← The Digital Executive
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Foreign.
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Welcome to Coruscant Technologies, home of the Digital Executive podcast. Do you work in emerging tech? Working on something innovative? Maybe an entrepreneur? Apply to be a guest at www.corazon.com brand welcome to the Digital Executive. Today's guest is Paul Breitenbach. Paul Breitenbeck is CEO and founder of R4 Technologies and a founding member of Priceline.com, where he helped build one of the most iconic data driven companies in modern commerce. As co founder and chief marketing officer at Priceline, Paul pioneered e commerce strategy and created a global recognized brand. At Priceline, he and his team mastered converting data into profit, developing a disruptive business model that transformed the travel sector and generated over 100 billion in shareholder value. Paul and three Priceline Co founders established R4 Technologies to bring this expertise in technology, data and mathematics to large enterprises. Built on Priceline's DNA and decades of experience, his team has developed a leading edge AI platform that empowers organizations to leverage their data and systems for decision dominance and competitive advantage. Well, good afternoon Paul. Welcome to the show.
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Hey Brian, it's great to be with you.
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B
Absolutely, my friend. I appreciate it and taking the time out of your busy schedule to schedule a podcast with me. You're in Connecticut, I'm in Kansas City. So an hour apart is not too bad. But I always appreciate that when the guest makes the time. So Paul, if you don't mind, let's jump into that first question. You were a founding member and chief marketing officer of Priceline.com helping turn data into one of the most disruptive business models in commerce. What core insight about data and pricing unlocked that early breakthrough?
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A
Yeah, that's a great question. Really. I mean the, the breakthrough that was Priceline was using data and math in real time to make predictions about what would happen tomorrow. And in the case, it's all about matching supply and demand, right? When you think about the, before the Internet, before Priceline, you know, there was, there were all these unsold seats in airplanes, all these unsold hotel rooms, right? All this extra capacity that went down, went wasted, never was used, but yet you had billions, tens of billions of dollars of demand on the consumer side. So really the big idea behind what we did at price, and it was so powerful, was using data and math to match supply and demand predictively in real time to make, to give consumers a 70%, 80% discount, make producers and suppliers tens of billions of dollars of profit. And it was one of the best performing stocks in all time of nasdaq.
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Thank you. I Appreciate that. There's a lot that goes into that and it's really cool that you were part of such a brand name like Priceline. Right. I always think of James Kirk and Shatner anyway, so I appreciate that. The fact that you're leveraging predictive modeling, predictive analytics with data and math in real time was huge for its time. And I remember it was exploded. I've used it several times actually in the past, so that's pretty cool. So thank you for sharing that, Paul. Let's jump into your next question. R4's platform combines AI, mathematics and existing enterprise systems. How important is it to augment rather than replace legacy infrastructure when driving transformation at scale?
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Yeah, now that's another great question, right? Really the big idea behind R4 is we're using the same DNA that was so successful at priceline. Right. In R4, we wanted to make a technology that could be deployed, a predictive AI capability that could be deployed without the customer needing data scientists. Right. The big leapfrog, just like the Internet, really transformed the industry by putting E commerce directly in the hands of individuals. With R4 in the golden age of AI, now what we're able to do is put this incredible AI capability, these incredible predictive capabilities directly in the hands of business users at this huge enterprise scale without the need of all the data scientists that typically are associated in the old world of AI. So think of it as we put humans at the helm in the golden age of AI so that they're able to really drive incredible business performance improvement, whether it's revenues going up and costs going down. But your question is so important, right? This idea that in the golden age of AI you could leave the legacy infrastructure alone, that we can turn on this new decision operations capability, this decision layer that allows you to connect all the different silos and stovepipes within the organization. I think that's what makes R4 so transformative, is pulling all these siloed data together, helping drive optimized decisions predictably, but putting it all indirectly in the hands of business users with humans at the helm. I think this is really what drives scale. By leaving the legacy infrastructure alone and just turning on the new capability almost overnight.
- [05:09]
B
That's awesome. I love how you're taking that predictive analytics, building that in to your platform so your customers aren't having to hire the data analysts and data scientists. I think that's really important. And you did talk about something I think that's really important as well is leveraging AI today. And we see it, it's just leapfrogging, but you can actually use AI and keep those siloed or legacy infrastructure systems in place. And I can do the hard work of bringing that all together and really making sense of it all without having to upgrade or replace. So I appreciate that. And Paul, the next question I have. Many organizations are data rich but insight poor. We always hear that. What are the most common mistakes enterprises make when trying to apply AI to complex decision environments?
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Yeah, that's a great question. To build on what we're talking about, right? Just like the Internet, the rules change, right? With how to leverage the Internet and the same thing with an AI, right? The first mistake we find all the time is well, with what we do at R4, we can deploy it so quickly, put it directly in the hands of business people, whether it's driving supply chain or personalization predictions, right? The first rule of thumb, that is how you build the requirement, what do you want the system to do? Can be done in hours now, but remember how we all grew up with old system requirements, right? It takes months and sometimes years to try to build the requirements today. I think the first mistake you make is sit with business people, understand the problems that need to be optimized, that need to be changed, the decisions that have to be made. And that can be this agile ongoing process which is incredibly freeing and really unlocks revenue growth and in cost savings, right? So getting rid of this old concept that I got to go, I have a bunch of requirements that take weeks and months and years to try to pull together. I think is the first mistake a lot of people make, right? So we can eliminate that whole step, right? And the second part, I'd say the common mistakes are trying to build things manually, right? Think about like nobody tries to build SAP anymore, right? We just buy it, right? Because it's so well, it's so well the standard. And I think the same mindset is a common mistake with an AI that I've got to go build my own technology. I've got to go try to get teams, armies of people to try to agree on the data, on the math and then try to get it to scale on software that is an undoable thing. So I think the first mistake people typically make is use the months and years long requirements process when it should be literally in hours done by business people. And the second part is that then they take those requirements and then go try to build it, which of course are way out of date versus the idea of a buy, not build. I think is the new and the golden age of AI with humans at the helm. That's the big idea. That's the paradigm shift that I think people in organizations have to get comfortable with.
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B
Thank you. And that is a big paradigm shift. You know what I like about what you said is you can deploy your platform with your customers very quickly. You literally said within hours in some cases. But being agile allows for faster turnaround, seeing those results a lot faster and obviously that's going to impact the bottom line much faster as well. The main message I took away, Paul, is here you're saving a lot of time and money on that implementation versus what you're saying is there's no big business requirements in building of a new platform or infrastructure around the customers. So I really appreciate that. And Paul, the last question of the day, looking ahead, what will separate organizations that truly achieve AI driven competitive advantage from those that simply adopt the latest tools without meaningful results?
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Yeah, that's a great sort of climactic question here. Right. So just like the Internet, I think there's a good analogy from the Internet that we all experience, right. In the Internet era people thought like the Internet was just kind of a bolt on, right? And think about it now, 20 years later, right. The Internet is core to the operation of every organization, whether it's the government, whether it's commercial sector, in every sector. Right. And I think that same exact mindset in order to get meaningful results. This is AI is not a bolt on, this is a cultural shift. And remember, the cultural shift is differently we're talking about by having business users understand how to drive the business decisions predictively. But it's a cultural mindset that AI is not for it people only. The culture shift is that AI is for business people, humans at the helm. It's not some sort of thing that happens in the background. And a lot of companies learn that lesson in the Internet the hard way. Remember? Think about it. Oh, the Internet is not for me. And it's easy to make that same mental mistake here in the age of AI where somehow as a business person, ah, this AI thing isn't really relevant to me. And when you think about especially the promise of AI where it's continually evolving, it's always getting smarter, it's continuing to make better predictions every single moment, every single day. And you need to be at the forefront of that because in the new next three to five to 10, 20 years, if with AI is always going to be better the next day than it was the day before, especially when we can deploy it so quickly with business people at the helm. Right. So the cultural shift is you have to jump in and that's a cultural mindset. It is not a technical problem anymore. Right. We have the technology now. It's the mindset and the leadership to, to dive in and really innovate. I think that's the big, that's the big difference with AI.
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B
Right, absolutely. Thank you. And I like your analogy. The Internet, it's what is the core of connection information, that sort of thing. But AI is different. It's not a bolt on like the Internet, as you said. It's a cultural mind shift and we need to get people to embrace. And of course it takes a lot of leaders to get that message out to help people adapt and again embrace this technology needs to be adopted by all business lines, not just the technologists, as you mentioned. So I appreciate that and Paul, it was such a pleasure having you on today and I look forward to speaking with you real soon.
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A
Yeah, Brian, it was really great to be with you and stay warm. It's cold. It's really great to be with you. Look forward to speaking soon.
- [11:28]
B
Absolutely. Bye for now.