A Proven Way to ACTUALLY Integrate AI into Your Financial Practice

What if AI could save you an extra 8–10 hours a week?

I’m talking about actual time back. Without adding headcount or overhauling your process.

That’s what today’s guest, Steven Latow, is helping advisors do. He’s the VP of Operations at Zocks, an AI assistant built specifically for financial advisors.

But Zocks isn’t just a note-taker. It automates meeting prep, takes detailed notes from virtual, in-person, and phone conversations, captures action items, and even drafts client follow-ups—without recording a thing. It also integrates directly with your CRM to keep records updated, create tasks, and kick off workflows automatically.

In this conversation, we unpack how top advisory firms can leverage AI tools (including Zocks) to save time, streamline operations, and scale in a way that’s actually sustainable.

Steven makes one thing clear: AI isn’t here to replace you, it’s here to free you up to do more of what only you can do.

If you’re looking to grow your firm without growing your hours, this one’s worth a listen.

What if AI could save you an extra 8–10 hours a week?

I’m talking about actual time back. Without adding headcount or overhauling your process.

That’s what today’s guest, Steven Latow, is helping advisors do. He’s the VP of Operations at Zocks, an AI assistant built specifically for financial advisors.

But Zocks isn’t just a note-taker. It automates meeting prep, takes detailed notes from virtual, in-person, and phone conversations, captures action items, and even drafts client follow-ups—without recording a thing. It also integrates directly with your CRM to keep records updated, create tasks, and kick off workflows automatically.

In this conversation, we unpack how top advisory firms can leverage AI tools (including Zocks) to save time, streamline operations, and scale in a way that’s actually sustainable.

Steven makes one thing clear: AI isn’t here to replace you, it’s here to free you up to do more of what only you can do.

If you’re looking to grow your firm without growing your hours, this one’s worth a listen.

3 of the biggest insights from Steven Latow…

#1 You don’t need to hire more staff to get your time back

Steven shared how some advisors are saving 8–10 hours a week just by automating what happens after client meetings—like notes, tasks, and follow-ups. That’s a full workday, without adding headcount.

#2 AI is becoming the ultimate utility player on your team

It’s helping advisors tag client interests, prep for meetings, even support smooth handoffs from senior to junior advisors. Whether you’re growing or transitioning your firm, this tech quietly fills the gaps.

#3 Most CRMs are just holding info—AI helps make it usable

Steven broke down how AI is turning flat, static data into dynamic profiles that actually fuel your planning and marketing. The data’s already there, you just need a smarter way to use it.

KEY TAKEAWAYS: 

• AI Adoption for Financial Advisors
• How AI is Automating Advisor Workflows
• Saving 8-10 Hours/Week with Zocks
• Trusting AI to Properly Capture Data
• Why Compliance-Ready AI Isn’t Free
• Turning Static CRM Data into Dynamic, Actionable Insights
• Use Case: AI Tagging Client Interests
• How AI will Change the Way You Hire
• How AI Eases the Client Handoff to Junior Advisors
• How Zocks Integrates & Syncs with Your CRM
• Automate the Back Office, Elevate the Client Experience  
• Identifying Opportunities with Existing Clients
• The Future Belongs to Advisors Who Adopt, Not Resist
• Advice for Embracing AI as a Financial Advisor
• Building a Tech-Stack That Grows with You
• What Does “DBDL” Mean to Steven

SELECTED LINKS FROM THE EPISODE: 

• Zocks
• Zocks on LinkedIn | Facebook | X/Twitter
• Steven Latow on LinkedIn | Instagram
• T3
• Shiner Bock
• DocuSign
• Kitces Financial Advisor Technology Map
• Cobol
• Moore’s Law
• Redtail
• Orion
• Calendly
• Hearsay Systems
• The 7 Habits of Highly Effective People: Powerful Lessons in Personal Change by Stephen R. Covey
• Pokemon

PEOPLE MENTIONED IN THE EPISODE:

• Michael Kitces
• Gordon Moore
• Mark Gilbert
• Akos Ratku
• Quin Kilgore
• Stephen Covey

MIC DROP MOMENTS

• “AI is not going to replace the human touch. It’s going to make you drastically more efficient in how you’re able to execute tasks.” – Steven Latow

• “We’re not talking a five-year horizon. We’re talking like a two-year horizon where 80% of financial professionals are using AI in their day-to-day lives to just make things faster and get time back.” – Steven Latow

• “We are not trying to remove humans from the workflow. We are trying to superpower them.” – Steven Latow

• “The dream is current advisors will be able to get deeper with their existing clients and they can profitably serve a larger population.” – Steven Latow

Episode Transcript

Brad Johnson: Welcome back to another episode of Do Business. Do Life. Excited to have Steven Latow on the show here today. Welcome, Steven.

Steven Latow: Brad, how are we doing today? I’m excited to be here.

Brad Johnson: I’m excited to continue the deep dive we did at T3 out in Dallas, where we connected for the first time in person, and then immediately I think over at Shiner Bock on my side. We were diving deep into AI, finance, technology and it was just a super interesting conversation. I’m like, “Dude, we’ve got to have you on the podcast.” So, thanks for making the time.

Steven Latow: Yeah, anytime. I can’t say I’ll be opening at Shiner at 8:00 AM but it is a Friday, so we’ll see where the conversation goes.

Brad Johnson: We’ll see how long the conversation goes. Maybe we’ll make it to happy hour. You never know.

Steven Latow: Yeah.

Brad Johnson: Well, as we kick this thing off, I want to start with a big question. So, AI, I feel like it’s become a catchphrase these days in everywhere, but also in finance. And large language models, those have been around for a long time. They’ve just gotten to the point where they’re actually really starting to make massive impacts on all business. And so, philosophically, I would love to hear where you, Steven, where the Zocks team, where you see AI changing and evolving as far as finance is concerned when you look out three to five years? What are some of your predictions you see on that front?

Steven Latow: Yeah. Starting out with the big questions, Brad. So, I think when you think about new technology, and this is a really exciting time to be a financial advisor because traditionally this has not been a user base that has adopted new technology quickly. And as we saw at T3, the conversation is just moving fast. And so, usually, new tech has kind of a three-phase life cycle. The first phase is what I’ll describe as like the marketing life cycle. That’s where you’re talking a lot about something. Blockchain is a good example about this, where it’s a tagline, but you’re not really seeing practical use cases in your day-to-day workflow yet.

There’s then the second phase where you start to see, “Hey, this is actually ingrained in the system.” So, this is really where we’re at right now with AI systems, where you’re like, “Hey, I got this update on my Outlook that now a copilot is automatically going and generating email responses for me.” I didn’t have to do anything, but it’s saving me 30 seconds an email. It’s saving me a minute an email. It starts to impact your life in kind of silent or low-friction ways, but it’s still obvious to you that, “Oh, this is an AI system.” And the third phase, which is really where I think we’ll get over the next three to five years, is you stop thinking about it as a specific tech sector. So, AI is not the goal, it’s just in your workflows. And so, you’ll stop seeing the company’s brand thing as powered by AI or powered by whatever the provider is and it’ll just be that’s how it works.

And so, that’s when we know that this has really gotten ingrained into how technology is being used, is when it’s kind of silently adding value. And our view at Zocks is when we started, we thought this was going to be a 5 to 10-year journey that was going to go really slow and it was going to take a long time to figure out the compliance frameworks and all of that. And we now, like basically every quarter we’re like, “Okay. Where can we get in the next 60 days, in the next 90 days?” And the speed at which it’s evolving is just really fast. So, my view is we’re not talking a five-year horizon. We’re talking like a two-year horizon where 80% of financial professionals are using AI or AI kind of dependent systems in their day-to-day lives to just make things faster and get time back, which is do business, do life. How can we be more efficient and get time back? Like, AI is the answer to that.

Brad Johnson: AI is the door that opens do life. Is that what you’re saying?

Steven Latow: Yes, it is.

Brad Johnson: Less business, more life. You’re taking me back. So, I got into this industry in 2007 and I remember one of the early value propositions of the business I was in at the time that served financial advisors was, “We’ll help you build a website.” That was like a big thing back then. And so, back to the adoption part, you don’t hear an advisor today say, “Oh yeah, by the way, I’ve got a website. Go check it out.” That’s like it’s an assumption at this point. And so, I hear exactly what you’re saying. I love the framework you just laid out. We’re kind of in the adoption phase where three to five years out, it will just be integrated into everything in the daily use that we do.

How do you think that will change back to just the day-to-day for advisors? What are some things they’re doing today that you do not see them doing three to five years out because of AI?

Steven Latow: Yeah. So, right now where the tech is, I’ll talk about now and then like in the 18 months, what it’ll look like. So, right now, really AI is layering on top of systems and it’s helping you complete individual tasks faster. So, meeting notes is all the rage right now. It used to take an hour, hour and a half to get your follow-ups done, to document your notes. You have the back-and-forth with a team. There’s a lot of friction in that system, particularly if you’re running something like Surge where you have meetings back-to-back-to-back. And we can take that down to three to five minutes where your notes are taken better than you would’ve taken it.

The technology is actually able to hear things that probably the human might’ve missed because of the norms of conversation. And so, that specific task can get shrunk down. Email responses, that’s another thing where a lot of the email response time is actually going and figuring out what data you need to respond to it. So, going and looking at your planning system at your CRM, at your past emails, “Oh, actually my admin staff was the one that responded to this last time. Let me check what they said and make sure that’s still true and that it’s consistent with what I’m saying.” So, making that more efficient.

Now, where we’re going in 18 months is actually stringing together workflows. So, thinking about, “Okay, coming out of a meeting, we have this follow-up to open up an account. That means we need to gather this data and then we need to get a DocuSign signature, and then we need to pass the data to the custodian to open that account.” And an AI system will be able to actually go and execute each of those steps. So, you don’t have to be cognizant of what is the next step. You just have to be cognizant of, “Hey, this workflow is going on and my system is working for me to get it done.”

Brad Johnson: So, I’m thinking like, “Alexa, what’s the weather out today?” Like, basically, the verbal conversations that are going on in a meeting taking Zocks, and we’ll get more into Zocks here in a bit, but taking the discussion out of a meeting, grabbing that data, having certain keywords that instigate actions in a workflow that are automated. Of course, there’s probably going to be some, “Hey, have a human oversight. Check. Make sure we want to move this money from A to B.” But basically, it’s like Alexa for your business, where it’s like the verbal commands are dictating the actions on the backend. Is that a fair analogy?

Steven Latow: Yeah, I think that’s exactly it. And think of it as like, “Okay. If I had a new operations staff member that was good at their job, knew how to interact with clients, knew how to go gather data, but maybe didn’t know my specific style, that admin would be able to go and execute a workflow but you’d kind of want to check in that this is happening with the Brad Johnson flavor, with the Steven Latow flavor, that it’s being executed kind of the way that you want. And that’s really where we’re going to get in the next 18 months is the things that you would delegate naturally, we call them two-way doors in the industry.

So, things that are reversible and lower stakes, those things are going to get taken care of for you, which really it all comes down to buying back time. That means that you get to spend more time either generating more revenue, growing your team, or God forbid, being available to hang out with your kids and spend time on your hobbies. So, it’s not getting to a level where it’s going to go replace the human touch. It’s getting to a level where it’s going to go and make you drastically more efficient in how you’re able to execute tasks.

Brad Johnson: Yeah. Let’s go to the analogy you hit out in Dallas that I thought was really interesting, or this is getting down into the weeds a bit, advisors out there listening in, so buckle up. So, if we look at like the assembly line that kind of a financial advisor oversees, so I’m talking with my hands here for those that are listening on audio. So, on the input side of the assembly line, really, the capture of that starts with those meetings, right? The fact find kind of the before and after snapshot of, “Hey, can we help you fix these problems?” which then goes into engagement and then asset allocation, right, to just oversimplify the whole process. But really the raw material for the assembly line of the financial plan starts in those meetings.

So, Zocks is really gathering the data, and if we want to geek out a little bit on like what is Zocks, because there might be some on here that are completely unfamiliar, but you’re gathering this data and then you’re starting down the assembly line of the actions that need to come off this data, the team members or the different companies that need to be involved, which is kind of getting into that workflow stuff. So, let’s talk about what Zocks is doing today on that data-gathering piece, maybe where it’s headed in some of the cool things you see on the horizon. And then we’ll take it from there. I’m sure we’ll splinter a few different ways.

Steven Latow: Yeah. So, Zocks AI-based virtual assistant for financial advisors. That’s a lot of tag words fit into one thing. What does it do? It takes conversational data and it translates it into action. And so, describing what that means, like, really every action in the advisory space triggers from an input based off of a conversation or new information. And so, Zocks is listening to those conversations. We think about the value prop in three levels. The first is the gathering of the data. The second is structuring it. So, is this a financial fact? Is this a life event? Is this a family member? Is it a potential referral? Is it an interest tag? Thinking about the structure of how can we actually know from that initial data why it matters and what it means and what format it’s expected to be in.

And then the second level is sending that data to downstream systems. Can our planning systems finally talk with our CRMs and our email communication all in one cohesive workflow? And then the third level is really workflow. What do you do with that? How do you go and drive business, give better service, do more appropriate things for your clients based on having a healthy data ecosystem? So, it’s flowing from grabbing the conversational data, structuring it, and then driving action is really what we’re trying to do at Zocks. And right now, where are we at right now, right now we’re able to do those first two levels really well. So, listen in on conversations and structure that data for you and then get it down to downstream systems.

We’re already hearing advisors say that they’re saving 8 to 10 hours a week. I don’t know about you, Brad, but I would do a lot of things to get 8 to 10, one full workday back per week. And the next leap I actually think will be exponential to that because it’s going from completing one task to completing a series of tasks coming out of an event.

Brad Johnson: Yeah. So, 8 to 10 hours, that’s real value. So, let’s go ahead and hit some of the objections I know the listeners are thinking about. And we were laughing before we hit record here. The first time I used Siri, I’m like, “Oh, this would be much easier than texting.” Especially if you’re driving down the road, you’re like, “Cool, convenient. I’ll just do a voice memo.” And then you see the voice-to-text and it’s a jumbled mess and you’re like, “What is going on here? This is, actually, I’ll just go back to typing.” So, how do we know the technology is at a level where it captures this very, not only valuable information, but it’s important, you do not mess it up? I mean, you can’t redo retirement, right? So, let’s make sure the math is right. It’s captured properly.

So, how does Zocks go to the point of like, okay, names are spelled correctly, data is accurate, and it’s a true summary of the conversation that happened?

Steven Latow: Yeah. So, I think the biggest friction point is change is hard. So, if you are running a successful business, there are a lot of things in place to keep you on the thinking track of I should just do more of what I’m doing right now and figuring out how to do more of what I’m doing right now. And this is really a change to your day-to-day life. And so, what we’ve done at Zocks is really, intentionally, we are not trying to get to what I would call true automation. We are not trying to remove humans from the workflow. We are trying to superpower them. And so, what that looks like in practical terms is you are still going to go review your meeting notes.

You’re still going to go be the one clicking, “Yes. Take this to the next level. Send this to the CRM. Send this email. Send this workflow to my assistant or to my paraplanner, including this data.” It’s just going to go and aggregate it for you. So, the human is still intricately a part of the workflow, like the kind of, I don’t know, the fear around, “Are these jobs going to get taken away based off of AI systems?” I don’t share that fear. I think it’s just going to be a gross productivity increase. And so, humans will stay in the workflow. They’ll still validate data just like they would before you send a text.

You’re maybe distracted, you’re with your kids, you’re writing out a text. That’s something important for work. You’re going to look at that text before you send it and be like, “Man, I wasn’t paying attention. That doesn’t sound great. I’m going to go and edit that.” And what’s cool about the AI systems is, as you make those edits, they personalize to your kind of personality, to how you communicate, to how you execute workflows, and they can learn over time.

Brad Johnson: So, the AI is learning with you, it’s adapting to your individual behaviors.

Steven Latow: Exactly, just like a team member would. You hire a new team member. The first 90 days, you’re getting to know each other. You’re figuring out your style. You’re figuring out how to work efficiently together. These AI systems, for all intents and purposes, are kind of the same thing, so they will improve as you use them more and they’ll fit into your workflows. I think the Siri example is a great example of you started using Siri and you’re like, “This isn’t saving me time.” And then Siri started to go and take actions for you on your phone that you weren’t even conscious of.

And now it’s kind of built into your workflow where you’re in your car, you’re doing voice detect. Siri is executing that, but you’re like, “I just pressed the button and say who to send it to,” and it just works.

Brad Johnson: Most of the time.

Steven Latow: Yeah, most of the time.

Brad Johnson: Yeah. When your name is Brad Johnson, there’s a lot of Brad Johnsons in the world, so you have to be careful who you’re texting.

Steven Latow: It’s okay. My last name has been spelled wrong every single time someone has texted me for the first time. Turns out Latow is not really a phonetic spelling or a common last name.

Brad Johnson: There you go. Well, let’s go to, I heard it said and I think it was in reference to Facebook. If you don’t pay for the product, you are the product aka they’re harvesting your data, selling it on the back end, monetizing it to ad buyers, all of that. I know that was one of the questions I asked you out in Dallas because I remember the very first AI note-taker that we used on Zoom, “Oh, it’s free.” And then I’m like, “Wait, wait, wait,” and we had compliance dig in and come to find out they’re using your data on the backend to train their AI models or to sell it to monetize the company. You have a cool background. You actually came from hearsay, which is heavy into compliance and suitability and finance.

And so, let’s talk about personally identifying information and how to make sure just the data and the sensitive topics that come up in meetings are secure when you’re using AI and how to kind of filter for that if you’re an advisor.

Steven Latow: Yeah. So, I think that’s actually kind of the biggest filtering criteria is what you just said is, was this purpose-built for an advisor? And a lot of the general solutions, the Facebook analogy is perfect, that are free. They are getting something out of it. There’s no such thing as a free launch. So, they are using that data to train. And when you’re talking about financial conversations like that is the last thing you want to do is have these really private and personal conversations be genericized into these large language models.

And so, number one is, was this product built for my use case? This is something you should do anytime you’re looking at technology. Number two is, how does our compliance department feel about adding on technology? And do they have the tools that they need to analyze an AI system and say whether it’s safe? So, a few of the things that we do at Zocks are, one, we’re never recording your conversations, and we think this is important because of the archiving and books and records implications of recordings. Two, we’re anonymizing the data before we actually send it to the large language models for processing, so that we’re taking out that PII, we’re taking out the names of who said what, the things that would make it easy to identify.

And then number three is we actually have enterprise contracts with the LLMs that make it so that they cannot persist or save or train off of the data. So, we use them for processing, but then they have to drop that from local memory. Sorry to get a little nerdy. The meaning there is that they can’t actually save that data. What does that mean? It means we’re a premium product. We have to pay more to our underlying service providers to have that structure in place. And so, you can’t really compare a Zocks to some of the other horizontal solutions out there because the whole monetization strategy is totally different.

So, a relationship that a more horizontal one has with an LLM is they feed it data, they get results back. It’s a happy ecosystem, but there isn’t any privacy. We have to pay for that privacy. And so, that gets passed on to our users. So, what happens is it’s a premium product. We think that there’s still a ton of efficiency to be gained by being vertically aligned. So, not only is it safer for your clients to use, it’s safer from a data perspective, but also it’s going to be more efficient because it actually looks for the types of things that you care about as a financial advisor.

Brad Johnson: Love it. Thanks for hitting that because that is something that I think is completely a blind spot for a lot of advisors out there like, “Oh, cool. I’m using AI.” Even ChatGPT, there’s a privacy setting on the backend most advisors don’t know. And if you don’t have that checked, it is literally feeding all of your information to their models to train them. And even the paid version, which is wild. Yeah, go ahead.

Steven Latow: You know, I’m a 15-year technologist. This is what I live, eat, and breathe. And it’s hard right now. You look at like that Kitces map that he publishes of technology for financial advisors, and it’s getting crowded on there. There’s 250 solutions. And I feel deep empathy for advisors who are like, “Hey, I think I can be more efficient. I don’t want to be a lud. I want to go and adopt new technology, but where do I start? This isn’t what my core competency is. How do I get started?” And I think it’s really exciting how fast it’s moving, but it’s also introducing a ton of new technology to an industry that traditionally does not move quickly.

Brad Johnson: 100%. Yeah. I mean, obviously, the wealth side of the business has had to, from a technology standpoint, just transferring assets, trades, all of that. It’s had to advance. And I think COVID, obviously, accelerated that, but you go to the insurance side of the house. Oh my gosh, we’re talking like legacy mainframes running on Cobol, which was what I learned how to code back in like 2003, right, when I was an IT major in college. So, let’s go to the speed of advancement. I think that’s a good segue. So, Moore’s Law, for those that want to nerd out a little bit, so, chip, I actually had to look this up so I won’t take full credit for this, but chip pioneer, Gordon Moore, who discovered that the number of transistors on a chip doubled every two years.

So, it was basically with technology as it evolves, it speeds up as it evolves because it gets more efficient and more compute power. And we’re definitely seeing that in AI right now. So, going back to Zocks and kind of like we’re saying, “Here’s where we are today, but here’s where it’s headed,” you’re grabbing the data and the data is the foundation of any business. You’re only as good as your data and if you’ve got a CRM that spells it differently than some other place in your business, now, the data can’t talk to each other. And one of the things that you shared with me that I thought was really interesting is the AI models are actually evolving and changing the build of the data, the data lakes, and how it’s stored and how you access that data.

So, without getting too nerdy and geeky, how would you explain what that’s going to create and then how to build your tech stack on top of that?

Steven Latow: Yeah. So, I think there’s a few things there. From like a Moore’s Law perspective, when we were starting 2018, the AI systems were really like a preschooler. So, that’s like that Siri analogy where you’re like, “Wait, why am I using this? It’s faster to type.” Where we’re at right now so fast forward, what, seven years is it’s really like an advanced high schooler or an undergraduate. And as we anticipate given Moore’s law, we’re going to get to where it’s a graduate degree kind of advanced person with knowledge of your specific industry over the next 24 months.

So, with that in mind, how is this impacting data between systems? Well, if you had an advanced high schooler, you would be able to say, “Hey, take this transcript and go figure out in my planning system, in my CRM, in my custodial system, are all these data points consistent? And if they’re not, figure out what the accurate data point is and make it that one for everything.” And it would take them forever. That sounds like the worst job in the world. But they would be able to go household by household and figure out, okay, this is the primary source of data, this is what it should be in the underlying systems. And so, the AI systems right now are able to reconcile that data across systems.

And what it means is if you think about your CRM, it’s really a bunch of Excel sheets that are going and talking to each other and have relationships. It’s called a relational database that are then presenting in a UX the information that you want. But those Excel sheets are pretty stacked.

Brad Johnson: Can you explain UX for those unfamiliar?

Steven Latow: Oh, user experience, so what you see when you log in. But those Excel sheets are static. You can add on a column or you can add a row, but you can’t change kind of the model of what it is. And what the AI systems can do is from a conversation, it’s really taking a dynamic view on what do we know about this person. So, it’s going and creating the household, and then it’s filling out all of these different tables based off of that conversation. So, it’s not dependent on a specific data format. It’s able to go and tag things, whether it’s a life event, whether it’s a financial event, maybe it’s a referral, maybe it’s a dog’s name, and we actually care about that data point, or it’s something about the husband’s height and weight so that we can go and get an interest so we can get them in a marketing campaign.

So, it’s able to dynamically give you a view of everything we know about that household and then send it out to those underlying systems appropriately, which is really exciting. This is the basis for how we get into workflows, how you save those two days of work a week, how you spend more time doing the things you like is that you’re doing less time getting your systems to talk to each other and less time curating information.

Brad Johnson: Okay. So, here’s the analogy that’s coming to my mind, and you just correct it if I’m off base here. So, I’m picturing the manual process of, “Hey, have this conversation.” Either you’re taking notes in the meeting as a human or you’ve got maybe an assistant or like a second chair advisor taking notes. And they’re doing the best they can. They’re hacking away at the computer, feeding it into a spreadsheet or just a note where it’s like, “Oh, like red wine. Oh, dog’s name is Woofy,” just all the stuff that comes up. Sorry, that’s all I could come up with. And so, this is like a 2D like, okay, I’ve got a format, hobbies on the spreadsheet. Here’s clients down the left-hand side.

I’m picturing AI automating that, just extracting all of that. And now I’m picturing like a Rubik’s cube of it’s like just the data’s being piled and now it’s like, “Okay, hobbies.” You’re flipping the Rubik’s cube and the AI’s extracting it. Oh, husband, wife, age, birthday. It’s all the stuff that you need to actually plan. So, it’s flipping the Rubik’s cube the other way. Okay. Extract this data for this need. Is that an oversimplification of just, I don’t want to geek out too much, but I’m trying to give the audience a visual of like what’s changing with how that data’s gathered and then used.

Steven Latow: Yeah. I think that’s exactly it is it’s essentially gathering all of the information and then able to serve it to the right system or to the right person based on the context. And that’s a big change from how things have been done in the past.

Brad Johnson: Well, here’s a case study for you that we talked about in Dallas. And we always talk about the riches and the niches in our industry. And so, it’s like, okay, I’ve got all these high net worth clients. Obviously, there’s definitely some similarities, there’s some differences. Here’s my group of exotic car people. Here’s my group of wine lovers. Here’s my group of golfers, so fill in the blank of like going around the circle of their interests. Now, you’re utilizing Zocks. And if this is too simplified, correct me, but you’re using Zocks for a year. You’ve got a year worth of client conversations. You’re like, “Build me a list of all of my wine lovers, including the specific wines they love if mentioned, and create a list because we want to do a wine-tasting client event where they can bring a friend.” At this point, can we extract that right now? Are we able to?

Steven Latow: We can extract that. And there are two things that are really cool about that. One is no programming is necessary. So, you can query and say, “Hey, I’m running an event. I want to curate it towards my clients. Tell me what wines do they prefer and who would be interested in a wine event. So, you don’t actually have to say, “Match to this data point in this system and this data point in this system.”

Brad Johnson: And list specific wines they might have mentioned they like as well.

Steven Latow: Yes. And the second thing is you can actually say, “I’d also like you to prioritize them based off of who is likely to join my event.” And what the AI systems can do is say, “Okay, I know that this underlying data shows me past events. I’m going to go and assume that people that traveled to past events are more likely to travel to this event. And so, I can actually go and prioritize the return set or the return of the data that you’re getting.” So, not only give you the kind of holistic, these are the people that like wine and what wines they like but actually go and give you context on how popular is this event likely to be. And when you think about how long would it take me to curate that list on my own, it would be quite difficult to go look at all the…

Brad Johnson: Yeah. Especially the high schooler analogy, like a lot of people literally hire an intern. They’re like, “Okay, I wish I would’ve done a better job with my CRM. I didn’t tag all my wine lovers. Can you go back through this stack of past wine events, RSVPs, retrofit it into the CRM and the whole workflow deal?” I mean, like if you think about Redtail workflows, everybody knows they should have them, but they haven’t built them. They’re like, “Oh, I’m going to borrow them from my other advisor friend that actually had somebody that geeked out on this.” So, now you’re almost getting to the point you don’t need to create them because they’re literally on demand. You speak them into existence. Is that what’s happening?

Steven Latow: Exactly. And so, if I look back at the last year in Zocks, the number one data point that I think is the most encouraging is when advisors tell us, “Hey, instead of adding an intern, instead of adding an ops person to take notes, instead of adding additional person to just keep the business moving, we’re actually hiring a second chair, or we’re hiring a junior advisor, or we’re supporting one of our current staff to go get certified so that they can grow the business.” And so, instead of spending that time hiring an intern to say, “Go clean up our data over the summer,” you’re able to put that effort into going and doing revenue-generating activities or having more time for yourself.

And we talk a lot about burnout within the industry, how we’re scaling horizontally instead of scaling vertically. And this tech change is what is going to allow advisors to grow their business without just having to layer on employment costs.

Brad Johnson: What are some other use cases you’ve seen? I think that’s number one. I think a lot of offices out there that have heard like, “Oh, I could literally curate my group of clients in all of these different interesting ways.” I think there’s probably some light bulbs going off right now for listeners or viewers, but what are some other real-life use cases you’ve seen advisors out there utilizing with Zocks or team members if it’s something kind of more on the administrative side?

Steven Latow: Yeah, I think I was talking to a shop last week and they are all in-person, it’s high net worth, and it’s also a transition planning where the senior advisor is handing off to one of their kids and the kid is quite a bit younger, hasn’t been involved in the business for as long, and this advisor…

Brad Johnson: Real quick to clarify. Dad or mom is senior advisor that’s been doing this a long time. Son or daughter is coming into the business as junior advisor. So, it’s like kind of passing the clients down to a…

Steven Latow: Yeah.

Brad Johnson: Okay.

Steven Latow: Yep. And junior advisor is super bright but doesn’t know the clients. And dad has been doing this for 40 years and has not been diligent in writing up profiles of all of their clients.

Brad Johnson: Sounds pretty standard in our industry.

Steven Latow: Yep. And so, what they built was a workflow where they’re like, “Hey, we can’t scale to having daughter in every single meeting.” We know that we need to start introducing her. And so, how can we use tech to facilitate that? And so, what they did is they actually went and used the system to pull into pre-meeting prep the preferred drink order of every single client. And so, as the client comes, the daughter runs that pre-meeting prep, sees the drink order, brings in the drinks to introduce herself to the new client and to the advisor, turns on Zocks so dad doesn’t even have to change his workflow. He doesn’t have to do anything, and then sometimes sticks around for the conversation and sometimes leaves. And when the client’s done, goes and grabs the drink, says goodbye, and ends the meeting analysis.

And it’s just a simple way of, hey, now we’re starting to build out the data. Our clients are starting to know the person taking over the business, and we’re giving a more personalized experience of we’re not spending the first 10 minutes of the conversation talking about what you want to drink, going and getting it, coming back, sitting down, standing up, all that stuff. And so, it’s just going and making that kind of getting to know you and getting into the financial conversation more fluid. And I thought it’s such a minor use case, but it really is a big change and so easy to do with the current systems.

Brad Johnson: And it solves the need where I’ve seen that screwed up. It’s the throwing of the baton versus the passing of the baton where literally dad’s like, “I got too many clients. I don’t know what to do.” And he just leaves the room and then daughter comes in and is like, “Good luck.” And by the way, no client wants that experience where the person you trust for the last 10 or 20 years just disappears from the meeting. So, you’re creating a way where kind of old guard can be passed to new guard in a seamless way, even if they don’t understand the tech.

Because I can tell you, that’s one of the biggest gaps I’ve seen is if you grew up yellow pad in a meeting taking notes, you are not going to all of a sudden change 30 years later and say, “Oh, now I’ll start typing out meeting notes or recording audios after the meeting to pass this information.” But now you can do that passively, which actually gets the information to the new guard that needs it. So, that’s really interesting.

Steven Latow: Well, I think that’s the coolest thing is in that old guard model, he’s trying to retire. He doesn’t want to learn a new system. He doesn’t want to go spend all of his time writing up profiles on each of the clients. He’s trying to work less. And so, without asking anything of the senior advisor, he’s able to set up his daughter for success in this business. And that’s pretty cool.

Brad Johnson: What if procrastination was a source of wisdom? That’s one of my favorite little business lessons. And you tell dad, “Hey, you’ve got 200 clients. Go ahead and write up a profile on all of them.” He’ll be looking at that stack of papers three years from now because it’s never going to get done. So, the wisdom is what if we found a different way to do the same thing? I love that.

Okay. So, let’s go back because I know you guys have built some integration. So, let’s use that case study you just shared there. And let’s say this firm actually did a decent job of capturing in their CRM their favorite drink orders for clients. Is there a way for Zocks to plug into the data at this point in a CRM? I know Redtail’s kind of the de facto for like 70% of our industry at this point, but how does that work if some of that data has already been captured? Or do we have to wait for it to verbally be shared for Zocks to benefit it?

Steven Latow: Yeah. That’s a great question. So, the first two integrations that we built out were into email and into your CRMs, including Redtail. And there’s a bunch of things, but I’ll focus on three things that are really cool about the integration. One is keeping the client file up to date. So, right after a meeting, within 90 seconds, you’re able to save down your note to the client file. This is what we talked about. This is what’s important to them. Here are the follow-ups. Get those follow-up tasks there to the downstream system. The second thing is it’s actually able to look, understand contextually what do we know about this client, and do we have any new information.

So, with a click of a button, you actually get this cool… And I’m using my hands because I’m a hand talker. You get this cool interface that says, “Hey, you met with Brad Johnson. Here are all of the facts about Brad that exist in your CRM right now, or that don’t exist. Here’s new information from this conversation. Do you want to update the profile?” And you can say, “Yes, Brad is 6’2” and so we’re going to check off that one.”

Brad Johnson: Never made it there. Dang it. I’d be in a league right now if I was. No, just kidding.

Steven Latow: We learned about Brad’s new card shop.

Brad Johnson: There we go.

Steven Latow: So, we’re going to add that in as a new asset. Definitely an asset. I’m rooting for you, Brad. We learned a kid’s name that we didn’t previously know, or a new puppy’s name. So, these discrete data points then are going into the system. So, not only do we know, are we able to pull kind of what the drink order is, but if it changes or if there’s clients that we don’t know it for, well, then in the beginning of that conversation you’re going to say, “Hey, we like to have coffee or tea ready. What do you prefer? How do you take your coffee?” And coming out of the conversation, you have your drink order field in Redtail and we’re going to say, “Hey, we learned in this conversation that it’s two sugars, one cream. And coffee, not tea.” So, now we have that moving forward.

So, a long-winded answer to your short question of, yes, the CRM integration is there, email integration is there and it’s deep, and it’s grabbing things that you just wouldn’t go and necessarily keep up to date in your CRM otherwise.

Brad Johnson: So, I think one of the keys with data is like one single point of truth, right? That’s where data gets all jacked up is we’ve got five different versions where this system got updated from this conversation, but not the conversation two years ago. And so, I would say most firms, and I wanted your take, the single source of truth should be the CRM or at least the underlying data that the CRM sits on top of. Let’s use Redtail because I think that’s the most adopted one. How intuitive is the Zocks-to-CRM process where like the tagging of information where, “Hey, this is their drink order, which might be coffee,” but then the added details is, “Two sugars, one cream.”

So, do you have to hand key that? Is it like in a, “Hey, this is what we have. Click approval.” Like, what’s that process look like?

Steven Latow: Yeah. So, it’s really intuitive. Getting back to like your high schooler analogy, how smart is the AI system? A high schooler would be able to look at a Redtail form and know, “Okay, these details apply to the coffee order so I filled that out there. These details apply to pet’s names or referrals so I fill that out there.” And the AI system is able to do the same thing. So, you’re not having to go through a big mapping exercise. You’re not having to do anything challenging. You’re clicking a button after the conversation and you’re getting told, “Hey, do you want to update these fields?” And it’s doing the matching and the processing for you which is a change. Like, it’s a big change.

Brad Johnson: Let’s get to name spellings. That’s a common one. So, how does Zocks handle spelling things? Like, there’s Steven, there’s Stephan, aka sometimes also pronounced Steven. Like, how does it handle things like that?

Steven Latow: Yeah. So, because it’s using conversational language, unless you spell out the name, it either needs previous context or it’s going to go and spell it phonetically and then when you correct it, it can learn. And so, what we do is we will look at the past context from the CRM and we’ll match that to a calendar. And that can give you a pretty good idea of who are we going to be meeting with. And what happens then is if, say the calendar appointment was with the parents and they happened to bring their kid, but their kid wasn’t on the conversation. And in the CRM, we actually don’t have a record for that beneficiary or for that kid set up.

Well, then it’s going to go and listen to, do we say the name? And that’s where it could get the name wrong. So, it might hear Steven, it’s spelled with a PH, it spells it with a V. What’s cool is because Zocks is learning with you, when you go and correct that name, it will remember that in the future. So, it then knows that this husband and wife or this household has a kid named Stephen and it’s spelled with a PH. And all of the underlying systems should also get that PH spelling. And so, it’s kind of correct it once and then have that forever.

Brad Johnson: So, I mean, that’s better than a human because a human’s going to be like, “Hey, how was that spelled again? I forgot. That was a year ago.” So, it’s literally permanently on record. Obviously, that’s the beauty of a computer versus a human. And so, now it’s feeding that into future but you still have the opportunity because it’s going to say, “Hey, here’s updates from meeting.” Say somebody decides to change their name or gets married, now you can correct it. It’s updated. And now we’ll use that new use case in the future.

Steven Latow: Yep. Exactly. And what’s cool is we, within Zocks, have what we call our virtual assistant. And what the virtual assistant is really good at is updating data. So, if there’s a name change where you’re like, “Hey, we recognize this person got married, and you’re like the new last name is this, you go and say, “Hey, virtual assistant, Stephen got married. The last name is now this.” And it’s going to update for all of your past records as well as in the downstream systems. That’s like a bread-and-butter use case. And then it’s going to remember that moving forward.

Brad Johnson: Cool. Okay. Since we’re on record here, April 4th, 2025, I want to do a little three-year prediction. By the way, we won’t roast you three years from now if none of this comes true. But I think the listeners and the viewers at this point have a pretty good idea of, okay, here’s current capabilities. Here’s kind of maybe some future use cases where some automation of workflows, but let’s just say this. Brad and Steven conversation right now, we had Zocks in the background recording it. Let’s say I’m a client and I’ve got $1 million and we want to do a few things like after this meeting, three years from now, what do you see Zocks doing automated versus today that has to obviously have some human interface and some human help along the way?

Steven Latow: Yeah. So, three years from now, I think most systems will be workflow systems that survive. And so, coming out of a conversation, it’ll dynamically recognize, okay, this is an onboarding scenario. We are moving this amount of money. This is all of the suitability information that we gathered. Here are the compliance forms that we need to fill out. And so, the first thing is it’s going to say, “Hey, we curated your intake form and we sent it out to Brad for DocuSign.” It’s then going to notify you when Brad updates the DocuSign. It’s going to say, “Do you want to open this account in Orion?” You’re going to click, “Yes, open the account in Orion with that information.” It’s then going to notify Brad of the next steps. So, it’s going to send Brad an email saying, “Hey, we need you to wire the money. This is your new account information.”

And we’re using secure mail for this one because it includes that account information, so it knows what should be secure mail, what should be a DocuSign, what should be a direct email. And then it’s also going to schedule that follow-up meeting for, “Hey, we’re really excited to get started. We meet with our A clients four times a year. Here’s my Calendly. Can you schedule something in November or whatever it might be?” So, it’s going to go and automate. I shouldn’t say automate. It’s going to go and know the appropriate workflow, serve the data for that workflow, and all the advisor is doing is saying, “Yes, go to the next step. Yes, go to the next step,” and validating.

And what that means is, my general view is that these AI systems are going to really take over the work that people don’t want to be doing anyway. It’s going to go do a great job of task execution, it’s going to do a great job of data curation, and it’s going to allow individuals, in this case, advisors to really spend most of their time on the human element, which is building relationships. So, hosting, marketing campaigns, going out to dinners, meeting people, and talking about their estate plans. All of these things that require trust, that’s where the AI systems aren’t going to get to. They’re not going to have that personal touch, but everything in the back office is going to get more seamless.

Brad Johnson: So, the picture that comes to my mind is today’s version of finance, back to that assembly line, we’ve got a lot of labor workers on that assembly line through that whole process you just walked through, literally printing the piece of paper, filling it out, or going to some sort of an e-app, keying the information, copy pasting from the CRM. So, what I just heard you preview is three years from now that will essentially be like a Tesla, which by the way, let’s stay out of politics. I’m using the example of the way the factory works. But basically, you’ve got like a robotic factory assembling the car or assembling the financial plan, the workflow, where really the humans going up on the supervisor panel, overseeing it, making sure, “Okay, everything’s in order. Approve. Approve. Approve.”

And so, now getting out of the manual labor, more into the supervisor role, which where my head goes, I mean, you look at a CFP standard plan, income investments, taxes, healthcare, legacy estate, it’s wild how so many advisors sell that benefit, but they rarely have time to go back and complete it because they’re so busy, just all the manual labor that gets in the way. Now, it should take the ability to truly build world-class financial plans to a whole another level because you’re putting time back in the advisor’s hands where they can actually go do their job.

Steven Latow: Exactly. The dream is current advisors will be able to get deeper with their existing clients and they can profitably serve a larger population. One of the big challenges is there’s this huge population, particularly within the United States, that is just not cost-effective to service right now. And you start to be able to service those customers, which this is my why at Zocks. I grew up middle-class in rural northern California. My parents really could have benefited from working with a planner, but they are not the ideal target right now. And so, these systems will allow those types of people to get the financial education and get the plan at a cost that makes sense for both the advisor and for the family.

And so, you’ll see both advisors getting deeper as well as serving more people. And you’ll see their operational and administrative staff actually being able to do kind of revenue-generating activities, getting to know the clients, helping with the communication, doing more from a marketing perspective, getting deeper with the current clients versus just execution and data cleanup all the time.

Brad Johnson: Let’s go to the other side. We’ve talked a lot about a prospect converting to a client. Let’s go to an existing client. So, now you do a review meeting. And it sounds like where we’re headed there is, hey, this client brought up something in this meeting. Hey, I inherited 200k from my great uncle that just passed away. Oh, flag planning opportunity. Number one, do you have that functionality now? Or number two, when will you or where’s that side going? Where’s identifying opportunities with existing clients when it comes to planning?

Steven Latow: So, right now, the flags exist. So, coming out of a conversation, it’ll be like, oh, assets held away. We should know about that. Oh, client concern around tariffs or interest rates. Like, we need to make sure that we’re communicating well about how…

Brad Johnson: Is that a thing right now? I haven’t…

Steven Latow: Yeah. Yep. Oh, there’s a potential tax liability that was unforeseen. We need to make sure that we have a plan for how we’re going to save and deal with that. Oh, there’s an inheritance. And so, those flags already exist. And where it gets to is what do you do with those flags? What are the next steps? And that’s where we’re getting right now is into those workflows. And what’s cool about the AI systems is their dynamic.

So, we have a firm we’re working with that’s like, hey, we just acquired a law firm. We’re going to start doing estate planning. There’s a whole bunch of different workflows that have to do with that. There’s different data that needs to be gathered. They don’t have any of the historical data. They need to have workflow on what should get shared with the lawyers versus what should not get shared with the lawyers and the systems can, within like an hour, we had them all hooked up to where it was just part of the workflow and the client was literally like, we had budgeted months to get this working and we are getting to do our first estate plan faster because the technology is enabling these new systems to talk together.

Brad Johnson: Very cool. I should ask you this at the beginning. How long has Zocks, the company, been in existence?

Steven Latow: Yeah. So, Mark and Akos are the founders, and going back to Hearsay Systems, I led product. Mark was the CTO and Akos was the head of engineering. And we all kind of win our different ways. And then around 2019, Mark and Akos were like, I think, there’s something with these AI systems. I think they’ve gotten to a place where if you think about core problems for advisors, they’re either burned out or they’re not able to build the business that they want. There’s a ton of operational and compliance overhead and there’s also pretty significant compliance and security concerns.

And they’re like, well, we know about the compliance and security side from our time at Hearsay. These AI systems are starting to be able to go and execute tasks, so that’s going to make them more efficient. And we think we can build a good user experience around it to make it easy to use. That’s the other thing. Advisors, it’s got to be easy to use. It can’t interrupt their workflow. It can’t be an expert system.

And so, they started building in 2019 and really built for a couple years. So, at the beginning, it was just going to be a secure data layer. They pivoted a few times. And we’re now in 2025. So, was it in the summer of 2023? They actually did a six-month early adopter program where they took 100 advisors and they gave them free access to the system. And they said, “We just want your feedback. We want to go and run a meeting where we’re watching what you do after, after you get your transcript.” So, what things do you grab? How do you organize it? What systems do they go to? And they built around those for six months. And then we launched a little over a year ago.

Brad Johnson: That’s it. That’s wild.

Steven Latow: Yeah. T3 last year was our coming-out party where we started taking on new advisors, and that was when I joined full-time. I had been an advisor to Zocks beforehand, and then when we launched, I came on to help facilitate growing the business.

Brad Johnson: Probably been pretty chill. Not much going on in the last year then?

Steven Latow: Brad, I have never seen a new technology get adopted this fast. We thought it was going to be 18-month compliance review cycles that no one, not that they didn’t want to use new tech, but that they wouldn’t know how to go and give a thumbs up or a thumbs down, so they would just stay away from it. T3 last year, a lot of education and a lot of people saying, “Hey, demo looks great. I’ll talk to you in two years because there’s no way this is going to get approved before that.” And it’s a credit to the industry that they’ve really seen the value-add and adopted quickly, and also, a credit to advisors who are banging down the doors of their OSJ, of their compliance department, of their broker/dealer saying, “Hey, this is so important to my business that if you do not approve something, I am going to leave. I am going to change my bottle.” And so, it’s just moved three, four times as fast as we thought it would.

Brad Johnson: Yeah, which is not normal for technology and finance. I do believe, looking back, COVID kind of cracked open this door. Actually, it blasted it open, I should say, because there was a problem that everybody knew needed to be fixed, but there was not the motivation to move these big companies that were almost archaic from a technology. Obviously, there’s a lot there. They’ve got to comply this stuff there. It’s got to be suitable. So, I don’t want to downplay that, but nobody was on Zoom. Hardly anybody was on Zoom before COVID. And now, everybody’s running meetings via Zoom, because they had to. It was a necessity to stay in business.

So, I think as that evolved, because I remember in the early days, we were trying to get everybody to use e-Apps on the insurance side of the world. And it goes back to changing old behaviors. And then the moment COVID happened and they had to, because you could not meet with people in person, and now it’s like, they were writing letters in the old days. Now, they’re sending emails when it comes to paperwork. It’s like, oh, this is so much faster, more efficient. There’s checks and balances so you don’t have NIGOs every other app. And it’s just, we will never go back to the old way. And I just kind of see that same evolution happening with AI right now.

And to your point, I don’t see any time soon AI replacing humans. It will be the humans that use AI and know how to use it that replace the humans or the advisors. And so, you definitely need to embrace it or you’re going to get left behind. Do you have any other thesis around that, just high level?

Steven Latow: I totally agree. I think the nice thing about these AI systems is there’s a lot of competition, which means that cost is going to go down and the benefit is really going to accrue to the advisor. Like, this is an exciting time to be an advisor from a technology perspective, which are words that haven’t been said since planning software came out, since online trading systems started happening. Like, that’s a new thing. And so, my view is, when– what’s it? What’s a good analogy? When the internet came out, it wasn’t actually the technologists that were building the internet that ended up accruing most of the value. It was the businesses that adopted it.

Brad Johnson: That’s true.

Steven Latow: And I think we’re going to see the same thing with the AI systems of the value accruing up the chain to the actual users. And that’s a really fun and exciting place to be.

Brad Johnson: Well, we have a mutual friend in Quin, our new CTO, and we were actually trading texts early this morning. I sent him a– it was a conversation on YouTube about AI actually coding now. And it’s like, hey, let’s make sure we’re exploring this. And so, everything is changing all at once. And Quin, we were trading a few texts and I was like, “Well, we got the right guy. It’s you. So, no pressure.” But it’s a really exciting time, but it’s also a time that can be a bit overwhelming.

So, if you were going to speak to the advisors out there and say, hey, so we’re not going back to the Kitces map with 250 options and trying to sort through that. And obviously, I know the answer’s going to be Zocks needs to be in there somewhere, but if you were like zooming out, what would be the first or second step as far as embracing AI as a financial advisor?

Steven Latow: Yeah, I think there’s two things. One is start with a use case. So, we always talk about this on the technology side of what problem are we trying to solve. So, for some advisors, that’s really easy. They’re like, “Hey, I’m a growing business. I’m doing 100 conversations, 150 conversations a month. I just need help with that note taking and I need to get it into downstream systems.” It might be advanced planning. It might be, hey, we think that within our current base, we have this opportunity. So, figure out what problem you’re trying to solve, number one, and that’ll whittle down that list.

The second thing is really think about who is going to be the appropriate partner over three to five years. All these systems are new. They will give you a free trial. Try a bunch of them out, spend the time to do that, and choose the one who you’re like, yes, this is who I want to work with for three to five years, because all of these systems are going to expand. They’re going to do more and more and they’re going to solve use cases that you wouldn’t even think about.

So, the team really, really matters. So, number one, choose the first problem you’re going to solve and use that to whittle down what you’re looking at. Number two, pick the platform that’s going to be flexible and grow with your needs and grow with your business. Those are the two places I would start.

Brad Johnson: I think that’s solid advice. It goes back to Covey’s 7 Habits, start with the end in mind. What problem are you trying to solve before you figure out the solution? And then, on the second one, one other thing you brought up the dot-com days. We talked about kind of blockchain and crypto before we hit record here. I think no one would argue if you said, did the internet change the world? Yes. Is blockchain currently changing the world? Yes. Is AI changing the world? Yes.

So, I think these are technologies that are here to stay, but go back to the dot-com days. Is it going to be pets.com or is it going to be Amazon.com? Only one of those exists today. And so, back to your– you said this a little bit, but I want to expand on it. It’s kind of the biblical analogy of build on a rock, not on sand. I think you need to be very, very careful on what you integrate into the tech stack because it sure is painful if one of those key pillars in your tech stack is out of business a year or two from now. That’s going to be painful. So, what advice would you give there? Obviously, none of us can predict the future, but what are some things to be looking for in technology partners of like, should I be building all of my foundation on top of this?

Steven Latow: Yeah. It really gets into the second point of pick the team and the platform that’s going to grow with me. So, can the team articulate what their vision is and how they fit into your ecosystem in three to five years? Or have they built an app that is kind of solving a pain point right now? That’s a big differentiator. Funding really matters. So, do they have the funds to survive the next 24 months while we see rapid expansion and really being thoughtful about have they invested in something that is flexible? So, do they have APIs that you might never use them? But those are what they’re going to use to go and integrate to other systems. So, it indicates that they’re thinking about how to expand broadly. Do they have advanced user management? So, when I sign up, am I able to go and add and manage my team? Or is it really an app built just for me? These are all the things that are kind of like, they should throw up a little yellow flag in your head of this is someone trying to kind of grab some money and grab a use case right now. They’re probably not going to be around. Or this is a team that is here to stay, that’s going to grow with me.

Brad Johnson: Good advice. Well, my man, this has been rapid fire for right at an hour. And I got to give you a compliment because you took what can be very technical content and obviously, getting into AI and all of the different use cases. Just want to compliment you on being– keeping it accessible to where I know, this was very interesting and I don’t think advisors’ eyes were glazing over out there. So, compliment to you and being great at your job and the vision that you all have built Zocks on. Yeah, go ahead.

Steven Latow: I appreciate that, Brad. I had so much fun chatting with you and Quin. Quin has been just such a good partner to us as a business and I’m really excited to see what we build together at Triad. But you got a good one there and I’m really excited that kind of, we all got to meet up and start this conversation at T3.

Brad Johnson: Awesome. Likewise. And as you know, this podcast is Do Business, Do Life for a reason, and we talk about helping our members build a business that blesses their life. Doesn’t become it. Obviously, we have a selfish pursuit of that as well at Triad for all of us and our team. But I would love to hear Steven’s definition of what does Do Business, Do Life mean to you.

Steven Latow: Yeah, Do Business, Do Life to me means getting to show up authentically as myself, whether it’s in my life or in my professional life. And I know my why. I love solving problems. I love delivering value. So, whether that’s with friends, with my partner, it’s with our dogs, or it’s with advisors, I get to experience that same joy. And so, really, Do Business, Do Life is getting to be me, whether it’s on a podcast or answering emails or hanging out at home.

Brad Johnson: Love it. Well, on the do life side, I know we were chatting before we hit record. If you need a spot for all those Magic: The Gathering cards that your wife needs to get out of the house, I now have a card shop, so bring it on out. We have no Magic: The Gathering cards yet, but that doesn’t mean we won’t in the future. So, open invite anytime you’re in Kansas.

Steven Latow: All right. I’ll let you get your feet under you and then we should get our first Friday night Magic draft night going. I bet there’s a few advisors out there that we could get in into the card shop for a good lock-in and some gathering.

Brad Johnson: Like, I am a self-professed nerd as well. I was an IT major. I played sports, but I also have a heavy nerd side to me. And I would love– I’ve heard like my kids were into Pokemon cards, right? And so, what I’ve heard is playing Pokemon is like kind of a dumbed-down version of playing magic. So, I will say Pokemon, the game is weirdly addicting. So, if Magic is like the next more advanced level of that, I’d probably be into it.

Steven Latow: All right, all right, I’ll take you up on that, Brad.

Brad Johnson: All right. Open invite. Till next time, my man.

Steven Latow: Till next time. Thank you.

Brad Johnson: All right. We’ll see you.

Steven Latow: Have a great afternoon. Happy Friday.

Brad Johnson: You too. Bye.

Steven Latow: Bye.

Disclosure

DBDL podcast episode conversations are intended to provide financial advisors with ideas, strategies, concepts and tools that could be incorporated into their business and their life. Financial professionals are responsible for ensuring implementation of anything discussed related to business is done so in accordance with any and all regulatory, compliance responsibilities and obligations.

The Triad member statements reflect their own experience which may not be representative of all Triad Member experiences, and their appearances were not paid for.

Triad Wealth Partners, LLC is an SEC Registered Investment Adviser. Please visit Triadwealthpartners.com for more information. Triad Wealth Partners, LLC and Triad Partners, LLC are affiliated companies. Triad partners is a Zocks client and is also a Zocks Ambassador.