Paul Breitenbach Podcast Transcript
Paul Breitenbach joins host Brian Thomas on The Digital Executive Podcast.
Brian Thomas: Welcome to Coruzant Technologies. Home of The Digital Executive Podcast.
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Welcome to The Digital Executive. Today’s guest is Paul Breitenbach. Paul Breitenbach 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.
Paul Breitenbach: Brian, it’s great to be with you.
Brian Thomas: Absolutely, my friend. I appreciate it. And taking the time outta your busy schedule to schedule a podcast with me, you’re in Connecticut. I’m in Kansas City, so an hour aparts, 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 unlock that early breakthrough?
Paul Breitenbach: Yeah, that’s a great question.
Really. I mean, the breakthrough that was price on 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. There was, there were all these unsold seats and airplanes, all these unsold hotel rooms, right?
All this extra capacity that went, that 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 on all times of Nasdaq.
Brian Thomas: 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, William 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 and. 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?
Paul Breitenbach: Yeah, no, 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? And 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, by putting e-commerce directly in the hands of individuals.
With r4 and 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, 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, 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. And 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 in directly in the hands of business users with humans at the helm.
And I think this is really what drives scale by leaving the legacy infrastructure alone and just turning on the new capability, almost overnight.
Brian Thomas: That’s awesome. I love how you’re taking that. Predictive analytics, building that into 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 it’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 AI 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?
Paul Breitenbach: Yeah, that’s a great question to build on what we’re talking about, right?
The, 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 the how you build the requirement. What do you want the system to do? Can be done in hours now. Remember how we all grew up with old, system requirements, right? It take 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 cost savings. Right?
So, getting rid of this old concept that I gotta go, you know, 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, 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 gotta go build my own technology. I’ve gotta go try to get teams, armies of people to try to agree on the data, on the math.
Then try to get it to scale and 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 businesspeople. And the second part is that then they take those requirements and then go try to build it, which of course are way outta 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 and organizations have to get comfortable with.
Brian Thomas: 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, for faster turnaround. Seeing those results a lot faster, and obviously that’s gonna 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 for 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 customer. 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?
Paul Breitenbach: Yeah, that’s a great sort of climactic question here, right? The, 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 Bolton. This is a cultural shift. And remember, the cultural shift is differently.
We’re talking about by having business users understand how to, to drive the business decisions predictably. Like, I think if, but it’s a cultural mindset, right? That, that AI is not for it people only, right? The culture shift is that AI is for businesspeople, humans at the helm, right? 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, right? Remember, think about it. Oh, the internet is not for me. And it’s easy to make that same mental mistake here in, 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.
You, you need to be at the forefront of that because in the new next five, three to five to 10, 20 years, if with AI is always gonna 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 di difference with ai. Right?
Brian Thomas: Absolutely. Thank you. And I liked your analogy, the internet, it’s. What’s the core of connection, information, that sort of thing.
But AI is different. It’s not a Bolton 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.
Paul Breitenbach: Yeah, Brian, it was really great to be with you and stay warm. It’s it’s cold. It’s really great to be with you. Look forward to speaking soon.
Brian Thomas: Absolutely. Bye for now.
Paul Breitenbach Podcast Transcript. Listen to the audio on the guest’s Podcast Page.
