Practical AI For Real Businesses

AI is getting smarter by the month, but it is still perfectly capable of giving you a confident answer that is flat-out wrong. 

That tension sits at the heart of our conversation with Kieran Gilmurray, an award-winning author and board-level strategist who works at the sharp end of AI, automation, data analytics, and organisational change. 

We start with a simple truth that applies to both humans and machines: progress comes from mistakes, but only if we are willing to spot them, admit them, and refine what we do next.

TL;DR / At A Glance

• why admitting mistakes is a strength in business and in AI adoption
• how generative AI answers anyway and why that can mislead
• where AI bias comes from and how training data shapes outcomes
• what digital transformation means beyond installing new software
• starting points for AI in a company, from chatbots to marketing and retention analytics
• choosing a lighthouse project that is meaningful but delivers value quickly
• a real retention model story that raises ethical questions about pricing and vulnerable groups
• responsible AI habits, from looking around corners to keeping humans accountable

From there, we unpack what “digital transformation” really means. For us, it is not a tech shopping spree. It is a practical shift towards better customer outcomes and stronger business performance, with digital technology as the enabler. 

Kieran explains how to slow down, get clear on the decisions your organisation needs to make, build a data-centric strategy, and invest in people so the tools actually improve the way work gets done.

We also dig into the most common AI stereotype, the chatbot, and why modern conversational AI can be a powerful first step when it is trained, measured, and continuously improved.

The conversation takes a sharp turn into ethical AI with a real pricing and retention model that delivered big gains yet created uncomfortable consequences. That story becomes a blueprint for responsible AI: look around corners, test for bias, expect unintended outcomes, and stay accountable even when the model is “working”. 

If you care about generative AI for business, AI governance, customer experience, and using data to make better decisions, this one will give you both momentum and caution. 

Subscribe, share with a colleague, and leave a review with the biggest AI question you are wrestling with right now.

If you want to read, or listen to, my new book on Agentic AI then pop over to Amazon or Audible.

Transcript

Welcome And Why Mistakes Matter

SPEAKER_00 0:00

Hey, we are back on the podcast with Kieran Gilmurray. And Kieran is a multiple award-winning author. He's an expert on AI, automation, data analytics, and he's a real-world strategist and implementer with extensive board-level experience. So we're going to find out all about Kieran's book, his expertise, his adventures, his misadventures, and so much more. So, Kieran, glad to be speaking with you. Glad to be speaking with you.

SPEAKER_01 0:27

And yes, lots of misadventures in there, Robert. We could be there for days, but let's narrow it down a little bit. You asked me the mistakes, and I'll admit to every one of them.

SPEAKER_00 0:36

Right. And that's a big part of being a grown-up and of personal growth is to say, hey, you know, I made this mistake. It wasn't that bad. I'll make it again. I've learned. I'll do better next time. And AI is full of mistakes, right? I mean, I use uh ChatGBT especially every day, and I say, hey, what I just told you to make, I don't want that exactly. And it's on me because of the way I defined it, but I'll refine it, I'll reiterate, I'll fix my mistakes. Many things can be corrected. And so, you know, we jumped in and explained a little bit about what you're all about, but in your own words, what is your passion and focus? What just gets you so excited these days?

SPEAKER_01 1:14

Yeah, Link, uh, it goes to what you were saying a moment ago there. You when you grow up, you learn to admit your mistakes. I'm excited by AI, particularly generative AI. Now, you have to be careful here because I love great technology, not for technology's sake. I love what technology can do for business. And that's where I think lots of people get really confused. They talk about digital transformation, AI, the age of AI, and whatever else, what they're forgetting. And this is what I've put out in my first book, The Aid is Z of Organizational Digital Transformation. In my second book, The A to Z of Generative AI, uh Guide to Leverage for Businesses, you're not doing any of that. That's the transport, not the end destination. There are mistakes in AI. To date, look, like you and I, as we have grown up and we have learned, and we've gone from junior business people to senior business people to experienced business people, you learn that you're going to make mistakes along the way. If we look at AI, it's as I describe it, Robert, an 80-year-old overnight success story. Uh, it's full of mistakes. Why? Because we as humans created it, full of our own frailties, full of our own biases. There are some classic stories. So, for example, AI applications or HR applications that discriminate, and I use these words not lightly, you know, white, male, peel, steel software engineers. Why? Because when you looked around the room and you designed the system and you fed it with CVs or resumes that looked like they should be the best software engineers, you were you were fishing in a very narrow pool. Do I blame anyone who wrote those systems? Absolutely not. Every time, and it doesn't matter, and I've worked in multiple countries across the world, and I meet people every day from different countries. There's very few people I know set out to do harm or say, I'm going to build an AI system that's biased because I can. Not a mission. They're just learning. No one would have thought of that, but looking back in hindsight, now they see those mistakes and they fix them. Now we're off with generative AI. Guess what? Full of human bias. Guess what? It was trained on what is out on the internet. Oh my God, you'll remember the classic stories of Tay and Microsoft. They trained it on Twitter. I thought, oh my goodness, you trained it on what of all the places on God's earth to train the chatbot. And you wondered why you got bile and bitterness in amongst the madness and the goodness and everything else. The AI spits back what we put into it. So it is going to learn mistakes and prompting that you mentioned a moment ago. When you dig behind generative AI, what's it there to do? Answer your questions. When it doesn't know how to answer your question, it is designed and programmed to do its best. To do its best, it's going to give you an answer, but it doesn't mean that that answer is actually correct. But we have to remember a little bit of forgiveness, a little bit of responsibility on our own part, a little bit of questioning. This technology is very much at stage one. Imagine how good it's going to be. Imagine how accurate it's going to be at stage 10. And that isn't in 10 years' time. That's probably in a couple of years' time. The old phrase, Robert, we always overestimate what technology can do in the short term, and we always underestimate just how impactful technology can be in the long term. That's where we are at the moment with Gen AI, and that's the one that's exciting me the most.

SPEAKER_00 4:31

Wonderful. And I'm doing my best to keep up with you as far as the excitement, because when there's some new technology that's on its way up, whether it's internet, WordPress, ChatGBT, whatever it is, my mindset is I want to be part of it in its infancy and not have to play catch up later on, right? I don't want to wait the five years or the two years, and now everyone's using it. And I say, wait, hang on a second. Like, how do I now start a few years behind you? And you fear, well, once I get caught up, you'll be ahead of me. So I'm like, okay, there's no reason to delay. I need to learn some of this now. And that's very helpful there if someone is feeling frustrated with their generative AI experience, whether it's Chat GBT, Dolly, whatever it is, you're saying that it will rarely say I can't do anything, will rarely say no. It'll really try to help you. And sometimes I've been amazed when I'm like, well, I want to write this article or this book or something, and I give it some output, and I'm like, hey, that gives me an idea of like what I don't want. So at the very least, even if these responses are bad, you're like, well, hey, it gave me an attempt at it, but at least that narrowed in the focus of like, if I'm outlining a book or something, I'm like, okay, we'll listed all this stuff. I definitely want to stay away from that. So even the negativity part is helpful. And so you're all about this thing called digital transformation, right? And that that sounds like big and scary and very businessy. So if someone says, okay, digital transformation, the way Kieran Gilmore teaches it, how do I know what that's all about? And how do I know if that's a good fit for my company?

SPEAKER_01 6:06

Yeah, look, uh, let's be honest, we're in the age of digital, and by digital, I mean digital technology, AI, and everything computer related. So what we find is organizations usually aren't born native digital or native AI. And therefore, most of the organizations that are in the world have to actually implement digital, and therefore they need to transform. And let's be clear about transformation, because everybody assumes I put in a piece of technology, I'm now digital. Not really. You know, I put in the internet, it doesn't mean I'm a cloud computing company. You know, I put in Zoom during the uh the downturn, it doesn't mean I'm a remote or hybrid company, it's just the technology. What I mean by digital transformation is reorientating your business, using digital technologies as a medium or as the transport mechanism that allow you to do different things. My focus is not the technology. Technology is the enabler, and it might actually redesign how you do things. But I'm focused on business and customer outcomes. Those two things are the same thing. Why I say they're the same thing is businesses that exist to solve, meet or exceed customer problems. And therefore, I'm going, okay, I've got a bit a customer focus. I know that I need to deliver a product or a service. How do I do it? Chances are these days it's digitally more than anything else. And therefore, okay, I've got a business strategy to meet the need. Now I need to put strategies and architectures around my business to make sure I can deliver that service, product, or whatever else to that customer to delight them in a way that's obviously cheaper to manufacture than it is to sell, so I can make a margin to reinvest. And therefore, I've got people strategies, I've technology strategies, I have business architectures, people architectures, procurement architectures, all these things need built inside of a business. It can seem scary because all of a sudden you're going, oh my God, I have all this AI, I've all this digital technology. There's so many things that are out there on the planet that I don't know where to invest. The key thing is, look, slow down, focus again on the business outcomes, and then go, okay, what is the technology that I can implement, the AI, that serves me in delivering my customer outcomes, my business outcomes. What do I want to do? Well, I want to get better data-driven decision making inside of my organization. Okay, I need to be data-centric for that. I need to work out what are the business decisions I need to make. And therefore, when I understand that the questions I need to ask and the answers I need to get, then I can go and get the data that allows me to make better decisions. Notice I've not removed people here because I believe great people with great technology and great decision-making capability make for great businesses. What do I want to do then? Okay, now I'm into using AI and automation technology and implementing that my core business processes, and I'm automating end-to-end processes to make sure that my decision making does improve, my data gets to where it needs to be to allow me to uh produce better answers. I'm increasing my efficiency, I'm increasing my agility, I'm increasing my innovation across all of my better uh my operations. I'm not doing this in isolation, Robert. I'm bringing my customers and my employees along because I want to make sure that as I'm doing all these things, their experiences, their personal experiences, their emotional experiences, transactional experience are really ramping up and improving their engagement, their satisfaction, their buy-in all the time. Because if I'm in business and I'm implementing all of this technology and all this AI, and it's not actually delighting anyone, it's hard to work with, I'm probably not going to use it. And therefore, what do I need to do? Having selected the technology, worked out the decisions that I need to make and do so really efficiently operationally to deliver my business strategy. Well, that all takes time, that all takes training, and therefore, again, it goes back to investing in talent and training to make things happen. So let me summarize that. I'm never focused on the tech and focused on the business outcome. I need people to make great decisions. For that, I need great data. For great data, I need great AI and great at great data strategy. To make decisions, I need great people to make great decisions and allow those people to deliver for customers. I need great employee and great customer experiences. Therefore, I need exceptional technology and exceptional people, and I need to invest in both to allow me to digitally transform my organization. Because if I don't, and this is the downside, and I wish it known, Robert, we are in a phase of what you might call AI Darwinism or digital darwinism. Whereas if I don't invest in these technologies, then I'm not moving in the way the market and my customers move, and therefore I won't be in business. Not the greatest incentive, but sometimes you need a burning platform to get businesses to change. Because, like the dinosaurs, if they don't, they're going to go the way of uh extinction, and that's not something I wish in anyone else. So big scary topic, but when you break it down into simple steps and simple components and work through the noise, and if you can't do it yourself, get a consultant in who does, then it's suddenly less scary. And businesses that have digitally transformed Amazons, eBay, Facebook, lots and lots of examples around the world when digital works to support you, deliver your strategy. By goodness, isn't that an experience and a product and a service that every customer and every employee wants and every shareholder wants as well?

Where To Start With AI

SPEAKER_00 11:34

Oh, yes. You think about how easy, painless, simple, fast, fun it is just to take out your phone and bring up the Amazon app and buy something, and then it appears, and then and it seems simple on the surface, right? If you just look at it from like just one perspective from just the customer, it's like, oh, I hit a button and it's simple. But then someone like you who would be thinking about the back end and the process map and the people and the redundancies, it is pretty crazy that it all somehow works. And like a lot of these sort of business cases we have to solve are this way, aren't they? Like on the surface, you're like, oh, it's just easy. Like some customer goes and they they order something or there's a step-by-step process, but someone like you is like, well, hey, this thing that's a very simple thing to accomplish, it requires all these other steps and all these things to do. And so it can be kind of scary. And that's why we need someone like you with your kind of process type thinking, because you were mentioning things about, well, having goals and moving people along and kind of having whatever your KPIs are and saying, well, are the uh are the customers are they satisfied, are they happy? Are they enthralled? Do things get done quickly? Do they get done correctly? And so when you mention like this whole digital transformation thing and putting AI in, like I know it means a lot of different things, right? And it can mean things to different people. And so what do people usually say when you talk about like putting AI in a business? Do you think do you think in terms of like a chat bot or in terms of like uh automated answering of emails? Like what is the usual AI stereotype? And is there like a kind of a low-hanging fruit to just like I I know like you mentioned a minute ago, like it's not just about the technology, but is there like a usual default for plugging AI into a business?

SPEAKER_01 13:22

Yes, is the shortest answer you'd be delighted to hear. Look, there's probably two parts. Let me break it down. First part is I always worry when people just want AI for the sake of AI. You know, the board member has one out on the golf course. She's heard from a fellow CEO and they've gone, Well, I've got AI in my business, maybe they do, maybe they don't. And therefore they come back going, we need AI in the business, just get me a project. That reminds me of the old adaj and technology says, I've got a hammer. Now I've got a hammer, everything looks like a nail. My job is to take the very complex and simplified and go, okay, what is the business strategy? How do we compete better than our competitors? How do we do things more efficiently, more profitably, more with better customer experience? And you always need that lighthouse project to what you're describing there, so that people can actually develop AI or digital muscle or transformation muscle. That first thing that you pick has to be meaty enough to be important and meaty enough to make an impact, but it can't be so meaty that I'm not going to get any benefit in 18 months because then everybody gets bored. So, therefore, you're right, Robert. When you're looking at bringing AI into the business, there's a couple of very obvious starting points for me. So, for example, you mentioned chatbots. Well, convers there I extend beyond chatbot because chatbot brings very often to people's minds the computer says no. If I didn't ask a question in the precise syntax and question style that the programmer programmed in, then I got the computer said no, and we all ended up frustrated. Now you have chatbots infused with AI. Now you have generative AI infused into the conversational AI infused into the chatbot. Long story short, that is a really intelligent answering tool that can accurately answer any question you want in the business. So, for example, I can put a chatbot into my contact center, and then customers who now expect it, because they used to Amazon and Netflix and eBay and everything else, having AI built in and being available on an app, on their desktop, in any shape or tool that they want 24-7, expect answers. I can put in a chatbot, and as quickly as I can train a person, and sometimes that can take months and months, I can train a chatbot, put in all the frequently asked questions, then people can ask more questions. I can get the chatbot through generative AI to make a good stab at answering it. And every day I can use the statistics and I can use the answer where it got it wrong to refine and refine and refine. And over time I can have a very brilliant conversational AI agent answering questions on customer service, finance queries, IT queries, HR queries, or whatever it is, wherever there's a service desk or a question, I can get it to do that within a reasonable short period of time. And if something changes, guess what? Instead of training a thousand agents across the world, I train one chatbot that's available 24-7 to anyone. When you want to get a little bit more sophisticated, remember there has to be a business case behind this, then I might want to do marketing at the front end. Therefore, I can optimize my ads on Google. And therefore, I can use AI to test all of my ads. I can get customers in, I can get a bank of customers and I can run analytics on my book of existing customers. I can work out the best customers. And by the best customers, I'm talking about the highest customer lifetime value, the lowest complaint, the lowest debt, the highest spend, those who will advocate for my business. And I can run analytics or AI in that, and I can go out and get more of them. I can find the customers, and remember, it's cheaper to keep a customer than get a new one, and I can build out retention analytics using AI. So I can work out the profit and the likelihood of every customer staying based on me adjusting price or me adjusting services. So over time, I can, yes, put in something very simple like a chatbot at the front end, an email answering tool, and I can work my way all the way up to acquisition analytics, SEO analytics, or Google Analytics, whatever you want with AI. I can do retention models, simulation analytics, debt and credit models. Where there's data, Robert, I can create brilliant insight. And that decision insight can be used by brilliant individuals inside of a company to allow them to make better decisions, to make a better, more efficient, more profitable operation that you can then use to satisfy more customers in a much better experienced way.

A Costly Lesson In Ethical AI

SPEAKER_00 17:56

Very nice. And so you're getting excited just thinking about all the possibilities, right? Because it's so easy to get lost in the mess. And what's way more excited is to get in action mode or pursuit of a goal mode. And it's just, it's really fun just kind of hearing about your thought process here where you're saying, like, okay, well, we we have like a knowledge base, right? We have the company's blog posts and the frequently asked questions and the help desk tickets. And it's so amazing now to just be able to dump all of it in, to like, you know, import it and create your own custom GPT to use a fan fancy technical terms and just pile in the knowledge, and then like, as you said, kind of inch closer and closer and not quite get to 100%, but get so that way the the chat bot answers more and more of the possible questions. And then you, as kind of the human quality control, you go and look and see what's not being answered quite right or falling short. And it's just like it makes the mind light on fire, right? Because you think like, man, now I can go back to that knowledge base with all the customer questions and like generate blog posts and press releases and content and like go the other way, right? If you have a lot of content already, like kind of feed that in and make that all work as far as the knowledge-based answers, the nurturing sequence. And then you're also getting less excited thinking about that customer profile, right? And kind of looking at what's your your top customers, what's that criteria and what's that data? And then you then go and look, go back out to your ads and get more of that. And it's like there's so much excitement that's available for us, and but we only have enough time to talk about podcast possibilities and not quite on actually the doing, right? That's what they need you for. And so in these last few minutes, do you have one of those stressful stories that we talked about that we teased at the beginning? Like, have you been in the middle of a project and you were just like stuck, uh not sure where to go from here, stressed out, had to change. Anything like that come to mind since we're talking about digital transformation? Has there been like a Kieran transformation as far as like being stuck solving a problem and having to really push to get through it?

SPEAKER_01 20:13

Yeah, look, uh, my goodness, it's interesting because we're talking about ChatGPT and everything else and generative AI and what it is is prompt engineered. What is prompt engineered means? It's made mistakes over time and someone has corrected it. Robert, well, we started out at the front end here saying, look, what is experience? It just means you and I have made more mistakes than anyone else. And we have taken the time to admit them and we have taken the time to actually learn something from them. I couldn't begin to tell you the number of mistakes that I've made. Non-intentional. Let me talk about one because we were talking about AI. And when I look back in it, and I look back with no pride, you'll remember back in the conversation I said, no one set out to generate biased AI. But you learn over time, it's a little bit like computer security. These days you couldn't imagine writing code without security built in by design. You couldn't imagine opening up a website without putting firewalls and everything else in place. But back in the day, nobody knew that. The mistakes had to happen, then you learn from them and off we go. My biggest fear is that AI is going out and wider and wider. And even the people who are designing it cannot work billions of neurons deep in a neural network-based uh generative AI model. So we can't possibly work out what the outcome is. My biggest mistake was years ago. I was building a retention model for an insurance brokerage. I could tell you, based on everybody's characteristic, how likely you were to stay and leave that company based on the price that I put in. Now, those people who were the most likely to stay, of course, what do we do? We charge them more money. Those people who are least likely to stay, we charge them less money to remain more competitive to get them to stay. For you and for I, on the surface of that, you're going, well, that makes business sense. And by goodness, the company three hundred percent improved over three and five years based on great decision insight like that. Looking Back today, I'll look back with no pride because guess who didn't leave? Usually elderly. So, what we're actually doing is making the elderly who could least afford it pay more and more from their insurance because the data taught you how. Now, decade and more after that, I've been building responsible and ethical AI into and practices into everything that I do because I look around corners now to understand the impact of everything that I'm trying to do. And that's what I'm encouraging in my new book and in my articles and my content all the time. It's get excited by the technology, get in there and experiment and learn, but do so responsibly. Don't just get so excited that you run and run and run and don't think, because there will be problems around the corner. I've been writing about this for two years. Now people are talking about deep fakes and election scandals and everything else. You heard Came Brown, uh, a Cambridge analytic a year ago. Now we're 60% of the world's population is voting this year, and you can already see things like that happening. Go back in the day, there's nothing new, by the way, I'm not blaming AI. Dynamite was invented to clear roads and mountains to improve transport. Guess what happened? It was used for bad things. People didn't think beyond, or the nefarious might get involved. We need to be careful, similarly with AI, and anything we do as human beings on a daily basis. If I take an action, what's going to be the consequence now, tomorrow and next year, if I think those things through and I learn from my mistakes and learn from my wins, we will end up in a better place. So I've made lots of mistakes. The key bit, though, is admitting that, a sign of maturity, confidence, maybe, doing something about it, and sharing those lessons with other people so that they can avoid some of the mistakes I made, so that we end up as better businesses, better business leaders, and you know, aspirationally, a better society as well.

Responsible AI And Final Business Advice

SPEAKER_00 23:51

Wonderful. I love the looking around the corners concept because, like, like you said, there's m many different, almost they they seem like they they could be hilarious types of cases, right? Where things go wrong, and then later on you say, oh no, the the chat bot was racist or the algorithm discriminated against the elderly, these kind of unanticipated consequences. And so what I'm getting from you is to say, well, know that the mistakes will happen, embrace the mistakes, try to build in your guardrails against what you anticipate, but also know that you can't anticipate everything. And so when you do make these mistakes, when things do go wrong, like fix them, but have the kind of more long-term strategic multi-step thinking common sense to catch when the logic gets out of control or where it just goes into unanticipated areas and fix them. And so this kind of it more solidifies the need for someone like you. Because, like you said, the the technology is easy, right? You have a smartphone, you have app, you have the cloud, you have Zapier, you have all these systems, you have the chatbots, but then it's really easy to just set up like the the first version of something, right? The rough draft, not accounting for human error, for weird inputs, for for you know, Stack Oflow and all the other like geeky things that could go wrong. So it's not enough, like and uh this term automation, like it's kind of a loaded word, right? Because like uh you think, oh, it just runs on its own, it's a hundred percent you know automated, but then there's people like you that need to course correct things. And even some of what you mentioned as far as like your mindset and your stories, that kind of makes it clear for us that like, well, you need a good mind setting up the these kind of business processes, but once they're set up, have someone like you always fixing and refining them and keeping them from going wrong. And so you mentioned a little bit here about how you have these books, you have this writing, you do the consulting. And so if someone's curiosity has been ignited and energized with this AI talk, what problem do you solve? And how can someone reach out and find out more about you and your services?

SPEAKER_01 25:59

Yeah, so like I solve everything from automation, AI, generative AI, operational excellence, process excellence problems for businesses. And by the way, even if you haven't got a problem, come and talk to me because again, I can help you design a much better way of operating if you're at the start of your journey here. Uh, if you want to get in touch with me, go to caringvillmarie.com. All of my services and everything that I do is built onto that. All of my writing, all of my podcasts, all of my videos, everything's put into one place for me to give as much away as I humanly can, Robert, to allow as many people to leverage my three plus decades of knowledge so that they can do better. I'm a massive believer in all boats rise on a floating tide. And therefore, I'm talking about making a bigger pie rather than me owning a segment of it. So, whatever I can do for folks, reach out. I have lots of years of bad practice, good practice, experience, learning, and humility to admit that I make mistakes, but I've learned from every one of them. That's the knowledge that I'm going to put at everyone's disposal to try and help them do better for their company, for their family, for their businesses, and again for the customers that they're dealing with on a daily basis.

SPEAKER_00 27:12

Very nice. That is generous and inspiring because we have all this helpful information for free. It helps you raise your profile, and I'm sure it also helps you to get your thoughts figured out. Because if once you get it in writing, you know what you're about and you're not about. And I'm sure that kind of inspires for you the future books and blog posts and content and everything that goes along with that. And so you said the website is Kieran Gilmurrie.com. We'll spell that out. That's K-I-E-R-A-N. Last name is G I L M U R R A Y. That's Kieran Gilmurray.com. So we go there, that's the central hub, and that way you can check out what Kieran's latest and greatest news and updates and insights and advice are about AI. And so as people are going there, Kieran, to Kieran Gilmurray.com to check out books, podcasts, whatever. Do you have a final thought, a final parting word of advice as far as uh AI about business? Does a quote or lesson come to mind for you?

SPEAKER_01 28:17

Yeah, there's probably a couple. So let me try uh let me try both. Look, with AI, this is the most exciting and frightening technology that I have seen in some time. Exciting because of what it can do and how it can augment great people. Frightening because we really do need to question a little bit more. Question deep fakes, question how we responsibly implement AI. Question, are we actually handing over too much control and thinking that the computer is actually right? So use it, use it responsibly. Learn quickly because this technology is not going to go away. You need your innate human skills plus great AI, because those people with AI and great people skills will replace those people that uh do not. Uh, another big lesson for me, and this is the final one for anyone in business, remember it's never a deal until the money's actually in your bank. That I've learned too many times over the years. So if you are thinking about running a business, yes, do the deal, get the invoice sent, get the money in the bank, and then celebrate. And after that, uh everything is fine. Up until that point where there's no cash in the bank, you're in trouble. Very helpful and successful. In both endeavors, business and AI.

SPEAKER_00 29:29

Right. I love it. So both both in business and AI, it's not a done deal until the money's in your bank account. And in the meantime, as far as all these advancements about AI, we need to be responsible about it and use it to improve lives, take action, make money, grow the world, and help everyone else out. So KieranGilmurray.com is the place to go. It's been wonderful having this philosophical but also useful conversation with you about AI. Really appreciate it.

SPEAKER_01 29:56

Absolutely delighted to be on, and I wish all your readers every success. I enjoy listening to all the podcasts. There's a lot of value in there as well. You're talking about the value I bring. I wish you every success going forward, Robert, the same to every single one of your listeners, sir.

unknown 30:12

All right.