Episode Transcript
[00:00:00] Paul Burani: One person can do the work of 10 people, of 100 people. And you have amazing companies doing incredible things incredibly quickly and with very lean business models.
[00:00:11] Elizabeth Gearhart: The people using it are making strides. The people not using it are having a hard time right now.
[00:00:16] Richard Gearhart: They tell us about their legal issue and in 30 seconds they get a complete solution.
[00:00:23] Elizabeth Gearhart: The beauty of AI is it can take a ton of data and it can get a cohesive result from it. It would have taken us weeks to dig that out. Right.
[00:00:32] Richard Gearhart: The moral of the story is that we're a long way away from a completely humanless workforce.
[00:00:39] Paul Burani: Let the machines do what the machines do best. Double down on the things that are uniquely human.
[00:00:45] Elizabeth Gearhart: This is real AI Use Cases Business Owners Roundtable. Here's how real companies are using AI right now.
Welcome to Real AI Use Business Owners Roundtable. I'm Elizabeth Gearhart.
[00:00:58] Richard Gearhart: And I'm Richard Gearhart.
[00:00:59] Elizabeth Gearhart: And we're here with Paul Bharrani with Atlas Primer. So, Paul, what is one way you are using AI in your business today?
[00:01:07] Paul Burani: To pick just one to share with you? That's a creative decision. But I would say one thing that I'm very interested in is how AI connects disparate resources and technologies and makes each better. We all write emails, we all connect with customers and partners and stakeholders, and a lot of us use a CRM to manage our relationships. What I quite like is using AI agents to connect the two and to achieve a one plus one equals three effect, whereby the intelligence that is absorbed from conversations, what we learn from having dialogue with our customers or managing relationships with new prospects actually trains the CRM to better understand how to communicate, how to write better emails, when to write them so that they are most likely to be read. Imagine that we're optimizing for attention.
And so I love having agents where I can ask questions in plain English and say, hey, what's the right angle to use with somebody in the B2B SaaS software category about a trend that I'm seeing and how could we use that to make a case for our product?
So there's really the only limit there is creativity.
[00:02:33] Elizabeth Gearhart: Well, it's funny you say agents, because a few months ago it was like, agents are coming and people didn't really know what they were. And it's like, agents are the future of AI and now they're here and. And I see the different LLMs going into agent mode sometimes when I query them.
So, Richard Gearhart with Gearhart Law, what is one way you're using AI?
[00:02:55] Richard Gearhart: Just to continue on, Paul's theme about marketing and networking. There's a lot of ways to reach out to potential business partners, using AI to find people who would be a good fit for our law firm and then coming up with ways to creatively contact them, which is the basis of every attorney client relationship. So obviously the relationship is important, but the relationship has to start somewhere and there has to be interest on both sides to form a relationship and make it. Certainly one of the eight things that AI can do is help us position ourselves so that the potential client sees all of the things that we can offer for them and make ways to improve their business.
[00:03:44] Elizabeth Gearhart: I'm Elizabeth Gearhart with Gear Media Studios in conjunction with what Richard was just saying. Paul, you're talking about the CRM. Richard asked me, can you have one of the LLMs identify this subset of business owners or business people for me and get their email addresses?
So I asked Perplexity. And perplexity gave me this answer. Oh, I have agents working on it. When the agents come back with their data, then I'll give you your answer.
And it gave me this spreadsheet of exactly what I asked for. And then it said, what are you going to do with it because you don't want to spam people? I said, no, I'm going to. I use Zoho. And Zoho's very careful about unsubscribes and all that. So it said, okay, here, I'll give it to you in a form that you can upload it to Zoho campaigns.
[00:04:27] Richard Gearhart: And so I think after we received the list, I said, maybe the first step is just to reach out to these people on LinkedIn and establish a connection that way. And then we'll probably send them a newsletter with content that is geared toward their offering. But it doesn't have to be like a huge 5000 person email list. I think we're looking at doing outreach to maybe 200 people.
And so by finding the right fit right away, we don't have to worry about spamming and we can go right to the people who we think would be a good fit for us and who we would be a good fit for. So I think it has a lot of strengths.
[00:05:10] Elizabeth Gearhart: Now let's get into the guts of the conversation.
Paul, what do you think of AI?
You have been in business for 20 years, sales and marketing.
Did you see this coming or did it hit you like a ton of bricks? Because the people using it are making strides. The people not using it are having a hard time right now.
[00:05:27] Paul Burani: Good question.
So let's keep in mind that artificial intelligence really was born somewhere in the middle of the 20th century. So the same way our Department of Defense spawned the Internet when nobody was paying attention. And then in the 90s, we started getting email addresses and all of a sudden we're power users. The same thing that's happening with AI. It's funny, when I was a sophomore in high school, I wrote a paper, so this is the mid-90s, I wrote a paper on the chemistry applications for artificial intelligence. And it was funny because I picked the topic thinking this would be cool. And then as I got into it, I was like, oh my God, how am I going to connect this to chemistry? This is way outside of my strike zone. And then I think I landed it got a B. So I did a good enough job.
And in hindsight, the stuff that I was writing about was so esoteric. But in 2022, the shot heard around the world was OpenAI releasing ChatGPT to the general public.
Now, you could call it a killer app, to use an overused term. It's not like that was some technological breakthrough. That was more of a user experience breakthrough that now taught the layperson, hey, I can use this very powerful technology myself.
What people are mainly doing with ChatGPT, you mentioned perplexity. Claude, Gemini. What? They're largely using it the same way they've been using search engines. But there is an evolution. We're becoming more sophisticated about what we ask and how we follow up with clarifying questions and how we embellish our prompts and so on. Earlier we talked about agents. That's sort of a quantum leap because now we're effectively training the AI to go out and ask the questions and clarify the prompts and to do all of that by itself without us having to put that energy in. And so I think that's what's really exciting, is the force multiplier for our creative and operational energy. Right. And that's where you see it. I think as entrepreneurs, we see it very acutely in an enterprise environment.
We have to jump through hoops, get approvals, and we got to be careful about what we share.
And there's power, but it's also capped and limited and diluted in certain ways. As an entrepreneur, we are truly the frontier people. We're like Lewis and Clark navigating the river, right, and seeing what's around the bend. And I think that's where it gets really interesting because we're finding that one person can do the work of 10 people, of 100 people, and you have amazing companies doing incredible things incredibly quickly and with very lean business models, which changes the whole economics of how you found a company and compete nowadays. So that's where I think a lot of the sort of transformative impact is happening.
[00:08:17] Elizabeth Gearhart: I agree. And I have to say what's interesting is over this past year I have seen a cottage industry explode with people who are willing to help you implement AI in your company, write AI agents for you.
I think what's happening though is when you have a hammer, everything is a nail. And I think that some people are over enthusiastic and they're not strategically deploying AI in their businesses and that's why they're not seeing the ROI that they had hoped for. So I think that you have to really take a critical look. Have you found that to be true for yourself?
[00:08:53] Paul Burani: Absolutely. As a career salesperson, we have another way of saying it, which is that a lot of times misguided sales and business development efforts look like a solution in search of a problem. And we're basically answering the same thing, right? Which is are you here just to sell something or are you truly in a fix it mindset? So the best example I can give you is in my business today with Atlas Primer. One of the things that we find is that our conversations go much further when we're talking to a leadership team that is thinking long term about AI. They are thoughtfully building their AI stack rather than chasing shiny objects and really using point shaving as the sort of guiding ethos. By that I mean are we onboarding AI just to trim a little bit of margin here, a little bit of cost there? Cause ultimately what happens is everybody under the leadership team on that org chart is going to start thinking, okay, it's only a matter of time before I'm on the outside looking in. And so what we're looking for is the organizations that, to your point, Elizabeth, are thinking strategically and are saying, no, we want to use this technology to actually further entrench our people in the business. People are incredibly valuable and important. We're not here to onboard this technology to make people irrelevant. And it's a choice we have to make. We're going to be on one side or the other.
[00:10:24] Richard Gearhart: So can you provide some examples of steps that a forward thinking management team would take versus the low hanging fruit approach? There's nothing wrong with taking the low hanging fruit. Right, but what would be a strategic approach, a long term plan?
[00:10:42] Paul Burani: I think the first thing would be to ask what do we hope to achieve here?
And it's amazing. How often people don't ask that question, right. Why are we doing this?
So let's work on answering that question and having, let's say it's a committee.
Sometimes that word's overused, but it needs to be more than one person. You need to get different points of view. So imagine getting your leadership team together and saying, all right, there's this new technology. There's an opportunity cost to not using it. But we're not just going to deploy it because I, our board or our customers or Wall street says we need to. We're going to do it because we see an opportunity. And if we don't do it, we have an opportunity cost that we're not willing to absorb.
[00:11:27] Richard Gearhart: So when you're going through this process, are you visioning a new business model or how the business could operate in the future, or are you looking at just making current systems more efficient? So, for example, for a law firm, I could try visualizing. People just call in, they tell us about their legal issue, and in 30 seconds, they get a complete solution. Right. That would be like the most efficient way to deliver legal services.
But that's pretty pie in the sky, right? So maybe that's 30 years away. I don't know. So how do you manage what is possible versus what is likely?
[00:12:12] Elizabeth Gearhart: Yeah, because I always have ideas for things that are impossible.
[00:12:16] Paul Burani: That's good. That's a healthy sign of your business and its fundamentals, I think. But I think the answer to your question is actually the result of that. Why exercise?
So if we know why we are intimately connecting not just to our AI objectives, but our business objectives, why do we exist? Your law firm, tell me more about what it does and what it does uniquely.
[00:12:42] Richard Gearhart: Well, we're an intellectual property law firm, so we protect ideas through legal means. Patents, trademarks, copyrights, and trade secrets. And we help companies at early stages incubate and protect the initial ideas. And then as they grow, we help them manage a portfolio of their intellectual property, we help them monetize the assets as they become mature, and then we are able to defend and enforce those rights if necessary.
So that's the spectrum that we work with.
[00:13:16] Elizabeth Gearhart: One of the strengths of Gearhart Law is that it is an entrepreneurial law firm. So Richard started it with zero clients in the attic of our house 20 years ago and has had all the ups and downs that a business owner has and has a unique perspective from having worked in corporate of how intellectual property can affect the bottom line of your company and where it fits into any business structure. Because Anybody could just get a patent, but then how do they use it? Right. So he has a lot of background and can counsel people on that.
[00:13:49] Richard Gearhart: Yeah.
[00:13:49] Paul Burani: So the reason I ask is because there's a spectrum of growth or innovation coming out of this fundamental question, what can we do with AI? So on one extreme, there's going to be incremental gains. They're easy, they're low hanging fruit, they're at arm's length, but there's limited upside. And then on the other side of the spectrum, you have these transformational gains.
So imagine if on one side of the spectrum, the act of asking, why are we using this technology?
Yields a number of ideas, possibly seminal concepts for real innovations that involve a better client intake process or a shorter cycle to achieving the outcome you want. Maybe there is some ability to allow in a segment of customers that previously were not profitable for you to work with, thus contributing to business growth. So those are all things that could be the end game. And then you work backwards and say, how would AI allow us to achieve that? Conversely, on the transformational side, maybe you would say, hey, we're an IP firm, we work with early stage. I'm doing a little bit of speculative talk here, but we're working with early stage founders. They have really exciting technology products and there's a lot of IP there. They live and die by their ability to protect their turf.
Now, we are the best in the business at doing exactly that, protecting their turf. But while we're here building these trusted relationships and being a valued member of that ecosystem, what if we took what we've learned from those relationships and transformed it into some sort of a risk mitigation mechanism that actually allowed us to make really smart investments in these companies at an early stage when the returns are potentially astronomical. And what we're talking about is a law firm eventually using its expertise to actually become an investor. Now there might be regulatory guardrails. That's the how, but the why would be, we believe in entrepreneurship as the backbone of the innovation economy. And we think that we have a head start, but because we know our business so well. So that's like a real transformational aspect. And AI would be where you leverage the intelligence you have, augment it with intelligence you don't have, fit it into a new business model.
So I think there's always gonna be that spectrum of different types of how leading up to the same why, if I'm making sense, that's brilliant.
[00:16:29] Elizabeth Gearhart: Yeah.
[00:16:29] Richard Gearhart: And so part of the process that you work with the teams is how do you Tease all these things out.
[00:16:35] Paul Burani: So I should probably give a little context as to what my company does with AI. So Atlas Primer is a platform to maximize human potential in the age of AI. Now that sounds really lofty. So I'm going to qualify that. We have a problem in today's workplace, which is that AI seems to be coming for all of our jobs. Now, regardless of how you feel about that statement, the truth is everybody is showing up to work. In the last few years, we've been thinking a little differently about our work because we're seeing that our work consists of a lot of different tasks that could be done better, faster, cheaper, or all of the above by AI. And we'd be fools not to leverage those opportunities and say, hey, look, if my work was a pie chart and I could take 60% of what I do and, and automate it at a fraction of the cost and then take the remaining 40% and be the best in the world at that, that's the model for the new career path. Right? What does that mean? That means we're basically saying, let the machines do what the machines do best. Double down on the things that are uniquely human. And by that I mean the ability to have meaningful conversations, build rapport and trust, exercise judgment, give people confidence in our wisdom as human beings. These are things that the bots are not replacing.
So you mentioned earlier, I've spent a lot of time in sales and marketing. I spent my whole career basically in some part of the revenue organization. I've touched all of them at this point. And so everybody can intuitively understand that's a great use case for this type of application because you live and die by your ability to influence others. Machines are not writing seven figure checks to other machines. It's going to be a long time before that happens. That still happens between people. So as a salesperson, if I was making 200 cold calls a day or writing 200 cold emails, if I was spending a lot of time doing desk research or putting notes painstakingly into the CRM, those days are over and I'm now spending time practicing. But the problem is we don't have that muscle memory. The modern workplace doesn't have a practice environment. We don't have infrastructure where people can go to refine their human skills in a safe space, without judgment, without embarrassment. And that's what Atlas Primer is building. So we have a roleplay app where you can define a specific Persona. I work in a call center. My customer is a stranded traveler calling the airline, irate with the situation they're in. So I need to model that customer and all of their nuances. And my goals are, let's say I need to de escalate, I need to make them feel heard, I need to make them trust that I'm here to solve their problem and then I need to solve it. So if those were my objectives, I could go through a role play scenario.
And once I get through it, I find out how I did. And that is my personal learning experience.
Having that objective, chipping away at it, seeing how I did, and getting real coaching and feedback.
So did I use the right type of language to articulate our solution? Did I remain within compliance guardrails? Did I actively listen and play back to that irate customer? The things they said that are at the core of the problem they're feeling. So we're really trying to attach that to business outcomes and to say, hey, look, nobody disputes that. Practice makes perfect. In the age of AI, you now have the ability to practice. We've given you that infrastructure. You can do this as many times as you want 24, 7. Do it at 3am when you've got insomnia. Whatever it takes, you're not stuck waiting for that coach to be in the room with you.
[00:20:35] Elizabeth Gearhart: I love this. And what I want to say is that less than a year ago, one of the first people that came on said that he had a customer service company and that they were recording the phone calls and having AI analyze them and then giving feedback I thought was awesome. You're the next step on that. You're one beyond that. So that's really an inventive way to use it. I do want to ask you, Paul.
So entrepreneurs starting new companies now should be designing their processes around AI, instead of trying to retrofit AI onto a process they're designing. What do you think about that?
[00:21:12] Paul Burani: The devil's in the details, so each process would need to go under the microscope. I do agree that native AI companies have an advantage. That's why a lot of the small fries are doing real battle with their bigger competition these days. Retrofitting it is tricky, but you can also build solutions in a sandbox without undoing what exists today. That's one thing. You can build it in a safe space and only replace it once it's been stress tested. But also, you don't necessarily have to take over your whole business operating system all at once. So. So maybe of all the different things you could improve with AI, let's look at the recruiting process, how we bring in new Talent. Let's see if we can get some incremental gains there. Maybe we've got a moonshot or two. We want to try and do things a little differently and get people bought in and bring them along slowly. I think the slow and steady wins the race.
[00:22:11] Richard Gearhart: I will say it was interesting. We are in the process of doing this software upgrade, and I went through the statement of work and I put it into Copilot, which is the software LLM that we use for the law firm, because it's secure, it's Microsoft, and we put it in there. And I asked it, based on all of these upgrades, how much money are we going to save if we make the investment in the software? And he came up with a number. And I looked at it and I said, did you consider the staff time associated with implementing the software and not just the vendor cost?
And it went bloop and came back. And my point there is that AI is not able yet to see the big picture. I would have thought that would have been something that would have figured into the analysis, but it took somebody who's more familiar with how business operations run to notice and ask the question.
[00:23:11] Elizabeth Gearhart: Well, I do want to relate a very recent AI experience that just ended today, actually. I have a personal website, Elizabeth Gearhart, PhD. I post blogs on there about how I'm using AI, because I use it every day and for different things, but it's an agent.
It is amazing. And so I had to build my website and it just did it. I was like, oh, my God. I didn't have to do anything. It just did it. And it was amazing. I was shocked. Like, in five minutes I was like, oh, my gosh, these guys are really good. But then I tried to publish a new blog and it wouldn't publish because of some really weird error. They had to send me to a person online and a person was able to figure out the problem and tell me how to fix it, and now it works again.
[00:23:57] Richard Gearhart: So I guess the moral of the story is that we're a long way away from a completely humanless workforce. Right.
And I think it sounds to me like the steps that your company is taking are really important. And I love the analogy where get rid of the 60% that can be done with AI and then focus on the 40%, just do more of it. And that's what's going to drive business growth.
[00:24:27] Elizabeth Gearhart: Yeah. And I love the role playing because I think that teaches people skills that we've lost.
[00:24:34] Richard Gearhart: So how do you set up a program like that? I don't know if this is proprietary technology to your company, but what's involved in actually setting up a program like that? Maybe just on a top level.
[00:24:46] Paul Burani: Yeah, so the product itself we license to our customers. But your implication is correct, Richard, that there is a certain amount of programmatic support that's needed.
So typically there is like a program owner. Sometimes, if it's more of an executive leadership type application, it might be like a chief of staff. But the point is you need an owner, you need a practitioner. But what we're effectively trying to do, it goes back to our earlier conversation around the why and the how.
So there's a capability gap that exists in some way, shape or form. Now, they may ask us for help in diagnosing it and refining it and maybe quantifying it, but someone's got a hunch that, hey, maybe we're not so good on the phones, or maybe we're not making the best use of these trade shows and the face to face relationships. The hypothesis will help structure a program to test it. And usually our customers will start with a pilot where they'll do eight weeks. The most important thing is to be able to measure it objectively in a sales organization. We could look at the CRM and we could say, how quickly are new reps being onboarded? Or how long is the deal cycle, how big is the average deal size, that kind of thing. And then you have an objective metric to say it improved or it didn't. And you can back into the training intervention where we helped identify some scenarios. So if onboarding new reps is a problem because it's taken them too long, we can start to take some of those onboarding materials, weave them into the scenario and say, all right, look, you've gone through all these decks, you've sat through all these sessions. Now you're going to pitch the AI, and until you score a 95 or better, you don't get to go out in front of clients, that kind of thing. So every single one is different.
[00:26:35] Elizabeth Gearhart: So, Paul, let's say you have. We have a couple super closers at the law firm. Richard's one of them. He's very good at converting leads into clients.
So how do we capture his special sauce and where does it live? Because I feel like he could be training people to do it. He just doesn't have time. I've thought maybe we have an internal Gearhart Law LLM where there's a spot on sales calls or something. But now you have that information, you know what works, where does it live? And how do people access it?
[00:27:07] Paul Burani: So this right here is an open door meeting. I would conduct this a little differently if it was a closed door meeting. And I would ask questions like, how is your sales intelligence documented today?
So the things that make Richard good at closing, what are they? How easily can we retrieve those and codify those? If we can't, it's not the end of the world. We'll just have a little more work to do up front. But presumably there are notes in the CRM, maybe there's testimonials from satisfied clients, whatever it might be, that starts to draw some correlations and say, hey, the relationships that go well, they tend to have these hallmarks. And it might be the way that certain legal proceedings are handled, it might be the way they were qualified in the first place. There's a million and one different variables, but you wanna arrive at some sort of a formula that says, when we do this, it goes well, when we do this, it doesn't go well. And then from there, at the time of scenario creation, you can say, hey, here's the fictitious person that we're gonna be practicing with. And by the way, we're gonna drag and drop some documentation into the SC scenario as well. And that becomes a starting point. But the beauty of AI is that it learns dynamically. And so not only can Richard go and train it better by merely showing up as himself, but also every new rep that comes in the system will learn about their strengths and weaknesses and it will compensate if you want it to. So if they really don't understand how to close, we're just going to keep hammering away until we get it.
[00:28:42] Elizabeth Gearhart: Where does that information live? Should we do an internal LLM in Claude, which we've been considering, and put that information there so it would live
[00:28:52] Paul Burani: within your instance of Atlas Primer? A lot of it is public domain, so if you think about whatever underlying LLM you use for anything, most of the information is actually the same. Whether you're going from OpenAI to Anthropic to Google or what have you, it doesn't get shared with our other customers. It's not visible to the outside world. Every recording that Johnny does generates data, but the recording is discarded. And that's really important because we take the security of this data very seriously. But it really becomes your own knowledge base. Data coming into Atlas Primer is going to be contextual data that achieves one of two things. Either it makes scenario creation more efficient or more effective. So gong, for example, is a tool that a lot of sales Organizations use to record their phone calls. We have a connection to gong where you flip a switch. And now whenever you create a scenario, it has up to the minute intelligence of what happens on these calls. So that makes it quicker and more precise when you're developing scenarios. The other way to look at it would be data that comes out of Atlas Primer that can inform other applications.
So an easy one to visualize would be a CRM. Imagine if for every single rep, you're looking at their data and you're saying, okay, this person's gotta hit a million dollars by the end of the fiscal year. They look like they're tracking, but are there any other signals that suggest maybe they won't? You could imagine for public companies, the ability to forecast with precision. You want as much signal as you can get.
So that would be an instance where a connection to the CRM could create additional signals and raise or lower that confidence that this person is going to hit their number.
[00:30:36] Richard Gearhart: Yeah, Paul, all of this sounds great. Can you give us some examples of some results that companies have achieved using
[00:30:45] Elizabeth Gearhart: your system and company size, who your clients are?
[00:30:49] Paul Burani: So we do like to work with organizations of a certain size for one reason, which is that you really need to have a multitude of people, a critical mass of people using it to generate data so you can compare and contrast the outcomes. So typically the organizations we'll work with, they might be like 100, 200 people or more. And then maybe it's their sales organization, maybe it's the recruiters or the exec team or whatever across industries, by the way, because what we found is that the problems we're solving are pretty universal. You see them in pharma and manufacturing and retail and media.
So you asked for a specific success story. I'll give you one easy one, which is in the Nordic region, we work with a pan regional travel company. They operate a lot of different brands and these brands have a lot of relevance in the eyes of their customers for specific destinations, specific experiences.
So what we found is that these teams have been working very much in silos. The intelligence gathered at one part of the process could be so useful later on, but the systems haven't been in place in order to actually transfer that knowledge. And so they're all left fighting their own battles. They happen to be the same ones, but they're not armed with the collective knowledge. And this tribal knowledge is a real problem in any industry.
So what we found was that by getting everybody onto the Atlas Primer platform, we were able to see certain patterns and in one instance, it raised a very sensitive topic with the leadership team because a product launch that was deemed to have been executed very well wasn't performing well. And what we found was it's because the team wasn't universally bought into the value based messaging. So imagine that you launched a new product and you thought everybody was on board and. And half of them actually didn't get it, but went out to market anyway. Big problem.
So they were able to use the data and take some corrective action at an enterprise level to say, hey, each brand you're going to do your own thing. You have that autonomy. But there are a couple of things that as a holding company, we believe, you know, are more important.
And supporting this product is one that we're going to lean in on. And so we're just in the process now of rolling out 2.0. It's very promising.
[00:33:15] Elizabeth Gearhart: I think the beauty of AI is it can take a ton of data that other programs you tried to use couldn't really handle anymore and it can get a cohesive result from it and it goes online, it looks everywhere. It would have taken us weeks to dig that out. Right. And we had different parameters that it had to look at and it was able to put them all together. It sounds like what you're doing is really powerful for businesses.
[00:33:41] Paul Burani: We certainly think so. But our job is to get others to come to the table with their problems and see a different way of doing it. And that's why it's a very collaborative process. We're not a services organization, but we apply that soft touch. And yes, we use our own product because anyone can get better with practice.
[00:34:01] Elizabeth Gearhart: Excellent. Thank you very much. Paul Borani with Atlas Primer. Thank you. Richard Gearhart with Gearhart Law.
[00:34:07] Richard Gearhart: My pleasure.
[00:34:07] Elizabeth Gearhart: Hi, I'm Elizabeth Gearhart with GEAR Media Studios. We'll see you next time.
[00:34:11] Paul Burani: You have been listening to Real AI Use Cases Business Owners Roundtable with hosts Elizabeth Gearhart, marketing expert and PhD researcher using AI every day. And Richard Gearhart, entrepreneur, business owner and intellectual property attorney specializing in innovation. We hope you found this valuable. Join us again for more stories. Because the future of business is driven by AI.
