"Billion Miles of Data" — How AI Finds Hidden Profit In A Flat Market | Greg Uland, VP of Marketing, The Reynolds and Reynolds
Are you leaving money on the table in a flat market? While some see limited growth, others are leveraging technology to find profitability others miss. The key isn't working harder, it's working smarter.
In this episode, Greg Uland, VP of Marketing at The Reynolds and Reynolds, reveals how unified data and AI can transform your dealership's operational efficiency. He breaks down how AI goes far beyond chatbots, tapping into every data point to maximize profit on every single vehicle and service interaction.
What you will get from this episode:
Understand how robust AI provides a competitive edge in today's no-growth automotive retail climate.
Discover how unified data is the non-negotiable foundation for effective AI strategies that impact your bottom line.
Learn how to identify hidden profit opportunities in your inventory, F&I, and fixed ops departments.
Gain insights into reconciling new AI possibilities with the "people, process, technology" mantra of dealership leadership.
Anticipate future cybersecurity watch-outs and infrastructure considerations for AI adoption without fear.
Greg Uland is the VP of Marketing at The Reynolds and Reynolds Company, bringing a wealth of knowledge on automotive technology and data integration.
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Timestamps
00:00 Dealers and AI Reality
00:18 Unified Data Foundation
02:18 AI Beyond Chatbots
05:01 Flat Market Efficiency Play
08:41 Dynamic Pricing and Inventory
12:50 AI Flywheel Use Cases
19:33 Cloud vs On Prem Future
22:01 AI in Marketing Workflows
24:36 Connect and Closing
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Episode Transcript
[00:00] — Dealers and AI: The Real Picture
Michael Cirillo: I want to ask you this — I think most dealers, when they hear AI as of right now, they're thinking chatbot, some sort of agent, some sort of automation. But from your perspective, what's the real picture that we should be considering? What should the dealer body actually be thinking about when it comes to this stuff?
[00:18] — The Unified Data Foundation
Greg Uland: I think the important thing with those types of examples — the ones that can get overlooked — is having a unified data set, a unified data layer for your AI tools to use and to leverage. That is really important for them to get it right.
[00:30] — AI in Marketing: What Excites and What Gives Pause
Michael Cirillo: As a marketer — you're the VP of Marketing at Reynolds — where do you see AI going? I know as a marketer running a marketing agency, there are things I have to reconcile when it comes to AI. What are those thoughts for you? What excites you? What's something that gives you pause? How are you reconciling the age of AI in marketing?
Greg Uland: When you think about AI, or when I think about it at least, I do think that...
[00:56] — Sponsor: FlexDealer
Michael Cirillo: One of the things I enjoy most about producing the Dealer Playbook is hearing from you — the messages I get from people who are getting so much value out of the podcast, applying it to their day-to-day workflows, and finding a thriving career right here in the retail auto industry. It means the world to me.
One of the ways that we make doing this possible is through my agency, Flex Dealer. And of course, in the spirit of providing value, I think this is a perfect time to head over to flexdealer.com.
To show even further support for you, my beloved DPB gang — right now, if you go to flexdealer.com, you can get a full free PDF of my number one bestselling book, Don't Wait, Dominate. A lot of the topics discussed in this book are even more relevant today than ever with this surge in popularized AI and people wondering, what can I do next? How can I have a competitive advantage? It's all in this book. I'd love to offer you a free copy at flexdealer.com. It means the world to me, because that is how we continue to produce this show for you.
[02:18] — AI Beyond Chatbots: A Vantage Point Most Dealers Never Get
Michael Cirillo: I'm fascinated any time I get to chat with an individual who sits inside of such a massive mover and shaker in our industry. With Reynolds and Reynolds, I can't even imagine what's floating across your desk and the conversations you're part of. That to me is so beneficial, because it's a vantage point that few in our industry will ever get to have.
The AI conversation is full steam in our industry. There's lots of buzz and there's lots of hype. There are those that are all hype, and there are those that are dismissive — like, whatever, right? And here you have such a unique vantage point. It's almost like I picture you, Greg, standing on the summit of a mountain looking down into the valley where most of us are, just looking from street view.
So what should the dealer body actually be thinking about when it comes to this stuff? Most dealers are thinking chatbot, some sort of agent, some sort of automation. But what is the real picture?
Greg Uland: That's a great question. It's great to talk to you again. And that's a really interesting visual — I hope everybody doesn't share that visual you have.
When you think about AI, what you described — chatbots, agents, those types of things — those do have a place, and they do have value, and they absolutely are creating efficiencies. Think about a good, simple, tangible example. Somebody calls in to schedule a service appointment and it's 7:30 at night. They want to schedule that appointment. They're doing it on their phone because they're driving home right now. They don't want to have to remember to do it at 10:30 after they get the kids to bed.
So you have an agent answer that call, and it schedules the appointment. But the important thing with those types of examples — the thing that can get overlooked — is true access to the data and the information about that customer, about that dealership, about the technicians available tomorrow.
Having a unified data set, a unified data layer for your AI tools to use and leverage is really important for them to get it right. Because you don't want that agent to answer the call and say, "Yep, we'll schedule you at 8:15 tomorrow morning," and lo and behold, you're planning on waiting for your oil change but you're not going to get that car for three hours because everybody else has an oil change at 8:15 tomorrow.
Making sure your agents and your tools have access to unified data is really important. That's the first thing.
[05:01] — The Flat Market Efficiency Play
Greg Uland: The second thing kind of builds off of that. When you think about what's possible and where we're going, some of the tools available today are really in robust operational use cases. How can we create giant efficiency gains?
Think about where we are as an industry and how exciting it is to have these tools at this particular time — because we're pretty much in a no-growth environment right now. We're going to be flat for the foreseeable future, barring some odd circumstances that we've all seen before and will bounce back from if they come. But as things progress right now, we're going to be in a pretty flat growth area.
That means we need to find ways to be more efficient if we're going to see growth and sustained success. When we find those operational areas for AI to impact, that's when you're going to see the biggest increases and the biggest opportunities.
Michael Cirillo: That's got me thinking about how challenges — a flat market, for instance — are going to create two groups of people. There are those that look through it and say, "Hey, there's opportunity here. There's a tremendous opportunity to differentiate." And then there's the other group, which is doom and gloom.
But to your point about this ability to know more and present a better experience for the customer — that leans into option A. There's an opportunity here.
And I think about this — I'm going to share a quick story. Everybody who listens to this show knows my one guilty pleasure: a Monster Energy drink, Peachy Keen in particular. If you see me at SODU CON with a Peachy Keen, don't be surprised.
I roll into a 7-Eleven near my office, and they always have their deal — buy two, get one free or something like that. So I buy six just for the office fridge. The guy at the counter says, "Oh man, you really like these. These are a good deal. Do you come here often?" This is 7-Eleven. And I said, "Well, yeah, it's a good deal on the way to work." He said, "I'm going to make sure I stock these just for you." Whether that's true or not, I drove away thinking, that's kind.
Greg, two weeks later I roll into that same 7-Eleven. The guy not only remembers me, he says, "I ordered a whole pallet of these things just for you because I know you come in and buy six. And then I bought another pallet for everyone else."
Again, whether or not that's true — the way it made me feel. At a 7-Eleven, Greg. Over a Monster Energy drink. He's using first-party information. He's using customer-centric, unified data information without any technical infrastructure to support it — which you guys have — to give me a better experience.
I don't get that when I buy a $100,000 truck.
So what are some examples you're seeing from your sphere of how unified data feeding into AI can actually benefit the dealer and create that spread of difference for them in a moment when markets are contracting?
[08:41] — Dynamic Pricing and Inventory Intelligence
Greg Uland: Let me build off that example. The clerk at the 7-Eleven — he's one guy. He knows you. It's a one-to-one relationship. He's got that first-party data in his head.
But what if he had all these other inputs? What if he knew that based on market conditions and the calculations, he's going to make more if he prices these at "buy three, get the fourth free"? He's going to sell more, his margin's going to be higher. And he knows that because of demand right now, he'll probably get a better deal if he buys three pallets instead of two.
What if he didn't have to figure all of that out manually? What if he had an AI agent that could analyze every single particle of data he owns? It knows everything about the buying conditions and everything about the selling conditions. That AI can dynamically decide how much to pay for that Monster, and dynamically decide how to price it so it generates the most revenue and ultimately the most profit possible.
You can translate that directly to a vehicle. To inventory. On both sides of the transaction — how do you make sure you're paying the least for it, and how do you make sure you're pricing it as advantageously as possible? And doing it in real time. Not once a week.
There are complications — window stickers and things like that — but there are real implications when we start looking at our market in a much more dynamic way, both from purchasing and merchandising. You might not sell another unit because we're in a flat growth market, but if you can start to maximize on the edges — you mentioned a $100,000 truck, what if it's a $101,000 truck because that's what the market will bear right now? You spread that across 100 vehicles a month and we're talking about a pretty massive impact.
That's really what I mean by operational efficiencies. How do we operationalize the AI? How do we leverage it to make the most profitable decisions possible?
Inventory is a really interesting one. Service is another. There are so many interactions, so many transactions in service, and each customer-pay transaction has an impact on what your warranty rate is going to be. How do we maximize across the board?
But again, it's only going to be effective if that AI has a unified data layer to act on and pull from. If it's operating on its own little data set in the corner, there's going to be missing information, duplicate information, inaccurate information — and then it's going to do the wrong thing. That's not helping anybody, and it can have pretty significant implications.
Michael Cirillo: You'd be like that car rental company where the agent deleted all of their customer data. No, no, no, no.
I love this because the thing that fascinates me most about AI — beyond the analysis piece — is that most people are going to a Claude or a GPT and giving it one piece of data. Maybe they're doing a CRM export and saying, "Tell me about this."
But this goes back to one of the first conversations I think we ever had. It came up that Reynolds prints something like a billion miles of paper.
Greg Uland: Well, it was 37 feet of paper per deal. And then we did the math — 16 million cars times 37 feet — and it's basically enough to get to the moon and back or something like that.
[12:50] — The AI Flywheel: Use Cases That Compound
Michael Cirillo: I bring that up because the amount of data that no human could look at in their lifetime and draw conclusions from — that's where AI fascinates me. That AI can look at that billion miles of data all at once, understand it all at once, and with that unification, know exactly what to do with it.
I just love the possibility of that. This is such a remarkable moment in history. Today, an AI — with all of the data access you described — can look at it all at once, analyze it all at once, and cross-reference it to the nuance of your dealership, your market, the economic drivers, the social drivers, the customer household nuances. That to me is such a tremendous feat. And here we are in an age where that's possible.
Greg Uland: Yeah, it really is pretty unbelievable. And when you start seeing this stuff work, you sit back and you go...
You hear about a flywheel in business, and that term gets used in different contexts. But I see it when we look at AI tools and start to see what's possible. The wheels and the gears just spin faster and faster, and more and more use cases pop up. You think, "What if we could do this? What if we could do this?" And lo and behold, you can — as long as you have access to all the information.
Instead of having somebody spend six hours pulling 14 different reports, downloading them into a CSV, trying to put them together, dealing with formatting issues, and then getting the report tomorrow morning when it's already a day old — you can just get that information right now.
And then you start thinking, "If you can do that, what else can you do?" The use cases keep piling up and compounding. That's what makes it really exciting.
And we're the perfect industry for this. Think about the entrepreneurial spirit inside of auto retail. Every dealer, every salesperson, everybody that works in this industry is at their heart an entrepreneur, chasing the next deal and figuring out ways to get things done. I think we have the right collective personality to really leverage these modern tools and create new ways of doing things.
[15:42] — Watch-Outs: People, Process, and Cybersecurity
Michael Cirillo: Let me ask you about the watch-out side. You and I are probably around the same age, so we lived through the transition from print to web, from website hype to SEO and digital marketing and all of those shifts. And here we are again with AI — another reset, a level playing field. In some ways we don't know what we don't know. I'm really bullish on this, but what are the watch-outs we need to consider today? And where do you see those evolving over the next six months?
Greg Uland: I think those are going to come about as fast — or faster — than the new use cases we come across.
A couple that pop into my mind: our business, automotive retail, has always been and I think always will be about people, then process, then technology. In that order. Whatever the technology is — an electronic ledger, a DMS, a CRM, an AI agent — it starts with the people, then the process, and then the technology enables it all.
Not forgetting that is really important. When we find these use cases, we need to make sure we have that foundation and that mindset. Because we'll be able to leverage AI to create the process — and that can help — but we have to think it through. We have to make sure it's logical, it fits the business, it fits the customer, and it takes care of both sides of the transaction.
The other thing that stands out to me right now — and it might feel a little off-topic — is cybersecurity. The speed with which these AI tools can work has changed the threat landscape significantly.
We used to live in a world where a hacker would send out 100,000 emails, hope one person clicked on a link, gain access, and do their bad thing. In today's world, they can send out 100 million emails in different iterations, tested in real time, written by AI and optimized by AI to get the most clicks. Their attack surface got dramatically larger.
A higher awareness and alertness around cybersecurity is really important right now. And the flip side of that — making sure that if and when somebody inside your store does make a mistake, you have the tools and the partners in place to react quickly, isolate that incident, isolate that PC, and get things remediated fast. If you don't have those partners and tools in place, it can mean a lot of money and a lot of downtime. That's a scary but very real proposition right now.
[19:33] — Cloud vs. On-Premise: Where Infrastructure Is Headed
Michael Cirillo: Here's something I want to reconcile. It is early — early in the sense of broad-scale adoption, even though the ideas behind AI go back roughly 80 years. You've got cybersecurity as a concern, and the first thing that came to my mind was: are we going back to a time when a dealer has to have its own server room on site? And if so, what's the infrastructure cost around that — hiring, hardware?
Or is there a way to reconcile it on the other side and say, yes, there may be some upfront investment, but the backend benefit far outweighs it?
Greg Uland: I don't think we're going back to on-premise servers. What I think we'll see in the near future is landing on the most effective way to manage this being some sort of device in the dealership — not a server, but a device that connects to infrastructure in the cloud. Most things will just run through a browser. But there will be some sort of network inside the store connecting to cloud-based servers.
A lot of the way cloud infrastructure is built out today — certainly the way we handle it at Reynolds — is segmented, where every dealership has their own instance. Your risk is truly isolated. There are a lot of ways to approach it, but I don't see a reversion back to on-premise servers and dedicated on-site IT staff. It'll be an option for a very long time, and it can certainly be supported, but I don't see that as the future.
[22:01] — AI in Marketing Workflows
Michael Cirillo: Now, as a marketer — you're the VP of Marketing at Reynolds — where do you see AI? I run a marketing agency, and there are things I have to reconcile when it comes to AI. What are those thoughts for you? What excites you? What gives you pause? How are you reconciling the age of AI in marketing?
Greg Uland: We were just having this conversation with our team last week, and it's overwhelmingly positive the way our team views what's possible.
Think about the people in any given job. They really like doing certain things. A lot of people love to write. A lot of people love design work. A lot of people love video production — and they're really good at it, often because they love it. But for all of us, there are things we have to do that we don't love. They're not at the top of the list every morning. They're repetitive. They have to get done, but they don't energize anyone.
We can start to leverage AI tools to handle a lot of those things. That frees us up to spend more time and energy on the things we're really good at and that we really love doing. Our output increases because the minutiae that used to eat up hours can now be handled by tools.
There are a million different examples of that, but that's what our team is most excited about — the opportunity to focus on the work you truly love. And it's different for everybody. But if you can focus on the things you're good at and quick at, and offload some of the other pieces, your output is going to increase. You become more efficient and more effective.
[24:13] — Closing Thoughts
Michael Cirillo: Well, thank you. You've talked me off a ledge. This is fantastic. We've talked about unified data, we've talked about the possibilities. We are living in the greatest time ever to be on this planet — I don't care what anybody says. The possibilities are endless. I thought they were endless with Web 2.0, and now here we are with artificial intelligence and everything that's possible.
Greg, how can those listening and watching connect with you?
[24:36] — Connect With Greg Uland
Greg Uland: You can find me on LinkedIn. You can also check out our Connected podcast — we put out a new episode every other week with a lot of great guests. And Greg, we've got to get you back on Connected, Michael. It's been two or three years, I think. Always happy to connect there or on LinkedIn.
Michael Cirillo: Greg, thanks so much for joining me on the Dealer Playbook Podcast.
Greg Uland: Thank you.
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