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In an industry where seconds count and margins are thin, delayed feedback, manual processes, and limited visibility make it extremely difficult to improve safety and efficiency. According to the American Transportation Research Institute’s 2025 Operating Cost Report, the trucking industry’s average operating cost will reach $2.260 per mile in 2024, with non-fuel marginal costs rising 3.6% to $1.779 per mile, squeezing profitability amid high accident risk and downtime.
Truck fleet accidents are a major problem across the region. According to data from the Federal Motor Carrier Safety Administration (FMCSA) cited in the truck safety analysis, there will be 153,342 commercial truck and bus crashes in the United States in 2025, highlighting the sheer volume of vehicle operations. The death toll highlights the human toll. FMCSA also notes that 3,781 large trucks and buses were involved in fatal crashes in 2025.
Peer-reviewed research on driver monitoring systems highlights the importance of early risk detection in accident prevention. Comprehensive review published in IEEE Transactions on Vehicle Technology found that human errors such as drowsiness, distraction, and panic cause 90% of road accidents and directly exacerbate vehicle challenges such as delayed feedback and lack of real-time visibility.
Peer-reviewed research from Wright State University, citing IEEE numbers, shows that the company’s Edge AI system reaches 98.6% accuracy in detecting these risks using low-power in-cab hardware, and works reliably even in poorly lit areas, uneven roads, or partial facial views.
Tested with a variety of drivers and conditions, ATRI’s record-breaking $2.260 per mile cost and scalability to large fleets facing FMCSA’s 153,342 truck accidents annually turns reactive reviews into proactive safety.
On a recent episode of the AI in Business podcast, Emerj Editorial Director Matthew DeMello was joined by Hemant Banavar, Chief Product Officer at Motive. In our conversation, Hemant will discuss how edge AI can transform the physical economy by providing real-time safety-critical insights to prevent accidents, modernize operations, and make high-risk industries safer and more efficient.
Their conversation highlights two important insights about the physical economy.
- Modernizing the physical economy with AI: Deploy an AI-powered operational platform to replace manual processes, improve vehicle safety, and increase productivity across sectors.
- Drive safer operations with real-time AI: Leverage edge AI to instantly detect risks and provide feedback to drivers to prevent accidents, improve safety, and realize significant cost savings.
Listen to the full episode below.
[Embed – TBA]
guest: Hemant Banavar, Motive Chief Product Officer
Expertise: Products, Artificial Intelligence, Data Science
Easy recognition: Hemant leads product, design, data science, partnerships and strategy at Motive, driving innovation across fleet management, driver safety, spend management and workforce operations. In the past, we’ve worked with Uber, Stripe, and Microsoft. He holds a master’s degree in business administration from the University of California, Berkeley.
Modernizing the physical economy with edge AI
Hemant begins by explaining that many physical surgeries become largely ineffective as feedback is received hours, days, or even weeks after the event. In environments where operators make split-second decisions, such as applying the brakes on a vehicle to avoid a collision, timely feedback is critical because delays can directly impact safety.
In such situations, humans cannot provide feedback fast enough and delays are unacceptable. He emphasizes that the disconnect between humans and feedback is why AI models deployed at the edge are essential. AI models detect risks and alert operators in real-time, allowing them to immediately mitigate dangerous situations.
These models are trained using human judgment, but operate autonomously and provide instant feedback. Hemant emphasizes that accuracy, reliability, and trust are essential in these high-stakes environments, and these principles guide how AI systems are built.
He emphasized that edge AI is valuable here because it provides real-time feedback and allows operators to take immediate action. By providing insights when needed, edge AI can prevent accidents and ensure employees return home safely, highlighting the importance of immediate, actionable data in the physical economy.
In another episode of the “AI in Business” podcast, Adam Burns, vice president of network and edge and director of edge AI development tools at Intel, emphasized that the true impact of AI comes when insights are provided in the moment a decision has to be made, transforming data from retrospective tools to real-time operational capabilities.
“AI becomes much more valuable when data is processed in real-time. Insights are generated as events occur, allowing organizations to act immediately to improve outcomes, increase efficiency, and prevent problems before they become serious. That immediacy transforms AI from a retrospective analytical tool to a core operational capability that enables entirely new ways of running physical systems.”
– Adam Burns, Intel Vice President of Network and Edge, Director of Edge AI Development Tools
Listen to Adam’s full episode below as he shares these insights and more.
To help his audience understand, Hemant defines the “physical” economy (a relatively new term compared to what is called physical AI) as the part of the economy that involves people delivering groceries, stocking stores, building infrastructure, and managing public transportation. This is essentially sectors such as trucking, construction, oil and gas, and public services, which together account for about 50% of GDP.
He said that despite its size, investment in technology in this area is disproportionately underfunded, with estimates that around 30% of investment over the past decade has gone into physical economy solutions.
As a result, many businesses still rely on manual processes, pen and paper, and telephones to get their work done. Hemant highlighted that his company was one of the first to bring technology to this space, building an AI-powered operational platform that enables operators in the physical economy to operate their fleets more safely, productively and profitably.
Promote safer operations with real-time AI
Hemant further explains that edge AI works by placing devices directly in the customer’s environment (often in a taxi or vehicle) to monitor both the road and the driver in real-time and detect risks. These systems use video and telematics data such as engine RPM, speed, and external temperature, which are important for risk assessment. In a separate conversation on the AI in Business podcast, Naveen Kumar (then director of financial crimes at Walmart and now head of insider risk, analytics and detection at TD Bank) emphasized that the real value of video intelligence comes when it’s integrated with other operational data to create a richer, more actionable view of risk in real time.
“Organizations that do this well use video to enhance rather than replace other data. When visual data is integrated with transaction logs, access controls, and operational systems, decisions become more contextual and the picture becomes richer. That unified view allows teams to move beyond isolated signals to understand risk, behavior, and operations in a more complete and actionable way.”
– Naveen Kumar, Head of Insider Risk, Analytics and Detection, TD Bank
You can listen to the full episode with Naveen Kumar sharing these insights below.
Building hardware that can handle this across multiple vehicles and scenarios is difficult. He highlighted Motive’s new AI Dash Cam Plus, which is powered by a Qualcomm AI processor that can run up to 30 AI models simultaneously, enabling real-time monitoring of numerous behaviors.
The device also features dual forward-facing lenses that enable accurate distance perception for improved hands-free communication, enterprise-grade reliability, and collision detection. Hemant emphasizes that this advancement not only enhances current risk detection, but also enables the monitoring of more complex behaviors, opening the door to the next phase of edge-based AI innovation.
Hemant highlights that physical economy customers are prioritizing accident reduction and safer driving, and edge-based AI enables this by providing drivers with real-time video monitoring, alerts and actionable feedback, rather than delayed feedback.
Similarly, in another “AI in Business” podcast episode, Joe Troy, senior manager of site risk at Amazon, explains that as real-time video analytics matures, it’s evolving from simple monitoring tools to cross-functional intelligence systems that help organizations operate more effectively across the enterprise.
“AI-powered video does not replace human insight. By eliminating manual labor, teams can focus on actually driving the business. By automating detection and displaying meaningful signals, these systems enable organizations to respond faster and make more informed decisions across their operations. When implemented correctly, video intelligence becomes a cross-functional layer that improves training, safety, and overall operational performance.”
– Joe Troy, Senior Manager, Site Risk, Amazon
Listen to Joe’s full episode below with these insights and more.
Hemant also points out that independent studies have shown that Motive’s AI alerts are two to four times more effective at detecting risky behavior than competitors, helping to prevent accidents and change driver behavior. Hemant claims that Motive’s technology is estimated to have prevented around 170,000 accidents, reducing crashes by up to 80% and saving 1,500 lives.
He goes on to cite examples of clients who have delivered robust and measurable results.
“One of our customers, Ernest Concrete, an 80-year-old concrete company, came to Motive after their previous dashcams failed to capture two serious accidents. Within 13 months of implementing our solution, they saw a 97% reduction in driver cell phone use and an 83% reduction in distracted driving.”
Overall, these improvements resulted in savings of $6.5 million during that period. This is important for companies operating in industries where profit margins are highly constrained. ”
– Hemant Banavar, Chief Product Officer, Motive
Hemant went on to share that another customer, a national home services company called South Wind, achieved $2.5 million in savings, including $2 million in reduced insurance costs and $500,000 in fuel savings, thanks to Motive’s real-time insights, fraud prevention, and operational optimization.
