say it in a year or two where this is the expectation, but we're we're making that real now.

[00:16:27] - [Speaker 1]
And this is what we're seeing our clients start to ask for, and they're asking it same way, which is this is this possible? And I think the answer is yes, but very soon this will become the norm and the expectation.

[00:16:38] - [Speaker 0]
And I guess the elephant in the room for people working in this space is that those concerns, those very real concerns that AI could replace the human side of financial advice. So how do you see that relationship between human advisors and intelligence systems? How do you see that relationship evolving over the next few years?

[00:16:57] - [Speaker 1]
So Adiport strongly believes that there is no substitute for human connection, human relationships, nor human judgment. And I and I think that we've thought a lot about this in terms of AI impacting our our workforce more broadly. And and we've even seen how thought about how it impacts, Atypar internally and the disruption it's it's caused us. What we've come to the conclusion is that if I were to sort of boil this down to a few key traits that differentiate, an adviser, an investor, an expert in the industry, and why AI can't disrupt that is because you need to have a few things. You need to have taste, you need to have judgment, you need to have precision.

[00:17:41] - [Speaker 1]
And those things are truly human, but also differentiated. Because the reality is everyone who's used AI knows that when you challenge it, it will agree with you and change its mind and tell you something different. You also know that it will it tends to get very list oriented. Well, here's five great things you can go do. Well, alright.

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Which one should I do? Which one do you recommend I do? And it might be forced to give you an answer, but now you risk the, well, is it doing that because I asked it to do that and told it to do that? So you have to take the good with the bad, so to speak, in that you ultimately have to bear the responsibility of making the final recommendation, giving the advice, and making the choice. And this is why I don't think AI is meant to displace or replace advice.

[00:18:31] - [Speaker 1]
I think, ultimately, what it does is it changes the the tasks or the type of work that a wealth manager or an adviser tends to focus on. And many of the things we've been talking about through this pie, through this discussion have been where, frankly, they spend most of their time. And I think that's that's a a terrible waste to to frankly for so many years. As much as Adaport's been making that easier and better, I think AI gives us a a step a function improvement on the fact that now you can spend far less time with the data, understanding and fixing problems, and ultimately even being reactive to your earlier question in terms of the types of information that someone may be interested in because of market events or because of a life, a life event that may occur for someone and spending more time being proactive and spending time with clients building relationships. Because at the end of the day, this entire system is predicated on trust.

[00:19:30] - [Speaker 1]
And I think AI has gotten so much better, rev in a revolutionary way in the last two plus years. But in the next two plus years, will it replace human judgment? Will it replace human taste and human precision? No. And we are fundamentally invested in ensuring that our clients, our advisers, our wealth managers, our investors are enabled to capitalize on AI and the high quality data the Atapar platform delivers, but we are in no way looking to disintermediate that or see a path towards that happening outside of Atapar.

[00:20:05] - [Speaker 1]
And we're very excited about this because quite frankly, bringing power tools to bear will only make their expertise and their credibility shine.

[00:20:15] - [Speaker 0]
And it feels like there could be quite a big gap that slowly develops here. I mean, looking ahead, what what do you think will separate those firms that thrive from that struggle as AI automation and real time intelligence all become increasingly embedded into, investment operations and and client expectations. We've almost got two sets of firms here, one from the old analog days and the the new AI era. Do you think that that gap will increase?

[00:20:39] - [Speaker 1]
I think that gap is already starting to increase, but I think it will become like very very wide very quickly such that you'll you'll effectively have the haves and the have nots. Yeah. And those that have not, and this is not dissimilar to other technological revolutions, whether it be mobile or cloud or frankly even the days of the Internet. You know, this this type of technology transformation, which AI is the biggest I've ever seen in my career, will absolutely create those who are not only, I think, early adopters and the adopters, but those that are late to adopt or laggards are really gonna suffer the consequences. Because it's it's a non linear impact.

[00:21:23] - [Speaker 1]
It's not just, okay, well now I'll just turn AI on and suddenly be able to catch up. You will be far behind and being far behind will be measured in probably three to six month increments And it's hard to catch up very quickly because innovation is the rate of innovation, the model evolution, the model power and intelligence that's occurring happens very quickly. And so now, if you're late to adopt, if you don't invest in solving your data challenges, if you don't invest in a platform that can enable AI for you and think holistically across your disparate systems and find a way to connect them, to unlock the information and the insights that's trapped in your data through AI and through modernization efforts, you're gonna be left always being reactive, and your clients are now going to be highly sought after. And you have a competition problem now, only for your clients, but also for your own talent. Because if your advisers, your investors, your operations team are stuck using tools that are antiquated, and every one of their friends in the industry is talking about, oh my goodness, I can spend so much more time doing high value activities, you will now have a talent problem.

[00:22:34] - [Speaker 1]
Because it's not even that your systems are a problem, your talent will leave you and your clients will leave you. So it's it's about competitiveness and frankly retention. And I think that's a a far bigger risk than even just the modernization lag that you're that you're speaking to.

[00:22:49] - [Speaker 0]
Wow. Such a powerful moment to end on and a lot of things to take away and think about there. And for anybody listening that would like to find you or your team online or dig a little bit deeper on anything we talked about today, where would you like me to point them?

[00:23:03] - [Speaker 1]
Well, certainly, have our website, aadapar.com. We're on social, both on LinkedIn and X. We post a lot. We're also at many industry conferences.

[00:23:12] - [Speaker 0]
Well, we covered a lot in our conversation today from legacy tech transformation, AI that drives outcomes, time and tech as wealth adviser's most valuable assets, and how you unlock new capabilities over there. So I will include links to everything that you mentioned. I would encourage anyone listening that that this has struck a chord with. Maybe set off a light bulb moment to get in touch, and, equally, let me know what you're experiencing out there. But, more than anything, just thank you for sharing your story and, everything that you're doing here, Bob.

[00:23:40] - [Speaker 0]
Really appreciate your time today.

[00:23:41] - [Speaker 1]
Neil, thank you so much. It's been great. Pleasure chatting.

[00:23:44] - [Speaker 0]
One of the things that really stayed with me after today's conversation was Bob's warning that the gap between firms embracing AI and those resisting is about to widen dramatically. Because this isn't simply another software upgrade cycle. It's a fundamental shift in how advisers work, how clients expect to be served, and how intelligence is surfaced in real time. And as Bob explained today, firms that fail to modernize, they don't just lose operational efficiency. They also risk losing talent and clients.

[00:24:18] - [Speaker 0]
I also appreciate his balanced perspective on AI and human expertise because, yes, AI can accelerate analysis and surface opportunities faster than ever before, but none of this matters without trust, judgment, and relationships. These things still sit firmly in human hands, and that's something that we should applaud. Because in financial services, that human connection still matters more than ever. But if you work in this area, I'd love to hear what you're experiencing, your challenges, what you're doing to overcome them. Please visit techtalksnetwork.com.

[00:24:53] - [Speaker 0]
There's 4,000 interviews there, but more specifically for you, there will be a blog post associated for this episode, and there'll be all the links to everything we covered today. And speaking of which, I'm afraid we've reached the end of the line. It's time for me to go now. I'll be back again tomorrow with another guest, but thank you for listening as always. Bye for now.