Brad Johnson: Quin, this is long overdue. Welcome to the show.
Quin Kilgore: Super pumped to be here, Brad. Thanks for having me.
Brad Johnson: Well, it’s been a really, really fun journey alongside you. And when you became Triad’s first official CTO, I think we were kind of joking. I was like, “Well, I wish we could’ve hired you, like, two years ago,” because as all companies in finance start to grow, I think anybody listening to this, any advisor can relate. It’s like, “Oh, like, I’m the IT guy because I can set up a printer and a network,” or you got a buddy down the block that can. And then before long, it’s kind of this thing that’s grown out of control that you’re not really sure what you should do, how you should protect the information. And I know we’re going to get into that today and AI and all of the other crazy things that change on the daily now.
But before we do, I want to hear Quin’s backstory because you got a really unique one. Midwest guy like myself, grew up in finance, got into technology. So, how did you get here? Were you just, like, geeking out on computers at a young age? Was it an accident? Like, what was your path into finance?
Quin Kilgore: It was a happy accident if it was an accident. But I grew up a massive computer nerd. My dad, I remember helping him set up the old Gateway 2000 computer, the box that came with a cow print on it.
Brad Johnson: Yeah, that was my first computer.
Quin Kilgore: Yep. Well, he was a financial advisor. So, what I grew up watching him do was go out to his Excel file and scrape the prices from Yahoo Finance. And then eventually we got DSL and then eventually broadband, so he started writing scripts to actually update all of his clients’ accounts so we can see that update in real time. To him, that was being a financial advisor, but turns out today, he’d probably be on a tech team. So, I ended up going to college for computer science and business. And then I moved to the big city, right? Like you said, Midwest. The big city around me is Omaha. So, inside of Omaha is Orion Advisor Services.
So, it’s a portfolio accounting management software. So, there I learned about how to calculate performance, how the connections are made from the custodian to the software, and then just how advisors function with their clients. From there, I jumped over to Carson Group, also here in Omaha, which is a large RIA, and then really saw the other side of it, how advisors kind of struggle to use software at times and to make the integration connections that are needed to really run a business. I spent about a decade there, or a little bit more than a decade, and really built the administration on a lot of products all the way down to when we got real big, above 40 billion in AUM, custom software.
And now I’m here at Triad, having a lot of fun building with the tech team. We’re building some really cool stuff, and we got a really cool team. And honestly, I’ve crossed the 2 billion AUM mark twice in my career now. Once was Carson, and now we got Triad, so it’s been a lot of fun.
Brad Johnson: Wow. So, it’s a really cool background. That was one of the things I remember when we first chatted. I feel like there’s a lot of CTOs in just the business world if you go, like, Fortune 500, but then you get into this smaller subset of finance, and a lot of rules, a lot of regulations. I don’t know how many times I’ve heard PII in the last two weeks, but there’s like we’ve got very important data that a lot of bad people would love to get their hands on. And so, as soon as you dive into the technology realm, there’s building technology, and there’s building technology and finance with all the safeguards we must adhere to. So, it’s like complexity level and importance level goes way up.
So, based on your background, it was really cool because you’re doing that at kind of the corporate level on one of the largest technology providers to a lot of advisors and a lot of RIAs. Then you flip over. How big was Carson? Because Ron Carson, like I remember him way back, like he was a mentor from afar, one of the original guys that was writing books, how to build practices, and he’s become a friend of mine. And I have a lot of admiration for him and what they built there. How big was that team when you got there, and then how big was the team when you left? And what was the advisor distribution when you got there to when you left, just for context?
Quin Kilgore: Sure, yeah. The tech team, it was a team of two or three when I first joined. I actually technically wasn’t on the tech team to begin with. I started with more of an operations role and then immediately jumped to tech. I’m coming from Orion there. And then when I left, we were a team of 25 plus, just on the technology team, 500 employees plus. So, again, when I started, the advisor footprint was pretty small. We would partner with offices that were sub-100 million, and then watch them compound and grow, all the way to the billion-dollar shops. And that’s how you kind of get that compounding, snowball effect.
Brad Johnson: What was the year you got there, and what was the year you left, just for context, technology-wise, because it changes so quickly?
Quin Kilgore: Yeah, got there in 2014 and left in 2025.
Brad Johnson: Okay. So, if you look back and kind of choose some of your learnings along the way, what were some of the biggest changes of how you were thinking about technology in 2014 versus in 2025? Because then we’re going to jump forward here because it’s gotten pretty wild with the AI. But what were your learnings from that run?
Quin Kilgore: Sure. So, think about the Michael Kitces map, that map that shows all the different tools that exist. Back then, there were maybe one or two names in every category, right? A couple of CRMs, a couple of portfolio accounting tools, maybe a planning tool or two, and then maybe a risk tool. And basically, what you did is you’d pick your favorites there. So, one in every category, and then you’d try to basically glue it together to make it work for a financial advisor. Now, if you look at the Kitces map, it looks like Where’s Waldo of logos. You can build any sort of technology stack and then get confused and wrapped up with all the vendor management and AI governance that comes with all that.
So, it’s been a real big change, and people have had to learn how to wear that technology hat within their office, or they have to partner with someone like a Triad to help scale their business in an appropriate way.
Brad Johnson: Well, I’m glad you bring that up. I mean, number one, love Kitces. He’s been on the show. He, I mean, self-proclaimed nerd. I was an IT major, like just a bunch of nerds now in finance. But the complexity has skyrocketed. And what’s wild like I had an IT background. Like before you got here, I was the de facto CTO, kind of. And it’s still overwhelming and complex to a guy that actually went to school for it. And so, I can’t imagine being an advisor with none of that background, trying to piece it together, trying to figure out tech stack, what pillars to build on. What was something historically in your career, like looking at a tech stack or if you were going to give advice to an advisor, that you’re like, “I think I learned, like, this was kind of a gold standard rule that we did well, and here’s something, like looking back, we probably messed up a few times”?
Quin Kilgore: Yeah. Let’s go with what we got right that still absolutely applies to technology stacks today in 2026. Your data is gold. Yeah, clean data will super matter in the technology stack because it needs to be able to be portable to all these new tools that are getting stood up. What we’re seeing with AI is the ability to reach out into other tools and actually get that data and bring it and service to you, where you need it, when you need it. Now, back then, it became difficult to align the data. Everything needed to be API first is the term that we would use, and you try to connect.
Brad Johnson: And what does that mean to a non-tech person? What’s an API?
Quin Kilgore: So, API means application programming interface. It’s a way for one tool to integrate with another tool. However, when you do that…
Brad Johnson: So, tool A, tool B, they can just talk to each other.
Quin Kilgore: In theory, they can talk to each other. That means that they have a more modern technology stack to be able to actually do that and authenticate into other systems, think username and password into other systems, and to be able to port that data. However, when you port from system A to system B via API, your data’s not going to align. Household name might not map to client name, or maybe it’s a two-person household that maps to a contact in another system. You have to have some sort of layer between there to be able to make system A and system B talk. What we’re seeing now, with MCP and AI, now, sorry for the abbreviations.
Brad Johnson: MCP?
Quin Kilgore: Model Context Protocol. Think about it as like kind of USB plug for technology. They all can talk to each other easier. So, one, AI. You can prompt an AI, and it can reach out into that tool and have the data in the format that it needs right there without having to create that extra layer. So, that’s the big change that we’ve seen from back then to now. I think from a learning perspective, it was really difficult to switch systems. There was a lot of cost involved, probably a lot of customization to, again, get that…
Brad Johnson: Can you give an example of, like, a system? What would be a system that like, “Hey, here’s something that switched that was sucked and was cost-prohibitive”?
Quin Kilgore: I wouldn’t go as far as sucked. It was just one of those that it was the only choice at the time, right? A big behemoth in the room that I know a lot of offices use is Salesforce. And that’s not just in financial services, that’s in all industries. They take their CRM. It was cheap to customize, without having to hire a software development team to make that work. Now, we’re seeing a super personalized CRM surface that have really native AI features that are going to be game-changing. I’m really excited about where our industry’s heading from that perspective of having AI built in to be able to go reach out and grab that context that an advisor might need when they need it.
Brad Johnson: You made me think of an analogy. Tell me if this is off base, but what I see happening right now with technology, it’s almost AI has created the ability for a democratization of it, and if I look at a parallel historically, back in the day when I was growing up, small town Kansas, I don’t know how many channels you had, but literally we had the old school antenna and we got CBS and Fox. Those were the only two. So, at least I could watch football, but not a lot of options. But whatever was on that station was what you were watching. There was no YouTube. There was no on-demand. And then YouTube came out, and now I remember my kids when they were, like, four, they’re like… Like, my wife did this little birthday questionnaire, and my son’s like, “What’s your favorite TV show?” And he said, “YouTube.”
Quin Kilgore: Nice.
Brad Johnson: Because he just chooses whatever he wants. And I feel like that same shift is happening in technology, where back in the day, okay, I’ve got two options for CRMs, Salesforce, Redtail, maybe Wealthbox, maybe three. And now with the ability to build and code and customize, we’re seeing these tools pop up out of nowhere, and it’s like, oh, here’s a CRM specifically built for financial advisors.
Quin Kilgore: Yep.
Brad Johnson: So, that’s essentially what you’re talking about. That’s the shift that’s happening right now.
Quin Kilgore: Exactly right. It’s been incredible to witness, just not even over 24 months. It’s been 18 months, 12 months. These things are popping up that are incredibly useful, because it’s the ability for these software developers to take feedback directly from advisors and then build the exact thing that they’re asking for, instead of having to build a very general-purpose tool and then try to pigeonhole people into that tool. So, that’s what’s changed with agentic vibe coding, that’s kind of been popping up all over the place. So, AI is making a huge impact in our industry.
Brad Johnson: Okay, I want to go back, because I don’t want to skip over this. And you said something, one thing we got right that I believe will continue to be very important is your data’s gold. And one of the shifts that, as we’ve kind of collaborated on how does Triad build our tech stack that in turn serves our members, independent financial advisors around the country, which by the way, a lot of similarities, but they’re all different. They’re all their own entrepreneurs.
Quin Kilgore: Correct.
Brad Johnson: So, you can’t have super stiff, rigid. You got to be able to have, like, be able to meet people where they are as well. But the data, which has always been a struggle, especially like I grew up in insurance, you grew up more in wealth management. Those two never… Like, the tech was built for each individual industry. They were never built to like talk to each other. And that’s one of the questions that I’ve been hearing for the last 20 years from every advisor I’ve ever talked to is like, “Could I just have one dashboard that’s got like the families I serve and all the tools we’re using to serve them? That would be cool.”
Quin Kilgore: Yep.
Brad Johnson: Tech didn’t make it easy. Back to the data. If we now build to where we have the data pouring in that’s serving your clients, insurance, wealth management, annuities, life, whatever else, how has AI shifted how you would have built the tech stack on top of the data even five years ago to today? Because there’s been a massive shift in, like, your thinking as a CTO, and I think sharing that with some of the advisors out there would be super helpful.
Quin Kilgore: Sure. You’ll have to save me if I go down the nerdy rabbit hole here a little bit.
Brad Johnson: No, let’s go nerdy. It’s good.
Quin Kilgore: But, just building out, we have a data warehouse behind the scenes, right? That’s basically collecting all of the data pipes from all of our tools. That way, we can then take things out of our data lake and centralize them and make them repeatable in a dashboard-type format.
Brad Johnson: I’m going to kind of slow you down a little bit here. So, data lake, like if we start at the foundational level, is data lake the bottom level of data in your opinion? And then we’re building up from there.
Quin Kilgore: Yeah, it’s a good way to think about it. Think about just a pipe of water flowing into a lake. The pipe just being piped from any sort of tool that an advisor’s using into a lake of data. So, then we…
Brad Johnson: So, real-world examples, just to, I’m going to slow you down because I really want to break this down.
Quin Kilgore: Oh, please. Yep.
Brad Johnson: Zocks, a very commonly used tool, Jump AI. So, AI transcription that is grabbing data from real-life advisor conversations with clients and prospects. That would be one of those pipes that could be flowing into this data lake.
Quin Kilgore: Absolutely could be, yes. Now, one part there to be thoughtful of that we have to really consider is the compliance piece of that, too, right? We have to make sure that whatever data is feeding from essentially one server to another does not contain any client PII or any NPI. So, we want to make sure that we work with our technology partners and really do hardcore vetting to make sure that if we do pull that data down, there’s nothing that could get lost or breached, because then we’re sending out letters to all these clients saying, “Hey, you have one more breach from one more vendor, that didn’t take their time and build it out the right way.” But yes, that’s exactly how it gets built. And then we…
Brad Johnson: So, we’ve got that pipe. Here’s my insurance pipe, here’s my wealth management pipe that might come from a tool like Orion.
Quin Kilgore: Exactly.
Brad Johnson: So, we’ve got all these pipes flowing in. We’ve got this lake of random data.
Quin Kilgore: Yep.
Brad Johnson: Where do we go from there?
Quin Kilgore: So, then we start organizing it. So, we will then start adding the time element to all these things. So, every day the data changes, and we need to be able to go back in time and say, “Hey, as of June 1st, 2026, what did our data look like?” So, we’ll add the time element and create. You can kind of mentally model this like a spreadsheet. So, date, here’s that row of data and what that looked like. And then we keep stacking on row on top of row, and all of a sudden we have just built out a data warehouse. And a data warehouse is exactly what a real warehouse would look like if you went behind the scenes of any retail store, right? Went behind those two double doors that they have and saw, like, if it was Best Buy, you’d see all the TVs lined up.
That’s what we’re doing with our data. We’re making sure that it’s all in a spot where if we need to go grab it, we know exactly where to go get that thing. So, once we have the…
Brad Johnson: So, the level up from a data lake to data warehouse is organization and structure. Okay.
Quin Kilgore: Yep.
Brad Johnson: Keep going. You’re good. Haven’t lost me yet.
Quin Kilgore: Once the structure exists, then you start combining different tables together into our own views that we’ll use repeatedly, like your dashboard thing that you talked about there for advisors, where they want to have wealth and life and annuity data all together in one easily readable dashboard. We would go grab those tables and form our own view that it would update every single day as we get new data in our pipes, right? So, it layers itself on itself to become very readable and updatable on its own. So, it’s kind of cool, right, from all these different tools, but it takes some effort. And I think the question we started with was what’s changed? Like how has that… This has existed for a long time, but it took a lot of people, a lot of effort.
I just talked about the compliance function. That’s also a compliance team that have to help us make sure our tools were all built the right way. Now, we are able to code these a lot faster because of the use of AI to help build these pipes as fast as we can normalize it, check the date element, and then place the data where it needs to run. It can also monitor to see if anything’s failed, and then automatically or agentically troubleshoot whatever error it just saw without even having to surface to a human. Eventually, it will then prompt a human to verify that we did everything correctly, but that’s what’s really changed. You used to have to go find a tool for that pipe that was pumping in the data from the tools, then a tool for the lake, and then a tool for the warehouse, and then a dashboarding tool.
These are all tools you had to piece together, very much so like the old school 2015 technology stack. Now you can actually code up your own custom pieces of the pipe, and it ends up being maybe one or two tools that we’re using to create that data warehouse.
Brad Johnson: All right. I’m going to throw some analogies out there. You tell me if they’re bad.
Quin Kilgore: Yep.
Brad Johnson: So, here’s what I hear you saying back to the question of how has this changed. Like, how has building a tech stack changed from even 5, 10 years ago to today with AI, and what that’s allowing us to do? So, data lake, got it. I think most people can picture that. We’ve got all these pipes, random data. Now we take that up a level where it’s organized. Back in the day, to go from lake to warehouse, you had to have a team of coders, developers, humans, compliance to make sure you structured it proper. Probably some other tools, so stuff could talk or you could display it. Now, today with AI, it’s almost like having this automated robot over top that’s building on demand and putting the data in the shelves in the warehouse wherever it needs to go.
And then you change your mind, and you’re like, “Oh, never mind, I don’t want to see it that way. I want to see it this way.” And now it’s spinning up a dashboard on demand, versus you having to throw that software away, go buy a new software to display. It’s almost like building Legos. Yep, throw that one away, rebuild it this way. Like, am I thinking about that right?
Quin Kilgore: Yeah. I mean, exactly right. The only thing I would add to it is you would go purchase a Lego piece from the store. You spend a bunch of time building it, and then you’ll just set it on the shelf and make sure that you never break it. Now, you can actually build the Legos, break it all within one session, and do it repeatedly over and over and over. Everything can essentially be thrown away with AI, right? Because it can build it so quickly from scratch to a fully formed Lego set. That’s the massive change that we’re seeing.
Brad Johnson: Okay. So, it’s funny, I was just having a random conversation with an advisor named Mark from Ireland. So, if you’re listening to this, Mark, shout out. But we connected through the podcast, and one of the things we were talking about is how, I mean, I’ve been in finance 20 years now, working directly with advisors, coaching calls, and I feel like, unfortunately, technology companies kind of prey on advisors from a sales standpoint. Like, I’ve seen it well before AI. Oh, here’s the online marketing tool that’s going to fill your calendar, two clicks, and you’re going to be the biggest advisor in the world. Like, that play’s been going for a long time. I feel like AI has now sped up the creation and the noise in the space.
Quin Kilgore: Yep.
Brad Johnson: So, just thinking philosophically, if we were to give advisors advice out there, they get it, data, capture it, how do we now start to organize it? But what are some ways to cut through the noise of the 74 AI tools that are getting pitched at them every day? How they should actually start to build on top of that data once they start to get it flowing in?
Quin Kilgore: Sure. I would think the one thing I’m really pumped about with artificial intelligence is the death of the workflow. There are two things I’m out here to kill. I’m out here to kill spreadsheets, and I’m out here to kill workflow. Now with artificial intelligence…
Brad Johnson: You might have triggered a few advisors with that comment.
Quin Kilgore: Oh, I’m sorry. Yeah.
Brad Johnson: Spreadsheets, I mean, we love spreadsheets in our space.
Quin Kilgore: Artificial intelligence makes it so easy to create spreadsheets now. It can also just, instead of creating a spreadsheet, just create you a spun-up app right there in your browser screen if you’re using a Claude or a ChatGPT, right? With workflows, agentic AI can take your existing workflow, break it into its sub-task, and go execute those tasks on their own. They can also run on a schedule or on demand. So, instead of your workflows having to be triggered by something that exists likely in your CRM, the agent or sub-agents can go out and run every single day, helping clean up whatever they need to clean up or schedule whatever they need to schedule, run their client review workflow.
Those are the things that are going to exist and do already exist right now. It’s only going to continue to get better and better. So, as advisors are building out their technology stack, they need to… Everyone needs to do this, not just financial advisors. Everyone needs to document their processes. The better documentation you have of your processes, the easier it’s going to be to hand off to AI and have him actually go execute that process for you. So, understanding your data footprint with processes plus AI is going to make everyone’s lives, and especially financial advisors, a lot easier.
Brad Johnson: All right. So, what I hear you actually saying is not killing spreadsheets, reimagining how spreadsheets are built, which is instead of the old school manual way, using AI to spin it up and probably show it on a dashboard, versus having to log into Excel, more like web-based or app-based. And even workflows, like the way I think about a workflow in my brain, it’s like an assembly line for your business of a process that happens over and over. We have assets hit. Those assets have to be allocated. There’s obviously steps along the way. So, instead of throwing the workflow away, it’s kind of the automation of the process where essentially AI and robots are doing it versus humans manually doing it.
It’s more like a human at the end of the assembly line that’s approving the move before it becomes official. I don’t want to take away from what you’re saying, but that’s kind of what I hear you saying. It’s like how you go about doing it is changing completely.
Quin Kilgore: 100%. And like everything with artificial intelligence, it requires a human in the loop, right? We still have pilots fly our planes, even though we all know it’s likely running on autopilot most of the time, right? I don’t think any role is going to be any different than that. You can let your AI go do a bunch of stuff, but you will still need to go check to make sure everything it just did is accurate, because ultimately, if it breaks, it’s going to be on the person that set it in the first place, right? So, that’s going to be kind of the air traffic controller role, I think we’re all going to have to run going forward. Instead of having 1,000 tabs across our browsers all the time, having to flip from one context to another, we can watch all of this happen in real time and make sure that everything that finishes is done accurately.
Brad Johnson: Well, and one thing, we’ve mentioned PII a couple of times, but just to make Shannon happy on the compliance team. One of the things, so I hear a couple of things, just to identify themes here. So, your data is gold. Make sure you’re capturing it. It’s very important data, and it’s PII, so it’s got to be secured. SOC 2 compliance, something I hear quite a bit. So, there’s certain regulations, if you’re an advisor out there, that might be a good question. Is it SOC 2 compliant? Are there other questions they should ask around tools like that?
Quin Kilgore: Yeah. So, SOC stands for security of control, so to make sure that they understand the data lineage of where their client’s data is going to be sent. Now, just about every tool has an AI component. They need to understand what are the underlying AI models to make sure that the tools that they use have an enterprise contract, that they don’t train on your client’s data. The one thing no business, and especially no financial advisor would ever want is for these hyperscalers to create your own business, because they have all of your data to do that. So, it’s your job to actually read all the agreements and make sure that you are keeping your business and your clients safe. That’s why, at Triad, we have a vendor management committee to help us sort through all of these different security protocols that exist.
And then we also have an AI governance protocol that goes through and also reviews the AI stuff on top of the vendors that we use. And that is a requirement. I think it’s a Reg S-P requirement that is brand new from the SEC.
Brad Johnson: All right. So, we’re nerding out, but we’re trying to speak in normal human talk here. So, data lake, get the data in there, make sure whatever technology tools you’re using, that data is protected at the highest level, SOC 2, everything Quin just talked about. And then if we look at the infrastructure of how you use that data to serve your clients, to serve your business, now we’ve got all of these random AI tools getting created. I picture them, this is probably a horrible analogy, like a pontoon boat floating over your data lake that has little pipes that suck the data up. I don’t know. There’s probably a better analogy out there. But you’ve got all these different AI options out there.
One of the things that has been really cool as I’ve collaborated with you, it’s like you actually have to think differently about building technology today, post-AI, than you did before, because the tools on top of the data rarely changed. They were like the big 800-pound gorillas of the industry. Now, you’ve got to build in a way they could be swapped out.
Quin Kilgore: Yep.
Brad Johnson: So, how has that changed your thinking around how you architect the build?
Quin Kilgore: I mean, not to reiterate our point of your data is gold, you need to be able to port your data from one system to another, and it might not even be a year between having to move from tool A to tool B. I think we’re going to see new interfaces entirely that get created because of artificial intelligence. We’re all now very used to working within a chat window, and largely nothing else but that chat window, because we’re now used to prompting, because it’s been around since 2023. What is the reason to go view a client record when you can view all of your client records all at the same time, all with one prompt that you threw in your tool?
With that, as long as your data or your context transfers from system A to new system B, I don’t think there’s going to be much difference, right? So, as an advisor thinking through everything, it really means a lot to keep clean data, keep good processes that make sure, hey, if you get a new prospect and turn them into a client, do you have a complete client record? Do you have all those fields that you might need? Do you have all of their preferences in a spot that you can easily run a prompt and say, “Hey, show me all my wine lovers. Let’s schedule an event for them”? You need to be able to port that data quickly and easily from system A to system B. So, it’s a completely different paradigm that exists now, that didn’t exist back then.
Brad Johnson: Well, as we geek out on the future and where it could go, so keyboards have been how we’ve got the data into computers in the past. Now, you actually, Zocks, we mentioned before, now I can speak it, and if my system is set up properly, you’re still probably following a fact finder for advisors to keep them on track, obviously, gather the data you need. But the truth is, if they mention, “Hey, what’s a hobby?” “Oh, we love red wine. We love to go to wine country,” that’s now captured in conversation. That’s now plugged into the data lake. Now, AI on top of that, with a prompt instead of a team member spending a day looking through tags they didn’t do in some CRM that they didn’t think about back in the day, now if that phrase was uttered, you could spin up in five seconds all of your wine lovers in your entire client prospect list. Is that fair?
Quin Kilgore: That’s fair, and I’d take it even further, right? You can have it automatically create the email. You can have it automatically go out and find you three places to book that event that you want to have. You can have it go ask for quotes for what the wine’s actually going to cost for that event, and you can have it spit back to you in a way that you just have to approve which one you want to go with, right? It can take it every little chunk a little bit further than ever before. Where previously, think about how many people would’ve had to touch that concept. It’d be the financial advisor, probably an Op staff, probably an event coordinator, all need to go out and make these things work.
And think about the time savings there of just being able to ask this thing, to go spin up and see how many wine lovers that we have. You can also go a different route with that and say, instead of wine, like, “Hey, I would love to host an event for my clients. What is the common thing that most of my clients like, to hit a majority of the firm?” Right? You don’t even have to specify wine.
Brad Johnson: Just based on the conversations it’s captured, what are the themes coming out of all of these retirement-level conversations, which by the way, hobbies and travel almost always comes up in a first if you’re asking?
Quin Kilgore: You know what I was thinking about the other day was the ability of think about a new prospect that comes in. You probably know very, very little other than first name, last name, email, maybe phone. It’s on the financial advisors, especially the good ones, to build that relationship, extract that information from the client, so they remember that to build the trust with them. Now, with lead enrichment, you can say, “Hey, you grew up in the Midwest on a farm. You probably love football. Oh, you do love football. You know what? I bet you’re deeply into sports cards because of your age,” right? Like, that’s not a big jump.
Brad Johnson: Oh, now you’re hitting on a topic close to my heart. I see where you’re going with this.
Quin Kilgore: Yeah, but like it’s going to make those connections largely in real time before the advisor even steps into their first meeting with that prospect. So, the ability to actually make the connection, build that trust is going to just help our entire industry.
Brad Johnson: Well, and truth is most humans today have a lot of their life on the internet. It’s on Facebook, it’s on Instagram, it’s on LinkedIn, depending on where they’re kind of what they’re into. I mean, all it has to do is have a plugin that searches for an email or something. It’s probably pulling a lot of publicly available data. Now, obviously, in the world of finance, make sure it’s compliant, whatever you do. But the future of where things are headed, instead of filling out a fact finder, you could have the fact finder half-filled out by the time they step in, if you’ve got the proper data feeding into your system and not having to re-enter it every single time into 50 different systems.
Quin Kilgore: Yep.
Brad Johnson: Here’s an observation, too. I want to get your take. We run an AI mastermind here at Triad. I’ve learned a ton. Michael Hyatt is the one that helps facilitate it for us. Is it fair to say he’s gone deep down the rabbit hole, Quin, on AI?
Quin Kilgore: Oh, absolutely. Yeah. He’s extremely fun to talk to about anything AI because he’s researched it way more thoroughly than just about anyone I’ve ever talked to about artificial intelligence.
Brad Johnson: And he’s running a lot of his business on it today, so he’s not just talking about it. He’s doing it, putting it into action. But one of the things inside of this mastermind that’s been a learning there’s a lot of, back to all the shiny AI objects floating around finance and getting pitched to advisors every day, what we keep coming back to is, are they vetted? Nothing against China, but if it’s DeepSeek AI that’s backing it, do you want your client’s data being fed to China? Probably not. So, one of the things that I’ve started to think about with the tools we build here at Triad is, how do we take the sandbox back to the data lake? And as we pull that data into tools that serve our members and their clients, it’s got to have a sandbox of SOC 2 compliance or all of the compliance regulations surrounding that data.
And instead of saying, “Oh, can’t upload this to Claude because it’s not enterprise level, and they’re training on your data and all that, so nope, can’t do that, can’t do that, can’t do that,” how do we start to bring these AI tools inside of the sandbox? I think you said something earlier, they’re like a lot of the tools you’re seeing are kind of native AI that lives inside the tool. So, like, to the just average advisor that doesn’t geek out on this every day, how do you start to look at how AI tools could serve them, and you’re not constantly running into this firewall of PII, PII all the time?
Quin Kilgore: The easiest way is to have the AI native within whatever tool that you’re using, without it having to reach out into other tools. You just have the backend artificial intelligence model running within, like, what you’re saying is a sandbox. Don’t ever let it leave that sandbox, so it never goes out, never comes back, with that data, right? That’s one concept. I think the new thing that we’re really exploring, and this is where it gets extremely complicated, so please help create me metaphors and analogies here on how to make it make sense. But when you say sandbox, that’s another term in nerd talk for virtual private cloud. Our own sandbox, right? Within our own VPC, we can put whatever tools that we want to have, and we could have models live natively within that sandbox, that VPC, so it never leaves the sandbox.
And then you can have little tools, the CRM, the planning tool, the risk tool, maybe the annuity, the writing app software, right? All within the same sandbox, all within the same VPC, and you have a model expand all of that, right? It takes a lot from a tool called Data Loss Prevention, DLP, to make sure that it pulls out all the data that you should never have hitting these AI models, right? So, if you build this all together within one technology stack, you could have a really cool, thorough AI system. The best company I can think of that’s doing this is Palantir, right? You definitely don’t want your enemies having the same access to your data or your AI models, as you have, right? So, they built out a software within their own sandbox. So, we’ll have to do something very similar.
Brad Johnson: Because they’re doing it at, like, the highest level of national defense.
Quin Kilgore: Exactly right. They have the most confidential data, likely in the world. And we’re not far from that in the financial services space here, where we have to really protect our client data. So, to reiterate, for now, we’re keeping AI native within certain tools, and eventually that sandbox is going to get bigger and bigger and bigger as we find ways to protect ourselves from these connections we’re making from AI models to CRMs to planning tools, all of that.
Brad Johnson: All right. Just to bring a few advisors back here, because I know if we go too much nerd talk, we’re going to lose some people, so I’m going to bring it back. Let’s talk about AI and marketing. Oh, their ears just perked up. We’re good to go. So, one of the cool projects we’re working on right now at Triad that’s in the infancy, the beta test, or what do we call that in technology? Is it a beta test, or is that the second test?
Quin Kilgore: Yeah, we’re running a beta. So, initially, you’d run an alpha. An alpha is when you basically don’t even have a wall set, right? Just kind of a concept. Beta is like, we have the software, we’re looking for active feedback, and then you go live after we’ve implemented the feedback from beta.
Brad Johnson: Cool. So, this is currently in beta, by the way. If you’re a Triad member listening to this, you’ll have updates in the near future, should be actually before this goes live. So, we have a small group of Triad members that we’re actually actively onboarding right now. We’ve kind of code-named it Triad AI. But back to building tools that are AI native, that are pulling the data, that empower you to serve your clients at a higher level, we’re really excited about this. And what’s cool on the front end, it really started as kind of a marketing ROI software, and one of the big needs that I’ve heard, well, once again, for two decades, all of the marketing shiny objects get pitched to everybody in our space.
And I remember back in the day, it was dinner seminars when I first got in the business in ’07. That’s still a pretty standard marketing funnel. Then it became radio shows. Then it became write a book. Then it became TV shows. Then it was, like, do a podcast, YouTube. Like, it’s continued to evolve, but the bottom level is a one-to-many that gets prospects to book an appointment that eventually could become a client. Well, this software, back to your data and how do we use it to serve our business, it’s actually automating a lot of the front end, such as APIs from mail houses that could say, “Oh, here’s your results on a dinner seminar versus a non-dinner seminar educational event, versus your radio show, versus your TV show,” just go on down the stack.
But instead of some team member filling out a spreadsheet and then reminding them when they forget every two months, it’s now just feeding. It’s automated. So, I’m going to throw it back to you. As you foresee the future, and technology, and some of the things that as we have this data flowing in where advisors can run their business smarter, where it’s actually, “Oh, put more money into the marketing funnel where we’re getting a five-to-one versus the one where we’re getting a one-to-one,” what are things you see playing out, like, if you were back in the AI workshop, mad scientist style, that this is going to allow and create for advisors in the future? What are some of the fun ideas with where you see this going?
Quin Kilgore: You know, the idea that I like the most, especially with that marketing automation engine that you just brought up, we are going to have the ability, along with our coaching that Triad provides, to be able to say, “Hey, let’s look at your ROI on your marketing campaigns. Which one gives you the best bang for your buck? How can you get the most clients per dollar spent?” And with that, we could probably offer up some dollars to our top-performing advisors and say, “Hey, we also get an ROI because when you grow, we grow on these sorts of campaigns.” I’m really excited about that concept of where we can help really hone in the spend, across all of these shops, because we’re going to be able to see a ton of data that we can run, like, data science-like models against, to see what is actually performing well.
And maybe something performs really well on the East Coast but doesn’t apply to the West Coast. It’s going to be able to think through those sorts of concepts that would be very, very complicated if we were just using spreadsheets now. The other thing that I’m just cheesing out about is the ability for our advisors to have agentic AI actually work for them without them having to spend the hours, if not days, if not months, thinking through how they build these agents. We’re going to help build them for them and implement them. Now, we have to figure out are we the ones playing air traffic controller for them, or are we going to hand that off to our offices. And that’s what we’re going to try to figure out with the beta. So, I’m extremely excited about where we’re headed, especially in 2026 with artificial intelligence.
Brad Johnson: So, on your first point there, something that, I mean, especially when I was doing coaching calls all day long, what I would always hear is, “Hey, how’s this working for other people?” You know, whether it’s the latest dinner seminar invite, whether it’s some email campaign, and the truth was I was their best resource because I was the guy just having conversations. So, I was like the newspaper guy on the corner that’s just talking to all the random strangers back in the day. Well, now it’s only as good as what I remember, what advisors I talk to. And so, like true benchmarking has kind of sucked in our industry forever because the data wasn’t there.
Back to your first point. Now that we have the data, now we can truly start to benchmark, and we can say, “Hey, here’s how dinner seminars compare to educational events. Oh, wealthier people show up to educational events, just less of them,” right? “Oh, here’s the ROI here.” But now take that a step down. Pretty much every advisor I’ve ever talked to, especially at Triad, that want to scale businesses bigger than them, I don’t want to be the founder that is the only salesperson or the only advisor building a book on my team. Chick-fil-A has more than one cash register for a reason. They probably want to make multiple sales.
So, now, how do I start to coach my team up? “You’re doing well. You’re not doing well.” What is a benchmark on what’s a standard closing rate from a first to a second, second to client? How can I start to see what are the skill sets on my advisor team, where, “Wow, this guy’s really good at working with referrals, but not so good at working with colder prospects that come from a dinner seminar”? And now allocating the proper appointments on the proper calendars to maximize ROI. So, we’re still at the top level, but the further down the funnel you go, the more opportunities you identify organization-wide. And I’m kind of getting excited, like in a geeky way, about all the opportunities this is going to open up as this continues to evolve.
Quin Kilgore: There’s so much opportunity with being able to capture all of the phone calls, all of the emails, all the text messages, and the transcripts from client meetings and also seminars, right? Between all of that, that data ingest, there is so much coaching opportunity, and there’s also like self-service coaching opportunity, right? If you get out of a seminar and you have, let’s say, a 25% close rate, your first prompt then on that Monday when you get back is, “Hey, how do I turn it from 25% to 50%?” “Oh, you use this sort of language that you’re losing your audience at this time,” right? That can be real-time feedback within artificial intelligence without even having to ask anybody.
So, these are the things that we’re building that’s going to be incredibly useful, along with the actual coaching that we’re working to provide, from a sales and marketing perspective, all integrated within our tool set.
Brad Johnson: Well, back to data, we’re going to pound this drum. When you’re a coach, you can only coach on the data that you’re provided. And there’s a serious deficiency of real data in finance. Like, I remember when I was coaching, and I’m looking at Ryan and Nick right out here in front of me, they have such a heart to serve, and they have coached some of the top, if not the top seminar presenters in all of America, because a number of them are Triad members. And It goes back to the old Jim Rohn quote, “You’re the average of the five people you surround yourself with.” Well, guess what? If I’m a pretty good seminar presenter, and then I’ve got five other amazing seminar presenters and we kind of crowdsource best practices and wisdom, everybody gets better. That’s just how it works.
But now if we have the data that actually takes the audio, has the themes, says this guy books 25% more consistently, and by the way, it’s weird, he talks about these two additional topics every time that you don’t. Guess what? The coaching just got a ton better. So, I’m just like so excited how it’s serving our members, serving their clients, serving our company, because we can now serve everybody better because we have better data that we can actually extract learnings from and deploy across the organization. So, I know we’re getting towards the end here. We’ve talked a lot about kind of the shift of thinking. I’m going to just throw this one at you. All right. Quin resigns as CTO. Please don’t do that, by the way.
But let’s just say in an imaginary world you do, and Quin decides to become a financial advisor in Omaha, Nebraska. Day one, you’re building your firm. Give me, like, I don’t know, maybe first 3, 6, 12 months of how you would start to think about how you would build the technology. And I know one of the things we’re doing with our members is we’re partnering with them to help. But if you were just out there, you’re a solo guy, how would you start to think about the basics of building a tech stack as a financial advisor?
Quin Kilgore: Wow. So, I think the place that I would start is still the same place you would start 20 years ago. You would start with a CRM. Actually, you know what? Let me backtrack. How would I build a tech stack as a solo financial advisor? The only way to succeed is to actually partner with a Triad type. That’s the only way I believe that smaller advisors are going to be able to succeed, is you can take advantage of the economies of scale that these bigger platformers can provide you. With that, I would get access to a lot of tool sets, like a CRM, right? So, assuming I can’t partner with a platformer like a…
Brad Johnson: So, real quick, just the why behind that.
Quin Kilgore: Yep.
Brad Johnson: Because it starts to get extremely expensive and complicated to build your own tech stack, because you don’t have the economies of scale.
Quin Kilgore: Exactly right. You’re an advisor. What do you need to go get to make revenue? You need clients, right? You can’t spend your time customizing a technology stack at the same time that you’re servicing your clients, at a super high level. To get going, you’d need a CRM so you can track your prospect pipeline. You can track your close rates to know how many clients you’re having, right? So, you can set goals and say, “Hey, at the end of year one, I want to be here. Year two, I want to be here.” With that, you would need to partner with someone to help on the investment and planning side. Right now, again, I’m going back to Triad, right?
Like, the economy as a scale that we can provide financial advisors really helps grow their business and makes it possible to have a small footprint of people in your office. So, with that, you’re probably missing a risk tool. And the one thing that is very 2026 that you absolutely have to have is you have to have an enterprise AI tool to be able to connect all these dots between these systems, is going to be incredibly important going forward for any financial advisor shop.
Brad Johnson: Okay. And just so everyone knows, like Quin didn’t know I was throwing that at him, so it’s not a veiled sales pitch for Triad. What I would say industry-wide that I’ve seen, I grew up in the FMO space, like insurance, brokerage, distribution. You grew up a lot more in the wealth management, the RIA space. It’s the same problem on both sides. It is very complicated, very expensive, very quickly, and when you’re starting out as an advisor, you don’t have the money, you don’t have the clients to pay for it. That’s the truth. But the other disconnect right now is what I’ve seen happen over the last 20 years outside of technology, just how financial advisors build financial plans and serve clients. There was like back in the day, between insurance and wealth management, like this separation between church and state, right?
Quin Kilgore: Yep.
Brad Johnson: Well, what I found, most clients don’t care. They don’t care that you grew up in this BD that does or doesn’t believe in insurance, or this RIA that’s fee only that does or doesn’t, or this insurance shop that doesn’t believe in wealth management, whatever it is. There’s the internet today. There’s AI. They can search and fact-check every single product you throw at them. What I believe is the thesis here that we’re building towards, they want world-class financial planning that is product agnostic and finds the right financial tool to solve the problem in the most efficient way, and they want a great customer and technology experience along the way to enable that.
And that’s this. That’s bringing these two worlds together from a technology standpoint, which, by the way, Quin, is really hard, because they don’t talk or weren’t built for each other. But back to the data, if we have all the data pouring in, now what AI is enabling is the build on top of that allows for that dashboard to make that happen. Is that fair?
Quin Kilgore: That’s completely fair. That’s exactly right, yeah. These walls don’t exist to AI because they can make these fuzzy type connections in real time, right? They can say like, hey, an annuity has an account number, so does a wealth management account. Okay, they’re accounts. What else do they have that are similar? And they can create those tables in real time. That way, it can surface the dashboard and show you your whole book of business. Again, just like that Lego analogy we used, you can spin this up, look at it, say it’s great, and then rebuild the entire next day, same amount of time. We’re talking seconds, not even minutes.
Brad Johnson: Where back in the day, let’s go five years ago, if you were to build it, you would’ve had to have a team of engineers coding or…
Quin Kilgore: Think about that process, right? It’d be a financial advisor, not necessarily knowing exactly what they’re after, probably picking up the phone or writing an email to somebody to then go put it in their roadmap to actually go build that thing with SQL or code, that then creates the report. Then you have to put a visual layer on top of that report to get a dashboard, right?
Brad Johnson: Do you have nightmares about Salesforce reporting back in the day?
Quin Kilgore: I really do, especially when tables don’t connect, how we can actually make that work, I absolutely have those nightmares.
Brad Johnson: Well, yeah, it’d be one data connection goes wrong, now this whole side of this report’s broken. Now, you’ve got 17 emails the next morning of upset team members, which is, by the way, why a lot of these software-as-a-service models are really getting crushed right now, because like AI can just spin it up on demand like that, versus have a clunky rebuild that takes some consultant a million-dollar payday to build for you, you know?
Quin Kilgore: Yep.
Brad Johnson: So, it’s pretty wild. I’m looking at my notes. Dude, hey, congratulations, Quin. You’ve done a great job of making a technology conversation very approachable. I think any advisor listening to this will be like, “Hey, I appreciate Quin like talking like a normal person when it comes to technology.”
Quin Kilgore: Thanks, Brad.
Brad Johnson: Which isn’t easy. Let’s kind of close with this. I think a standard theme through this conversation has been, even with AI tools of today, you’re only as good as your data. I will say, like even going back to the conversation I just had with Michael Hyatt, where we talked about like the six levels of AI and finding out where you’re at. So, if you’re an advisor listening in and you haven’t, if you want to geek out on technology, go back, check out that episode if you haven’t already. But when it comes to AI, you can have the world’s best AI, but if the data below it is garbage, then it’s going to be garbage that’s getting sucked up into the AI.
So, are there standard ways that an advisor can think about, “How do I get my data clean so that it’s kind of like systematized? Hey, I’ve got a team member, they quit, now a new team member comes in and is running the CRM. I want to make sure we kind of have a standardization process”? Are there certain industry norms that they should be following? Is it, “Here’s how to make insurance and wealth management language kind of come together”? What would be thoughts of just keeping the data clean and having systems around that?
Quin Kilgore: Sure. What you’re talking about there is artificial intelligence is an amplifier. So, if you have a bad database, right, it’s going to amplify bad data. If you have good data, it’s going to amplify great data and allow your team to really be more efficient going forward. I’m a huge fan, and as agentic AI gets better and better, we can start offloading these tools to AI, of these data quality rules.
Brad Johnson: Can you define agentic AI versus regular AI?
Quin Kilgore: Yeah. Agentic AI is artificial intelligence that takes a task and breaks it into sub-tasks and then figures out how to solve that problem by chunking up the work, so it can run all at the same time. So, instead of, you know…
Brad Johnson: So, is that an agent? Is that like you’ve created an agent?
Quin Kilgore: Exactly right, and they can all run at the same time. So, it’s part of a task.
Brad Johnson: So, it’s essentially instead of Brad being the team member doing it, I now have an AI agent acting as if they’re Brad that’s hired to do this thing.
Quin Kilgore: Exactly right. You can clone Brad five times and send Brad out to do five different things all at the same time. That’s agentic AI, right? And back to cleaning up your data, and having data quality rules to your business, you should have rules within your business on what a solid client record looks like. You know, husband, wife, couple, do you have their names? Do you have their birth dates? Do you have when they started as a client? Do you have their AUM, where you started, where it’s at today? When was their client anniversary? What are their preferences? All of those sorts of things need to be broken out and consistent from Client A to Client B across all of your records.
What I’ve seen really work is having someone, usually an ops role, schedule about an hour every week, and sometimes on Fridays, to go through and find missing data, and then go work with the advisor to get that sort of information filled back in. Because again, having this data clean makes it very portable to whatever systems we’re going to have in the future.
Brad Johnson: So, if I think of that in my head like an old-school Rolodex, and this will date us because some of the young ones have no clue what that is, so go use AI to figure out what a Rolodex is. Here’s a little note card, name, contact info, email, all that. We can’t have half-filled-out Rolodexes because now we’re not going to have all the data we need. So, now we have a role on the team once a week. AI could even deliver the not-filled-out ones.
Quin Kilgore: Correct. Yep.
Brad Johnson: “Show me all of our not complete.” What’s that? A client profile? Is that what we call it?
Quin Kilgore: Yep.
Brad Johnson: And the other thing, like just learning about AUM language versus insurance, typically it’s a household on the AUM side. I think we’ve kind of adopted that same philosophy on the insurance side as well, right? Like it’s household either way. So, here’s the household, which could be a widow, could be a married couple, could be a lot of different things, but at the household level, kind of here is best practices of a profile. The other thing I would say, back to the cool ideas, we were talking about wine tastings, things like that, you could have a scrub, “Oh, here’s all the ones we’re missing hobbies on or favorite travel preferences.” How cool of a phone call would that be every Friday?
The best, like, kind of huggy person on the team, I don’t know if that’s a word, but your people lovers, they could call out every Friday, “Hey, Quin, I noticed on your client profile, like we always want to understand our clients at a deeper level, and I noticed we hadn’t asked you, like, what your favorite hobby is. And do you have any favorite drinks, favorite snacks? We’d love to know more about you so we can serve you at a higher level next time you come into the office.” How good does that call feel?
Quin Kilgore: 100%, right?
Brad Johnson: I mean, now you’ve created some client connection, and technically, what you’re doing is you’re filling out the client profile so that you can then invite them to fun stuff that connects with them in the future. But that call’s never going to go wrong, so.
Quin Kilgore: Right. Wild stuff. And you can take that even nerdier, right? Let’s say they don’t pick up that phone call. You can then follow up that phone call with a text, a survey style, and say, “Hey, of these hobbies, which one interests you the most? I’m thinking about running an event and wonder if you’re curious in joining us,” right? You can follow up those campaigns. You can also send out an email campaign, all feeding back to the exact same field that is currently missing from the client profile.
Brad Johnson: And back to integrated AI tools inside of that sandbox, we were talking about, if that’s an integrated AI tool, they respond to that email or text with, like, their response. It feeds right back into their client profile because it’s linked, so you don’t have to have a human digging through a bunch of emails.
Quin Kilgore: Exactly right.
Brad Johnson: Well, it’s going to get really fun.
Quin Kilgore: It already is really fun.
Brad Johnson: Yeah. More fun, I should say. Well, I think the cool thing about that, too, I had an advisor that did this probably 15 years ago now. He wanted to do fun client events, and he thought, “Well, what better way to do than to survey my clients and say, like, ‘What cool stuff should I do that you would like to attend?’” Well, we take that idea, just simply getting better data, better knowing your clients, so you can better serve them, and now you run this little campaign for a month. You get all your profiles built out. Now, you send another email or a communication: “Hey, thank you so much. We’ve identified these are the top three highest interest client events from all of you. Thanks for the information. Please select your favorite. And it’s based on your feedback.”
So, humans love to be asked for their opinion. You ask them for your opinion. Now you create experiences around their opinions that they’re more likely to want to go to. Guess what they’re going to bring their friends to? So, data can make sales. This is the…
Quin Kilgore: There’s the headline.
Brad Johnson: There it is. Data equals sales. It took us a while to get there, but we got there.
Quin Kilgore: We’re there.
Brad Johnson: All right, buddy. Well, it’s always fun, and I truly, when I say to nerd out with you, I mean that as a compliment. It’s always fun to nerd out with you. Any closing thoughts, just AI, tech, finance in general, as we close here?
Quin Kilgore: Yeah. Same thing I tell our Triad stakeholders, right? It’s time to lean into artificial intelligence. This is not going away. It’s not like robo-advisors or crypto. AI is going to make everything easier and more efficient. So, if you’ve tried something in the past that hasn’t quite worked, try it again. And then if it doesn’t work this time, try it again in a year. Chances are it’s going to be able to figure itself out. Really excited about the state of our industry, and also the changes that we’re going to have to make to make sure that all these tools are safe to connect together. So, thanks again for having me, Brad. This has been awesome.
Brad Johnson: Yeah. Always love it, Quin, and the whole concept of this show is do business, do life. As I continue to dive deeper down this rabbit hole of AI, Quin, you just sent me a video on this about there’s all this kind of fear-mongering of everybody’s job is over. I think with technology, it always, yeah, it happens fast, but not as fast as the headlines say it will.
Quin Kilgore: Yep.
Brad Johnson: And I think what’s really cool is the AI-enabled teams are going to be able to serve their clients at an exponentially higher level, and they’re going to reap the rewards from it, which is why Triad’s taken the stance to lead on this front, not to put our head in the sand. So, back to do business, do life, done well. You’re going to create more margin to do some life and serve your clients at a higher level. So, that sounds like a win-win on my scoreboard.
Quin Kilgore: Absolutely.
Brad Johnson: So, all right, dude. Well, I’ll be seeing you here later this week in person. Looking forward to that. Thanks as always for the wisdom.
Quin Kilgore: Absolutely. Anytime, Brad. Had a lot of fun.
Brad Johnson: Until next time.
Quin Kilgore: See you, man.
