Car Dealership Guy - The Next Phase of AI That Will Reshape Car Dealerships Forever

CDG: AJ McGowan on the CDG podcast. Founder and resident company was acquired by Reynolds and Reynolds about three years ago. That'll be an interesting story to talk to you about.

AM: Yeah. Yeah. It's been a fun ride so far.

CDG: Was this before ChatGPT or after ChatGPT?

AM: This was when ChatGPT was still pretty nascent. Not too long before we got acquired, we were definitely starting to play with it and definitely saying, “This is not quite ready for prime time. It can't be trusted without a lot of guardrails.” But we were starting to really get a lot of interest in what we could do with LLMs in the big picture, for sure.

CDG: Talk about not listening to the haters. You know, there's this viral photo of Sam Altman, the founder or co-founder of OpenAI, where he's like, "Hey, we just launched this thing, ChatGPT. I think you'll like it. Check it out."

And there's this famous comment underneath which is like, "Dude, this is one of your worst ideas yet. This is going to flop."

And it has not. So, it actually has revolutionized a lot of things.

Well, I think this will be a particularly fun conversation because, while we've discussed about the trend of AI, you are trailblazing this within one of the most impactful companies in our industry, which, of course, Reynolds is integrated into many thousands and thousands of dealerships across the country.

Number one, so you have lots of impact there.

Number two, I heard you say a line which I haven't heard anyone say before about AI or just talk about in this space, which is: we are now entering a new phase of AI called cognitive AI.

We initially spoke about generative AI, which every dealer here uses or has used, right? ChatGPT and all these LLMs, that's at least a form of it.

We then started talking about a year and a half ago about agentic AI, where some vendors that serve dealers started implementing agents, which actually do things for you—tasks, whether it's in your CRM, your DMS, things are actually happening automatically.

Many dealers have adopted that, and some have even done it themselves.

Now there's this next iteration here, which you call cognitive. We’ll talk about what that means.

Before we get into that, given you're such an interesting technologist, I want to start with if we can hit on a theme. Like thematically, what is interesting to you nowadays? What's exciting to you from your work in this industry?

AM: What's so interesting to me about automotive specifically--especially when it comes to AI--is the fact that in automotive it's really all about the customers. It's really all about the people and it's about that relationship that dealers ultimately have with their customers.

I think in a lot of places and in a lot of other industries, we're seeing AI being looked at as a tool that can just sort of replace that relationship or replace those people or just do a job.

But in automotive, at the end of the day, that relationship with the customer is sort of the most important thing.

So, I think in automotive specifically, there's a unique opportunity to really leverage the promise of AI to help employees be better at building and maintaining customer relationships.

And I think that that sort of unique retail, if you will, outlook on virtually every aspect of the business means that we can leverage AI to create new kinds of software and think about software differently than we did in the past.

CDG: Dealers are not asking you, “Hey, build me the next Carvana.” They're saying, "Hey, build me this omnichannel buying experience where I can run a tighter ship and let my people focus on what they're great at." That's semi-accurate.

AM: Yeah. For the most part, I think, just tying that together with what you mentioned a second ago, I've been talking about this sort of cognitive software thing.

When I think about the phases that AI has gone through, there's sort of this like preliminary, prehistoric AI, which we called machine learning and we've been working with for a long time, right? And then you have the advent, and by long time I'm talking in AI terms. So long time meaning the last like 8 to 10 years. That's been something that you could do interesting things with.

I think when most people start to really think about AI was, to your point, ChatGPT. It was the invention of the transformer which led to the LLM, ultimately, and then ChatGPT and others that have come out and sort of brought that out to the masses.

When you think about what that phase really looked like, that was generative AI, right? It was: “How do I summarize this email?”  or “How do I come up with a great recipe?”

Or any of these other things where it was a pretty bounded task that you would just ask for.

Then, in the agentic realm, as we talk about the last year and a half, that term's really been popularized. What we're fundamentally talking about is true companion software.

I like to say sometimes generative is like an intern, and agentic is like having an employee, where it's a real companion that we are teaching how to use the other software that we use.

So, that whole phase and everything that we're doing around that is super super exciting and I think there's massive potential.

We're working on an agentic framework that we call Rey at Reynolds. That's something that we've kind of been spearheading in the industry.

But when we talk about cognitive software, what I'm really talking about, the thing that gets me the most excited right now is: Okay, great. We take AI sort of to the point where we can use these LLMs to manipulate the software that we're already using. But what does it look like when you start to think about how software works, period, with AI embedded directly into the software?

One kind of fun definition that I've heard about cognitive software is the way to think about it is if you remove the AI, then the software doesn't work anymore.

That's the jump from agentic to cognitive where it truly becomes not just a system that's built for agents to be able to manipulate it easily, but a system where AI is deeply embedded inside of every transaction, every interaction and the software itself in a lot of ways melts with the AI to become truly a learning system.

CDG: What dealership problems are you trying to solve with cognitive AI? Like if I'm today saying, "Hey, I'm committing to a DMS and I choose Reynolds, right?” What am I expecting? This is a question that dealers have. I said, "Hey, I'm speaking with AJ from Reynolds, ask questions.” And I have some AutoVision stuff as well. 

But I think with regard to the overall bet on Reynolds, what should I expect from your team as I think about cognitive AI.

At the end of the day, I want to know what problems are you going to solve for me? What's the next phase there?

AM: Sure. Yeah. I mean, so let me answer that question more broadly. So, what can you expect for the bet for Reynolds?

The first is continued work on all of the generative AI tools that we have.  We were first to market with a lot of those tools, made big bets years and years ago and have great services that will do all kinds of interesting, cool things that are embedded into the platform.

The second piece is the agent piece where we're investing massively in our own hardware inference, our own data centers to actually run this AI, building out and training our own models like real agentic infrastructure and doing a lot of the hard science.

CDG: Can you touch on that for a second? Why are you investing in hardware? Reynolds is a software company, as far as I know. Why are you investing in hardware? You don't hear that too often.

AM: Yeah. So, it's a great question. So, when we look at where the component or the place that AI has in sort of the future of technology, there are a couple reasons for us to sort of own that infrastructure.

One of those is sort of the easy, obvious one, which is: We want this to be deeply embedded and endemic in all of our platforms, which means that we need to control costs. We need to make sure that we can control what the cost of that inference is, so that we can continue to provide value to all of our customers at a great rate. So that's kind of number one.

Same thing around quality, right? We don't want to be beholden on somebody else that might go down. If we're going to treat this, not just as sort of a novel addition to the software, but as something that's really core, we need to control that infrastructure.

The third piece is privacy. I mean, a lot of the most interesting use cases for AI inference involve data that is either on the line or that you definitely wouldn't want to hand out to a third party.

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CDG: The way I would explain this to a dealer or simplify, I would just simply say that it makes sense that every time we are using some querying AI in some way.

Even today, I use certain tools, even the app we're recording this podcast on right now. When this podcast ends, I will not automatically get a transcript and companion products or whatever you want to say, I have to click to get it. So, I emailed the CEO. I said, "Why do I have to click every time and wait 10 minutes?" He's like, "Well, because only a small subset of users do this and if we just ran it automatically for every single user, we would be spending a ton of money on inference, and it just wouldn't be economical."

So, I think what you're essentially saying is for us to create the DMS of the future, where a dealer has AI deeply embedded or it's AI first, you have to control the cost in order to use that effectively, which means going into hardware, which I think is fascinating.

AM: So, it's the cost, the stability, and the security. Those are sort of the three main tenets. To your point, we want all of the software that we build going forward to be the best possible user experience. We don't want to have to have trade-offs like the one you just mentioned.

I’ll pretend that I'm the product person now on your recording software. We'd love for a transcript to get recorded every single time and then send you an email afterwards to say, "Here's how you did, and here's some of the key highlights and some cut scenes that you might want to pull out and, by the way, if you want the short clips, then here's the links to that. You can send them to your social, we've already done that work for you, right?"

Well, that's the kind of workflow that you can enable and that you can create these sort of magical user experiences when you can count on the AI to be just another component in the toolbox as you build out software.

CDG: So, AJ, there's Avery, there's Rey, of course, your claim to fame initially, AutoVision. There’s a lot happening at Reynolds. I guess you wedged into this industry by way of inventory management, right? That was where you started. You clearly saw that this one big, fat line item on the dealer's balance sheet called inventory could probably be handled a bit more efficiently, and so you built an inventory management, and you know analysis optimization.

I mean many, many things on top of it, but pretty robust inventory suite. By the way, we had some fun where we went on our CDG marketplace, which is where we have our dealer conversations, it's cdgapp.ai AI, and I wrote “AutoVision.” I just wanted to see what, because I've never personally used AutoVision. It was interesting. People had some pretty good feedback for you. So, dealers can see who are verified through our platform but, nonetheless, you're clearly doing something right in that realm.

I would say that primarily as I'm reading this, people were very excited. Dealers are excited about it. They wrote about more AI recommendations versus the market. So, I would have to assume they're referring to when it comes to pricing a car, acquiring a car, whether you're aware of it or not.

That's something that people find a lot of value in that you've built for dealers--the ability to, as they say, use raw market data and just create better recommendations for my inventory management through AutoVision.

But I don't want to monopolize the conversation on that. I want to let your brain wander here and say between Avery, Rey, AutoVision, where are you working to solve most problems for dealers?

AM: So, when we talk about AutoVision, the thing that really distinguished us in the marketplace--and the problem that we set out to solve--was how do we get better at valuing cars? That was the fundamental, before, frankly, we'd even decided we wanted to build an inventory management platform. The initial idea was if we can get really good at valuing cars, there are cool interesting use cases for being able to do that. And so that was the kind of fundamental data science problem that we started out with AutoVision.

What we realized, over the course of time, was to get it into dealers’ hands, because I think we all know like the last thing the dealer wants is one more tool. One more tab, one more login.

CDG: No one wants another tool, right?

AM: So, we almost got forced into building an inventory management platform because otherwise dealers weren't going to be able to access this mechanism that we had built for valuing cars more accurately. And so, we ended up building the rest of the platform sort of around that. So, when you think about AutoVision, it's really like a data science product.

And then on top of that, we built an inventory management platform to make it accessible. And what was interesting was, right around the time of the acquisition, when Reynolds acquired us, we had sort of reached a point where, I think the most generous way that I've heard this put is “AutoVision became a Bloomberg terminal for used car dealers.” I think that the generous part of that is that it acknowledges that we surface a lot of data, a lot of historical trending analysis, and different ways to sort of look at--what is the value relative to wholesale or other people etc. etc. But the reality was--and to a certain extent still is--that in AutoVision, we can help you make hundreds of dollars of more per copy than have seen now as we've scaled out through Reynolds proven over and over again.

But it might take you five or ten minutes to go through and look at all of the AutoVision data and understand the decision that you wanted to make. So, one of the things that we did, a few years ago, when we built this, it's funny because it's before we started talking about agentic AI, right? So, I like to say sometimes that Avery is kind of like the hipster agent because she was an agent before it was cool.

But it was an attempt to basically say, how could we train AI to use all of these tools that we have in AutoVision to value cars, instead of just having like this arbitrary black box of some weird model that we trained and all this other stuff.

Instead, we said, “can we train the AI to use these tools and to then evaluate it the same way that we would train a user to use AutoVision?” And that ultimately became Avery, and is the thing that you're talking about with being able to recommend like “Here's the number you should put on this car, where you should reprice, etc., etc.”

But that experience was sort of the direct through line into Rey, where we said we can trade.

So, Rey is the agentic platform that we're working on right now. So, it is an agent or a series of agents inside of this overall architecture that we're teaching how to use all of the individual products now within the Reynolds ecosystem, so that it can actually go and goal seek against all of those individual data sources to try to answer users questions. So, sort of objective number one.

CDG: Yeah. AJ if I start the month and I say, "Hey, we need a bit higher volume this month. I want to blow out some of the older units and maybe the stuff that just came in, try to claw a bit more margin.” Then it's going to find how to do this for me.

AM: That's right. That's exactly right. It’s to go and figure that out and say, "Hey, I need to look at some DMS data here. I need to find some automation data here. Maybe I need to go and pull how we're doing out of FOCUS in CRM. I need to look at: what does lead volume look like on these cars?" And Rey will go and goal seek for you to come back and try to answer your question.  

Then, ultimately, where we want to take that is Rey becomes the agentic infrastructure then that can also goal seek to go and do something for you. That'll be sort of the next step here is teaching her how to find data in the platform, teaching her how to analyze that data appropriately to give you real business value, and then ultimately to let her take action on those insights.

To say, like in your example, “Go find these cars.” Well, why don't you just let her go reprice them and if you're happy with those results, maybe you let her reprice them every morning. Yeah. So those types of things are what we're building the Rey infrastructure for.

CDG: Take me down to the dealership level, right? What’s the workflow today of a dealer? Has it changed meaningfully in the last 24 months? What does it look like today? How are dealers really using this software?

AM: It hasn't changed meaningfully yet, is the honest answer.

CDG: I think workflow you're talking about.

AM: When we talk about the generative stuff, in the course of Avery for instance, when you talk about the individual tools that are built into the platform, dealers’ workflows have changed massively. I mean, we have generative AI tools that will analyze transcripts, like what you were talking about a second ago, and use that to surface things up inside of the BDC that otherwise, I mean, we talk about it generously, it would have taken them hours and hours and hours to do it. But the reality is because it was so time intensive and laborious, most of the time it just didn't happen.

So, for most dealers, that insight into what their BDC is doing is net new and has huge changes to their behavior. Or if you look at AutoVision and Avery being able to recommend on cars, we can actually run that both at appraisal time, so it helps in their workflow when they're inspecting cars. But it also can run every single night and look at all of their inventory and say “Above or below a threshold. Here's where you're at relative to where Rey says that you should be.”

So instead of a used car manager now having to go through and hunt through 150 cars and say, "How do I look relative to market? Why isn't this moving? Am I not getting enough leads on it?" Avery looks at all of that stuff and just says, "Hey, here's one I think you should go take a look at. Here's where I would recommend that you price it.”

So, all of those sort of discrete tools are changing dealer workflows every single day. The way that I think about Rey is now how do you wire together those tools to then produce even bigger outcomes? To produce both strategic insight outcomes on a daily basis.  I'd like for Rey to be able to say things like, “Hey, you have six cars coming into the service drive today and we're missing parts on two of them. Would you like to go ahead and order those parts in?" And those are the types of things that we would expect in this next wave as agentic goes beyond just where it's at now, which is go find me this thing, and goes to being proactive with giving you information and then, ultimately being able to take action on that for you.

CDG: So, based on that last example you just provided, AJ, where is a dealer to find an edge nowadays? Like what has staying power, right? Because if you're saying that the software is increasingly doing all these tasks and we'll do more of them? Parts ordering, inventory management. I'm sure, I mean, you already have big companies doing automated inventory acquisitions to a certain extent. Not that dealers can, but I'm sure that's going to proliferate over time.

Where is the edge for the dealer if I'm saying “Okay, what can I invest in, whether it be my time, energy, capital to create differentiation over time?” AJ, you used to have, “He's been on the block for 40 years. He knows all the prices and he knows the market.” But you cannot compete with an LLM in what you're building and what you're working on. You just cannot do that. So, that is not going to be an edge in the future. Where is the edge for me to invest in today as a dealer?

AM: Well, so I think that surprisingly it's still there. It's still in your people. The ultimate ceiling here, right? So, we talk like everything that's happening in agentic right now is super exciting and there's a lot more work before we're going to bump our heads on the ceiling. But the ceiling here is going to be running these agents. They only have context for what's happening right now, unless you go and drag in a bunch of logs and analysis and other things from other places and give them this kind of massive context to say, “Make a decision right now about what I should do.”

CDG: And that's the money right there, by the way. You're investing in hardware to be able to do that.

AM: That's right. That's exactly right. And that is being able to do that more and more effectively is going to make these agents more and more effective. But there's a point where we're going to hit our heads on--that's just not efficient. That's not efficient. And it also still requires that a lot of very specific contextual data goes in and it's wasteful, right? Because in a lot of cases, context is for a particular question at a given time. Given other contexts, some things are required and some things aren't. And so, where we're going to see the sort of ceiling there is that ability to understand what is the right context at the right time. That requires that we go and say, “Go look at this, go look at this, go look at this.”

And so, the hardware investment, as you said a second ago, is sort of critical to that and making that scale up and scale up well. But we're still going to bump our head on the ceiling because it's only going to get better and better and we're going to have more and more data.

So, when we start talking about that third wave that I see in the future, which is cognitive software, the part of the idea there is that the software itself retains a memory and understands the context that's happening at every given moment because the AI is constantly looking at all of the factors as everything is changing in the system. And so, you end up with, a lot of people in AI more generally call this concept like institutional memory, where you know it's baking the institutional memory into the software.

But the problem is one way to look at that is that gives you, like I said earlier it's like the intern and the employee with cognitive software, to be like the veteran. It's the veteran that's been there 30 years, like you were just talking about, and the reality is that, yes, it can make that new hire have the kind of insights because it has access to that context all the time that a veteran would.

But investing in that new hire so that they can have that relationship with the customer, and they know what to do with that data still is the most important thing. And that's going all the way back to the beginning of our conversation, why I'm so excited about doing this at automotive is that people part, where the people meet the customer is something that I don't think you can ever get rid of inside of automotive. It's something that is the core, when you strip away all the operational complexity. The core of the dealership's business is that trust and that relationship with that customer.

So, I think on a system side, what dealers can be thinking about investing in is trying to get their data onto common platforms, so that AI can have contextual access to it. And from a business standpoint, I think it's going to be the same answer that it's always been, which is that the dealership is really about the people that work there. And in a cognitive software world, those people continue to feed the software in this virtuous loop that gets you better and better and better. Then, the software decisions tighten in around the dealership and the people and the market and the cars and the customers. So, you're getting higher and higher quality results from it. But investing in the people is the thing that keeps that thing working.

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CDG: So, two things you just said there, and I want to touch on the first one. You mentioned context or having the systems talk. I want to understand what you mean by that or how a dealer can do that as best as possible and what's in it for them.

As a sidebar, we love to talk about like picks and shovels on this podcast and I can't help but wonder as you talk about this, where people will be where the dealership excels because that's essentially the last part that's not commoditized over a long enough time horizon. It makes you wonder the opportunities like that lie ahead of us in stuff that is, as it sounds simple but like training, right? Like investing in culture like these types of activities that historically were not anything sexy or fancy, anything special.

But when you think about the future and if every dealership is increasingly using software which is commoditized, how do you stand out? You stand out by having the best people. And how do you have the best people and get there?

So anyways, this is just where my mind wanders. Going back to the first point you made, you said contextual databases. I believe you're talking about having, as a dealer, invest in having your systems work with each other, talk with each other. Can you tell us what do you mean by that?

AM: Yeah, what I mean by that is that the more advanced this AI becomes, the more that you're going to want your systems to be able to talk to each other in a deep way.

Let me give you an example. We have somebody that pulls up on a service drive and they're just there for an oil change and they've been sitting on the car for three years. What we want the AI to notice and be able to surface to the user is this particular car, they've got two payments left on their lease and, by the way, this is a great car for us in the market right now. And, by the way, we usually sell, you know, $3,000 in warranty and those types of things on it, as well as $2000 on the front end for these types of cars. This would be a great trade for us. And we've got one that looks really good right now. And she is a repeat customer that always talks back to her service rep right away and she prefers to be texted and so on and so forth, right? The net out of which would be, “Hey, this is a car we probably want to try to acquire. Let's send her a text and say we may be able to give her a great offer and have her walk onto the sales floor to go talk to somebody.”

That type of insight, while it seems straightforward, actually requires a lot of different pieces of data that come from different parts of the system. AI can't listen to what it can't hear. And you know that is 100% the case when we talk about these systems because if it doesn't have access to the context, if all it knows is “Hey, we gross well on these kinds of cars and there's somebody in the service drive.” That you're interested in it, maybe you text your used car manager and say “Hey, there's somebody out there that you could go take a look at.” But you don't really have the full context to be able to say “No, this is a hot opportunity for us. Here's why we need to get a hold of this person and if they don't come back in, go get a hold. Send somebody out talk to them. This is their preferred communication method. Let's go.”

CDG: Is that my job or is that my CDP's job or is that my DMS's job? Whose job is it to do what you just said to put all the information together? Who should I be leaning on?  Who’s best positioned to do that successfully?

AM: I think quite frankly we're best positioned to do that effectively.

CDG: Who’s we? Is it like? Oh, you're saying like you being a platform, which, well you're a DMS and a CDP, so you're kind of a CRM and many other things and under one roof.

AM: When you think about an agentic future, having all of your tools under one roof is going to become much more important than it was in the past.

CDG: That's exactly where I'm going too, yeah.

AM: It’s hard for me to say because it sounds like a commercial, like “Hey, you're going to want all of your tools to be with Reynolds,” but the reality is, it's just the truth. You're going to want all of your data to live in one place that your AI can see it and you're going to want it to be able to see it in real time.

CDG: Yeah. I think and I would like to uncommercialize that. I think the point you're making is you're going to want to have all your stuff consolidated and that increasingly, the opportunity cost of not having all your software and data together, will become higher over time, which is to say that we'll probably see more consolidation in the industry.

And because dealers are not dumb. They’re going to notice that they're going to see they're getting better results, better profitability, better customer experience as they consolidate all their tools under one provider and there's not many players out there that can do most things under one roof.

But yes, I agree with that. I think that it just makes sense. I would say historically, and the reason I even personally was very keen on moving to a platform, which was everything consolidated under one roof, was simply because, from a training perspective, it was just too difficult when you have to switch between platforms and click on all these different tabs. But I think the era we're heading to now is, to your point, the opportunity cost will just continue rising by not having things combined and being able to leverage the best possible insights and intelligence.

AM: One of the interesting things about AI, going back 15-16 years, one of my really good friends is a pretty well-known AI machine learning engineer, just sort of in those circles. And he taught me most of what I initially knew about machine learning 15-20 years ago. And I've kind of followed through from there.

But one of the things that he said to me when we were going through it was, AJ, one thing to bear in mind is until the moment of singularity, if a person can't do it, the AI can't do it. Like if you could not teach a person to do this, then the AI is not going to be able to do it. And this is way before LLMs or anything like that, but it's sort of borne out to be true over the course of all the iterations that we've done and it's still true. The very best AI is only going to be able to do as well as the very best person. And so, if you'd expect for a person to have the problem, you should expect for the AI to have the problem.

CDG: I like it. So basically, here's my takeaways from that part of the conversation, right? The opportunity cost of consolidation will rise. It's in my best interest to work with a consolidator, in that sense, who can leverage AI the best. The cost is obviously going to be increasingly very meaningful, and I think inquiring into the hardware investments that my partners are making, so that I can have the best software and use just the best quality product out there. And, lastly, that cognitive stage, which you mentioned, which is next after agentic, which just takes it to, like you said, it's AI first, if without the AI can't even function. That's how I know I'm really solving my problems as efficiently as possible, and you know squeezing the lemon just to the fullest extent.

AM: I'd love to use your lemon metaphor. Yeah. Because I've never thought about it that way. Yeah. It’s almost like in the first phase with generative; it's like we were taking a slice of lemon and dropping it into a drink. And in the second phase with agentic, we're trying to squeeze that lemon as hard as we can. In the third phase with cognitive, it truly should be transformative. It's almost like we're making lemonade. We've got something that's completely net new. And that's kind of how I look at the future, that we're trying to improve and improve and improve until we've squeezed all the juice out that we can. And then it's like, okay, well now what can we do with all this juice?

CDG: Amazing. Look at that. We're even coming up with analogies. AJ's going to send me some royalties for that one, folks. AJ, any closing thoughts?

AM: I think just to close out, it's a really exciting time. I mean this is, as I hope has come across in our conversation. I think automotive is a place where we can be at the absolute bleeding edge of this technology and get the most squeeze out of it. And so, it's really exciting for me to be in the middle of it, working with customers, getting their feedback as we're building these tools in real time. And, quite frankly, as we're all figuring out together as a species--what place AI has in our lives? We get to have a front row seat for--what place does AI have in our dealerships? And that's a really fun thing. And it's really fun to be at Reynolds and Reynolds where we've committed resources, time, attention, the whole thing to being all in on AI and getting to lead that charge. So, we're really really excited about what the next few years hold.

CDG: Amazingly well said, AJ McGowan, founder of AutoVision, acquired by Reynolds and Reynolds, and now a senior leader at Reynolds. AJ, thanks so much for joining us on the podcast.

AM: Thanks for having me.

CDG: All right, hope you enjoyed that episode. Please give the podcast a rating. Consider subscribing to the show and check the show notes for links to the sponsors of today's episode. Matador AI, Zurich, and of course, Reynolds and Reynolds. Thanks for tuning in and I'll see you guys next time.