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Featured on the WealthTech Today Podcast by Craig Iskowitz | Published on 6/23/2025
Ep. 293: The Compatibility Quotient: How AI is Transforming RIA M&A Deals, with Allen Darby, Alaris
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The traditional M&A process in wealth management has a fundamental flaw: it prioritizes financial metrics over the one thing both buyers and sellers say matters most—cultural fit. Allen Darby, CEO of Alaris Acquisitions, has built an AI-powered solution that’s turning this approach on its head.
In this episode of WealthTech Today, Darby explains how his company’s LENS technology uses artificial intelligence to create compatibility matches between buyers and sellers, moving beyond the typical financial auction model that dominates the industry. With his background running M&A at United Capital before its Goldman Sachs acquisition, Darby brings unique insights into both traditional and innovative approaches to wealth management transactions.
Key Takeaways:
- Cultural Compatibility Drives Better Deals: When buyers and sellers achieve strong cultural alignment, valuations and deal terms actually improve as buyers become more aggressive in their offers
- AI Enables Complex Matching: LENS technology processes 20+ contextual data points to rank buyer-seller compatibility, something impossible with traditional static databases
- Technology Integration Remains a Major Pitfall: The biggest post-acquisition frustrations occur when sellers insist on keeping legacy technology that buyers don’t support, limiting their access to business lift
- Cybersecurity Due Diligence is Often Overlooked: Many M&A processes fail to adequately assess cybersecurity capabilities, despite the critical importance of data security in acquisitions
- AI Will Reshape Advisor Valuations: As knowledge becomes freely accessible through AI, the traditional value proposition of advisors may shift, potentially impacting industry valuations
Notable Quotes:
“The sell-side advisors today largely use a financial auction methodology to match buyers and sellers, which totally ignores what both the buyer and the seller would tell you is the most important aspect of a deal, which is cultural fit.”
“If they fall in love with you, they’re going to be more aggressive in their posture from the valuation perspective. They’re going to give you better deal terms.”
“I think that you can’t measure culture. Culture is something you experience by spending time with one another. What we can measure is compatibility.”
“Part of the value of an advisor is knowledge. The other part is the human aspect, empathy, guidance, things. Well, knowledge is going to be free, in the very near term.”
Episode Highlights:
- How Alaris processes 20-30 hours of buyer data including video interviews and on-site visits
- The difference between measuring culture versus compatibility
- Why financial planning software creates the most M&A friction
- Alaris’s roadmap for building an agentic AI workforce
- The potential impact of AI on university education and management consulting
Podcast Intro
Here at Ezra Group, we’re experts on everything wealthtech, including CRM, portfolio management, trading, rebalancing, performance reporting, just to name a few. When we start working with an RIA or broker dealer, the first thing we do is comprehensive tech stack assessment. This provides a top to bottom view of all systems and processes, and it’s a critical part of the firm’s growth plan, since the tech stack is the foundation for building towards the future.
So if you’d like to see your tech stack converted from a liability into an asset you need to run not walk to our website, EzraGroup.com, and click on the golden Contact Us button at the top of the homepage, the experienced team at Ezra Group will conduct a detailed tech stack assessment for you, delivering targeted recommendations that will optimize your existing software platforms. Or we can run an RFP process and help you select and then implement a new solution to help take your firm to the next level. You can schedule a free consultation by going to EzraGroup.com.
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Episode Transcript
Craig: Here we go, up with the next guest. I’m happy to introduce Allen Darby, CEO of Alaris Acquisitions. Allen, welcome to the program.
Allen: Thank you, Craig. I’m looking forward to it.
Craig: It should be fun. It’s always fun. Everyone comes to the WealthTech Today podcast to have fun. That’s what it’s all about, man.
Allen: Well, hopefully I won’t disappoint. I appreciate you having me on.
Craig: It’s not possible. Where are you calling in from?
Allen: Charlotte, North Carolina, technically just north of Charlotte, but Charlotte Metro. I live in a suburb called Lake Norman.
Craig: Beautiful. I’m jealous. I’m stuck here in New Jersey.
Allen: It’s paradise here. We love it. I’ve been here about 30 years.
Craig: Yeah, it’s gray and chilly today. I’m not happy, but all right. Yeah, that’s awesome. We won’t talk about my geographic jealousy. We’ll just jump right into this episode. So please, if anyone who’s not familiar with Alaris, give us a 30-second elevator pitch.
Allen: Sure. So traditionally, the market would refer to us as a sell-side advisor, meaning we represent sellers in the wealth management industry, evaluate and affect a partnership opportunity, which is code for acquisition, but we say partnership because in the vast majority of transactions we’re involved in, the seller is not exiting the business. And so they’re going to join a larger wealth management firm’s business through the acquisition.
And so we help the seller get educated on what the process looks like, organize their data both quantitatively and qualitatively, and then we go out into the market and matchmake with them, which is a very unique aspect of our practice in that we are not using the financial auction mechanism that most of the advisors use in the marketplace.
If we’re not familiar with that, it’s just a math-driven process where they cast a very wide net into the buyer pool, invite 50-plus buyers to their process, and then they vet them based on the sufficiency or insufficiency of their financial offer, which is part of the topic of today’s call. But we think that’s a ridiculous process. We don’t use the auction to matchmake. We run a two-way compatibility screen, which is the technology that we’ve built. But so very unique way of doing it, but we’re matchmakers in the wealth management industry.
Craig: This product is called LENS. Correct?
Allen: Correct.
The Dating App for M&A
Craig: So when I heard about it, what interested me, this is a wealth tech podcast, and this is a form of wealth tech, right? This isn’t used by advisors, but it’s still technology, which I thought was interesting. But what I liked about it was about 10 or 15 years ago, I had the idea, because this sounds a lot like dating, cultural compatibility, right? You’re looking for that. And I thought that when people get hired for the hiring process, it’s kind of the same thing.
Most people, when they’re looking for a new job, only look at the data. They only look at the company, the job description, the role, the salary, maybe the location. But none of those are important when it comes to, are you happy at your job? It’s about culture. Are you a culture fit for the firm? And that’s not gathered by anybody. So it’s more like a dating app, which would be much more useful, or at least a dating style app, where you’re learning more about the personality of each person.
Allen: That’s exactly what we’re doing. The reality is that when you talk to a seller or you talk to a buyer and you ask them, what’s the most important part of this transaction that we’re contemplating, they’re both going to answer that with something like cultural fit.
They mean different things by it, but they’re basically saying, I want to be happy on the other side of the transaction. Obviously, the math in the deal is important to both parties. The seller wants to maximize the value of the business they’ve created. The buyer wants a fair transaction, but they wouldn’t sacrifice, particularly the seller, wouldn’t sacrifice their culture, which would be their team, their clients, just for more money in the transaction.
The irony is, though, if you do have a great cultural fit, if the transaction is rooted in cultural conviction. The buyer will be more aggressive in price and terms. So often we lead with trying to get this cultural fit right. And sometimes people hear that we’re sacrificing valuation to get the cultural fit right. That’s not the case at all.
In fact, we would argue that the valuation and the terms improve when you measure culture and the buyer is rooted in the cultural conviction.
Craig: So basically saying is if they get to be friends beforehand, it’s a better deal.
Allen: That’s right. It makes total sense. If you spend time with one another, and from the buyer’s perspective, they conclude this is our type of people. They’re on the northbound train regarding how we see the world in terms of our value proposition to clients, our role as advisors, you get along with one another, all of these.
Obviously, there’s stuff with your business that has to look good, too. But assuming that is true, if they fall in love with you, they’re going to be more aggressive in their posture from the valuation perspective. They’re going to give you better deal terms. And that just makes common sense. So focusing on culture or compatibility first leads to a better outcome financially.
Craig: It seems that way. It seems logical. So how does it work? Is it a bunch of questions? And what’s the AI part? Is it AI or is it?
Allen: Yeah, no, it’s real AI. And I’ll describe it to you. So we represent the seller, but we have engagements with the buyers. And the buyers are agreeing to give us their data. So it’s easily 20 to 30 hours worth of work when we onboard a buyer.
They are giving us answers to a very robust questionnaire, describing their business fundamentals, everything that you want to know, fee model, custodians, tech stack, investment platform. So it’s the basic stuff. But then we also learned their deal, their model, how they structure deals, how they value businesses.
We’re going to also capture what are they going to require the seller to do, change, or adopt on the other side of the transaction, which is highly relevant, obviously to determine if you can live within that model. We capture what we call their mandatory points of alignment. These are the things that the seller is going to have to agree to do if they join their team.
So, but that’s just data. We also go on site and we spend typically a day with the buyer’s team meeting all of their people. We record video interviews with their leadership, their departments, all of the stuff to add color to the raw data.
So, so that’s, that’s a straightforward practice of capturing buyer data. We also do the same with the seller. So when a seller engages us, we’re bringing them through a pretty elaborate, we call it our ideal outcome phase, where we’re capturing and organizing all of their quantitative and qualitative data.
So if you think about that, given that we’ve done that with the buyers, we know all the answers to the questions. Once we do it with the seller, it puts us in a very good position to matchmake. But we’re not trying to measure culture, Craig.
I think that you can’t measure culture. Culture is something you experience by spending time with one another. What we can measure is compatibility. These are objectively measurable data points. So we have the buyer data, we capture the seller data. The AI behind the scenes can reason to a match.
And that’s why it requires AI, is that reasoning to a match. We tried to do this with legacy tech, just call them static databases. And it was virtually impossible because, let’s say that, and I’m just making this up, but let’s say that there are 20 relevant data points that go into the decision of whether we are compatible or not.
Each one of those data points is going to have context around it. I’ll give you an example. Let’s say that the buyer, when they onboard with us, they’re going to give us certain parameters that they want to focus on. It could be a metropolitan market that they’re interested in. It could be size of the business that they want to acquire.
They give us all these things who is their ideal avatar or practice that they want to partner with or via acquisition. So let’s take one data point, revenue. So the buyer says we will only acquire businesses that have a minimum of $2 million of revenue. Okay, great. Well, if I throw a couple pieces of context around that minimum requirement, their answer is going to change.
So if they said that $2 million threshold was their floor, but I came back to them and said, we have this million seven revenue business. It’s in a major metro market, there’s two partners. Their age is 40 and 42. Their net new asset growth rate for the past three years has been north of 10%. Their average client age is 45 to 50. You give them all these contextual data points around that stated minimum, their answer will change.
They’ll say, oh, yeah, we’ll change our $2 million requirement for that practice, given all those additional layers of context. That’s on one data point. Try to do that with 20 data points and layer in the context. You need something as sophisticated as artificial intelligence to be able to understand the data points and the context and reason to the best fit.
And so that’s what the AI is doing, is it’s score ranking the compatibility between the buyer and the seller using this very hyper-layered contextual-driven data set.
Craig: So I get the mandatory points of alignment. I get all the video interviews and the on-site, makes all perfect sense. I’m just not understanding how the AI is evaluating. So did you train the AI to say, here’s the way we operate this, here’s how we understand this? We’re doing cultural fits, we evaluate things this way, and you trained it on something. Where’d you get the data to train the AI on?
Allen: Okay. So we start with the onboarding data that we’re capturing from the buyers. I mentioned it’s 20 to 30 hours worth of input that they’re giving into our system where they’re going through a very linear progression of questions, where they’re answering basic things, who are your custodians? What’s your fee model? What’s your tech stack? But then it gets into descriptions of who, describe your ideal partner, right?
We’ll leave it as an open-ended question. And they will articulate typically through a video interview where we’re capturing the… So they’re giving us the real data points, but we’re also capturing the context through the language, just having a dialogue, which picks up sentiment. It picks up very nuanced pieces of data that wouldn’t be captured in a check the box type scenario for data onboarding.
Okay, so once we have that data on the buyer, then we provide the AI a very detailed description of when we say, well, one, we’re not measuring, we’re measuring compatibility. So what would those things be? So we’re going to tell it, we need to understand general alignment between the two parties.
General alignment would be things how they articulate their retail value proposition to clients. It would be things their fee model. It would. Then we’re going to move into fundamentals. These would be points of alignment on custodians. Maybe the seller has a specific piece of technology that they’re in love with that they want to make sure that the buyer has. It’s very easy to measure data points.
Then we’re going to go into deal needs and objectives. So the seller might say something, ideally, I would roll 20% of my valuation into the buyer’s equity. Well, we have to understand, will the buyer permit that? What’s their equity allocation range in their transaction? Perhaps the buyer has a very robust earn-out component to the valuation mechanism. Well, is that relevant to the seller? So it’s deal needs and objectives.
Then we’re going to go into partnership preferences. These would be things that the seller would identify as, hey, if I’m going to transact with a buyer, ideally, they would be able to improve my life in this manner. It could be things expansion of services and capabilities. I would to add tax preparation and corporate trust services to my suite of services I offer to clients. Well, do the buyers have that? And then how good are they at that?
It could be things, I have a strong Gen 2 mentoring and training program. Is that relevant to the seller? And if so, does the buyer possess that? And again, how competent are they at that? So these are partnership preferences. Kind of a wish list.
Then it’s the points of alignment. This is the autonomy profile from the seller’s perspective. The reality is if you transact with anybody, you’re going to lose a degree of autonomy. But it’s not a binary thing. It’s not you make all the decisions today, and if you transact, you’re not going to make any decisions. It’s a spectrum of autonomy that exists.
There are buyers that are going to require you change, adopt, and do a lot of things. There are also buyers on the other end of the spectrum who very entrepreneurial. So we need to capture what the buyer is going to require of the seller if they join them to do change or adopt.
And then organic growth. Sometimes buyers bring to the table a lot of organic growth capabilities. Is it relevant to the seller? Then it’s unique seller attributes. It’s what does the seller possess that maybe the buyer is seeking? So often a buyer will tell us, we have this focus on physicians or something. That’s a niche client that we serve. Well, perhaps the seller has a client concentration in physicians. That would be something obscure and unique.
So you just go through and you capture all of these data points from the buyer, and the AI then takes that and reasons to fit. And then it ranks the buyers based on their degree of fit, if that makes sense.
Craig: It does make sense. I like it. So how many acquisitions do you guys do a year?
Allen: Right, we’re averaging, I would say, 15 to 20 a year. With that being an increasing, if you look back, we started Alaris. My background was I ran M&A at United Capital, and then we sold to Goldman Sachs, and when Goldman bought us, the show was over on the acquisition front.
So we started Alaris in late 2020, early 2021. So if you look at our acquisition history, it probably started out with four or five that first year, and this year we’ll be close to 20.
Craig: So you have a lot of data.
Allen: Oh, so that’s the biggest challenge that we have is managing it, staying on top of the buyer data. You have a lot of history of data, so you’ve got – do you save data from all the acquisitions you’ve ever made? Oh, yeah. From the seller’s perspective, yeah. So we have all of that information cataloged, but the buyers, today we have over 80 buyers that have engaged us in the process.
So if you look at the volume information, it’s not static data. It changes over time. And so we have full-time people that that’s all they do is maintain the relationships with the buyers, make sure that their data is accurate and up-to-date, and our system is reflecting what they’re present.
Craig: Indeed. Cool. All right. So another thing you mentioned was the buyer may or the seller may have a piece of technology that they’re in love with that they want to move over. So that’s something we get involved in a lot here as a group. So we’re always looking at technology. And I see two camps when it comes to RIA, wealth management acquisitions.
There’s the Wild West camp, or whatever tech you have, bring it over. You can use it. I remember thinking United Capital was that way. Whatever you got, you just use whatever you have. You’re fine. Then the other is the, here’s what our structure is. You use our tech stack. Everything you have is going away. We’re going to move everything over onto our tech platform.
Allen: Yep.
When Legacy Tech Becomes a Deal Breaker
Craig: So we do both, right? We work with our aggregators that are in both camps. But a lot of times we see technology as a sticking point in many deals, especially if you’re keeping something and they’re trying to integrate it. Or manage it going forward. So what technology pitfalls have you observed in all the deals that, the ones that underperformed or failed, what was there a technology pitfall that caused that to happen?
Allen: So I’m going to answer it, but it’s probably in a little bit different light than you might expect. So the buyers are even the most dogmatic about adopt our systems. They will, to your point, if a seller had something that they just had to have that wasn’t in the tech stack of the buyer, the buyer may say, okay, you can continue to use that.
We’re not going to force you off of it, but we’re not going to support it. It’s not going to integrate with all of our systems. And so that would be the place where I would see the most, let’s say, lack of success is when the seller is arguing to keep or use a piece of legacy technology that the buyer doesn’t support.
And then post-acquisition. They’re frustrated that their tech isn’t as useful or seamless as their partners because they’re not integrating that legacy tech that the seller had to keep into their new environment. And so one of the biggest areas where a buyer improves the life of a seller is in, we call it business lift.
And that’s a lot of things, but one of them is the advisor experience, which is what’s rooted in technology. The buyer has invested millions, tens of millions in some cases, in either creating their own technology or more commonly integrating disparate systems together.
And if the seller wants to keep something that the buyer doesn’t support, they’re not going to get the business lift that everyone else around them is. And therefore, they become frustrated and they may blame it on the buyer, but it’s not the buyer. It’s the seller forcing them or asking them to support a piece of technology that they’re not using throughout their entire organization.
Craig: Okay, I’ll buy that. Not bad.
Allen: It’s not as common, I don’t think, as one might expect. Probably the area we see the most is in the financial planning software. If the seller is just convinced that one is superior to another for their reasons and the buyer uses a different system, that’s typically where we see the most pushback. Otherwise, it’s not as big of a deal.
Craig: And you have a lot of experience coming from United Capital where you had the financial planning tools that you built.
Allen: Yeah, FinLife. Right?
Craig: FinLife, right?
Allen: Yeah, that was the one. You’re right to say that it was a pretty open architecture platform except for that piece. But that FinLife piece was the onboarding of new clients. It was the steps, the vernacular, the technology that we used to bring a client into United Capital. After they were a client, it wasn’t as relevant, I don’t think. But the FinLife was definitely something that if we were talking about joining United, if you looked at that and said, no, I don’t see any use in that, I would tell you you’re not a fit for United. There you go, right there.
Craig: Have you worked with any buyers that remind you of United Capital? They’ve got some of their own tech that encapsulate the way they think about financial planning or the way they think about managing holistic wealth, which they’ve encapsulated their best practices inside some software.
Allen: Yeah, I would say United was pretty, well, not pretty, they were ahead of their time in terms of having a tech platform that undergirded a definitive client experience. I don’t see anyone out there who’s taken it to that level. And this goes, you’re talking about the firms that are 300 billion and then assets under management all the way down to the $5 to $10 billion type of firm.
What we see is their tech is their integration of all the other different systems. I don’t see a lot of people investing so much in building proprietary stuff. What’s proprietary about them is the degree of integration that they’ve stitched all these different tools together.
So the advisor just, they come into the office and they have a single sign on into that point of integration. Let’s say it was a Salesforce and all of the different tools, the financial planning software, the investment software, all of that stuff integrates into their hub.
And so data gets keyed in once and it goes out to the financial planning software. comes back and forth. It reduces the amount of stress the advisor has in moving between different systems. That’s what we see a lot of firms investing in.
The Missing Cybersecurity Question
Craig: How about cyber? Cybersecurity. So that’s often an issue when it comes to the tech stacks. And as I mentioned, we do a lot of RIA integrations and working with aggregators. And one of the things on our checklist when we are doing due diligence on an acquisition is cyber and going through their tech stack and making sure they’ve got all the capabilities that we expect them to have and all the security. Do you have that as part of any of the questions you’re asking?
Allen: Uh, it’s not a major focus on the buyer onboarding that we do.
Craig: Probably should be, to your point. Cybersecurity is an increasingly important topic.
Allen: But it’s not something that we’ve tracked. All of the buyers will be able to provide an elaborate answer to that question when posed. But we’re not tracking today their depth of cybersecurity.
Craig: Well, it’s something we should talk about.
Allen: Probably something we should be doing.
Craig: After the episode.
Allen: Yeah, learn something. I gotta add that.
Craig: I’ll follow up with you on that. We’ve got a whole cyber, we have our own cyber evaluation and due diligence on firms. Because even if you’re not taking any of the technology, you’re bringing over their data and their information.
Allen: Yeah, absolutely. It’s a major point, and I will take you up on that, Craig.
Craig: All right, you’re done. We’re closing the deal on podcast here, folks.
Allen: You heard that. That’s right.
Craig: This is business occurring on the WealthTech Today podcast. All right, so let’s look down the road a bit. So you’ve got a lot of experience with M&A. You’ve got experience with technology. You’ve got your AI work. So where do you see – do you see any other areas where these technology aspects and integrations can provide value down the road in the wealth management space? What else do you see AI being able to provide?
Allen: Well, in our business, we’re spending a lot of time – well, let me just say this. Going through building LENS and integrating AI into that tool has opened our eyes to the power and leverage that AI can deliver.
Building Tomorrow’s AI Workforce
Specifically – We’re, the next on our roadmap is, is how to build an agentic workforce to support what we’re doing manually. And so I think from my, I don’t know if that’s your question, but in terms of the use case for M&A, leveraging agents is the next thing that we’re going to focus on that can take our team, which today we have 11 people.
And it’s not, I don’t look at it as going to replace anybody on the workforce, but it will limit the amount of hiring that we have to do because the process that we’re running, obviously being a very heavy data oriented process that doesn’t scale very well with people. And so having, leveraging those types of tools to the degree I understand them today, I think is going to be the next big thing for us.
Craig: There’s so much out there. It’s pretty exciting, I’d say. There’s a new announcement, a new development.
Allen: It is, and that’s both exciting and frustrating because when you think you see something that’s got a huge wow factor and you acquire it or start to integrate it into your current stack, only to see 60 days later that something else comes out, which is double wow, and you just spend all this time, energy, and effort on that legacy system.
The pace of the development and the things that are coming out is sort of daunting. It’s scary. It’s exciting, but it’s also scary to me. It’s how much money I’m going to… I spent seven figures on the legacy tech that the AI tool that we have rendered that worth zero at a tenth of the cost.
Craig: So… That’s going to continue. We started working on agents six months ago, and then there were already companies popping up that offer agents to build agents.
Allen: That’s right. work you did building your agent is wasted because they have an agent that can build it. Well, also kind of tangentially, what’s going to happen to the advisory businesses, which are largely, think of it it’s two components of value.
The Knowledge Revolution’s Impact on Advisor Value
Part of the value of an advisor is knowledge. The other part is the human aspect, empathy, guidance, things. Well, let’s say that the fee that we’re charging is equally allocated to both of those components of value. Well, knowledge is going to be free, in the very near term.
And so does it have an impact to the fees that we can charge, which that if does, it will certainly impact the valuations that are currently available to advisors seeking to monetize their business. That sort of thing – I watched a it was a TV interview recently, and it was a well-known anchorman that was interviewing this college kid, and he had this device hooked up to his ear, and he could just think a thought, a question, and it would go out to the internet. Did see that?
Craig: Thinking in Google searches, yeah.
Allen: Yes, he was – and the guy was asking him these random questions that he would probably never be able to answer, live, and he was able to – vocalizing?
Craig: Wasn’t he just saying it? I thought it was a jaw thing. He was saying it, but you couldn’t hear anything.
Allen: sort of just vocalizing it. Maybe it was. I thought – but I couldn’t hear it. But the immediate thing that I thought was, I can, from a knowledge perspective, be the best advisor in the world. When I have instant access to all information, that’s awesome, but it also renders that sort of worthless to pay for, when anybody can do that.
What happens when AI, I mean, we saw AI can pass the bar. Right.
Craig: I’m sure AI can pass the CFP exam, or Series 65, Series 6, any of them, right? And once it does that, then as long as it’s an RIA overseeing it, it could do anything you wanted, right? It could open up accounts, could do onboarding, could make recommendations, it could do a financial plan, because everything behind the scenes is software.
Allen: Exactly. Someone asked me, I was speaking at a conference, and someone asked me, what do you worry about? And that was my answer. It was I worry about the impact of what I don’t understand and don’t know, which AI sort of squarely fits in there. I’ve thought about writing an article about, I don’t see any way where it doesn’t impact our valuations when knowledge is essentially free.
Craig: People better get good. Well, knowledge is free. Yeah. The whole point of university, not to take it on a tangent, was to impart knowledge to students, right? But knowledge is free, it’s available anyway, so why are we going to university? I agree.
Allen: I agree. The university model should be concerned about what’s happening.
Craig: I think so.
Allen: When I can hire the best professor on any topic, I go somewhere.
Craig: And there was a, I just posted something, one of my relatives, is a professor, and someone posted something saying, every student, students are coming in demanding, we’re designing your tests, and we’re doing only in-class essays, so you can’t use AI.
Allen: Can’t use AI.
Craig: Students are complaining that you’re impacting our learning methodology or something, or our learning process is being discriminated against, because we learn with AI. It’s you’re going to write with a pencil, with a pen and paper, and a blue book.
Allen: Well, unfortunately, that is going happen. A lot of people are going to be, they’re just going to become so reliant. I try to think, when will I become irrelevant?
Craig: What about If you could just ask ChatGPT any question you would ask a consultant, some kind of answer. won’t be right necessarily, but it could be something. At some point, I think generic management consultants are doomed. Because I can just say, hey, I’m a CFO, and I need to do this and this, and what’s the best way to do it?
I want to do another offering. I want to revamp this. want to look at my capital structure, look at my depreciation. How can I fix this? How can I do it better? What are the best firms doing?
Allen: And ChatGPT is going build that for me.
Craig: Why do I need to hire McKinsey anymore? Every McKinsey book is in ChatGPT.
Allen: That’s right.
Craig: But it’s the consultants us will be the last to go because we’ve got a very niche.
Allen: Very niche.
Craig: Yeah, very niche book. It’s hard to say, well, which CRM should we be using and how can you convert the data over? Well, I can give you generic CRM knowledge, but that’s not going to be very helpful in a wealth management context.
So that’s the way people hire us is because we have very specific knowledge of very specific software. People on our team have either been operators of RIAs, COOs, or CTOs, or have been on the vendor side for many years deploying software. We know all the bodies are buried. We know all the people, which no AI can do that yet. At some point, it might start to catch up, but we’ve got a couple of years of runway, at least.
Allen: Honestly, I’ve thought about that. It’s man, I need to, you have to do great the next two to three years before, even our niche gets changed.
Craig: AI. Crushed. Well, if need any help with any integrations on technology side, we are very good with data moving.
Allen: you want to move. I am dead serious about that cybersecurity thing.
Craig: It is something that we need to focus on. have something. I’m working with a partner on that, and we’ve got a little bit of a little overview of how that would work, so I’ll send it over to you. Very good. All right, my friend. Well, nice to meet you. Call me if you need anything. Oh, thanks, Allen. Cheers. Cheers. Bye. Cheers.
Conclusion
AI wealth management M&A is evolving beyond traditional financial metrics toward compatibility-based matching. Allen Darby’s insights from Alaris Acquisitions demonstrate how LENS technology addresses cultural fit—the factor both buyers and sellers identify as most critical to successful transactions. As the wealth management industry continues embracing AI innovations, firms that prioritize cultural alignment alongside financial considerations may achieve superior M&A outcomes. The integration challenges and cybersecurity considerations discussed highlight the ongoing importance of comprehensive due diligence in an increasingly technology-driven M&A landscape. For wealth management executives evaluating M&A strategies, understanding these AI-powered approaches to buyer-seller matching could prove essential for future transaction success.
https://wealthtechtoday.com/2025/06/23/ep-293-the-compatibility-quotient-how-ai-is-transforming-ria-ma-deals-with-allen-darby-alaris/?nab=1
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