The commercial vehicle industry has spent the last decade digitizing operations through connected vehicles, telematics platforms, and real-time monitoring systems. The result is an unprecedented volume of data flowing from vehicles, drivers, and manufacturing operations.
The challenge now is turning that data into business value.
At Daimler India Commercial Vehicles (DICV), artificial intelligence is increasingly becoming the mechanism through which information is translated into action. Rather than treating AI as a standalone technology initiative, the company is embedding intelligence across the commercial vehicle lifecycle—from vehicle production and quality control to fleet management, maintenance, and driver development.
For Abhinav Srivastava, Chief Information Officer at DICV, the next phase of transformation is less about visibility and more about decision-making. “Connected vehicles today generate vast amounts of data, but what fleet operators truly need is not more information—it is clearer, faster decisions.”
That perspective reflects a broader shift underway across the transportation sector. As fleets become more connected and operations more complex, competitive advantage increasingly depends on how effectively organisations can convert data into operational outcomes.
Moving AI Beyond Experimentation
While many organisations continue to explore AI through pilot projects, DICV is already seeing measurable impact in core business functions.
“If you look at where AI is already creating tangible impact, three areas clearly stand out,” says Srivastava. “First is predictive maintenance. By continuously analysing vehicle health data, AI enables us to anticipate potential failures before they occur, significantly improving vehicle uptime while reducing repair costs and unplanned downtime.”
The second area is fuel efficiency and driving optimization—critical priorities in an industry where operating margins are heavily influenced by fuel costs.
“Even marginal improvements in driving patterns—such as smoother acceleration, optimal gear usage, and reduced idling—translate into substantial cost savings when scaled across large fleets.”
The third area is manufacturing.
“AI-powered vision systems and advanced analytics are helping us enhance precision, reduce defects, and improve first-time-right production rates.”
Together, these applications illustrate a key evolution in enterprise AI adoption.
“What’s important is that AI today is no longer experimental—it is delivering consistent, repeatable, and measurable outcomes across the value chain.”
From Monitoring Fleets to Guiding Decisions
The next frontier for AI in commercial transportation is emerging through connected vehicle platforms.
Traditionally, telematics systems have focused on reporting performance metrics and generating alerts. However, as data volumes continue to increase, fleet operators are looking for systems that can recommend actions rather than simply present information.
That shift is shaping the evolution of Truckonnect, DICV’s connected vehicle platform. “Truckonnect is evolving from a platform that simply reports what is happening to one that actively supports what should be done next. This shift is being enabled through predictive insights, contextual analytics, and prioritised recommendations.”
The goal is to reduce the burden of interpretation for fleet operators.
Rather than reviewing multiple dashboards and datasets, operators can focus on decisions supported by AI-generated recommendations. “For instance, instead of just flagging a potential issue, the system can suggest the optimal course of action—whether it is scheduling maintenance, rerouting a vehicle, or adjusting utilisation. The result is faster decision-making, reduced operational complexity, and improved fleet productivity.”
The same philosophy applies to telematics data more broadly.
“Fleet operators do not want raw data—they want clear, actionable insights.”
According to Srivastava, the focus is on correlating multiple operational variables—including driving behaviour, terrain, load conditions, and route profiles—to generate recommendations that are relevant at both driver and fleet levels. The company is also advancing toward condition-based maintenance models.
“For uptime and maintenance, we are transitioning from traditional time-based servicing to condition-based maintenance, where interventions are triggered by actual vehicle health data rather than fixed schedules.”
Using AI to Improve Driver Performance
Even as vehicles become increasingly intelligent, driver behavior remains one of the most influential factors affecting safety, fuel consumption, and overall fleet efficiency.
To address this challenge, DICV is incorporating AI into driver training through its BharatBenz Simulated Driver Trainer.
“Driver behaviour continues to be one of the most decisive factors in both safety and total cost of operations. With the BharatBenz Simulated Driver Trainer, we are moving beyond standardised training to a far more personalised, data-driven approach.”
The platform uses AI to analyze how individual drivers respond to different operating conditions and then adapts training modules accordingly.
“AI enables the system to understand how individual drivers respond to real-world scenarios—whether it is braking patterns, reaction times, or handling challenging road conditions—and then adapt the training modules accordingly.”
The objective is to create behavioral change rather than simply deliver training content.
“This ensures that each driver receives targeted feedback based on their specific driving style. Over time, this helps drivers internalise safer practices and adopt more fuel-efficient driving habits, leading to measurable improvements in both safety outcomes and operational efficiency.”
Building Intelligence Into Manufacturing
The role of AI at DICV extends beyond vehicles and fleet operations into manufacturing itself.
As factories become increasingly digitized, AI is helping improve both production efficiency and quality outcomes. “AI is playing an increasingly important role in strengthening manufacturing excellence at DICV.”
Computer vision systems are being deployed to strengthen inspection processes, while analytics-driven optimization is helping improve production flow and equipment utilization.
“On the shopfloor, computer vision systems are enhancing quality inspections by detecting even the smallest deviations with high accuracy. At the same time, advanced analytics are being used to optimise production flows, reduce bottlenecks, and improve overall throughput.”
Predictive models are also helping minimize disruptions.
“Predictive models also help identify potential equipment issues before they lead to unplanned downtime, ensuring smoother operations.”
The Road to Agentic AI
As enterprises explore more autonomous AI capabilities, the transportation industry is beginning to evaluate where Agentic AI can create value. Srivastava sees potential, particularly in operational orchestration and intelligent assistance, but believes adoption must remain grounded in safety and accountability.
“Agentic AI will certainly play a growing role, particularly in areas such as maintenance orchestration, route optimisation, and intelligent driver assistance.”
At the same time, he cautions against fully autonomous decision-making in safety-critical environments.
“However, in a safety-critical domain like commercial vehicles, the adoption of autonomous decision-making will be measured and clearly bounded. Reliability, accountability, and safety will always take precedence.”
Instead, the industry is likely to adopt a collaborative operating model.
“In the foreseeable future, the industry will move toward human-in-the-loop systems, where AI manages complexity, processes large datasets, and recommends actions—while human operators retain oversight and final responsibility.”
Human Expertise Remains Central
Despite rapid advances in AI, Srivastava does not see technology replacing drivers, fleet managers, or operations teams. Rather, he believes AI will elevate their roles by helping them make better decisions and focus on higher-value activities.
“The future is best seen as a strong partnership between human expertise and machine intelligence. Machines will increasingly handle tasks that require speed, scale, pattern recognition, and consistency. At the same time, humans will continue to bring critical strengths such as experience, contextual judgement, and accountability.”
For DICV, that balance between human expertise and machine intelligence ultimately defines the long-term vision for AI. “Rather than replacing drivers and fleet managers, AI will elevate their roles—enabling them to focus on higher-value decision-making supported by intelligent systems.”
As AI becomes increasingly embedded across vehicles, factories, and fleet operations, the organisations that derive the greatest value may not be those with the most data, but those that are best able to act on it. Srivastava opines: “Ultimately, the real competitive advantage will come from how effectively organisations can augment human capabilities with AI, creating a more efficient, safer, and smarter transport ecosystem.”