Artificial intelligence (AI) is reshaping mobility – not just through automation headlines, but in real operational terms but where does the true business value lie?
Data is fuelling many industries and telematics, in particular, is naturally converging with AI
Artificial intelligence is having a powerful impact on mobility, though not always in the ways the headlines promise. Behind the talk of automation and efficiency, many projects still hide unexpected costs or inflated expectations. That tension is forcing businesses to ask a more practical question: where does AI in telematics truly deliver measurable value and where does the hype still outweigh results?
That same question shaped one of the key discussions in Vilnius, where engineers and entrepreneurs from around the world gathered for the International Telematics & Connected Mobility Conference 2025. Hosted by Gurtam – a Lithuania-based global software company focused on fleet management and tracking solutions – the event covered a broad range of topics in transport and mobility, with AI emerging as a defining theme cutting across many of them.
For Lithuania, taking a closer look at AI is more than a passing trend – it’s becoming part of the country’s established role in the global telematics ecosystem. That awareness matters: before businesses or cities rush to apply new tools, they need to understand where AI actually adds value and where it just adds chaos and costs. For many tech companies, that means asking a simple but uncomfortable question: is AI really saving money, or quietly inflating expenses?
Where AI delivers real results
Data is fuelling many industries and telematics, in particular, is naturally converging with AI due to the sheer volume of information it generates. There are already cases of AI in telematics producing measurable business outcomes, signalling an efficient use and reallocation of business assets.
Smarter streets and cleaner cities
Waste management is an area where AI integration can ensure cleaner, safer and more environmentally aligned infrastructure. The work of Moaad El Aboudi, co-founder of Insight Solutions, offers a clear example of such innovation. Sharing his experience at the Vilnius conference, El Aboudi described how his team oversees digital waste collection across Casablanca, Morocco, which houses over seven million residents. The company coordinates an operational effort for over 6,000 collection points and 4,500 kilometres of city streets. That equals out to 3 million kilograms of waste every single day.
Insight Solutions introduced a hybrid system of GPS live tracking, IoT sensors and AI-driven route and load optimization. By analysing fill-level data and historical collection patterns, the system predicts where waste will accumulate next – a task that previously relied on fixed schedules and manual estimation. Everything from fill levels to vehicle routes and on-board weight sensors started working in concert to predict waste accumulation in real time. The result reduced costs by ~15%, cut the number of trucks and staff and led to boosted urban cleanliness. The achievement demonstrates how AI can align physical assets with labour efficiency, even in dense urban settings.
When fleet data meets predictive mobility
The same idea of using automation to reduce uncertainty was discussed by Arturs Burnins, CEO of ATOM Mobility, at the Gurtam conference. His company connects over 35,000 vehicles in more than 140 cities, creating a logistical web of challenges. According to Burnins, AI-based demand prediction is transforming the economics of shared mobility. By leveraging historical ride data, weather patterns and event schedules, ATOM Mobility can anticipate scooter and bike demand, allowing fleet managers to reposition vehicles before a rush.
This form of AI-driven rebalancing increased daily returns by 6.2% and fleet utilization by 10% in just 24 hours. When the team integrated AI tools, it enabled smarter allocation, equal service levels and higher satisfaction – all achieved with fewer vehicles and less downtime. These improvements demonstrate that predictive analytics deliver real results when AI is used to solve measurable inefficiencies.
Car rentals automation in the background
As Arturs Burnins noted, ATOM Mobility’s platform also supports car rental operators in multiple regions. In that model, inefficiencies arise when staff must manually verify damage, fuel levels and vehicle condition before cars return to service. By integrating AI-driven computer vision and 360-degree photo analysis at check-in, rental companies can now operate 24/7 without on-site inspectors. The automation quietly removes bottlenecks and accelerates vehicle turnaround.
Having this kind of ‘invisible’ AI running in the background creates visible efficiency. The technology’s biggest wins often go unnoticed – not because they are minor, but because they happen behind the scenes, filling operational gaps.
The hidden cost of AI integration
Even with all the impressive use cases shared by ATOM Mobility and others, AI is far from a magic fix. During his keynote in Vilnius, Aliaksei Shchurko, CEO of Gurtam, reminded the audience that “an AI answer to a business query can cost less than a cent – or several dollars, depending on its complexity”.
His point touched on a growing concern across industries: the hidden cost of AI operations. While some processes seem almost free, the price of computation and model usage can escalate rapidly once scaled across teams and systems. Simple queries may cost pennies, but large enterprise prompts – involving lengthy documents, custom instructions, or continuous automation – quickly add up. When multiplied by hundreds of employees and thousands of interactions per day, a ‘cheap’” AI can become an expensive habit. The real test, however, lies in whether customers are willing to pay for that added intelligence, a question that becomes sharper as AI scales.
Scalability’s price tag
AI’s promise has always been about scale – doing more, faster, with less. Yet as Arturs Burnins, CEO of ATOM Mobility, demonstrated, scaling AI often introduces new costs of its own. The company’s Vision AI detects scooters improperly parked across a region, with each image costing a fraction of a cent. When multiplied across tens of thousands of images each day in multiple cities, the expense of operating the AI becomes a significant budget item.
The technology delivers visible value but also consumes hidden resources – data, energy and human oversight. The hype around AI’s scalability is real, but so are the constraints. Every new city or fleet expansion brings not just opportunity, but a bill.
Lithuania’s emerging role
Having realistic conversations about the integration of AI is crucial for much of the global mobility and telematics industry. What stands out from these examples is Lithuania’s growing role as a hub connecting global AI in telematics trends with local opportunities.
Companies such as Gurtam, committed to developing telematics solutions that boost business efficiency, serve as bridges between global partners and Lithuania’s tech ecosystem, bringing international players into local conversations about the future of mobility. Sharing insights on AI in telematics, mobility, fleet management, IoT and city infrastructure allows new concepts to take root in the Lithuanian economy and inspire both startups and policymakers.
The result of these collaborations is a clear, loud signal to investors and partners that Lithuania is not only on the map of AI and mobility conversations, but also a stable foundation for growth and development. That pours more financial resources into the region, turning conferences such as Gurtam’s into hot testbeds for the next generation of mobility solutions.
“Lithuania might be small on the map, but it’s large in connectivity,” Shchurko noted. “We have the infrastructure, the talent and now, the conversations.” For local businesses and municipalities, such exchanges offer a shortcut to tested solutions – and a safer path for investing in innovation.