- AI and Domain Expertise in Commercial Vehicles drive future innovation.
- Software-defined trucks require continuous digital lifecycle advancements.
Artificial intelligence is expected to play an increasingly important role in the commercial vehicle sector, but its true value will come from being combined with engineering knowledge and industry expertise rather than functioning independently. Speaking during Siemens Realize LIVE APAC 2026, Radhakrishnan Kodakkal, Managing Director and CEO of Daimler Truck Innovation Center India, said future commercial vehicles will be shaped by the integration of digital intelligence with deep domain expertise across every stage of the product lifecycle.
According to Kodakkal, organisations that successfully blend engineering capabilities with digital technologies will gain a significant competitive advantage. He explained that digital transformation is no longer limited to individual processes, but now extends from product conception and engineering to manufacturing, validation, deployment, and long-term vehicle support. He emphasised that combining these capabilities will define the next generation of commercial vehicle development.
Modern commercial vehicles are increasingly evolving into software-defined platforms that continue to improve well after leaving production facilities. Since trucks generally remain operational for 10 to 14 years or longer, manufacturers must deliver continuous software enhancements, cybersecurity improvements, and digital feature upgrades throughout their service life. This long operational lifecycle makes software management an essential element of future commercial vehicle ownership.
Connected technologies, digital twins, and AI-powered analytics are also changing maintenance strategies across the industry. Rather than relying only on scheduled servicing, manufacturers are moving toward condition-based and predictive maintenance models. Future systems will not simply notify operators after a fault occurs but will remotely analyse vehicle conditions, assess issue severity, and recommend whether operations can continue safely, a workshop visit is required, or field service support should be dispatched.
Kodakkal noted that these capabilities cannot be achieved through artificial intelligence alone. He stated that meaningful digital solutions require combining operational data with extensive engineering expertise. Understanding vehicle physics, customer applications, and real-world operating conditions remains essential for developing intelligent systems that deliver practical value throughout the commercial vehicle lifecycle.
He also highlighted the growing importance of integrated digital twins in product engineering. Bringing together requirements management, product design, simulation, manufacturing, and validation within a unified digital workflow enables manufacturers to detect engineering issues much earlier. This integrated approach reduces dependence on physical prototypes while shortening development timelines and improving overall product quality.
Manufacturing simulation should begin during the product design phase because production processes frequently influence engineering decisions. Kodakkal explained that early manufacturing validation helps optimise product development while reducing later design changes. Technologies such as photorealistic rendering, augmented reality, and virtual reality also allow original equipment manufacturers to validate vehicle concepts with customers before building the first physical prototype.
Key Technologies Supporting Future Commercial Vehicle Development
| Technology | Primary Benefit |
|---|---|
| Software-defined engineering | Continuous vehicle improvements throughout lifecycle |
| Digital twins | Earlier validation and reduced physical prototyping |
| AI-driven analytics | Predictive and condition-based maintenance |
| Connected vehicle technologies | Remote diagnostics and lifecycle management |
| AR and VR validation | Customer evaluation before physical prototypes |
Looking ahead, Kodakkal said commercial vehicle manufacturers must remain agile as they respond to evolving regulations, customer expectations, and geopolitical developments. Organisations that rapidly adopt emerging technologies while maintaining strong engineering foundations will be better positioned to remain competitive in an increasingly digital automotive landscape.
He concluded that long-term success will depend on integrating software-defined engineering, AI-enabled digital intelligence, simulation-driven development, and deep domain expertise. Rather than relying solely on artificial intelligence, manufacturers that effectively combine these complementary capabilities will be best equipped to deliver safer, smarter, and more efficient commercial vehicles in the years ahead.
Frequently Asked Questions
Why does Daimler Truck Innovation Center India believe AI alone is not enough for future commercial vehicles?
According to Radhakrishnan Kodakkal, artificial intelligence becomes most effective when combined with engineering knowledge and domain expertise. While AI can process vast amounts of operational data, understanding vehicle physics, customer applications, manufacturing processes, and real-world operating environments is equally important. Integrating these capabilities enables manufacturers to develop software-defined commercial vehicles with predictive maintenance, digital twins, continuous software upgrades, and more effective lifecycle management.
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