From Guesswork to Growth: How AI is Rewriting the Pricing Playbook

July 23, 2025 00:46:57
From Guesswork to Growth: How AI is Rewriting the Pricing Playbook
Street Pricing with Marcos Rivera
From Guesswork to Growth: How AI is Rewriting the Pricing Playbook

Jul 23 2025 | 00:46:57

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[00:00:00] Speaker A: It is because it does force you to really sit down and make sure you are framing the problem in the right way. [00:00:06] Speaker B: And one of the most important challenges is usually framing the problem and realizing that we have a problem. [00:00:13] Speaker A: The clarity piece is really, I think, the key that folks need to spend a few more minutes on. It may sound boring, it may sound like, what? What do you mean? I have to sit down and think. But it's those steps, I think, pay very big dividends later. [00:00:25] Speaker B: Sometimes we ask customers, okay, you want to optimize prices. Which part of your business you want to optimize? You want to optimize profits, profitability, your revenue, your growth. And they say everything. [00:00:36] Speaker A: Yo, Mike check. What's up everybody? You're listening to the Street Pricing Podcast, the only show where proven SaaS leaders share their mindset and mistakes in pricing so we can all stop guessing and start growing. Enjoy, subscribe and tell a friend. Now let's break it down with your host and sought after slayer of bad pricing, Marcos Rivera. What's up and welcome to the Street Pricing Podcast. I'm Marcos Rivera, author, founder and CEO of Pricing IO and today's guest is an AI expert from Athens. You heard me right. Today I have George Baredos. He is the founder and CEO of FutureUp. George, welcome to the show. [00:01:14] Speaker B: Thank you, Marcos, and thank you for having me here. It's a great opportunity to share several experiences in AI and pricing. Thanks. [00:01:22] Speaker A: Yes, I mean, you have deep experience in pricing and AI, which is why I'm excited to talk to you today. You've also had posted some pretty thought provoking things out there around where AI is going. So no surprise, we're going to talk a little about AI today. Right. But before we jump into all that, George, just for the audience, tell them a little bit more about you and what you do. [00:01:42] Speaker B: Okay, so I've started my career before almost three decades before, 29 years or so. And initially I started as a business executive and I was mostly leading marketing, pricing, commercial functions. And I did my first year of both pricing and AI experimentation back then with primitive tools obviously. But I've learned a lot and this led me into my second chapter of my life, which is becoming a startup. So the past nine years I have launched three startup companies, all in the AI space, which is something that I really love and now everybody loves it as well. I'm not alone anymore and I have worked with all sorts of companies, including many big ones and for 500 companies. And my latest endeavor is called Future App and it's a Company that focuses especially in AI based price optimization. [00:02:37] Speaker A: Love it, love it. AI based pricing optimization. So much to unpack just in that, in that statement alone. But you've seen it all from the enterprise side and the startup side. So we're going to be able to really understand, I think some of the differences is where I want to dig in a little bit deeper today. So thank you for being here. I am excited to unpack this. Are you ready to get street with me? [00:02:57] Speaker B: Yes, definitely. Let's go. [00:02:58] Speaker A: Let's do it. Let's do it it guys. So quick for the audience here. This show, this podcast is based on the book Street Pricing. So I break it up into these three sections. First, rewind, we're going to get a story of a pricing past. What has happened, lessons learned and then play is all the things going on today, current trends, what's working. Fast forward is all about the future. Where are things going. So we're going to be able to jump all around there today. And George, I'm going to get a little special for you because of your expertise and because of all the things you've done around pricing and AI so far. I'm going to dig a little deeper in those areas together. All right, so what we're going to do is let's start with Rewind. Let's go back. You've done so much work. You've done it on the enterprise side, you've done it on the startup side. Right. And I'm really interested in knowing a pricing story where you're, you're seeing a company struggle and maybe even struggle with their AI strategy or where they want to go with AI. Let's see. Do you have anything on a company that was trying to push their way into AI and just had some challenges? Let's talk about that. [00:04:02] Speaker B: Well, I can think of many, many different stories, but I will pick up one that I did with one of my startups before some years because it was a very, very interesting case. It was a company that was selling mobile applications and multiple different mobile applications and they had the same flat pricing for all countries. They were selling to some richer and poorer countries and obviously with different spending capacity, they understood that they were leaving money on the table or perhaps they were losing some customers due to difference price elasticity in different countries. But they didn't know how to handle that. They didn't have any experience with AI. So they asked me to do something about it and this is what I did. The company that was my first startup company and I was using the Same AI model, not the same software, but the same AI model that I'm using with my third startup company. And so we analyzed everything and eventually we came up with some prices which were highly differentiated as opposed to the initial flat pricing. In some cases the system proposed to double the price, in other cases to give an 8% discount. So it was a risky proposal and they eventually did a price experiment. So for three months they sold with their initial pricing. So one price for all countries versus another group where they sold with our diversified prices. Eventually they almost doubled their revenue for these products and tripled their conversion rates, which was a huge success. And this opened up the window of opportunity for more AI applications into this company. And for me it was a great lesson because the price experiment really help to understand the business value, just the accuracy of the models, but also the business value by using AI in pricing. [00:05:58] Speaker A: Yeah, yeah. And that's a really tough area for startups too, especially your early stage. You're, you're maybe still defining the ICP or the, the value that you're trying to offer, especially in the AI space which is also fast changing and growing. So when you said in the beginning, I want to unpack this a little bit more, when you said we analyze some things, what did you analyze? At a startup world there's not a lot of data to grab onto. Right. What did you in a high level, what, what information we were able to grab onto to help make these, these decisions? [00:06:31] Speaker B: Well, different types of sources information. And this tends not just for this case but for most cases. So first of all there's sales data, the obvious thing, whatever sales, they have done up to this, more up to that moment. It's a useful starting point. But we blended in traditional information, especially some market factors. So mobile penetration for instance, it was a mobile application. So obviously this is interesting. It could be any other market related factor plus macro factors like exchange rates, inflation, things like that. All these were factored in and eventually the model decided that the most significant were some of these factors. Actually the mobile penetration was very important and exchange rates were also important as well and some other factors as well. So all these different market macro indicators plus sales data are usually factored into the model. Sometimes when we have competitors intelligence, usually we don't. If we are talking about B2B companies by the way, but for B2C companies, usually you get more data then you can factor this in as well. And based on all that, the system will try to figure out the demand curve, supply curve and shifts of these curves based on all these different indicators and based on that it creates forecasts and optimum price suggestions. And this is exactly what we did at this case and all the other cases got it. [00:07:58] Speaker A: And was there any pushback from the team on, on some of the outputs of the model? I mean, you fed it the data, right? You looked at it. How did you reality test some of these things? Because it sounded like they were just scared to change the prices around for the region. They just went flat. They wanted to keep everything simple. Maybe too simple, right, in that case. But how did you get them to buy into to the prices of the model? Like how did you get them to believe it, I guess is what I'm saying. [00:08:22] Speaker B: Well, there was a lot of pushback because of the nature of the actual propositions from the model because there were some heavy discounts or some heavy increases. So there were deeply diversified prices and they were scared. I mean, they were scared to slightly change the price, not to do something like that. And this is why they ended up doing a price experiment, because they were afraid. But when they saw that the price experiment was so successful, they understood that obviously there is value in this tool and AI price optimization and differentiation in this case. But we were lucky enough to have people there that were willing to do a price experiment. In most cases you can do a price experiment. It's difficult, it's expensive, it could be risky, and so on, so forth. And you do need to manage whatever is happening within the company. So usually I find that the best approach is to have gradual deployments, not to go for a big bet and change everything in one show. That's almost impossible. Not technically. Technically would be possible, but it's impossible for the whole organization to digest all changes at once. If you find, and I focus a lot on quick wins, some areas where you can have a quick win and it's easier for them to understand what is happening, to get some immediate benefits and gains out of the system and so on and so forth. That's easier because you have a quick win and you can build on that. Then you increase the appetite of the overall organization to experiment more because they have seen some success. It's limited, but it's there. And then you do something else and it's also successful. And then eventually everybody comes and say, okay, that's great, let's do it. Also in our area or function or whatever. So gradual steps, I will say it's the most important thing. And also to focus a lot in the human factor to understand that it's equally important, not just to have a great pricing strategy or a great AI pricing tool, but also to have great buy in from all people that are stakeholders and important factors for success for this initiative. [00:10:30] Speaker A: Yeah, it's, it's tough to do it with math only, right? You have to be able to get the human piece of it. I always said as long as humans are buying stuff from other humans, you got to factor in that component into it. But what you said I think is really important for a lot of people to hear, especially folks that are starting up, that are trying to figure out their journey, is that it doesn't have to be big bang. You're saying the gradual steps, right? So for me it's, it's build up versus Big bang, right? So let's build it up. Let's start with the first step, let's get a quick win like you said, and then let's build up from there. Build up versus Big bang, I think is a big lesson that a lot of entrepreneurs can take away. It doesn't have to be just AI by the way. It could be anything. But I think those, you were able to get some data, illuminate a few possibilities, right? And it wasn't all or nothing. Now let's run an experiment and then let's go ahead and push based on those results and then keep pushing up and up. I think that is a muscle that a lot of companies need to start building. And so when you frame this experiment, because I can tell you right now, like some folks are like, oh man, experiments are risky, they are expensive just like you said, right? Especially when the stakes are really high, like in B2B Enterprise Software Global. Like there's lots of reasons why companies don't experiment. No one wakes up in the morning and says I hate experiments. But they, they fear the, the effort, the work, the risk and all those key things. It slows them down. So maybe you can help me for this particular one you said, oh, they're luckily they were open minded to, to doing one. How did you frame an experiment? So they were willing to take that step forward and not be afraid with the time and effort and risk. [00:12:03] Speaker B: Well, one of the things that is different in this case than other cases is that these guys, they were also IT guys, they were in full control of the pricing and price execution engine. So it was easy for them to change pricing automatically and dynamically. For other cases it's perhaps a bit more difficult because the executional part is a different function or is external to a company, to ERP system or something like that. So It's a bigger project. So in this case at least we didn't have this problem. Now, the second factor which was extremely important is that we limited the scope of this, let's call it experiment or proof of concept or something like that. So it wasn't to all the sales of the company. Obviously it was just one part of the company, a few products and a few customers involved. So even if everything went south and there were problems or something like that, there weren't any problems in this case. But if there were any problems, okay, it would be a failed experiment. It's just a tiny small portion of the company, the business activity, so they could afford to perhaps perform another one and another one until they get it right. What I've seen in practice, and this stands for price experiments, also small pilots or proof of concepts, you should take something which is representative. So it's not extremely difficult, but not extremely easy somewhere in between to be representative of the whole organization. At the same time, you shouldn't exceed 5, 10% of the business activity at most for price experiments even less. So you don't jeopardize or put at stake the whole business by this experiment. But just a tiny part that you can easily change things afterwards. [00:13:51] Speaker A: That's interesting. You mentioned the 10% thing. There was a guy I used to work with and he used to talk about tests in terms of 10. He had this. With this rule that he always said, look, if I'm going to test something, I'm not going to go past 10% of my customer base. I'm not going to do more than 10 new deals and I'm not going to do more than 10 days. I think he had these 10 rules and I was wondering, like, man, you are way oversimplifying this thing, dude. However, whenever I talk to folks, it keeps coming back to those 10. So. So, you know, so Aaron, you were right, dude. Whatever. You're right with your tens, right? But. But the. But there. But you're right. But being. Being measured about it and then the ability to change it quickly when you're not seeing what you want to see. I think that's one of the key things to help reduce a lot of that risk. But the mindset, it does take a bit of curiosity and willingness to push a little bit in order to get the experiment going. But if you have the right controls, then you should be in a better position. So curiosity to do it and controls to make sure you don't screw things up. You need both, right? So that's, I think a Big lesson. Anything else you want to be able to is you think through that time and you double the revenue. And you probably did another change after you saw initial progress and new revenue. That usually opens things up and now they're willing to, to take it even further. Right. So if you had to think about the one thing that in that entire journey and experiment with that company that was, was really the pivot that pushed you in the right direction, what was that one thing? [00:15:24] Speaker B: Well, I think that the most important in this case was that they did the actual big problem. They did understand that they had a pain point and they understood more or less what the problem was. The source of the problem was the lack of price differentiation. And in this case they understood also that they were lacking the competencies, the skill sets, AI tooling and so on and so forth to move ahead. So more or less they had framed the problem correctly and they have understood more or less what the solution would be. But they were unable to do it themselves, so they needed external help. So in this case it was very, very well framed by the experiment. And after the experiment, obviously everything changed and they were not reluctant anymore to differentiate prices. It was the other way around. Now they understood that differentiating prices is actually a game changer because they did see it and they did see the value in their actual data, actual sales, before the price experiment, during the price experiment and after that. So that was the most important thing for this case. It's not the same for all companies. For other companies it's completely different thing. And one of the most important challenges is usually framing the problem and realizing that we have a problem, that the customer has a problem. Most cases they don't realize that. They see that profitability is falling and the first thing that they're trying to do is increase marketing intensity in order to improve revenues. For all pricing guys, we know that this is wrong. You need to figure out how to improve your pricing first and then your sales volume and so on, so forth. But for customers it's not so evident or a no brainer. They need to be educated and that's a difficult thing. For most companies it is because it. [00:17:20] Speaker A: Does force you to really sit down and make sure you are framing the problem in the right way and understanding it deep enough. And I think sometimes we may react at a surface level information or maybe you know, knee jerk react to something in the competitor did. But really sitting down, framing and understanding the problem in a deeper way before you solve it I think is it takes patience, takes a little discipline to be Honest with you. And I think it helps add what I call the clarity you need to decide your next steps. Right? So there's, I think a big. A big focus on clarity can help in so many ways. And I think without that, I think you would have been throwing what they say, darts in the dark, right? And kind of figure out, is this the problem? Is this the solution? Is that the problem is that the solution gets really difficult and frustrating to solve. [00:18:09] Speaker B: I like what you said about clarity, because strategy comes before AI, before the mathematical formation, foundation or anything. Because if you really don't understand exactly what you're trying to accomplish, then no AI tool is going to help you. Sometimes we ask customers, okay, you want to optimize prices, which part of your business you want to optimize. You want to optimize profits, profitability, your revenue, your growth. And they say everything. It doesn't work like that because these are mutually exclusive to some extent. So you need to decide, and that strategy, that's not AI. What's the most important thing for you? If you are a mature business, perhaps it's not growth, it's profitability. But if you are a startup, obviously it's growth. So you need to put some effort and some clarity in identifying your objectives first. And then comes AI processing and all these things. [00:19:02] Speaker A: Listen, man, you can read it in the books all day long. You can hear it on podcasts like this one. You can read different, you know, articles on medium. The clarity piece is. Is really, I think, the key that folks need to spend a few more minutes on. It's. It may sound boring, it may sound like, what? What do you mean? I have to sit down and think. But it's those steps, I think, pay very big dividends later. And I think it's one of those things that a lot of people know they kind of need to do, but end up just kind of, you know, in the. For the sake of moving fast, don't really spend a lot of time. So this actually is a nice segue into the next part, which is in play. So from we. Ryan to play, there's a. I want to talk a little bit more about what's going on today with AI, which is no shortage. It's just every day somebody's putting some new model out or some, you know, what was it? Claude's latest model was. Was being deceptive to somebody and trying to blackmail them and like, get all these news right, all these things popping up. It's. It's a fun, exciting time. I'm really happy to Be alive here now. But when it comes to AI and harnessing the power, monetizing it, applying it, the strategy piece, where do you see companies falling down? Right now it feels like everybody's clamoring, trying to get in on AI and maybe even just putting it on their website without even knowing what they're going to do next. So what are some of the areas do you think companies are struggling with adopting an AI strategy, getting a direction going? What have you seen out there? [00:20:26] Speaker B: Well, there are a couple of principal directions because there are many different uses or use cases for AI based price optimization. The two directions is first of all, if you have an existing product portfolio, it could be one or two products or it could be thousand products if you are talking about them, manufacturing company for instance. But if you have an established big business, then you need to audit it with AI and audit your product portfolio pricing wise. Obviously with AI means a number of things. Means first of all segmenting your market and your product portfolio, Understanding where there is different pricing behavior, consumer or businesses behavior depending on you, are B2B or B2C then optimizing prices. But this is even more sophisticated than it sounds. Okay, let's find the right price and that's it. It's not like that. Usually even the same product has different pricing behavior in different segments, different types of customers, even at different price ranges. It may be elastic or inelastic. So the AI system will identify areas of inelasticity which are hidden. Although generally speaking, a product may be elastic in most cases, but not in all cases. We must identify these few cases where it has a different behavior or cases where we have excess demand. Sometimes it happens and when you have excess demand, that's great because you can increase price and you can increase your volume this way and also revenue and profitability. This is one of the few cases where you can do everything looks great and you don't have conflicting indicators or KPIs. So all these types of optimizations plus the forecast that is relevant to this optimum price, what's going to be my uplift if I do this or that with my pricing, what's my expected sales uplift or profit, and so on, so forth, this is the type of AI applications that we see in pricing right now in this direction, having an established product portfolio, you need to audit it, identify problems and fix them or areas of opportunity and leverage them in order to improve something. Some of your indicators, that's the first category, the first direction, the second one, which is something like 10% of cases it's a minority right now, but it's important. It has to do with innovation or expansion. So if you have a product which is new, a new product launch, or you are expanding, so you are selling to let's say 10 countries or 30 countries, you are expanding to another four, five. But you don't have any data there. That's the problem with established products. You do have sales data. What happens if you don't have anything? There are workarounds. If you don't have anything at all like a new product launch, then you can do some pricing research first and use this as input instead of sales data for your AI model and training. And also, if you are already doing business in some countries, but you want to expand some other countries where you don't have any sales data, you can use the existing countries with some macro and market indicators for each and every country and then extrapolate based on that using your AI model. So that's another direction again. Still this is the minority because in most established companies it's something like 10% of their product portfolio is new or a new product launch or something like that. But this expands as more and more companies try to innovate. And we have many startups that are getting bigger and bigger. And obviously these startups have new product launches. This should help at least. So this is what I see, right? [00:24:03] Speaker A: Yeah. I think this the first step then that auditing that audit of the now. Right. And being able to, to see kind of where there's patterns that could potentially emerge. Right. That's the very, I think a step that a lot of companies skip. Right. They're going straight to the forecast. How could I make and what can I do? They're not auditing the today. But the other thing you said about the new product I think is even more interesting, right? Because there's a mix out there of companies that are existing incumbents who are trying to figure out their next wave of growth or trying to figure out how to stay relevant in this new world in rapid changing. But then you also have what I called AI first companies or companies that are born from AI and their entire solution is based on the AI technology. And that could also mean a new product that an incumbent build. So either way. And you're saying that if there's no sales data. Well, because there isn't, because you're brand new, you want to double down on pricing research. Right. And I think this is maybe a skill or knowledge gap that's out there today. And how do I do pricing research on this product that has no, no sales data whatsoever. And, and maybe you can illuminate of how you help companies like that. When there's a new product, they have to do pricing research. What, what can they grab that, that by the way, doesn't cost $100,000 in, you know, four months or whatever it is. But what's the, what's the play when you have no sales data to go off of? Nothing. [00:25:28] Speaker B: I can tell you an actual case and which is a very strange case of a new product and a new product category. The product is called Send Camera and the company is a startup company. Send Camera captures smell. It's called camera, but it captures smell. So you smell coffee, you capture it with this device and you can produce it somewhere. It's a smell camera. Send camera. So obviously these guys, they had all the difficulties of creating the technology for capturing smell, which is obviously innovative and very difficult. And then they didn't have a clue about their pricing because they told us, okay guys, now we don't have any sales data. Obviously it's a new product, there's no competition, there's no market, no market reports, there is nothing. The only thing that we know is just one figure, our production cost. That's it. They understood that they shouldn't go cost plus and they need to differentiate per country, but they didn't have a clue of how to price everything. So in this case, a company called Neuronsics, they are partners of mine, they did the neuroscience based, very advanced pricing research and they captured price sensitivity as research because obviously we didn't have sales data. And I use exactly this data, the output of this research, as input to train my AI model. And based on that I extrapolate it using several market and other indicators for all countries that wouldn't have any data from the pricing research. This way, these guys and Camera, they had first of all a price indication that was justified using the existing data, pricing data plus all the market indicators for different countries. They had a different price indicator indication for all countries plus and expected sales for each and every country based on that. They prioritize also. It wasn't just that they were fixing prices, but they wanted also to prioritize. They had something like 100 target markets, which is impossible, especially for a startup company. And they narrowed down then to approximately 30 to 40 countries. And again, there were priorities for all these countries. So it was a great strategic tool for them for their pricing, but also for their strategic direction and prioritization. And we manage it with pricing resets. [00:27:49] Speaker A: And AI modeling that Is that, is that is the key right there. Just finding a really good proxy and prioritizing. I think just those two alone, I think help a lot. And one of the things you're interesting, you said 100 market, which, you know, when you're in that startup mode, you go for the gusto. You want, like you're out there, you want to change the world. So I get that it's kind of hard to kind of peel it back and say, okay, no, no, let's just focus on these first. But I think that's, that's the key, the proxy. There's some extrapolating that happens too. As well. I think most prices end up being triangulated at some degree anyway. And then you have this, the prioritization helps you focus to see where, where the learning is happening. And that way you can continue to, to update and evolve the model from there. If you spread yourself too wide, too, too thin, it gets really hard to make the adaptations you need. Right. So I think that's, yeah, that's a. [00:28:40] Speaker B: Really big one and bigger startup. Myself, I know how important it is to narrow down your scope. If you go for a broader scope is a disaster. You never make it because you don't have enough resources, you don't have enough time, you are not big enough, you don't have anything. So you need to identify the best possible target audience, the best possible geographical region to target, and even the best possible use case. You can do everything with your software you need to do. Not a thousand things, a couple of things if we're talking about something new. And then you expand once you land somewhere and you see that everything looks great, you get some feedback, some learnings and so on, so forth. You understand that it's a nearby market that's even better. So let's go there and we know how to do it and expand step by step again, gradual, quick wins and eventually you'll get the big bet as well. [00:29:32] Speaker A: That resonates. That resonates a lot. But the, again, I think we folks need to temper that move fast and you know, break things mentality with this learn gradual step up approach. It's a, you obviously don't want to take forever, you don't want to take, you know, a year or two years to make a move. At the same time, you don't want to run around without any direction or any clear focus. And so the idea is finding the balance of, you know, being able to have progress, be a little patient, but have that progress and keep pushing up. Right? So one Thing for just this last piece here on, on Play before we move on to, to fast forward is I think this is really hitting on a major problem today that's holding companies back. Which leads to my mortal enemy, guesswork. I don't like the guesswork. I like when companies guess on their pricing. They put their finger in the air, try to throw a number out there. I think it, I think it leaves money on the table. I think it inhibits growth potential and opportunity. Even if it works a little bit, you probably left a ton of money on the table, right? So the idea is moving away from the guesswork. And so I like the, the getting a proxy, you know, I like the prioritizing like getting some, some kind of data. If you're, if, if all those doors slam in your face and you just can't get anything going. Is there any advice you would give a startup who's trying to figure out how to monetize their new innovation or even their new AI innovation? What would you tell those guys to do? [00:30:58] Speaker B: First ask the customers. I would say that's the most important thing. If you don't know what your customer is thinking then you don't know anything. And most startups, or lots of startups fail because they feel that product market fit is about testing the product. It's not, it's testing the product at the same time you are testing your pricing and monetization model. So it's time that you go out and demo your software, your AI solution, your new device, I don't know, whatever you are making and ask about the product features that you can feel if the customer likes it and so on so forth. Obviously you should ask all these things. You need to understand the product is right for their needs and you don't remember to ask in the end, okay, it costs 1,001 million, whatever is it okay? And it's going to be a one off payment or it's going to be a SaaS subscription or it's going to be on the go payment or whatever you have in mind. If you haven't tested all this, then you have tested part of the product. So practically you don't know if it's going to be successful. I think that's the single most important thing that I would say to all startups and actually to all companies. Product wise, love your product but remember, but your product is much more than its specs. It has also values attached to it plus a price and a monetization scheme. Everything is part of the product, everything needs to be tested. [00:32:20] Speaker A: I Love that. I love that there's an old product market fit to product price market fit. Like there's this whole expansion of, of that when it fits, the solution solves the problem and people are willing to pay for it. And that you can actually have a sustainable business based on that. You need all those things to kind of come together. I love that because that brings back that thinking to the forefront. And yet when, when all doors are slammed in your face and talk to your customers, that door should never be closed, it should always be open. And that would lead you down to better decision making there. So with all those, I think beautiful nuggets of gold for a lot of entrepreneurs out there and even SaaS operators, everybody moving into the future, the question you've been dying for me to ask you this whole time, right, which is around where AI is going, you know, what does it mean for us? And so I want to start opening about let's take a look into the future and let's do this in a way that's practical, right? We know that we've been predicting what AI is going to do and what's going to happen for a couple of years now. And you've even pointed out, hey, here's what we predicted in 2023 versus 25 and here's what happened and what didn't and all that. I think that's fantastic to see how things are shaping up. But I'm talking 26, 27, 2028, just thinking about a couple of years down, which the conversations could be very different than what we're having right now. But in your mind, you know, where is AI going? And you can apply to pricing and packaging, to B2B. Where is it going? What does it mean for us? I want to hear your latest thoughts and let's riff on those to discuss. [00:33:53] Speaker B: About where AI is going, especially for pricing. We need to first of all understand where we are right now and we are not in a very, very good shape. Although AI based price optimization, one of the best applications, and this is not my words, Gardner has said so in a survey, in a recent survey that AI based price optimization is practically the most valuable and also feasible technology to apply AI Wise among all other business applications. And we did with a partner of mine, Value Based Booster. We did a survey before a few months and the adoption of AI based price optimization was less than 20% whereas other marketing related or sales related AI applications had 40 or 50% adoption rates. So they were more mature. So this is surprising, but this is where we are right Now I think this is about to change because pricing is coming to the spotlight for a number of reasons. First of all, because we have all these macro disruptions. Think about economic slowdowns, think about the inflation. We had it before a few years, but it's still here. Now we have the disruption with tariffs and tomorrow we'll have something else, and so on, so forth. The same with market, we have many disruptions. AI is a disruptor, not just for AI companies, but for everyone. So there are many disruptions and that means that the value chain is disrupted and therefore your pricing is disrupted and you can stay at the same stage. Let's say you need to continuously change things and that's very difficult if you do it manually. So you need aids, so you need AI practically in order to help you optimize your pricing. So I think that both pricing and AI and their intersection AI and pricing is coming into the spotlight because of all these market turbulence and disruptions that we see. My feeling is, and I can see it already, there's a lot of interest questions, inquiries and so on, so forth and more initiatives that there were before a couple of years. I think that the penetration is going to accelerate a lot. I wouldn't be surprised if it reaches 40% or something like that in the next few years. That means something also because we are starting small and low and in most cases, one of these companies per industry will make the first move. That's important. If you are the first adapt, early adopter or the first mover, you will gain a competitive advantage against all others. So there will be areas that will accelerate things. Who is going to be the first one? If you are not the first one, you are losing, then you need to accelerate your efforts in order to be at least the second one, and so on, so forth. Companies now are starting to realize that and this is why there are more initiatives and more interesting about AI in pricing. So that's one major thing that I think that we will see in the future. A second thing is that we will see a blending of different AI technologies. So you have predictive AI. Predictive AI is the main AI technology for predictions and price optimization. It's not generative AI, it's not ChatGPT. These are general purpose systems for other things, a thousand things, great. But it's a different thing. I think there will be a merging of gen AI with predictive AI. Why? Because it can complement each other. So for instance, data collection and structuring generative AI is better. In doing that, you have unstructured Pricing information on your CRM system through text comments from your salespeople, and so on and so forth. And you can't do anything about it because it's unstructured. You use generative AI, you structure the information and then you fit it into your predictive AI system. The same for scraping websites where there might be some pricing info, especially for B2C or retail companies, E commerce platforms, so on, so forth. You can get a lot of information there. You can do that with gen AI, not predictive AI. And the last thing, which I think is very, very important, not just for pricing, but for all software companies, for all software tools. I think that generative AI is going to be the next operational system or a UI layer up on top of the operational system. So we are going to communicate with pricing or other software in the future, in the near future through gen AI prompts. So instead of having fixed menus, options, charts, tables, information, so and so forth, which is how most software is working right now, you will just prompt and ask the software about something, optimize my prices using data from this or that source. And I want to test against those two competitors and the system will know what to do, use the pricing software to do so, and then present you without. I think we will see this type of fluid and more flexible UI systems that work in tandem with pricing software and other software as well. [00:38:56] Speaker A: So this is what I feel is. [00:38:59] Speaker B: Going to be different. [00:39:00] Speaker A: I'm processing, I'm processing. That's a lot, but a lot of really interesting stuff, right? The, the acceleration, right, The. And listen, my point of view is, I think when ChatGPT really started opening up to the world a couple years ago took off like wildfire. I think people were really intrigued by how human, like the responses were. Okay. And I think what that led us down a path of, well, use cases that maybe AI replaces a human doing it. Right. And I'm thinking about simple things like writing copy or, you know, helping you with, you know, now, I think now you have AI SDRs and you know, it's like the, the next generation of chatbots and things like that going on. Right? And so I think that's. That it went there because it was the nearest and clearest association with what you were seeing with GPT, which was really just completely making everything mainstream. And so the natural, I think the natural use cases of the marketing use cases, the customer service use cases all kind of come from that initial zone of familiarity, if you will. Right. It's like, okay, I kind of get it. It's kind of person like, and if it does what this person does, I could save money and doing all that, that's fine. But as far as applying it to price optimization and using AI, predictive AI, not just descriptive but predictive and things like that I think really hasn't entered into a lot of the minds of folks yet. Right. I think they still need to be introduced to it, understand it and it's starting to happen. Like you're, like you said, pricing being in the spotlight. I'm already seeing dozens of new monetization and predictive AI related startups coming out and hitting and so you're going to start seeing this accumulation and this push in this direction. So I agree, I think that you're going to see an acceleration and with that acceleration is that blend you talked about of gen AI vs predictive AI because different AI can do different things. And I think that's another area that most people when they think of AI they just think of the A and the I and that's it. There's actually a sub layer of all types of AI going on and I don't think folks fully grasp that piece just yet. Right. But I think as people learn and see it and get exposed, you're going to see more and more of that happening. With that comes the blends and then from there you're right taking over from like you said, even an os. I mean Satya, Nadella or Microsoft talked about the new layer, right. Being more agentic or prompt related versus the old fashioned. Hey, you got a screen with a bunch of menu items and you click here and click there and that's how humans interact with software. Then of course APIs came out and now systems interact with systems. But in this case the system interacts with humans, other systems and even with itself. Right. Which is I think an incredible evolution forward. So I love, I love the thinking and I think it's a little safe to be honest with you because I kind of see it already happening a little bit more. So. So George, are these guys going to just take over and as robots and run the whole, run the whole planet Earth? What do you think in there? [00:41:59] Speaker B: No way this is happening. No way this is happening. I get this question a thousand times per day and my answer is always no way this is happening. And there is an example. ERP systems dominated the market before 20, 25 years doing accounting stuff. Do you still have accountants or not? Do you still have CFOs or not? Yes. Do you trust the ERP system or your CFO? Guess what? Your CFO. You don't care about the ERP system. It's just a tool. It's like saying that I have a great formula car and I gave it to George. Me, the worst driver in the world, and I don't understand why I'm losing, although I have the best formula car in the world. Because the driver is not there. You need the driver, and the driver is definitely not the AI. It's people. And one thing that people forget, business people, I mean forget, is accountability. I mean, people are accountable. AI is not accountable. So who are you to blame or to give credit to if something goes wrong or not? Obviously people. So people should be there. You said something somewhere before 10 minutes or so about guesswork that you don't like guesswork. I will surprise you all. Although I'm an AI guy, I love guesswork. And this is the starting point for all my AI discussions. First of all, I get the guesswork, and this is the first initial starting point for AI modeling. Exactly that. I guess the human element. I take all the guesswork and what are the instinctive directions that they feel prices or anything else should go? And it can be structured scientifically, by the way. But even without that, you have some directions for your AI modeling and you don't have to test a thousand different useless directions. That's important. So human AI collaboration is not important. It's mandatory. Without that, you'd get nowhere. So definitely it's not going to replace us, but it's definitely going to change the way that we work. And if we don't adapt and ignore AI, that's a problem. Then, yes, there is a possibility that we are going to be replaced. But it's our blame, not AIs. They understand that things are changing and we didn't do anything about it. [00:44:11] Speaker A: Yeah, it's on us. It's up to us. I like that a lot. And even your point about guesswork is really interesting as an input, as instincts. I love that as well. I think that makes sense. Incredible sense, man. And less dire and scary than what I've been hearing from some other folks, which I like. I like your point of view. Listen, I want to thank you for coming on today, giving us all of your. Your knowledge, your tips, your deep experience, man. I would love to have you back on the show someday in the future. Be willing to come back, of course. [00:44:37] Speaker B: Yes. It was a great, great discussion. I loved it. [00:44:41] Speaker A: Thank you so much. Also, I want to make sure we get our last question in here, which helps humanize you a little. More George, make sure you're not AI talking on here, Right? Give us a little glimpse into who George is. Why don't you tell me your. Your favorite, your favorite song, something that lights you up. You can hear it a hundred times. And why is that? [00:45:01] Speaker B: I think that would be Space Oddity from David Bowie. I have heard it. Not a hundred thousand, a thousand times. I think it's one of my favorite songs and it is about space and space is about technology and the future. And this is what I've been doing my whole life. So it's partly of who I am. This is why I love the song. But the song is obviously a beautiful song and I love Bowie anyway, so I really love this. [00:45:31] Speaker A: Who doesn't like David Bowie? That's wonderful. No, but I think that what you're telling me is that it just says a lot about you, the song in and of itself and what Space, science, everything around that. So fantastic choice there, my man. One last request here. Where can folks find you? [00:45:48] Speaker B: Well, I guess the most the easiest way is on LinkedIn. So just type my name George Borettos or Future up and you will find me there. Reach out and I will respond as the easiest thing. [00:46:02] Speaker A: Thank you so much, team. That was George Barrettos. He is the founder and CEO of Future Up. Find him on LinkedIn. Follow him. His content is actually really good. I would jump right into his latest news section if you're reading. He's got a lot of great stuff in there for everyone here. Now, taking everything that you learned today. Don't just sit on it, take it, apply it, move and get 1% better. Get 1% away from that guesswork. Guesswork as an output, not as an input. George, let's move away from that guesswork and let's get you pricing with intention and moving forward and learning forward. So with that, I want everyone to say, stop guessing and start growing. Until next time. Thank you and much love for listening to the Street Pricing podcast with Marcos Rivera. We hope you enjoyed this episode. And don't forget to like and subscribe. If you want to learn more about capturing value, pick up a copy of Street Pricing on Amazon. Until next time.

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