Episode Transcript
[00:00:00] Gleb Tsipursky: So my expertise is not just developing tools. My expertise is in teaching people to fish.
[00:00:07] Richard Gearhart: There's still a lot of suspicion about AI in the legal profession. Courts have sanctioned over 700 attorneys.
[00:00:15] Gleb Tsipursky: The best way of figuring out how to do something with an AI tool that's high quality is ask it how to do it.
[00:00:22] Elizabeth Gearhart: Yeah. Have it teach you. I find that's how I'm using them more than ever is having them teach me how to use them.
[00:00:28] Richard Gearhart: Clients come to us and they think that because its AI, because it looks nice and uses some legal language, that what they have is correct.
[00:00:39] This is Real AI Use Cases Business Owners Roundtable with hosts Elizabeth Gearhart, podcast consultant, marketing expert and PhD researcher using AI every day. And Richard Gearhart, entrepreneur, seasoned business owner and intellectual property attorney specializing in innovation.
Here's how real companies are using AI right now.
Welcome to Real AI Use Cases Business Owners Roundtable. I'm Elizabeth Gearhart here with my co host, Richard Gearhart. And we're here with Gleb Tsipursky today. Tell us how you're using AI. How are businesses using AI in your experience?
[00:01:14] Gleb Tsipursky: So, and this is all based on my book, the Psychology of AI Adoption at Work for Resistance to Results, peer reviewed out with Georgetown University Press. And so businesses are using AI in a variety of ways. I was just giving a training this morning for a company that is using AI for a financial investment firm. So financial advisor, wealth advisor, and one of the tools that we worked on building for the company is a tool that looks in the local area where the company provides client service, any announcements that somebody's about to come into quite a bit of money who might not have that much experience managing. So a business owner, let's say they're selling their business and $7 million is coming in, or there's a significant divorce settlement and one of the spouses doesn't know how to manage the money. The other spouse managed the money all this time. So several million dollar settlement. So through the tool, the AI tool finds this information, then it customizes the pitch to this person, it finds their email, and then it creates a pitch specific to that individual, saying, hey, we specialize in working with people who are recently divorced and we know it's a challenging situation.
And what we want to do is just help you take one of the number of loads that you're dealing with right now by helping you figure out how to invest this money safely and securely for your future.
And then it sends the email. So it automatically sends the email.
[00:02:55] Elizabeth Gearhart: Wait, do you review the email before it Goes out.
[00:02:58] Gleb Tsipursky: Not at this point. They worked with it until they were happy with it. So right now, instead of having to do outbound research and trying to reach out to people, they're just waiting for inbound emails, just saving their time, spending their time much more efficiently on providing client service and providing client and ensuring client retention. And they're getting inbound emails that are a result of their cold outreach.
[00:03:28] Elizabeth Gearhart: Yeah. Do you know have any idea what their return on investment is?
How much, how many leads do they get compared to how much money they spent?
[00:03:35] Gleb Tsipursky: They're spending very little. They just have a subscription to a cloud account.
And this cloud account, that's $20 per month per person for up to five. That's what's doing it. So as long as you already have a cloud account or chatgpt account, you use that, and that's not costing them any additional money. You just run into the scheduled tasks. That runs every hour. Looks, researches, various announcements, reaches out, sends an email. Doesn't cost any extra money.
[00:04:06] Richard Gearhart: Are they using agents to build a repeatable algorithm that consistently finds the information in the right way, identifies the emails in the right way, creates the content in the right way?
[00:04:20] Gleb Tsipursky: So it's an agent. Agent is a general concept for an AI task that does things in the real world in a repeatable way. So they scheduled it to run once an hour, and then they are automatically emailing and then they're getting just inbound leads. And they could spend their time on dealing with inbound leads while also, of course, serving their existing clients.
[00:04:43] Elizabeth Gearhart: So did you write the agent for them or did they do it themselves?
[00:04:46] Gleb Tsipursky: I taught them how to do it. So my expertise is not just developing tools. My expertise is in teaching people to fish.
So how do you actually build such agents? So another agent that they develop, we want to talk about this one, is a meeting pro. The client would put their information into like an intake form or as a result of a phone call with a client, either way, whichever the client prefers. And so then it analyzes that information and it recommends a portfolio for the client based on that information.
Then the account manager would go over the portfolio and then they would present to the client to talk about what to do and next steps.
[00:05:29] Elizabeth Gearhart: So it really is automating a lot of process. But you still need people in the mix, right? You still need people to. At the end of the task, maybe, yes.
[00:05:38] Gleb Tsipursky: So inbound emails, there is an email drafter tool that automatically drafts a response, but it doesn't send the response. A person needs to evaluate that and decide like, is this the right tone for this specific context?
And tweak the email and then send that with the next, whatever the next steps happen to be. Now the AI tool can automatically follow up and there are automatic follow ups based on that. But you still need the person who manages that account. That's one. Then you need to figure out if they want an intake call. Then of course you don't want to have an AI agent manage that. That's a high value client. And of course the account manager has to meet with the client, but they now have that pack prepared for them with recommendations for what to do next. Great. Much easier than they had otherwise.
[00:06:27] Elizabeth Gearhart: You wrote a book, the Psychology of AI Adoption at Work From Resistance to Results.
So Richard tried to bring AI in years ago. How'd that go?
[00:06:38] Richard Gearhart: Initially it met with some resistance, I would say, though a couple of years ago, AI was a lot different than it is now. And it's improved as a tool.
I think attitudes have shifted in part because it's probably more reliable and the output is generally better. There's still a lot of suspicion about AI in the legal profession. Courts have sanctioned over 700 attorneys in pleadings where the AI hallucinated cases.
And the court checked up on it and said like they didn't find anything like this. And then as of probably three or four months ago, it was still hallucinating cases.
But I do find it as like a good sounding board for testing legal theories, trying to understand the psychology of different people involved in a case. And I think the results there are usually worth considering. But I'm not at the point where I would adopt it on a wholesale basis. But I think part of the skepticism in the past is not technophobia, it's wanting to do a good job for the client. As it gets more reliable, I think more attorneys are going to be, are going to be using it. Right. And so I think that's part of the reluctance. There also may be some fears that because it can do things so quickly, it's going to undermine what we do and the service we provide.
For patents, for patents, they're usually pretty bad.
[00:08:20] Elizabeth Gearhart: I don't think it's gotten better in some ways. In some ways it seems like it's gotten worse.
[00:08:24] Richard Gearhart: I think drafting that kind of legal document is something that I think AI is well adapted for.
So we'll see. What sometimes happens is we'll have clients who will put their inventions into AI and it'll literally draft 14 patent applications for them, which is overkill for most expensive and expensive. If each application costs $20,000 from start to finish. And you're an entrepreneurial company starting out. It's ridiculous, right? And traditionally, the invention can be protected in one, maybe two applications.
And so AI still has a lot of redundancy. And the clients come to us and they think that because it's AI and because it looks nice and uses some legal language, that what they have is correct. And they get upset if you suggest, well, you know, maybe we don't need 14 applications, maybe we just need one. And they think AI is smarter than we are on that type of topic.
[00:09:32] Elizabeth Gearhart: But you still use AI every day?
[00:09:34] Richard Gearhart: I still use it every day, but of course I use it for all sorts of stuff. But I'm curious about if you would have an approach to handling that kind of situation.
[00:09:44] Gleb Tsipursky: Sure. I work with a number of law firms. As you can imagine, professional service firms are my expertise. So I'll talk about law firms that are general practice as well as they include patent and so you'll have hallucinations when you use tools like Copilot or something like that. If you use Westlaw or LexisNexis or specialized AI tools that are grounded in legal cases, you will not have hallucinations. You will not have cases that are made up. The worst that happens is that they are not as up to date as they should be. And so this is something like has to do with Westlaw or LexisNexis not having the latest court case inside, and they're not citing the latest court case. So I've heard complaints about that. That is not hallucination. That is just, oh, this case is slightly outdated. And so you should have cited something else to make a more convincing argument. Second, in terms of the, like, legal AI tools and what you can do.
So, one, if you use sophisticated tools, like, you can have very strong pleadings, you can have very strong drafts, you can have very strong outcomes, but you need to have humans review them. Absolutely no question about it. I'll tell you about an example I had myself with a personal situation where I'm involved in legal dispute. And I was talking to my lawyer, and he sent me, this is my draft of what we want to file.
And I looked at it, I ran through the AI tool and said, well, this is quite good. But here are some weaknesses of the lawyer's language, and here are ways it could be strengthened in accordance with the Ohio law, which it was filed in Columbus, Ohio. And here are some ways it can be strengthened to make the argument more robust against counterarguments. I worked with an AI tool to have some ideas for strengthening this pleading. If you think it's helpful, please integrate it. If you think it's not helpful, you're the expert, you make the judgment. And he said that oh yeah, you know, this will actually would make the pleading stronger. I'll integrate this language and then it's ready to file.
[00:11:55] Elizabeth Gearhart: I wanted to ask you about this. So I read about a research paper when I was scrolling and it was very recent and so I started asking questions about this topic to Perplexity, which supposedly uses rag retrieval augmented generation so that it supposedly goes out and it totally did not find the study.
[00:12:14] Gleb Tsipursky: No propoxities are outdated.
[00:12:16] Elizabeth Gearhart: So which tool do you think does find the most recent? You think Claude is the best one?
[00:12:21] Gleb Tsipursky: It's no question it's the smartest tool followed by chatgpt, it'll still be do a solid job. Perplexity is behind Copilot, so it's going to be worse than Copilot. You should not be using Perplexity.
[00:12:32] Elizabeth Gearhart: Have you used it lately? Because I hadn't used it for a long time and it seemed like it was a lot better all of a sudden.
[00:12:37] Gleb Tsipursky: I'm just talking about where the major labs are. So Claude, you have cloranthropic, you have OpenAI, you have Google Gemini, you have Microsoft Copilot. Those are the four that anyone should be using that and the latter two, you should only use them if your company policy requires it because they're less smart than the Andrap or Claude or ChatGPT from OpenAI.
[00:13:02] Richard Gearhart: So I started using ChatGPT and I created a lot of different project folders and it seems like ChatGPT knows me pretty well now. It brings in things from earlier discussions and chats into what I'm doing and it does it in a relevant way. I would like to maybe do more work in Claude, but I think about how do I rebuild the experience that have with ChatGPT and transfer that to Claude? Because I've been using ChatGPT for probably a year and probably doing seven or eight queries a day, something like that.
And I've put all sorts of different kinds of information in there and so now to duplicate that on Claude seems like it would be a big time investment. So is there a way to transfer information between the LLMs?
[00:14:02] Gleb Tsipursky: Absolutely. So ask Claude, tell me the prompt that I would have to give to ChatGPT in order to get the context from all of my conversations and then transfer to Claude and then Claude will create a prompt for you. Which you then go and copy paste into ChatGPT, and then ChatGPT will respond with the context from your previous conversations, and then you would input that into Claude, and then Claude would have that context. Now, it wouldn't be perfect. It wouldn't have all your projects, so it wouldn't have the structure, but it would have the context in the memory.
[00:14:36] Richard Gearhart: But that would save. Save at least some time. Do you think there's any advantage to rerunning the same prompts in different LLMs just for the purpose of having a backup? Because the technology is evolving differently at different organizations, it's a lot of extra
[00:14:56] Gleb Tsipursky: time that you don't need to do what I told you will work for every LLM. So if you want to, for some reason, if you want to transfer From Claude to ChatGPT, you would do the exact same thing in reverse.
[00:15:07] Elizabeth Gearhart: I just got hit with something really bad.
So I had my speaker website and my blog on Manus. Are you familiar with Manus?
[00:15:15] Gleb Tsipursky: Yes, I do.
[00:15:16] Elizabeth Gearhart: You know what happened, right?
[00:15:17] Gleb Tsipursky: Yeah.
[00:15:17] Elizabeth Gearhart: And then I was like, oh, no, no, no, Meta, you don't get to own Manus. Manus, you have to be a standalone company.
So I went in there today to post a blog and I got this message like, we're going to wipe out all your data, but you can back it up here. So I did back it up and put it on my Google Drive, but I don't know if I did it right.
I asked Claude if I did it right. It's. Yeah, it looks like you did it right.
[00:15:40] Gleb Tsipursky: So Claude is smarter.
[00:15:41] Elizabeth Gearhart: But the thing is, I know for my blog posts, though, I saved them all in Word because I wrote them myself and then I had ChatGPT optimize them for LLM Search. But so I do have a separate copy of them on my hard drive on my computer in Word. But you know that that happens, right? It's scary.
[00:16:00] Gleb Tsipursky: So Claude is valid. Last valuation of Claude was over a trillion. Last valuation of ChatGPT was over that. OpenAI was over 800 billion. And there's pretty much no company that can afford to buy either. You just. You don't need to worry about any of those. The same thing for Copilot or Gemini for someone who's using that, because obviously those are some of the two largest companies in the world. In the world with Microsoft and Google.
[00:16:29] Elizabeth Gearhart: I have another question for you too.
[00:16:31] Gleb Tsipursky: Sure.
[00:16:31] Elizabeth Gearhart: So I did buy some credits for Claude Fable 5, and I was using it, but I don't think I need something that powerful for the Queries. That I'm doing. Like, what are people normally using Fable 5 for? Like, what is the main use for that? Is that for programming more than anything else?
[00:16:49] Gleb Tsipursky: So if you want to review all the information on a legal case and make like a really strong legal brief for the court, like, that's going to be a critical filing. That's going to be something where you want to use Fable, something that's really important to get right and pretty complex.
[00:17:07] Elizabeth Gearhart: Yeah. Could I use it to. Or should I use it to try to do an agent? Like, I talked to a friend of mine, she said, well, you can like Fable 5 will just make an agent for you.
[00:17:15] Gleb Tsipursky: You can use Sana to make an agent. You can use OPUS to make an agent. You don't need. You don't need Fable 5 to create an agent for you. So Fable 5, Fable can be used to create code like you said, but it can be also used to create complex legal products, complex legal outcomes. Let's say you're doing your strategic planning and you want to pull together all the information for your strategic plan from everything that you had and then draft an initial strategic plan based on your goals and guidelines. And Fable can do that for you. So pretty. Something pretty sophisticated that you want to create something that's going to be really high level. Or let's say you're doing a presentation to make a really important sale to a client. That's like high stakes. Fable 5 is good for that. So those are the use cases.
Like something that's really sophisticated, something that's really high stakes, where it's worth paying for. Otherwise Opus is fine and for everyday tasks, Summit is fine.
[00:18:17] Elizabeth Gearhart: Okay. Yeah. I mean, it keeps saying, you know, you're using Fable 5. Do you really need to. Yeah, maybe this one. Because I did pay for credits.
[00:18:25] Gleb Tsipursky: Yeah, yeah. You don't need to be using it for everyday tasks. This is something that's. It needs to be worth the effort and the pain.
[00:18:33] Elizabeth Gearhart: So I did. I guess I have one more question.
So we have processes that people in the law firm do, and I want to have them screen, record the processes and then have an AI delineate the process and really define it so that maybe we could turn it into an agent at some point.
So what would you use for that?
[00:18:55] Gleb Tsipursky: Claude Cowork has that functionality. Download the Claude app, the desktop app, and turn on the Cowork functionality and then ask it, what are the steps for me to record this using Claude Cowork? It will explain it to you very clearly.
[00:19:11] Elizabeth Gearhart: So then once you've Recorded it with Claude Cowork. It'll break it down into like the series of steps that it sees. And then you can create an agent from that.
[00:19:21] Gleb Tsipursky: That's right. The best way of figuring out how to do something with an AI tool that's high quality is ask it how to do it.
[00:19:28] Elizabeth Gearhart: Yeah. Have it teach you. I find that's how I'm using them more than ever is having them teach me how to use them.
[00:19:34] Gleb Tsipursky: Wonderful. Exactly.
[00:19:35] Elizabeth Gearhart: Is there anything else we need to say before we go? That your book was the Psychology of AI Adoption at Work From Resistance to Results by Gleb Siporski. T S I P U R S K and where can we find your book?
[00:19:49] Gleb Tsipursky: So first of all, people can connect with me on LinkedIn, tell me you heard me on the real AI use case business Owners Round Table podcast, because I won't accept a random LinkedIn connection.
Too much spam these days, including from AI bots. And then you can find my book, obviously at Amazon, Barnes and Noble, any bookstores. So it's out with Georgetown University Press, traditionally published book, peer reviewed, all that good stuff. Now if you want the free copy of the introduction and a chapter of the book, you can go to my website, disasteravoidanceexperts.com AIBook that's disasteravoidanceexperts.com AiBook for a free book chapter and the introduction to the book. And if you already brought the book, whether pre order or ordered, just put the receipt number in there and you will get a free assessment on how to adopt AI effectively and a manual on the seven critical mistakes that leaders make in AI adoption.
[00:20:49] Elizabeth Gearhart: That is excellent and so very timely and it's awesome that people like you are helping people figure this out, really, because I think it can help businesses a lot. But you have to know how to use it. And there's so much, like you said, spam out there.
[00:21:04] Gleb Tsipursky: Thank you.
[00:21:04] Elizabeth Gearhart: Well, thank you.
Yeah. This has been Real AI Use Cases Business Owners Roundtable.
[00:21:10] Gleb Tsipursky: You have been listening to Real AI Use Cases Business Owners Roundtable. We hope you found this valuable. Join us again for more stories. Because the future of business is driven by AI. This podcast was recorded at the iHeart Studios in Manhattan as part of the Passage to Profit radio show.
