The Rise of AI Agents: What They Mean for Your Business | Ep 103

In a world where change is the only constant, Mo Choumil stands at the forefront, guiding title professionals to not just grow their businesses, but to master the art of innovation. With every episode, you're handed the keys to unlock unparalleled growth and stay ahead of the curve. Get ready for a transformative journey. Hello everyone, and welcome to another episode of the Title Agents Podcast. I am your host, Mo Choumil, CEO of Alltech National Title.

Today I have a repeat guest, my good buddy Clay Speakman from RocketHub AI, and a fellow GoBundance member. I've been talking about GoBundance a lot for the last few episodes, but A year ago, Clay joined us and we talked about AI in general, but before, I kept saying in my last few podcasts that AI has changed forever in the last 6 months. So we're going to dig deeper into what that means. Welcome, Clay. Welcome, Mo.

It's great to be back. For anyone who didn't catch the first episode you were on about a year ago, give us a quick version What is RocketHub and what do you actually do? Yeah, so RocketHub is an AI company with a mission to leverage AI for entrepreneurs and small businesses. It's a pretty broad mission, but AI changes so fast. Our job is to understand where are the best leverage points that AI can help entrepreneurs and small business owners to maximize the technology for whatever they want to achieve in their business.

Uh, we started off by building an AI health app called HealthRocket, uh, and then we built an AI productivity tool called AI Rocket. And you just mentioned it yourself, AI is changing. Now we are deploying custom agents inside of, uh, business owners', uh, companies, and we're seeing a, an incredible transformation with that tool. That's awesome. So you're deep into deploying AI agents right now.

I'm doing it at a smaller scale, but you're doing it at a scalable fashion, which is awesome. What changed recently that people aren't fully understanding yet? Well, when people talk about AI agents, the mistake is to think about, oh, that's ChatGPT, that's Claude Code, that's Google Gemini. Really, the intelligence in an agent is a small part of it. So think of a human body.

Our brain makes up a small part of our body, incredibly important, But it is, the agent is the entire body. And a lot of things are changing all the time. Automations are getting smarter. Software is getting smarter. AI intelligence is getting smarter.

The ability for AI to use tools, like to work with the CRM system, to run a website, to read your email and your calendar, those things are progressing. Agents have to do all of those things together at one time. If you want them to operate by understanding their job, what they can and cannot do, and what success looks like. And that's really what an agent is. It's a highly automated system.

Sometimes it can be fully autonomous if it is trained correctly. This is your job. This is what success looks like. These are the things you're allowed to do. And that's what makes agents incredible is it's not an answer engine anymore.

And that's what the old version of AI was. And a lot of people are still there. Oh, I use AI to answer questions. That's great, but that's not going to transform your business. You need autonomous guardrailed AI that knows its job and is doing its work.

So all what has changed is everything gets better every day. And we're now at a certain point where you can have mostly reliable, mostly safe, mostly autonomous agents. They're not perfect. There are issues. And they can do real things in your business.

And what, I know you and I know this, but what changed recently, like in December, with Claude Opus 4? Is that 4.5 or 4.6 that kind of transformed AI forever? Yeah, Opus 4.6 came out actually October, and I think 4.5 is what came out in October. And a lot of people didn't realize the power of Opus until the holiday break, until December happened. And really what happened is a lot of people that worked in technology, they had time off for the holidays and they started playing around in their personal time with what we call now Claude Code.

And they realized, oh my gosh, this thing is incredible. It can create all kinds of things. We were using Claude Code when it first came out about a year and a half ago. I remember you and I, you and I debated this. And so we were using Opus 4.

And we were using Sonnet 3.5. Wow. And we built our business on that, and we've been able to see the progression of changes. But, you know, the thing is, back then it was amazing. Now it's amazing.

Next month it's going to be amazing. It's just going to be a new level of amazing. And so what people realize is, oh my gosh, this thing can do anything if the instructions are correct and the outcome is defined. And there is the skill of how do you How do you build it to do what you want it to do? That's a visionary, that's an entrepreneur, that's a founder question.

Then how do you guardrail it so that it knows what it can and cannot do? That is a high tech. A lot of trial and error goes in that. It's where a lot of people get frustrated and a lot of people, they'll go down a path of setting up agents and systems and then they'll realize, oh my gosh, I don't have the skill or the experience to know how to navigate all these problems that are causing. One, it does one thing right, but then it does 2 things wrong.

Um, and so, you know, yeah, this is the early days. We're pioneering it. Uh, and, you know, we're running 100 different agents every day, 25,000 tasks that are running every day. And so we've got a large volume of how do we fix and improve things as they go. Give us a real example.

What's an AI agent you've deployed that's actually doing work today for one of your clients or for your company? Yeah. So every one of our customers gets a chief of staff. That's the first agent that they get. Uh, and the chief of staff is custom for their business.

So it understands what is the business, what's the core values of the company, what's the mission, what are the goals, what are the targets? Um, and it really tries to keep everything in line with that. So the first thing a customer gets after they do an interview about their business, their core values, their mission, their goals, because that's important. That's the foundation. You can't skip that.

Otherwise, an agent has no idea what success looks like if it doesn't know that. And a lot of people don't, they don't do that correctly. Um, the chief of staff introduces itself and says, hey, here I am. I'm alive. These are all the things I can already do.

These are the things that I'm going to help you with every day, which again is custom based on their business. No, no 2 chief of staff agents are the same. And here's the things I need to do my job better. I need information about these connections to these software tools that you use in your business. I need the ability to, to get these connections built and everything like that.

And by the way, send me feedback. Tell me when I'm doing something right. Tell me when I can do something better. Tell me if you want me to change something. Feedback is what the best agents learn from.

And a lot of people don't understand this. If you have a frontier-level agent, You need to treat it more like an employee than the technology or a software. You need to give it real usable feedback. You have to invest time just like you would hiring an employee and training the employee. Feedback is how we have our agents not only understand success, but how are they doing against it?

And our chiefs of staff can edit their own code. They can change their own directions. They can build new tools and integrations based on feedback. And then the other thing is we've got agents that are doing lead generation. They're doing inbound and outbound responses to support tickets and customer inquiries.

They are able to access the data of a company, understand an incoming message from a customer, a prospect, or a lead, and then understand where it fits in everything and how to answer, how to respond. And they can generate a draft and have it sit there for human approval. Or if the human is comfortable with it, they just send it out as long as it matches a certain level of confidence, right? So the agents can gate a certain level of confidence. Oh, we've handled a customer request or a lead request like this before.

This is 98% similar to all the other requests. So we're going to go ahead and send this out. But another one comes in, oh, this is like, this is only 50% similar to the other ones we have. This is below the threshold. We need to generate a draft, but we need to flag the human team, hey, you need to take a look at this before we send it because this is outside the norm.

That again, the agent can do it all. It's up to the humans in the business to decide how do we want to operate these agents? How do we want to set these guardrails? So we have that. We have CRM agents, we have lead source, we have answer ticket agents.

We have agents that are handling social media. for their customers. And it depends on the social media platform. There's some platforms that'll allow automated posting, others that'll just allow drafts. And so agents are not perfect, but a lot of what agents can't do is limited by the tools.

And we're always working around that, and the tools are getting more used to having agents. So those things will change in the future as well. So, um, it's— I've been talking about TitleGPT.ai, and you and I have talked about it as well. And what I'm building, what the company's building, is focus on sales and lead generation. It's a, there's definitely a void in our industry.

Like, I'm not aware of any platform or tool that actually addresses the lead generation piece. So how can agentic AI or an agent, or actually it's a virtual AI employee in a way, because when you onboard them properly, and so like there's no different than a real person. So how can we leverage, uh, like the agentic AI, like in sales and lead generation specifically? This is what interests into our audience to grow our business. Yeah.

What you, I want to double-click on what you said. Um, I think the next year or 2 is going to see a transition of every successful business employing agents in their org chart, on their team with roles, metrics, responsibilities. Just like their human workforce. I don't see a path for a company to be successful in 2 to 3 years without this strategy. And if everyone is trying this strategy, the differentiations will be the companies who understand it best.

They know how to use the tools best. They know how to set guardrails, responsibilities, jobs, and tasks, just like you do with your own team, right? This is a differentiator that exists today with your own employees, and it'll be a differentiator in the future with your agent employees as well. So the key to how to leverage AI for any specific task, whether it's sales generation, lead processing, customer service, sourcing new ideas, sourcing new revenue sources, keeping up with the news, managing your calendar, recapping your team leadership goals and what needs to happen in the future. I mean, there's, there's no use case that we haven't sort of seen and explored where an agent doesn't understand, okay, first, It's about understanding the goal.

What is the goal here? What does success look like? And again, that comes from the humans. 2, where does the data live that needs to be brought in for this? Every agent needs the data.

Okay, where is the input coming from? What do we know? Right? If it's sales and lead gen, where's the information coming from? What data do we have?

And if it lives in an app or if it's coming from a website, The agent needs to know, okay, where, how much data is actually there? Where can I get it? How can I use it? And I want to understand it. So that's the first thing the agents do is try to understand what is this data?

What does that have? How many different tables are there? What do I know? What can I access? And then the agent builds its own integration to interact with that.

The agent wants to make sure it can get all the data it needs. And then it needs to contextualize that. Okay. I have all the data from the, lead gen or the sales coming in, but now I need to understand how it fits in the company. Well, what success looks like here.

The company has these products. The company has these services. The company, this is what I know about the company. This is how the company responds. So it needs to match those 2 together.

And then the decision. So data collection is step number 2. The decision is driven by the The AI, this is where your Claude, Sonnet, or your ChatGPT-5.5 is going to take all this information in and say, okay, what's the optimal decision to be made here? Do I have several options and how do I score them from highest to lowest on which decisions will guide me to the success? So that happens steps 1, 2, and 3.

Now step 4 is the action. And the agent needs to know, what am I allowed to do? And if the agent has been told, well, if the criteria of the decision scores at, let's say, 90% or higher, you are allowed to take these actions. You're allowed to go put this information back into the CRM. You're allowed to send an email.

You're allowed to send a message. And where are you allowed to send those things? And that is again set up by the human team. What's acceptable here? So what you have is You have all those steps combined, but they can fire 100,000 times a day.

Right now you pay for every time it fires. There's token costs, there's energy costs, and either you pay for that or you buy it from somebody else. But the agents can execute these things once they have the system and the guardrails on any use case that you can do, as long as they know what the success looks like, where the data is, how to make a decision, and then what actions they can take. That is an agent in a nutshell. So what kind of, uh, kind of tasks are perfect for AI agents and what still requires humans and decision-making?

Yeah, so this is, this is funny. Um, before our call, since you sent me a framework, I had my agent review your criteria and give me some talking points. And one of the talking points my agent, uh, was going over with me is what kind of things are agents better at and what kind of things are humans better at in this? And really the agents are better at doing things that require automation, task gathering, decision scoring, framing the outcome of, okay, this would match to this, this would match to that. These are a lot of the redundancies, things that happen at high volume.

Things that are happening on a recurring task, things that need to be processed, set up. And where the human really probably owns a better decision tree is things that, number one, things that are high stakes. If this isn't correct, we might lose this customer. Things that require intuition and gut. There may be not a clear answer on something.

There may be no way to score which option is better. A human can look at that and go, well, I know which option is better. That's a human intuition. Now, we could be wrong. Also, we're often wrong in business, but it feels better to make that choice.

If there's no clear yes or no, if it can't be analytically scored, the agent will pass that up to the human. Again, that's in the guardrails. And then 3, things that cost real money. As humans, you know, If we're going to go for a big deal, if we're going to make a big negotiation, if we're going to put our foot in and we're going to draw a line in the sand and say, this is our best offer, this is all we can do, this is our discipline, it feels better if we make that decision. Again, win or lose, we don't want to look back and be like, oh, AI made that call and I wasn't involved.

That doesn't feel good. And I think the relationships with people, AI is not going to replace that. Uh, we need a human-to-human relationship. We need that emotion. We need that connection.

So even though AI can do all the work, you're still managing relationships with your core customers, your core vendors, your business partners, your team members. Um, and you know, I don't see AI being very helpful in that, nor do I want it to be. Are, uh, companies actually trusting these systems to run their own yet? I know we are in sales and marketing, but not operationally yet. Yeah.

Again, it's still early days of agenthood. The errors and mistakes are going to be there. I don't know that it's any more higher than of an error rate than you have with a human team. We certainly have that with our human team, but people are extra sensitive to when an agent makes a mistake. You know, they're more, you know, they're more likely to blame an agent for making a mistake, blame the technology than they are for blaming a human team for making a mistake and blaming humans as a whole.

So a lot of that's mindset. We're not used to that yet. But in our opinion, the customers who are open to, I'll take a little bit more of a risk, I'll take a little bit more of an acknowledgement that some things could be frustration, some things could go wrong, but they want the upside. So this is just, where is your tolerance for these things? Yeah.

And what we think is even if you get in now, Versus a year from now. And let's just say things are working 80 to 90% now, and you want to wait till they're working 99 or 100%. You could definitely wait, and that day may come, but what you may lose by waiting is a few things. Number one, you lose the experience you gain right now in using agents. So if they are 99% in a year, you've gotten a year head start, which means your agents are going to work way better than other people who are just getting into it and just trying to figure it out because the elements of The human element in working with agents is going to be the same then as it is now.

And you're going to have to have your own skillset as a founder and as a team on how you construct these agents, how you give them the vision, the goals, the mindset, all that stuff. And so that is needed. That's a needed skill that takes time. Doing it now is going to be a lot more beneficial than later. Plus, there are some clear advantages of doing it now because when you do something and 90% of your competitors are waiting to do something, You have all the delta of conquest to get to leads first, to handle sales first.

Uh, you might have a little bit more margin of error than they do, but the speed, you might be 5 times faster. You might have 5 times as much output. And we think that that far outweighs the, the error, uh, rate that you might be able to see. Uh, to piggyback off the blaming AI, uh, statement you made is like the, when Tesla gets into an accident, It's a massive deal when there's actually thousands of accidents a day, but it's one once every year, every 2 years, something happens. Oh, Tesla, it makes the news.

Yeah, absolutely. You know, and it's going to get even more pronounced when there's a lot of humanoid robots that are working in our homes, in our businesses, things like that. There's going to be accidents and incidents, right? There's going to be Things like that that are going to happen and people are going to be really quick to speak out against that. Oh, these things are unsafe.

We need to shut them all down. But there's 100,000 incidents like that in every city, like every day from human to humans, right? There's intended things. There's unintended things, right? And so it's just another thing that we're going to have to work through as a society.

Yeah. And, um, are we moving towards a world where companies have fewer employees, but more output, like with that productivity? Yeah, this is a real interesting debate and one that's all over most podcasts right now is, is AI and are agents going to take our jobs? I don't believe that agents take our jobs. I think that they allow us to redefine our work.

So agents will probably take a lot of our work, but allow us to redefine what work means for us and what we can do. In the last 12 months, there's been more hiring in tech than there was before. So we're not seeing a direct correlation, but it is also possible that companies can build a company and an org chart with a lot less humans on it than before. But what we may see is a lot more companies. So while the average company or startup or small business might have a reduction in the number of people, on the team, there might be twice as many of those companies.

So I don't think we know yet where it's going to net out, but I think we know that agents can do a tremendous amount of the work that humans are doing right now. And most of that work is targeted to work that honestly, the people don't want to be doing. It's not the highest value work that they are doing anyway. So if you can clear up that work and you can allow the people on your team to be doing more high value work, more human-centric work, things that drive more purpose and are more aligned to the things that we're uniquely good at. I don't see how a business has a downside to that.

If someone is listening and they're curious but overwhelmed, what's the first step? Well, we have a first step that we think is pretty fun. It's free, it's painless, it takes 30 minutes, and we have an agent that will interview you about your business, your reason why, Your pain points, your opportunities, your goals, things like that. What does success look like? These are the foundational questions that an agent needs to know.

And it will take that. You can have a voice conversation or a text conversation with our agent, and then it will map out, hey, this is what your chief of staff would do for you. These are some specialty agents. This is what they would do in your business. Here's how it'll all fit together.

Here's how the agents would work with your human team. Um, and it's what we think is it's a look into the future of what agents on your org chart will be like. And this is how every one of our customers got onboarded, but we do it for free just so people can take a look at it, just to know what it would look like. I think you've done it, Mo. Yeah.

And the idea is we just want people to understand what agents in the business or in their life. We have some people who've done it just for their personal life, like their family, their calendar, their trips, things like that. And if you haven't seen what that looks like yet, it's a great experiment and you could do whatever you want to with it. You could ignore it, you could build it yourself, you could open claw it, you could hire a development team, you could, you know, take it to your CEO and say, hey, I got an idea. This is what I want to build for us, right?

We just want people to understand what that looks like. So that is our answer to that question. So what's a simple, low-risk, use case they could test in the next 30, 60 days? Oh, you know, this is a difficult answer because there's so many paths you can go. You can just, you know, use whatever AI you're using right now with Claude Cowork or ChatGPT Projects or custom GPTs and Google Gems.

There's so many places you can do. But if you really want agent flows, You need to be able to connect your agent to your data, give it ability to take actions. And there's so many routes you can go with that. I mean, the route that we built is quite intensive, took us 6 months to build the framework, but we've got 100 agents running 25,000 times a day. Most people are not going to be able to build at that scale, right?

So what do you build for yourself? You just keep pushing the limits of your own AI to connect to more things, do more things, trial and error. I think the best way to learn AI is like riding a bike. You can watch a video, you can do a course, you can read a book, but just get on the bike is the best way to do it. You're going to fall, you're going to crash, you're going to figure out, oh, if I lean this way, it does this, if the handlebars do this, that trial and error is awesome.

But if you want to understand a full agentic system, Um, do the interview with our agent, um, and it'll give you an outlook of what it looks like. It won't teach you how to build it, but it will show you what it looks like if you were to build agents for your organization. Um, what do most people get wrong when they try to implement AI, specifically, uh, agentic AI? I think the biggest thing is people don't start with the foundation. People don't start by making sure their agents understand what is important.

What's the reason why? What is our business trying to do? What are our goals? Where are we trying to get to? What are our core values?

They just say, hey, I want this agent to go look at these sales leads and convert them. I want this agent to go into my CRM and do XYZ. And you would never bring an employee into your business and do that. No background. So why would you think agents are going to perform well?

Now they will perform because agents are, their job is honestly, their job is to please you and spend tokens. So what they will do is they will just go, go, go, go spend a bunch of tokens and be happy to send you the bill. And you're going to be like, well, this doesn't feel like me. It's not what I wanted. It didn't do its job right.

No, it did. It did exactly what you asked it to do. You just didn't ask it to do it with the right foundation, the right goals, the right guardrails. Um, so the best thing and the worst thing about AI is it knows everything that's, that's happened before. So if you don't give it enough focus, the response is going to be a mix of everything that's ever happened before, which is probably not the outcome that you want.

It's what I, um, uh, I've been preaching, like, um, in the podcast and also to our team. The foundational, most important thing is building that first document as a constitution or master memory file where you can ask, especially with Claude, it's very friendly. You can ask to interview you to find out more about you, who you are, your goals, values, more in depth so that way when you ask it to do something, it understands you, knows the guardrails, knows, has more framework, more context. It makes it a lot easier and more— Absolutely. Um, yeah, so that's why I think that, you know, there's, it's still early days, but if you get those things correct, then you can do a lot with your agents that might surprise you, might delight you.

And, and at the end of the day, you're going to learn a tremendous amount. This is the time to learn how to use agents. If we are going down this path, And I don't see a way that it gets changed over— now we don't know what's going to happen in 6 months, but, um, now is the time to get your feet wet, to learn how to work with these agents in your system, because honestly, the biggest hindrance to what we're seeing in agents is not the agent itself, it's what's coming from the humans in the business. Do you need clean data and systems first, or can AI help fix that mess first? For the people that are a little overwhelmed, don't know where to start.

I don't think you need clean data first, but that depends on how you are setting up your agents to get access to your data. Again, if you just say, hey, all my data's here, go get it, and the agent doesn't have any way to understand, filter, sort that, it might be problematic. We use a pretty sophisticated system that syncs data, that vectorizes data into basically agent code, like ones and zeros, things that we don't read, but the agents love to read. And then most importantly, we have an agent that reviews all of the data and attaches what we call smart tags. Oh, what is this information about?

What are the topics that were discussed? Who was involved in this? What are the key themes here? And it adds these little tags to each row of data, and there's millions of rows of data. And it says, okay, here are all the keywords.

Here are the discussion topics here that people mentioned. And it puts those in data columns. And then when our agents have to go find information, instead of reading a million records, which would eat up all their context, take a ton of tokens, it goes, I need information about this topic, this thing, on these dates that had these people. And it only looks at those tags. And it grabs all the information that it has.

Now, out of those million records, it might grab, say, 200 or 500 or 1,000. Then it grabs that information, still vectorized so it can read it like it can drink water. And then it goes, oh, this is the information I need. It passes it on. And then the intelligence goes, what do I do with this data?

So that, that's a very complicated, but also a strategic way because data takes time. And data takes tokens, which means costs, right? And if you're building an agent system and you want it to have access to all your data, but it takes it 5 minutes to give a response because it's got to read through everything, and now you're paying API costs of $20 or $30 a day on data, that's not sustainable. Um, so you've got to think about those, but that's more like in the weeds, right? But you do have to have a good data strategy.

If you were to look in the future, let's say if 3 to 5 years from now. What does a typical business like if they fully embrace AI agents? I think a typical business is 80 to 90% of the day-to-day work is done by the agent team. I think a business owner is the architect of the team. Let's put it in terms of a of a race team, like an Indy race car team.

You move the humans out of being the actual race car driver and you move them into the team owner role. Their job is to set up the team for success. Oh, we've got our manager, we've got pit crew, we've got mechanics, we've got the driver. And a lot of times the agents will become the driver of the team, not the humans. Now they're doing most of the work.

But the team owners and the humans on the team are deciding what races do we want to go for? How do we want to build our team? What investments do we want to make in new technology for our car? What sources of information do we want to be looking for that'll improve our team? It's a completely different focus on work.

And I think that's what we're going to see from the leading, most innovative companies. is shifting most of the day-to-day work off to the agents and allowing the humans to think about bigger pictures. What's something you believe about AI, like, that most people would disagree with? You and I are big optimists, and I've had conversations with people like, oh my God, it's like total opposite with how you and I think. Yeah.

One belief I have about AI that may be not too common is I believe that AI needs to be challenged to do the impossible. So if you are prompting or you're trying to use AI for something and you're going for a small outcome, you're going to get a limited result back. I, at least once a day, multiple times a day, I challenge AI to do something that's never been done before and to figure out how to do it. And, you know, it may or may not be able to be done, but that's just what I think is the frontier of using AI is let's solve a problem or do something that's never been done before. I've never seen it, never heard it.

I don't even think it's possible, but I want to challenge AI to figure out a way to make it possible. You'll find out that a couple times a week or maybe even a day, you'll go, oh my gosh, I can't believe that just happened. That was crazy. This was impossible a week ago. So I think you have to push AI just like you would push yourself to do an impossible goal.

You know, I'm a big adventure racer. Adventure is my number one core value. So, you know, when I've got 5 or 6 things on my calendar each year that go, oh my gosh, like, I don't know. I don't know how I'm going to do that. I don't know if I can do that.

That levels me up because then, you know, every Tuesday, every Wednesday, every Thursday, I'm thinking about that next thing I got to do. Oh my gosh, I don't want to fail at that. I don't want to die at that, right? So that becomes the motivation. I treat AI the same way.

Like, we have to figure out this problem. Like, go to every resource you can. We're going to figure out this problem. We're going to work on it together. And I find that, I don't know how it's working internally, but it's unlocking some really cool things.

Um, if you had to rebuild a company from scratch today using AI, what would you do differently? I know you've RocketHub.ai is all brand new and it's a, you're a perfect example of how that— Yeah. So we built our company from day one with a strategy and an org chart. And the strategy is we're going to use AI for our work. We're going to have a few managing directors, equity owners that oversee different parts of the business.

We're not going to have employees and That was our org chart, and our org chart had agents on it a year and a half, 2 years ago. Now, we had no idea what was possible, how to even make that happen, and it wasn't possible back then, but that was our mandate to figure out. And it's a large reason why we are here today is because we said that's what we're going to do. Even though it's going to be hard, it's not going to be reliable, and it's largely not possible today, we're going to grow into that. So if you were starting a new company, I would say, Again, back to the foundation.

Before we had our org chart, we had a mission, we had a VTO, uh, we understood what the purpose was, what we were trying to achieve. We had core values, and then we built the org chart around that. Um, and that's what I would say. Start with the mission, the reason why, the core values, the unique deliverables, then build a strategy of how you want to do it. And if you're comfortable using AI agents, try to find every part of your business where that can happen so that your humans can do truly human work.

That's awesome. Any last words for our audience? And this was very informational and educational, and I appreciate you as always. I appreciate you too. I'll go non-tech on this, but sort of as a consequence of tech, if we look forward And we assume that the current technology and innovation and adoption rate continues, we're going to have agents in every part of our life in the future, work, personal, family.

And so what I want to encourage people is 2 things. One, you have to build your personal mental mindset around this so that you don't get sucked down the productivity loophole of, oh my God, if I work One more prompt and one more thing and one more thing. What— there was a thinking a few years ago that once agents could do most of the work, then we would have a lot of time off as business owners and leaders. It's actually gone the other way. Now that we're using AI, we're working more.

We're spending more time because, oh my gosh, it's so much more productive. So I want to spend more time being more productive. And then we realize, oh, nights and weekends are full of AI work. Where is all this? Oh my gosh, I can enjoy my life now because AI is doing my work.

It is there. We're just choosing not to use it. We are choosing to believe that we have to now work a lot more because AI is more productive. That is a choice that we can make as humans, and we can only make that choice. AI is not going to do that for us.

So that is an investment that people need to make. And if you don't make it now, you're not going to make it in 6 months or a year because it's going to get more and more difficult to make that choice. And I think there's a reckoning to pay for that. As a human business owner or a human team leader, if you can't do that for yourself, as AI gets better, you may lose yourself in it. You may lose your purpose on the team.

You may lose your ability to be uniquely human in your business, in your family, in your personal life. And it, at that point, What is the gain worth? It's not going to be worth it. Even if the AI does everything that you would hope it would do and your business is more productive, is that the success that you really want? And the success that I think people really want is to feel more human, more alive, more connection, more freedom of time and choice, and to use AI to do that.

But you have to make the choice that you're not going to get sucked down the Absolutely. I couldn't agree with you more. And I get sucked into it and I've been working some weeks, 80, 100 hours a week, but it's so much, but it's fun. It's like we're literally going through a major shift in human history. Like I just, we're going through it.

People are not aware of it, but it's fun. But you do have to pause and like, hey, I kind of step back and enjoy life. Yeah. And I'm with you there. The pushback I give is this is not a major shift in— the major shift is going to continue on a continuous basis.

It's not like this major shift will end and then we will go back to being relaxed and get our time back. And it's not a moment in time. It is the new reality of everything. So to say, well, I'm just going to do this now. because now is the shift.

Now is the time that I need to take advantage of it. Well, you're going to wake up in a week, 3 months, 6 months, a year, 2 years, and it's going to still feel like the major shift is happening. And you will look back and go, man, I never really exited out of that. Yeah. So just a challenge for you and me together.

That's why I went on a 3-mile run up the mountain this morning instead of answering emails or getting my agents to do some work because, um, gotta protect that. Well, thank you so much, Clay. I truly appreciate you. Thank you, Mo. Well, that was an awesome conversation with Clay, um, talking about AI and specifically agentic AI.

Um, if you like this episode, I would appreciate if you can, uh, most importantly subscribe and second, give us a 5-star review. Until next time. Have a wonderful day. In a world where change is the only constant, Mo Choumil stands at the forefront, guiding title professionals to not just grow their businesses, but to master the art of innovation. With every episode, you're handed the keys to unlock unparalleled growth and stay ahead of the curve.

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