AI Won't Replace Local Expertise—It Will Make It More Powerful

In the latest episode of In the Vicinity, host Tim Hanlon sits down with Madhive Chief Innovation Officer Aaron Brown to explore why local advertising remains one of AI's toughest challenges. Brown explains how Madhive's AI-powered Maverick platform is connecting thousands of fragmented signals to help advertisers preserve the nuance that makes local campaigns successful.

Listen to the full In the Vicinity podcast above or get it on Apple Podcasts and Spotify.

---

Tim Hanlon: Hello. Welcome everybody. How are you? This is In the Vicinity, our little weekly podcast journey into all things local media. My name is Tim Hanlon, your humble and congenial host, and the founder and the CEO of the Vertere Group here in Chicago, where we consultatively advise bunches of companies in the media and technology spaces, some of them large, some of them small, and as I said before, some of them very much in between.

And there's a lot of in-betweenness going on as the traditional media landscape gets shaken vigorously by digital technology and AI and all those kinds of things. And that's where we head this week. In our conversation we're joined by Madhive's Chief Innovation Officer, Aaron Brown.

We're gonna talk about AI and how Madhive is using it to improve the local advertising experience both for buyers and sellers. But first, we kinda get into the nuts and bolts of what a chief innovation officer does at a place like Madhive, the technology involved, the various levels of data and analytics, and all the kinds of stuff that power this engine that the new Maverick platform of Madhive is delivering to the local media enterprise.

You're gonna learn a lot, as I did and you're gonna geek out a little bit, perhaps as you might, a little bit more than the average listener, in that there's a lot going on under the hood at Madhive with this Maverick platform. And I think it's trying to solve a lot of issues that have bedeviled local media since its inception.

And we get into lots of different digital touchpoints and how to smooth out those wrinkles that aren't digital or the signals that are not immediately discernible, and helping marketers better improve their results as they go into local marketplaces, plural, and try to gain success and improve upon it.

This is our conversation we had just last week with myself and Aaron. Please, as always, enjoy.

For our audience, give us a little bit of background on you. You've been at Madhive for some time. Maybe a little background as to how you got there and what you have done and are doing there to give our audience a bit of a sense of what you and Madhive are up to these days. 

Aaron Brown: Sure. My pleasure.

Thanks for having me on the show. I'm Aaron. I'm the chief innovation officer here at Madhive, responsible for just trying to infuse more and more innovation into the Madhive product, bringing it to our local advertisers, our broadcasters. There is a world of opportunity here, especially now in the AI era, just connecting technology, local media, and advertisers together.

Tim Hanlon: And that came from what background, and how did you get ensnared in the Madhive journey into making local media things better? 

Aaron Brown: I have absolutely no qualification to do business in local media. My background is in mathematics and law. I worked at a machine learning startup before Madhive. Madhive, the draw, the bridge was Madhive was a very, ML first, technology first-oriented company.

There was just a huge need in local media to infuse cutting-edge tech. We cut our teeth there. We made a name for ourselves. We support a huge amount of local media today. In fact, I would go, now in 2026, if you're not using Madhive you're not doing local, full out. That's just the breadth of our offering.

Tim Hanlon: It's a bold statement to say if you're not using Madhive, you're not truly local. Okay. So I'm gonna double click on that one. But also more importantly, what have you learned as a quote, unquote outsider about local media in your journey?

Because as we've talked about with Jim on numerous occasions, local media historically and then arguably in certain cases still is very much, I wouldn't call it ragtag, but it's certainly very, the diaspora of approaches and unaligned resources to connect and/or harmonize those things is I won't call it a hallmark of local, but your interpretation of local, what you've learned. And do you agree that there's still room for improvement in terms of harmonizing various local ad activities?

I suspect the answer is yes. 

Aaron Brown: Yeah, totally agree. Local full out is a really hard problem. And I don't think, as outsiders, the founding team here at Madhive had an appreciation for how much trouble we were in getting in here. So the first thing you do is learn.

You listen and learn. What's going on? Where are your pain points? And, all of the problems we heard could really be boiled down to there's a need for really, truly nuanced understanding. And what I mean by that is, local advertising is really about the success and the sort of the environment that these local advertisers thrive in.

They are custom fit to their community. They are integral. I have a saying, if you walk into a bar in Brooklyn and sit down and have a conversation, you walk into a bar in LA and sit down and have a conversation, you're gonna have two different conversations. And for advertisers who are essentially trying to have a conversation but at a distance through, mediated through the lens of programmatic RTB and data centers and DSPs and SSPs and all these things this is a really hard problem to do for one ad, for one impression, to really nail it.

And then now try to scale that up. Be the everything platform for everyone to make everyone successful, to appreciate and exploit the nuance there that is what is making the local advertiser successful in their community, and just run that at scale. And that's a conceptual problem for advertising.

That's a technology problem. And that is, I think, to date, an unsolved problem. We're doing it better than anybody else, but there's plenty of room to grow. 

Tim Hanlon: But in some respects, though, and maybe as an outsider you could appreciate this question, right? Because I would say this too if I were coming at it from the outside.

Is it imponderable? Isn't it maybe beyond solving? Can all these different local permutations and by market, by city, by town, by zip code, whatever that is it a fruitless pursuit to assume that you can aggregate that or scale that or get it to a point where it's all interoperable?

Because there's gotta be a point along the curve where that localism that you're talking about, that bar-by-bar uniqueness, is not, is the antithesis of being rolled up and or centralized and intra or inter communicable.

Aaron Brown: I think we have hope. If you think about advertising before digital there were humans all along the chain.

The advertiser knew the agency, knew the seller, knew the audience. And this is where local advertising started. This is where local media sort of grew up, and that worked. And so as a technologist, when you look at, okay here's a functioning ecosystem. How do I scale and grow this using technology?

Those technological problems are really daunting, because the amount of data that you have to soak up about the world and the amount of technology you have to throw at it, like AI, to, achieve those same kind of results is if we were doing this, 15 years ago, it would cost like a million dollars to successfully execute a single successful local media impression.

So our challenge is how do we bring that down, and how do we make that just accessible to every business, every advertiser, every budget size? Our sort of, when we started the company, we were very forward with our technology, cloud first, scalable ML, at the time, I don't think we appreciated how novel it was.

Over the years talking with other companies, so on the record, off the record conversations, I think the way we set up the whole tech stack here at Madhive is completely unique. We are the only business that is sifting through with full technological rigor 100% of the bid stream.

We are applying that kind of nuanced understanding about what advertisers need and who they need to connect with and looking for those little needles in the haystack. It's like taking a sip from a fire hydrant, just to get that one perfect little campaign on a small budget where success matters.

These are not businesses who can engage in these huge scale advertising campaigns and just take what worked and try to discover on the fly. This is a campaign that has to work from go with pretty limited information and they're gonna rely on the technology platform to figure that out.

And so starting out with that, very efficient scaled approach that was a win for our customers. And now doubling down on that with AI to try to draw the connections between all those little disparate threads to come up with new actionable insights both internally for our execution and then externally to give usable insights for our customers.

Tim Hanlon: All right, we'll get to AI in one second, but I do wanna ask this sort of, more pointed question. All this presumes that all these local touchpoints that you're describing to make easier, better, more harmonized are digital, right? So I think that's part of the local idiosyncrasies in that there are plenty of things out there on a local basis that aren't yet digital or are not translatable into digital yet.

And then maybe some of them will never be, right? Just because it's hard to, let’s say a parade sponsorship, right? It's a little hard to get into a digital, bit and a byte and put in a programmatic funnel. But regardless, even if the majority of the things that you're working on at Madhive are of digital origin, right?

There still is a gigantic problem, and I would love to hear how far along the curve you are in solving this problem in, shall we say, equating those digital touchpoints, maybe not harmonizing them in terms of value, but at least having them interconnected enough where a true locally digital campaign across all these different touchpoints can at least be optimized or assessed or comparatively measured and that kind of stuff, right?

I'm not even talking about equating them, but how far along is Madhive and/or the industry in at least doing that, getting that framework effectively working? 

Aaron Brown: I think the whole industry here, this is like day one for all of us. We talked at the top of the conversation about the huge potential that lies before us.

I hope that Madhive is leading here. We are, I think, paying attention to if you believe that this is really day one for us, I think pay attention to who's sprinting really fast here. There are people who are picking this up. The way we pick it up, you talked about the digital signals that are signals that cannot be digital.

Are just not knowable in a programmatic kind of environment. I think there's two ways really to approach that. One is this sort of, grassroots, Madhive sort of founding ethos. We are discoverers. We are just trying to find out what is right. We are talking.

We are connected to everyone. There's hardly a client that we haven't talked to, within the week kind of thing. So we're hearing things on the ground as humans, and this manifests itself in the products that we create. We're craft in the hand, and there is some side effect to those impacts, and so we're sharing that knowledge indirectly.

And then from the complete opposite side realizing early on how valuable those little tiny nuance signals are because in their sort of collective form they do paint a little picture. My daughter, she's getting really into art these days. We go to these museums in New York. I like the pointillism paintings, and so I'll draw on that.

If you're just some computer and you're, like, zoomed in on a couple of these dots, they're just dots. There's really not much data to gather there. Take a step back, soak in all of these dots together, and there's a really clear beautiful picture, and this is the sort of artistic metaphor for what the Maverick platform is doing.

So we have our AI product here at Madhive called Maverick. We've got a bit of a different approach. This is our foundational tech. It is connected to everything, and that kind of represents how we're thinking about the data problem that underpins local media. We have to tie together all of these little threads to draw these little insights, just like we would as people, but we have to do it in a digital scaled way.

And so going all the way back now to your question about some signals just can't be digital, that's true, but there's a lot of little weak signals that if you can really, if you can build the apparatus that can take in this information efficiently enough that you can feed the AI machine, then you can step back and see from those little points a really nice picture, and you can get enough of that to guide the campaign successfully.

Tim Hanlon: How much though do each of these let's say unique or specific touch points, the specific digital touch points need to be mature in their own respective silos before we can talk about them being more interoperable or interconnectable or at least being able to talk with each other?

There's plenty of vertical improvement in digital out of home and in audio and in streaming video, and all those kinds of things, right? There are historically four, five, or 10 companies that all kind of focus on that particular funnel or that particular silo. But how do we go horizontally across?

Do you need those things to be mature before you can horizontally get those things together? Or do you need to replicate and maybe tear down the silos? How do you approach that? 

Aaron Brown: I think you tapped into one of the fundamental problems in local media.

So as a technologist, we're thinking how do we optimize this campaign? We're sifting through data. If I'm running a national campaign I have a big budget. I'm running a lot of geographies. I might even be thinking about my national campaign through the lens of a bunch of smaller sub-campaigns.

And I run my campaign with all of these segments until I find some kind of differentiating factor. And up until that point, I have an undifferentiated campaign. I'm running media, I'm getting audiences at large scale, and these are genuinely scientific approaches.

I don't mean to,you know, disparage them at all. They're finding that signal in the noise net new for that campaign. That approach has underpinned successful advertising for decades. It doesn't scale to local, that you're never gonna have the budget or the variation to really get that out of the single campaign, so you won't learn on the fly.

Instead of just how you said, you gotta slice it the other way. You have to understand the community in which something works, and so it's less that one advertiser in a vertical sort of translates to another advertiser in a vertical. The sum aggregate of all of the interaction with the community gives you a digital impression of how that community reacts and responds to, how to talk to them, how to address them, what is that community's needs.

And that is the unifying data that works for any campaign that's trying to be successful in that community. 

Tim Hanlon: All right. Here's the crucial question I wanted to get to. Where and how is AI applicable, and how are you approaching the use of AI at Madhive?

To smooth out these wrinkles. Where is the application most useful? And how, frankly, are you going about it? 

Aaron Brown: Yeah, I think about Madhive's customers. So we are the scaled local platform. There are big broadcasters, agencies, direct advertisers who are all serving the local communities, local advertisers, and they are large scale operations.

So the first way we help them with AI is in these operational movements. We have to get a lot of campaigns, through the life cycle from, selling the campaign to planning the campaign, activating it, reporting on it, optimizing it, billing it, and there's a creative generation every bit of it.

And in order to move such a volume of small campaigns to the system, you have to make sure you have high tight operational efficiency. And that's at odds with the level of nuanced and bespoke understanding that I talked about earlier that you have to apply these campaigns to make them successful.

And so enter AI. This is foundational tech with the Maverick approach. It is beneath everything, and what we're doing there is we're tying together the little unstructured bits of data that help to make these campaigns successful. I'll draw an example. There are a lot of these systems in local media where, there's a human, there's a seller, they're talking to the advertiser.

At that moment in time, there is a wealth of important nuanced information that the human advertiser has about their community, their audience, and the seller is getting that. And they all also have context because of the other advertisers in the community. But they have to punch this in some computer somewhere, and there's this huge information loss where it gets standardized, it gets pushed into some other system.

Tim Hanlon: So you're saying at the data entry part of it, there's a huge disconnect?

Aaron Brown: Yeah, because they have to get it into some standard, ad campaign form. And now it moves over to ad operations teams, and they're doing their job optimizing and scaling, making sure campaigns are working and filling.

And if only they knew that the advertiser had these unspecified preferences that could be infused there in the operation. And the 2024 version of that is, "Oh, hi, while you're optimizing this campaign, I'm Maverick here, and I'm here to tell you that there's something important that I've carried through this campaign that I can tell you about."

This is great. This just made the ad operations professional’s job much easier. This made the seller's job much easier now when they have to go back and explain the results. This makes the advertiser's job much easier because they're just running their business, and their campaigns are running better. Enter the 2026 version of this, and now Maverick is pushing levers, adding these biases, and even making that step a little bit easier, letting that ad operations professional kind of scale up, take on more volume and be successful at that scale.

And so all of these we have to solve the white glove problem first because no campaign can run unsuccessfully before it can scale up. And this is where AI is directly helping at that level. And you can see already at the operational level how there're these nice side effects that are benefiting the advertisers.

That 2024 version was, "Hey, you can  vocally rattle off your campaign targeting and AI will help you match that to something, and you'll get a great campaign. We'll optimize it," that kind of thing. The 2026 version of Maverick is taking all of those insights and setting a new path.

"Hey, I found something new for you. There's a pattern maybe that you maybe haven't appreciated. You named three kinds of model customers, but there's a fourth one here for you. And we can begin to exploit that gap because you haven't been targeting them directly."

And being able to find that reliably on scale for local advertisers has been a great benefit for them. 

Tim Hanlon: So here's my sort of, final question on all of this. How do you, and we can scratch deeper into all of the dynamics stuff. We'll have you back maybe in a few months. We can get some more granular scenarios and some examples and maybe some use cases and stuff. But tell me how you and the rest of the company message that usage of AI. Where and how do you communicate the use of artificial intelligence in the offering? Because I have to think that depending on the client and/or vendor relationship, there's gonna be various levels of freak-out there or distrust or otherwise in that mix?

How much do you look under the hood with them to show what's going on or is it just a lost cause? AI is at once intriguing and opportunistic yet also at the same time frightening and uncertain to people who are just new to it. How do you communicate the good part of that in what Maverick is doing and what you're doing?

Aaron Brown: I think that's fair. I think we wrestled with that problem when we started our Maverick AI platform rollout. What we found was that our advertising audience was pretty sophisticated. Our local media customers, people are just using ChatGPT.

So they had some kind of what do you call it? Mechanical sympathy. They understood how AI worked. They understood that it was helpful and useful and that's just in their personal lives and a lot of times in their work lives too. With our first rollout we held back some of the tech because we were scared of the same concerns.

That quickly changed when our customer says, "I need this and I need 10 times more of this." We are all in a fight to continue our successful existence. We need you to arm all of us with the best tech that you have. And we took that message to heart and we have followed through to our customers, on top of the Maverick platform with Maverick Audiences where our customers can get a custom fitted audience just for that advertiser that matches that customer profile directly.

Maverick Outcomes, our cutting edge outcomes optimization platform, and this is for local advertisers who just state what they need. If you need your cash register to ring, Maverick will make your cash register ring. If you wanna go really deep with a new customer and you wanna appreciate, what's every bit of data in your system that you can give to me?

We have a pre-release product called Maverick Research where we go through and we just show plainly these are your customers, some of them you already know about. We'll tell you more about them. Some of them you may not have realized. Some of them you didn't realize that you knew, but as soon as we showed it to you there was a light bulb moment there.

And all of these have worked in different ways to help them, and the response has really been less trepidation and nothing short of enthusiasm because these are solving real problems for our local advertisers in some kind of way. 

Tim Hanlon: Even if that means them increasingly giving you their, let's call it proprietary data, their customer base and stuff, there's no fear there that data is gonna kind of waft away from them and that unique relationship data that they have with their customers might go awry or go somewhere else or worse?

Aaron Brown: I think the industry at large, I think that's a really valid concern. At Madhive, we do have this close relationship with our customers. We're not a vendor, we're a collaborator. We jointly go to market. Our success is their success, and vice versa. And two things happen. One, we don't wanna damage that relationship in any kind of way.

Their data is their data, and we're here to help. We'll take theirs, we'll help with their unique offering. And then the other side is this data is a little bit less fungible than it might appear. Every local media news outlet has co-evolved with their community.

And like the example I gave, the communities are different. And if you can't appreciate that from a data standpoint, you're not gonna be successful in local media. You're gonna just be more of the sort of the national noise that's happening in the background there. And so every go-to-market strategy, every bit of data is fitted for that, that media property, that audience, and there's really not too much temptation to try to mix it together.

Tim Hanlon: All right, last question. Where do you see in six months this is all going? Is it improving at an increasing rate? Is the state of the capabilities of Maverick and the AI engines and all that stuff improving exponentially or is it just gonna be a slow, steady, evolving kind of a dynamic with the variety of local media partners out there?

Aaron Brown: I think a bit of both. On the exponential front, the technology beneath us is definitely accelerating exponentially. The appetite and the receptiveness and then the realization of the need of both driven organically and then folks taking a look over the shoulder at their competitors and saying, "Oh, wow they're going fast. We wanna go fast, too." There's more reach out from our competitors. "What have you got? How can you help us?" And that's our role. We're here to help with that. We are in the fight to make sure that local media remains the best way to run your campaign, the best way to run your business.

And so on these fronts, this is exponential. I think the only sort of speed bumps here that could slow things down are there's a lot of people who have to make a lot of changes. We are just humans. It does take us just a little bit of time to adopt these new patterns and these new tools.

Again, this is where Madhive is making a name for itself. We're here to bridge that for them, but we'll meet you where you are. We'll bring you the best technology.

Tim Hanlon: All right, there you have it. My thanks to Aaron and of course we bow humbly in the general direction of his employer, Madhive, without whose support we couldn't do this show. So we thank them of course, as part of the ongoing adventure that is In the Vicinity. Our thanks, of course, as well to our friends at TVREV, whose production prowess allows us to get this show to you each and every week.

Mike Gasbara helming the knobs this week. Melissa Hourigan, of course, Jessika Walsten, and the inimitable Jason Damata, all part of that crew at TVREV. And of course, our complete and undying thanks to the wonderful Jerry Payne for his continued audio excellence this week as well. All right, many more episodes to come in the weeks ahead.

We appreciate your listening, and we'll see you again next week here In the Vicinity.