[AI Series] Transforming Client Services and Advisors’ Workflow Using AI With Mark Gilbert

Mark Gilbert is the Co-founder and CEO of Zocks, an AI-powered platform helping financial advisors streamline workflows by automating tasks like meeting notes, CRM updates, and client communications. Under his leadership, Zocks has raised over $19 million in funding and is recognized as a leader in WealthTech innovation. Before launching Zocks, Mark held senior product leadership roles at Twilio, Hearsay Systems, and Microsoft. He earned a bachelor's degree in applied science in electrical engineering from the University of Waterloo.

Here’s a glimpse of what you’ll learn:

• [2:10] Mark Gilbert discusses how Zocks helps financial advisors automate time-consuming tasks

• [6:24] How executive attitudes toward advisor support technology have evolved

• [9:09] Managing the unpredictability of AI models in real-world applications

• [10:09] Techniques Zocks uses to ensure AI-generated data is accurate and auditable

• [13:27] Bridging client conversations with structured backend systems in financial services

• [15:04] Mark talks about prioritizing real advisor needs over trendy AI features

• [18:55] Key differences in building an AI-first company versus traditional software firms

• [21:54] Challenges of encouraging AI adoption internally and among clients

• [25:13] The role of venture capital in AI-driven company growth
  

In this episode…

Financial advisors are overwhelmed with administrative tasks that distract them from client engagement and strategic planning. From manual data entry to prepping and debriefing after meetings, these time-consuming workflows reduce their ability to scale personalized service. With AI advancing rapidly, how can advisors reclaim their time while ensuring accuracy and compliance?

Mark Gilbert, an expert in product development and artificial intelligence, shares how financial professionals can leverage AI to automate unstructured client interactions — like emails and phone calls — into structured, actionable data. He explains how his team uses multiple large language models in parallel to ensure accurate output and eliminate hallucinations. This approach, he notes, is most effective when guided by real advisor pain points rather than by chasing the latest AI trends. Mark also offers practical strategies for increasing internal trust and adoption of AI through transparency, audit logs, and a user-friendly interface.

In this episode of The Customer Wins, Richard Walker interviews Mark Gilbert, CEO of Zocks, about how AI is transforming the advisor-client relationship. Mark discusses building trust in AI systems, adapting to fast-changing models, and the differences between bootstrapping and venture-backed growth. He also shares thoughts on leading AI adoption, product design priorities, and his approach to building a scalable, secure solution.

Resources Mentioned in this episode

• Quik! 

• Richard Walker on LinkedIn  

• Mark Gilbert on LinkedIn 

• Zocks 

• "[AI Series] Revolutionizing Compliance With AI With Larry Shumbres" on The Customer Wins 

• "[AI Series] Transforming Client Onboarding With AI With Peter Dun" on The Customer Wins 

• "[AI Series] How AI Is Transforming Business & Education With Michael Todasco" on The Customer Wins 

• "Revolutionizing Fintech Sales Strategies With Wim Van Lerberghe" on The Customer Wins 

• Geoff Woods on LinkedIn 

• The AI-Driven Leader: Harnessing AI to Make Faster, Smarter Decisions by Geoff Woods 

Quotable Moments:

• “Working on something that removes tasks that people hate is very rewarding.”

• “Technology is a way because there are so many old systems in the industry.”

• “With AI, like you're moving from a very deterministic system to probability outcomes, right?”

• “What we're trying to do is really give them better tools to help accelerate them.”

• “You don't have an AI strategy. You have a strategy for how to build your business.”

Action Steps:

1. Identify high-friction tasks advisors dislike: Pinpointing repetitive, manual tasks enables targeted automation that saves time and boosts advisor satisfaction.

2. Use AI to structure unstructured data: Transforming emails and meeting conversations into organized data helps streamline workflows and ensure accuracy.

3. Prioritize auditability in AI systems: Ensuring every data point can be traced builds user trust and supports compliance in regulated industries.

4. Test multiple AI models in parallel: Comparing outputs from different LLMs increases confidence in data accuracy and reduces hallucination risks.

5. Encourage gradual AI adoption with transparency: Providing clear logs and contextual insights eases resistance and helps users embrace new tools with confidence.

Sponsor for this episode...

This is brought to you by Quik!

At Quik!, we provide forms automation and management solutions for companies seeking to maximize their potential productivity.

Using our FormXtract API, you can submit your completed forms and get clean, context-rich data that is 99.9% accurate.

Our vision is to become the leading forms automation company by making paperwork the easiest part of every transaction.

Meanwhile, our mission is to help the top firms in the financial industry raise their bottom line by streamlining the customer experience with automated, convenient solutions.

Go to www.quickforms.com to learn more, or contact us with questions at support@quikforms.com.

Episode Transcript:

Intro: 00:02

Welcome to The Customer Wins podcast, where business leaders discuss their secrets and techniques for helping their customers succeed and in turn, grow their business.

Richard Walker: 00:16

Hi, I'm Rich Walker, the host of The Customer Wins, where I talk to business leaders about how they help their customers win and how their focus on customer experience leads to growth. Today is a special episode in my series on artificial intelligence, and today's guest is Mark Gilbert, co-founder and CEO of Zocks Communications. Some of our past guests in this series have included Larry Shumbres of Archive Intel, Peter Dun of Feathery, and Michael Todasco, the visiting fellow at San Diego State University. And today's episode is brought to you by Quik!, the leader in enterprise forms processing. When your business relies upon processing forms, don't waste your valuable time manually reviewing the forms. Worms. Instead, get Quik! using our Form Xtract API. Simply submit your completed forms and get back clean. Context-rich data that reduces manual reviews to only one out of a thousand submissions. Visit quickforms.com to get started.

Now, before I introduce today's guest, I want to give a big thank you to Wim Van Lerberghe, Co-Founder at Advintro, who is also a guest on my show. So go check out his episode and learn how adventurers help B2B companies improve their business development and sales efforts. All right, I'm excited to talk to Mark today. Mark Gilbert is the co-founder and CEO of Zocks Communications, using AI to help financial firms scale. Prior to Zocks, Mark served in a variety of executive and product leadership roles at Twilio, Hearsay Systems and Microsoft.

Mark, welcome to The Customer Wins.

Mark Gilbert: 01:48

Thanks, Rich. It's great to be here, I appreciate it.

Richard Walker: 01:50

Yeah, I'm excited to talk to you. Pardon me everybody, if you haven't heard this podcast before, I'd love to talk to business leaders about what they're doing to help their customers win. How they built and delivered a great customer experience and the challenges to growing their own company. So Mark, to understand your business a little bit better, how does your company help people?

Mark Gilbert: 02:10

Our company. So we focus purely on financial services. And I would say the people we primarily help are financial advisors. You know, independent broker-dealers and RIAs. And we really help, frankly, take a lot of the work off their plate that they don't like to do.

Specifically, we do things like listen to client meetings for them, look at their email from clients and can look in their CRM and get them the data they want from those meetings. And frankly, for any kind of downstream workflows. So as they're working with clients, they can focus purely on them. When they walk out of a meeting, all the information is there. We help them prep for those things as well.

And then the idea is that they actually offer better client service because they get back to them quicker. They can offer more personalized service at scale, and they don't have to do a bunch of data entry and data retrieval that they do, typically in preparing for meetings and following up. So that's how we help them. You know, we've been out for, I guess, 14, 15 months ago now. We launched a ton of users.

And it's great, frankly, to work in a spot where I worked on a lot of software in my career and working on something that removes tasks that people hate is very rewarding. I'll just say that.

Richard Walker: 03:22

You know, that's how I started my career. I was a technologist in the 90s, became a financial advisor, joined my mentor in his business. And while I was working to get my licenses and then my first client, I started automating everything that we were doing. And I mean, we had automatic charting systems with Yahoo data going into Excel. We built a Monte Carlo simulator, but this is how I stumbled across forms.

It's because I hated to do that work. So you weren't necessarily a financial advisor, right? How did you land on wealth management and these specific needs in general?

Mark Gilbert: 03:53

Yeah. So there's two really big things that happened for me. You mentioned this, but a bit ago I worked at a company called Hearsay Social or Hearsay Systems, which focuses purely on wealth, basically wealth, technology, financial firms and some insurance. And that was the first time I really saw, you know, what we think of as vertical SaaS. So essentially taking software and tuning it directly for an industry.

And frankly, you just see how much easier it is for that industry to use it. They get more value out of it, instead of taking more kind of horizontal general systems and trying to like, tune them for their scenarios. So that was the first big piece. And then I was at a company called Twilio, and I was the exec sponsor for a number of, frankly, financial institutions. And Twilio, for those who don't know, essentially is a is an online communication programmable communication layer.

So if you ever talk to an Uber driver or you ever get a text from Airbnb, that's all Twilio. And as we were working with these leaders in these banks and insurance companies, they really We're starting to hit on like how heavy and painful it is, frankly, for people to have all these conversations for them, typically on the phone and then having to turn around and like, do all this data entry. And so those two things clicked for us. We started Zocks. And, you know, it's just been one of those, even from our early reach out when we were trying to, like, validate the product, frankly, before we really had much code.

The feedback has just been super positive in the sense of like, it's again, it's an area that people just really don't want to be spending time. Like we find financial advisors are, are either very focused on wanting to engage with their clients and or plan, right, like plan and really helping the financial management side. And so I think everyone is super excited to not do this. We've had a lot of great feedback early. And then from that it really didn't take long to see the impact we could have.

So yeah. Super. Yeah.

Richard Walker: 05:45

So I have a kind of chicken and egg question, which I don't know that you're going to know the answer to this, but you talked to a lot of executives. And so here's the thing. Early on in my career with Quick, I would talk to, say, a broker-dealer, the enterprise, about their advisors losing time with their clients. I mean, it's this really the same sales pitch, like I'm going to help them recover all this time and effort to do their forms and paperwork. And a lot of the answer was, yeah, that's their problem.

That's the advisor's problem to deal with. That's just how business is done. So the chicken and egg kind of question is, has the mindset changed in executives, or is it really the advent of technology that's caused them to change their view of how to help people?

Mark Gilbert: 06:24

It so I think a few things have shifted, probably in the industry. So we started very much with the advisors themselves. And then what we saw right away is they were happy to go both use the software, the system and champion it. And as you probably know, like, you know, the larger broker-dealers do want to make sure that the advisors, especially some of their top advisers are happy. And I think increasingly in the industry.

Technology and systems is a way because there are so many old systems, as I think all of us know, in the industry. So having basically a modern tech stack and the ability to do these things and run your business more efficiently is becoming a pretty strong value for the broker-dealers and the enterprises themselves. And I think they realize that a lot more. It was a little surprising to me how quickly some of these systems like, like our system has been able to get reviewed and approved and rolled out across companies that we had worked with before in previous lives that would often take, you know, 18 to 36 months to do something. And they're now doing it in like, you know, a few months.

It's really it's really changed a lot.

Richard Walker: 07:35

Yeah. I mean, it's remarkable how much you guys have grown in such a short period of time. I'm envious, frankly.

Mark Gilbert: 07:43

Most days are crazy days. So I we were just talking before the show about, you know, your. you know, your ability essentially to just grow such a strong business as you have. And I think that's yeah, amazing. And also just gives you control over a lot of things that in the AI space, you know, it's so early.

We talk to people, we talk with clients and you know, prospects about it. And it's you know, people don't even really know how to like evaluate or see where things are going. So it is a little chaotic, frankly. But it's interesting. It's definitely there's no dull days.

That's a that's a promise we can make.

Richard Walker: 08:17

You know, you bring up something really fascinating I hadn't thought of because I've been in this business for a long time. I'm watching AI evolve and come out right. I'm seeing it from the early stage and I got on it really, really fast. I mean, I was on it before ChatGPT came out to the public and I was looking at things going. We got to figure out how to make this work for us.

And now, I mean, since 2023, we're an AI-driven company. I wouldn't call myself an AI company, but an AI-driven one. Everything we're doing is driven by that. So I looked at it from two perspectives. One is this is potentially a threat to my business, my existing legacy business, but also an amazing opportunity to shift and evolve and grow.

And now you've presented a different perspective. You started with the AI to build your business. So is it a threat? Is it all opportunity or is it just like quicksand shifting under your feet all the time? How do you manage what's happening every month?

Mark Gilbert: 09:09

It so from a technology point of view, it's quicksand under your feet all the time. Like it's you know, I think for those who don't know, with AI, like you're moving from a very deterministic system which most software is, right, which says like if this, then do this, you know.

Richard Walker: 09:22

Like.

Mark Gilbert: 09:23

A plus B equals C, it's very like, you know, you can run the code again and again and it runs the exact.

Richard Walker: 09:28

Same way. Linear. Yeah.

Mark Gilbert: 09:29

Yeah. Exactly. And with AI it's basically it's probability outcomes. Right. And so the probability can shift on things.

It's very hard. And so yeah like there's a lot that we've had to build and build around the AI systems to both to make them predictable. The other big thing is, you know, in this industry is to make them auditable. So, you know, like as we get data from a whole bunch of different sources, we know what came from where, why it came from there. Like, you know, just you have to have all of this auditability.

And frankly, the vast majority of AI out there is like, you know, give the AI system a bunch of stuff and see if it gives you the right answer. Like, that's kind of how they work.

Richard Walker: 10:05

It feels like R&D half the time. Like, is it going to work?

Mark Gilbert: 10:09

Yeah, totally. And so there's a lot of quicksand we've had to build a lot like put a lot of pillars down to help with that. We, you know, I think our world's a little different from yours. I totally agree with you. Right?

Is that, like, you know, I don't know your business, obviously super well, but, you know, you offer a huge amount of value to clients. And I just given what you're doing, which is this like almost translation between human and machine. Right. Like AI systems are really good at that. Like LLM specifically are really good at that.

And so, you know, at some level it can make your business awesome. But also if you do nothing like it's, you know, there's probably going to be a replacement there. Our view of this is like this. This wave is it's almost like the same as the initial software wave, right? So, like, you know, we always talk about AI like, you know, this is an AI series.

And honestly, like you can replace the word AI with software. And you're going to realize that, like how ludicrous this is going to sound in three years, right?

Richard Walker: 11:02

I know, I.

Mark Gilbert: 11:03

Know, I, I don't know if I'm ready for AI, right? This is like saying, oh, using software. I don't know if I'm ready for software. Do I need software to grow my business if I don't use and like, they just think of anyone running their business today without software, right? It's just like, you know, it's mind-boggling.

So that's going to happen. But yeah, we spend most of our time really thinking about where like additional things we can do frankly, for people. So our North Star is really like where are they burning their time? Like specifically, advisors are burning their time in areas they really don't like. That is a lot of like manual again, data entry pulling.

You know the space well where they don't have systems like yours already to go do that. And what we see is there's a huge opportunity connecting, you know, like we look at an advisor and you know, when they work with clients, everything on the client-facing side is really conversational, right? It's really unstructured. It could be emails, could be conversations in person, phone calls, whatever. So we can handle all those and turn that into structured data so that things on the back end basically operate.

Because all the back end, like all the custodial systems, all these different things, you know, planning systems, all of that is really structured, you know, for those less technical. Like it's essentially a database, right. Sitting with a number of things. But we're really bridging that. And there's frankly so many different areas that we can help.

That's really where we spend our time. The one thing we get asked regularly is like, you know, what does this all look like five years from now? Right? Like, is there one system that's my AI friend that helps me out? Or is it, you know, and the reality is, I don't I don't think any of us know.

Right. So we're just kind of towards this North Star.

Richard Walker: 12:37

Well, so I want to point out something you said that I'm not sure the listeners caught because you're a product person. I believe from your background I'm a product person. And there's a there's two ways essentially, that you can have a mindset around product, build it because it's cool or build it because it's needed. And I has been kind of a technology searching for problems to solve, and we've found a whole bunch of problems we can solve with it, which is amazing. There's a lot of technologies.

You're like, what would I use that for? But nonetheless, you're not looking at your business in terms of, oh, what new AI feature can I put in front of somebody? What new thing did Claude come out with that I can now use and help people? You're asking the customer, what do you need help with? You're coming to them first.

I mean, that's how it sounds. And that's, I think, the better approach to build a product. Because AI is only a tool set. At the end of the day, it's just a technology platform to build from. Right?

Mark Gilbert: 13:27

Yeah. No, that's exactly it. Right. That's exactly it. And we're what we're trying to do is really give them better tools to help accelerate them on the things they want to do, right, the things they need to do all the time.

And then, you know, I think as part of that, one of the nice things is that, like, as the industry continues to evolve and change, which it which it will, and frankly, regulations change and everything else, you know, ideally we can shift with them so that like, people can be comfortable doing what they're doing every day. And as you know, whatever the SEC and Finra kind of provide more guidance on things or anything else that these systems can just do whatever they need to do to, you know, to frankly, help them help their clients because, like, for us, you know, I can't think of a financial advisor we work with who's not primarily motivated with talking with and helping their clients. Right. Like that's ultimately where they want to be spending their time.

Richard Walker: 14:14

It's one of the reasons I love this industry. Their focus is on how do I help people. I mean, it's awesome. Yeah. Just one other thing I'll think about is that, you know, you said it.

If you're talking about AI and what is your AI strategy, that's like going to a carpenter. Say, what's your hammer strategy? It's a tool. It makes no sense to say that. But because it's so new, we don't realize that.

Yeah. I mean, unless you're OpenAI developing the AI itself. You don't have an AI strategy. You have a strategy for how to build your business and your product set. Yep.

Yeah. So I'm curious, you know, there's a lot of challenge with these predictive models being accurate. And if you are getting data from various systems and analyzing it and all this stuff, how do you make sure it's not hallucinating and inventing stuff and bringing other people's portfolios together or whatever?

Mark Gilbert: 15:04

Yeah, yeah, it's a good question. And that like as we were talking earlier about quicksand, like, that's frankly a lot of the, you know, the building pieces that we have to get around. So we at a high level, I'll say like one of the like, just as you mentioned, one of the harder things with AI is like, ideally, you want to be able to automatically measure the quality of the output just straight out, right. Like I think it's pretty easy for us if we were having this conversation and then reading something that AI generated to say whether it's accurate or not, but, you know, at the scale that we operate at, you want to be able to do that automatically. And so we do a number of things there.

One is without going too much into our secret sauce. But so first of all, there's a number of these AI systems and some of them are just better at certain things. And some of them you can actually easily run side by side. So underneath a lot of the like the latest wave of AI, like ChatGPT and things like this is are what are called large language models. So LLMs you'll always hear.

So we typically run, we have I guess, five different LLMs now that we can run that we use for different things, and we'll run them in parallel at different times on different things. And that gives us kind of a check and balance of like, hey, if we're trying to list every account this person mentions or every piece of KYC data that they need or whatever, whatever they want out of that, you know, we can actually run it twice in parallel, frankly, through two completely different code sets, code bases, and then and then say like, are they are they actually getting the same thing or not? And if they are, we're in good shape and if they're not, that likes us and starts to look so.

Richard Walker: 16:38

Sorry to interrupt. Do you use a third LM as referee to review the other two's responses?

Mark Gilbert: 16:43

So no, because what we do. That's a very good question. Our AI assistant model actually has layered agents. They're called. So essentially like layered LLMs exactly as you would as you would say.

But for this stuff, what you can do is basically pull it. We build out these large tables of all the data that we're capturing. And so you can just compare that. You can really look at two, you know, two tables in a database and say, do they have the same amount of things in them or not. So that's a huge piece.

The other thing that we have found though, kind of to your point, without going into too much detail on it, is that we have basically a pipeline of the AI and LLMs working, and what we find is that the more specific you ask them for things, the more accurate they are, and then it's much easier for us to validate whether they've gone off track or not. And so instead of like as we were talking about earlier, instead of saying like, hey, here's all my info, tell me what happened, which is really hard to validate whether that's actually true or not. If you kind of get it to extract data and then pull it together, you can validate whether those data pieces are true. And then, as always, we're running what we call known data through the system. Right.

So that kind of known conversations, known email threads, all that, where we know the output and we expect to see that. And then we also frankly have it structure the data in a way that is very hard, just the way LLMs work. It would be very weird for one of them to hallucinate in a way, and yet still produce the output exactly as we want, right? So it's almost like me saying, hey, I want, I want you to generate a picture with exactly these colors of a car, and, you know, the fact that it would use exactly those colors and not produce a car is extremely rare. And so we kind of add up all these different things that we use.

But those are the big three, and those, those have really, you know, essentially allowed us to, you know, just to kind of fully remove any hallucination problems at all.

Richard Walker: 18:28

Okay. So I want to kind of switch a little bit because this is a newer company, right? You're starting it from scratch. You've been in product development for a long time with other companies, and it seems like with plenty of resources to work with. But AI is different, right?

This is a different skill set. And as you said, it's changing and evolving so fast. So, how is it different to build a company that is so AI dependent, if you will, versus traditional software?

Mark Gilbert: 18:55

Yeah, that's a really good question. I would say the two big things that hit us pretty, pretty quick. One is that the space moves very quickly. And like when we, you know, we're talking earlier about kind of building on quicksand to give an idea of people like, you know, if you go build a piece of software for whatever the internet, like a website or, you know, for your Mac or your windows laptop, that just runs right, it runs the same way every day. It kind of, you know, the LLMs literally will change daily.

So like you need to have a number of things in place and frankly, no single points of failure on that, which for us means one of the reasons that we started using multiple LMS a while ago was explicitly to do failover, right. So, like one of the things that's happened is, as ChatGPT became so popular, literally OpenAI and now others like Anthropic and Google literally struggle sometimes to serve the results in a timely manner. And so like you need to you need to really look at your points of single points of failure and get those over the industry also just moves quicker. So our systems are set up now to ship software and updates much quicker than we've ever had before. And you know, like in like a long time ago.

You and I will probably remember this. But, you know, like we used to buy software in a box or like on a CD or download it and use it, what we call box software, on-prem software. Then it moved to the cloud and that ship cycle got a lot quicker with that, right? Like it used to be that every year we would get new software in the cloud. You know, it's like every day or two or a week or you know, we now basically can update.

We update our system typically multiple times a day, and different parts of the system can get updated very independently by design. And that's it's for a few reasons. But like the two big ones are that the AI systems themselves change a lot. And frankly, like we have a lot of customers who want really early access to things and we're happy to do that. It also gives us feedback, which is really good as we build for them.

But that means that we kind of need to have all these pieces. So I would say those are two big ones. One of the comical things I'll tell you and I don't, I don't know if you've had this at all, is you would think in an AI first company that it'd be very easy to get AI people to use AI for their core jobs, you know, and it's still it's still, you know, I'm sure if we compare notes, it'd be similar. There's a group of us we're very aggressive at using AI systems like internally just to even get internal stuff done. And another group that we've really had to like, push to be like, you know, just use these.

It's kind of comical. But yeah, so I would.

Richard Walker: 21:26

Say, is. No, I believe me. I experienced that when I said on January 3rd, 2023, we're going to be an AI-driven company. I gave my team two specific goals. One is we're going to build our first product that's built with driven by AI and release it that year, which we did.

And the second one is everybody's going to find a way to use AI in their daily job, buy a tool, use it, I don't care. And in six months, I think I got one other person to try to do that. And then for another six months I got more people.

Mark Gilbert: 21:54

But the interesting thing for us that's been really helpful is like, you know, as we're working with advisors, again, these systems are really, really helpful, right? Like it, you know, like we help them with a whole bunch of stuff they don't like. And yet still you end up in some situations where you're like, hey, why aren't you using it? Right. Like we'll, you know, like have them run a pilot and often their company will buy it for them.

And it's everything from like, and so our product design team is a little frustrated. Right. They're like how do we get them to use it? Yeah. And in the same way you can point at them and be like, well, why aren't you using, you know, like ChatGPT to start, you know, like generating the, the wireframes for your docs or something, right?

And they're like, oh yeah, I don't really. And they like, you realize just how much inertia there is in whatever people are doing. And there's also a trust that we've actually built quite a lot of things into the system that like. To your question on just kind of an AI company like this is something I've never really had to do in software before is, you know, like build things to make people like, you want to make the software easy to use. But like, we literally have added a number of things to get them to just build more and more trust with the system over time.

So that's like these audit logs of like, hey, where is this data coming from? Can I mouse over and see more context on that? Can I get you to explain things to me? All of these things are very unique to AI that you would never really, you know, do in traditional software. So a lot of, you know.

Richard Walker: 23:10

If you remember the days of having VHS tapes, you bought them and got them at Blockbuster, right? And our VCRs, it had 100 functions, but we only know play, stop, rewind, Eject. We didn't know how to set the clock. We didn't know how to time it to record our favorite shows. I mean, there are so many things we didn't know how to do.

So I think this is part of just human nature to not learn and not adapt, or adapt, except the most quintessential little things. And if you make it a tool for somebody, you guys are building a tool for advisors. Why aren't they doing this for themselves with ChatGPT and whatnot? You're waiting for somebody to make it the easy button for you?

Mark Gilbert: 23:47

Yeah, totally. And nobody like. Although ChatGPT is fun, nobody wants to sit there chatting with the thing, trying to get all these things out every time they have a meeting. Yeah, yeah. Yeah, totally.

Richard Walker: 23:56

Yeah. Well, I'll mention somebody who was on my show last year, Geoff Woods. He wrote a book called The AI-Driven Leader. And for me, that changed a lot in terms of getting people to adopt AI in my company, because one, it started with me and I was already ahead of the game using it. I remember talking to him and he's interviewing me.

He's like, Rich, you're really far ahead. But I learned so much from his book. It's one of my favorite books. And second, because I'm the leader and I'm demonstrating it over and over and over to the team. And I think somebody else was on my podcast.

I can't know who it is to give credit to. They said, why don't you, in your weekly standup, have somebody talk about what they did with AI every week, make it part of the cadence?

Mark Gilbert: 24:35

That's been super helpful for us. That trick, I should read that book. I haven't read it, but that trick I've heard and it's been super, super helpful.

Richard Walker: 24:40

Yeah. All right. So I'm going to ask you a biased question. Biased because you took a certain path. Okay.

Yep. Do you think you could build your company like. Well, let me just validate you did raise capital right for your company. Yeah, I think I read somewhere you guys raised millions of dollars. I don't know what the number is.

Mark Gilbert: 24:57

But venture-backed. So we have we have a fairly good war chest, I would say on that side.

Richard Walker: 25:02

Yeah. And I know that's necessary from a sales and outreach and market presence and all that. But do you think you could build what you've built technologically without raising capital?

Mark Gilbert: 25:13

That's a good question. I think for us, we hired a lot of engineers fairly early on because we wanted to get the privacy and security into the platform set up very well. And we would have. So when I say that, like, you know, theoretically we could convince a number of engineers to work without money for a while, but it'd be hard. I think, you know, if you have 1 or 2 people, that's a little easier.

It's hard when you have more. I think that would have been pretty hard for us to do. But I do think, you know, especially looking back and, you know, kind of what worked and what didn't that you, you know, you can always run it more like with venture capital. You know, you essentially are choosing speed over efficiency, for lack of a better phrase. I do think, like we've seen a number of different systems come out kind of in the broader area that we know are not venture-backed.

And I think some of those will do will do quite well. So I do think there's always the opportunity to do that. It's just a matter of like what your priorities are. And frankly, also just like what the goals of the company are, right? Like when you do raise capital there, there are certain things that you basically have to go do very quickly after.

Richard Walker: 26:17

Well, but also you're in financial services, highly regulated, high security thresholds to overcome. Like, have you guys achieved SOC2 at this point? If you don't mind me asking.

Mark Gilbert: 26:26

Yeah, we yeah we got our like exactly those things right. We got our SoC.

Richard Walker: 26:30

That's expensive.

Mark Gilbert: 26:31

It's expensive. You need to have the systems in place to do it. And then we build like we work with a lot of large enterprises now that we would not have been able to work with had we not raised money. Right. Like, I think you have to take more of an independent advisor and start to step up and things like that as you as you go, if you're a little bit more incremental.

Richard Walker: 26:50

You know, I asked this question because I'm always enamored with these, I don't know, people over the weekend build something amazing, because you can move fast with tech. And I'm a bootstrap company. I've done everything wrong over a long period of time, and I figured it out eventually. So we didn't have that pressure, nor did we have the speed or effectiveness of what venture backing would have done. But I don't.

I just don't know that somebody can take on what you've done without the capital to really push it.

Mark Gilbert: 27:17

I think it'd be hard. And I think when someone has capital, it's very hard to do it incrementally, you know, against them as well. Yeah.

Richard Walker: 27:24

So, Mark, as we get close to wrapping this up, I have another question for you. But before I ask it, what is the best way for people to find and connect with you?

Mark Gilbert: 27:33

Yeah. So for me our website is zocks.io. Or LinkedIn, I'm Mark Gilbert. So just linkedIn/in/markgil/ And my email is Mark@zocks.io. And you can just shoot me an email directly.

We'd love to chat with any of you. I'd love to chat with any of you. Feedback is always welcome on what we're doing. Input ideas. We love all of it.

So yeah, any of those ways are good. And we're getting a little bit more active on X, but don't you know, it's still a little new for us, I'll say.

Richard Walker: 28:08

Yeah. I'm not a big fan of the X platform myself because I don't know how to manage it and monitor it.

Mark Gilbert: 28:14

And it's hard, you know, like with LinkedIn, I think we can kind of like I can go and look in the morning and in the evening and it's okay. But like, you know, if you blink and something happens on X and you didn't watch it for an hour, you're kind of.

Richard Walker: 28:26

That's the thing. It just scrolls. It's gone. What happened?

Mark Gilbert: 28:29

That's exactly the problem. Exactly what I'm stuck with. So anyways. Yeah. Yeah.

Richard Walker: 28:34

Well, let me ask you one of my favorite questions to ask my guests. And that is who has had the biggest impact on your leadership style and how you approach your role today?

Mark Gilbert: 28:43

Yeah, that's actually fairly easy for me. I've been very lucky in this. I've had a lot of good mentors, but for me, I worked for a person very directly in a staff role at Microsoft. He used to run all of our enterprise business, all like Windows Server Azure as it as we built it out there and everything, and I was literally as technical assistant. So I did whatever the heck he asked me to do, which was mostly technical strategy, but he had a huge org.

His name was Bob Muglia. He actually then went on and I think was the first CEO of Snowflake for a long while until right before they went public and built that up. He. He also a lot of people don't know this was the first PM product manager for SQL Server at Microsoft. So he's a little older, but I've learned a ton from him.

He had a huge business at Microsoft, obviously built a huge business with Snowflake. Just the way he works, the way he works with people. He's very, very people-focused but also results-oriented is. Yeah, just, you know, I would say 80, 80 plus percent of my leadership style, how we run, you know, how I've run the businesses I've worked with since then with him. And honestly, how we're building out running Zocks is based on him.

So a huge, huge thank you. I yeah I don't think I'll ever be able to repay him. So we're just kind of trying to help, you know, as much as I can kind of help other people. Earlier in their career, I was pretty young when I started working with them, and it's been great.

Richard Walker: 30:03

Pay it forward. It is so awesome to hear you had such a successful person to model after and to be mentored by. That is awesome. Congrats!

Mark Gilbert: 30:11

Yeah, no, it's made a huge difference. It also just makes you realize what's possible. Like, you know, you see that there's some things that are really interestingly different. And then there's a lot that you realize like, oh, this is what I would have done anyways. That's great.

Like, you know, it just kind of gives you confidence in it.

Richard Walker: 30:23

Yeah. That validation.

Mark Gilbert: 30:25

I have one quick question for you, Rich, before we hop, if that's okay.

Richard Walker: 30:28

Yeah.

Mark Gilbert: 30:29

You had mentioned. So just as we're talking about using AI, you had mentioned a book and I didn't I didn't catch the author's name or Geoff Woods. Geoff Woods.

Richard Walker: 30:40

Woods, The AI-driven Leader. We'll link to it in the show notes. I'll be happy to follow up and send you.

Mark Gilbert: 30:45

I'm going to take it. I'm going to hopefully it's on audible. Audible. It. Oh, yeah.

I would love to say I read a lot of books now, but I really I really listen to a lot of books now. But Geoff Woods awesome.

Richard Walker: 30:54

Yeah, honestly, I've given that book to so many people. I gave it to my lead business development guy, changed his entire perspective on how to do AI. My entire team got copies of it that changed them. I had Geoff come in and present to my team. That changed their whole mentality.

And so then we saw like ten x more improvement in use of AI across my company. Wow. Yeah.

Mark Gilbert: 31:16

That's awesome. I'll definitely take a read.

Richard Walker: 31:18

Yeah. Very very.

Mark Gilbert: 31:19

Good. I appreciate it. Super helpful.

Richard Walker: 31:22

Yeah. So hey, I want to give a big thank you to Mark Gilbert, co-founder and CEO of Zocks Communications, for being on this episode of The Customer Wins. Go check out Mark's website at Zocks.io Oh, and he also has zocks.com, if that's easier. And don't forget to check out quick at Quickforms.com where we make processing forms easy.

I hope you've enjoyed this discussion. We'll click the like button, share this with someone, and subscribe to our channels for future episodes of The Customer Wins. Mark, thanks so much for joining me today.

Mark Gilbert: 31:49

Thanks, Rich. Really appreciate it. Have a great afternoon.

Outro: 31:53

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