In this segment from CES 2026, Andy Marchant, director of product marketing at TomTom, joins theCUBE’s Rob Strechay to discuss how the company is refining the driver experience through advanced AI integrations. Marchant details the evolution of TomTom’s AI agent, highlighting new collaborations with Amazon Alexa and SoundHound that streamline in-car voice interactions. The conversation unpacks how these integrations enable the system to handle complex, multi-layered queries – such as locating specific dining options with EV charging stations along a route – without forcing the user to switch between different platforms.
The interview also explores broader applications of TomTom’s technology, including the use of an MCP server to help smart cities optimize traffic management and the shift toward proactive AI that anticipates driver needs before they are voiced. Marchant further explains the importance of "4D maps" that use environmental context to adjust driving behavior and outlines the company’s push to expand lane-level coverage to local roads, a key enabler for the next generation of autonomous vehicles.
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Andy Marchant, TomTom | CES 2026
At CES 2026, innovations in artificial intelligence and navigation take center stage as Andy Marchant, the director of product marketing at TomTom, discusses the integration of AI with cutting-edge navigation technologies. Marchant provides insights into the advancements TomTom has achieved over the past year and their applications in automotive and smart city contexts, offering a glimpse into the future of navigation.
Throughout the video, Marchant presents an overview of TomTom's latest developments with their AI agent, which now integrates with platforms such as Amazon Alexa and SoundHound. This video, featuring Rob Strechay of theCUBE Research, explores the transformative power of AI in enhancing user experiences by enabling seamless navigation and addressing complex queries for drivers and city planners.
Key takeaways from the discussion include the significance of AI in advancing customer interactions with technology, as emphasized by Marchant. TomTom focuses on contextual data, paving the way for innovative applications in autonomous vehicles and urban planning. This approach enables organizations to optimize traffic management and improve driver safety through real-time insights.
play_circle_outlineTomTom Unveils AI Agent: Enhancements and New Integrations with Amazon Alexa and SoundHound for Superior Driver Assistance
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play_circle_outlineEnhancing Autonomous Driving: Advancements in AI, Country-level Coverage, and Lane-level Mapping for Improved Navigation Experiences
In this segment from CES 2026, Andy Marchant, director of product marketing at TomTom, joins theCUBE’s Rob Strechay to discuss how the company is refining the driver experience through advanced AI integrations. Marchant details the evolution of TomTom’s AI agent, highlighting new collaborations with Amazon Alexa and SoundHound that streamline in-car voice interactions. The conversation unpacks how these integrations enable the system to handle complex, multi-layered queries – such as locating specific dining options with EV charging stations along a route – w...Read more
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What improvements were made to the AI agent and what integrations were announced at CES?add
What advancements in AI and road information are expected to impact customer use cases in the coming year?add
>> Hello and welcome back to CES 2026, live from Las Vegas. We're here at TomTom and I'm joined by Andy, who's the director of product marketing for TomTom. Welcome on board, Andy.
Andy Marchant
>> Thank you. Good to see you.
Rob Strechay
>> Glad you could get here out of Europe with the snow, the freak snow they're having over there. You had proper guidance to get you here. How about that?
Andy Marchant
>> We certainly did. We certainly did.
Rob Strechay
>> But I think, again, I met with you guys last year and it was a lot of fun. I think it's really always interesting to see how AI is being built in and changing the customer experience for the end users that are taking on your products. Kind of help us understand some of the advancements that you've made in the past year that you're displaying here.
Andy Marchant
>> Sure, sure. So I think last year we introduced our AI agent and we spent a lot of time over the last year kind of building up and improving different elements of it. And it's really interesting to see what it can do now this year. So we announced at the beginning of CES two integrations. So one with Amazon with the Alexa and one with SoundHound. And what that does is that means that when you're a driver now, when you've got those systems in your vehicle, you're not using multiple systems to do different jobs. So the system is from Alexa and SoundHound is great for voice interaction and giving you information on HVAC and windows and your media player. But when it comes to navigation, that's where we come in. So our AI agent connects to those services. So when your query is location based, it pushes to our agent to give you the information.
Rob Strechay
>> So I think to me that was kind of the key. And I'd seen the announcements and I think that what I loved about it was you're not forcing the end user to change their experience, to go outside of something, to really be able to utilize your geo data and the mapping capabilities that you've brought. How has that been received by those customers that you guys sell into?
Andy Marchant
>> Really well, because I think one of the biggest challenges is currently today, it's very much you ask a question, you get a response. And now with the agent that we've created, we're able to support more complex queries. So imagine if you're driving to a location, especially in the age of autonomy, EVs, et cetera, you're not only wanting to drive there, but you're wanting to do a number of tasks along the way. So for example, maybe we're here in Las Vegas, maybe you want to drive to LA afterwards. We know it's about a five-hour drive, it's a few hundred miles, so you're going to have to have multiple stops on the way. So by asking it for the route, the agent will automatically calculate the best places to stop. But maybe you want to take some dinner while you're there. Maybe you have a preference for Mexican food or Italian food or something else. So again, the agent can give you the answer back as in, "Hey, find me a charging station around halfway my route and I want it near an Italian restaurant." And it will actually give you that query back. So it will find the best Italian restaurant with the recommendations and it will find one that has an EV charger next to it.
Rob Strechay
>> Yeah. I love that. You'd probably find an easier time getting the Mexican restaurant, but that's neither here nor there. When you look at AI, I mean, I noticed out here that you have an MCP server now and so that helps other people do integrations with you. How has that really also broadened your appeal to your customers?
Andy Marchant
>> Well, exactly what you said is the integration, right? I mean, there are so many products that we have where customers have used them for a number of years. A great example of the MCP server is actually outside of the automotive space. So we have a lot of customers in the smart city space and government space. And through the MCP server now, we're able to connect multiple traffic products that then allows a customer to make multiple queries. So for example, the city of Las Vegas could look and say, "Hey, show me the five busiest junctions in the city right now." And it will be able to identify based on our historic and live traffic data, which are the most impacted junctions, which then means if you're the city authority distributing the police for traffic management or signage, you can really direct it to the right places. Where in the past you would have to maybe tackle those one by one. Now, it's a much simpler process and way quicker as well.
Rob Strechay
>> Have you learned anything over the last year that maybe surprised you or was things that made you say, huh, about all of this AI and where it's taking you with your customers?
Andy Marchant
>> I think it's the range of use cases. So there are so many use cases where AI just makes things easier. I think one of the other things as well we've integrated is AI at the moment is you ask AI a question, you get a response. We're also flipping that on its head a little bit. So AI can be more proactive. So one of our demos that we're showing here today takes the scenario that you're driving home, but we know that on a typical day, most people don't need turn by turn directions, but they want to know what's happening ahead. So again, the AI system can say, "It's 5:00 PM, it's a Wednesday afternoon. We know you're probably driving home, so we're going to tell you if there's traffic on the route. Okay? We're not going to tell you you need to turn left or right, but hey, there's traffic ahead. It's going to give you a delay by 10 minutes. Would you like an alternative route?" You say yes, it then gives you the route to get you home. So again, it's taking AI, but just giving it, I guess, other options and more flexibility.
Rob Strechay
>> So are you seeing that organizations and your customers are taking you in new directions and this is helping you guide you on those different use cases and how you can build more sophisticated AI as well?
Andy Marchant
>> Yeah, I think so. I think there's a couple of things, right? I think it certainly opens up more of our data to support those use cases. So perhaps things that hadn't been considered in the past now are answering those questions much easier and ask for new directions, absolutely. I think as we gain more data from more use cases, our kind of exploration, if you will, into new areas which will ultimately result in perhaps new products and new data sets is coming from exactly the use of AI.
Rob Strechay
>> When I look at what you guys do, I mean, I know heritage of maps, but it's really the data and AI lives on data. Is that what you see as your value go forward?
Andy Marchant
>> I think it's the data and I think it's also the context. I mean, we've long talked about the 4D map. So maps are great. You can have buildings and you can have roads, but that fourth dimension becomes context. So what does it actually mean when I approach the curve in a road if it's raining? The context of rain will change the dynamic of the instruction. So maybe you need to break earlier and by that we can then start to look at driver behavior and use AI to say, "Okay. Well, on average, driver behavior changes into wet."
So maybe the breaking zone starts 20 meters earlier or something like that. So again, as you start to build up those systems in the future, the system can learn better because of the information it has. The other one is also, as these systems become more advanced, the benefits of collecting this data mean that you can really personalize it. So imagine if you put a US driver in the middle of Paris and use the same system or in the middle of Italy or put somebody from Germany in South America.
Rob Strechay
>> I'll give you an example. I drove from Budapest to Bratislava over to Vienna and up to Munich just three months ago. I could tell you, I could have used context.
Andy Marchant
>> Yeah. So if you put an autonomous system built around the profile of a US driver into those countries, it wouldn't work or vice versa, because the way and the habits of the way people drive is different. So I think AI helps us kind of build those training models as well.
Rob Strechay
>> Yeah, I love that. So final question. Was here last year, here today. When we look forward to next year, what do you hope that you can say next year that really has been driven and helped you further AI with your customers?
Andy Marchant
>> Yeah. So I think the big thing, what we see now is as autonomy is now kind of back on the table, there's an even greater need for more accurate lane level information so that car can really position itself in the right space. We are now expanding our coverage on a country level, so no longer are we looking purely at highways. We are now down to connecting roads and even kind of local, real urban roads. So I think hopefully within the next year, with machine learning and with the advancements of technology, you're going to see that, that lane model expands way beyond where we have it today so that we can support more use cases across North America, across Europe, and hopefully other regions as well.
Rob Strechay
>> Yeah, especially as people move beyond Level 2++ and things like that towards three and four. Well, this has been great, Andy. I really appreciate it. Thanks for coming on board.
Andy Marchant
>> No problem. Thank you.
Rob Strechay
>> Yeah. And thank you for watching this episode from CES 2026 here with TomTom. We'll be back.
