The commercial-vehicle industry is navigating a traffic jam of structural pressures, from market pressure to technology and business model shifts. At the same time, AI is emerging as a powerful force: Agentic AI combines, coordinates, and executes multistep workflows across all business functions, and physical AI extends automation into plants, warehouses, and service environments through intelligent robots, digital twins, and adaptive quality systems. Together, they define a new operating model in which people, AI agents, and intelligent machines collaborate across the commercial-vehicle value chain.
The opportunity unlocked by these complementary forces may be profound. For commercial-vehicle players, agentic AI can generate 2 to 3 percent EBIT uplift within 24 to 36 months through the combination of efficiency, effectiveness, and top-line growth. But while 62 percent of companies have experimented with AI agents, fewer than 10 percent have scaled agents within any function.1 This is the AI paradox: 80 percent of companies have deployed AI, but 80 percent of those companies are seeing little EBIT impact so far.2
Agentic AI for commercial vehicles
Commercial vehicles are rarely sold off the shelf. Customers require specific configurations spanning vehicle body, software, charging and refueling assumptions, service packages, financing, and uptime models, and 85 percent expect the same or higher customization levels in the future. The commercial logic is shifting toward life cycle value too, with nearly two-thirds of global OEM medium- and heavy-duty truck profits expected from recurring life cycle services by 2030.3 This mix—high configuration complexity and a profit model leaning toward life cycle services—is where agentic AI generates impact by moving from supporting work to executing it.
Agentic AI supports multistep workflow planning, enterprise-system interaction, cross-functional orchestration, task execution, and exception handling (for an example, see sidebar “In depth: Agentic AI across the lead-to-order process”). Its potential is especially pronounced in commercial vehicles, where requirements, engineering feasibility, pricing, life cycle services, and operations are tightly interconnected. Redesigning these workflows end to end, rather than deploying AI as an isolated tool, is what unlocks EBIT uplift.
The impact of agentic AI spans both revenue and cost. Faster cycle times could allow the same head count to pursue more of the pipeline, while better total cost of ownership modeling drives higher-margin service attachment. On the cost side, automating specification reading, configuration checking, and proposal assembly frees experts for higher-judgment work. For example, an international systems manufacturer achieved time savings of 20 to 30 percent and a 90 percent reduction in specification-reading time; a global truck OEM reduced configuration effort by 20 to 30 percent; and an international commercial vehicle company increased tender participation by 10 to 20 percent.
Physical AI extends technology’s reach
Physical AI represents the next level of productivity in commercial-vehicle operations. It also represents a fundamental break with the economics of traditional automation, which required high volumes and low task complexity for positive ROI—conditions that rarely applied to commercial-vehicle production.
Physical AI inverts this logic. It enables automation of low-volume, high-complexity tasks and unlocks productivity gains that were previously out of reach (for an example, see sidebar “In depth: Physical AI in production, logistics, and services operations”). In that respect, physical AI presents a strategic opportunity for European commercial-vehicle companies to narrow the gap with Chinese competitors through technology rather than labor. It supports adaptive production automation, robotics, autonomous material flows, AI-enabled quality systems, and digital-twin-enabled learning.
Potential benefits of starting now: Full-scale deployment—particularly of humanoid form factors—remains a post-2030 prospect in most commercial-vehicle environments. But there are three reasons to act now. First, positive ROI is already available: Near-term physical AI applications in vision-guided systems and autonomous material handling are generating returns today. Second, the required skills take years to build. Deploying, operating, and continuously improving physical AI systems demands new capabilities that cannot be developed overnight, such as robot integration, simulation engineering, and AI-in-the-loop quality management. Third, social acceptance is earned over time. Introducing robots in factories is as much a change challenge as a technical one, requiring sustained engagement to build trust, collaboration protocols, and workforce relationships.
Realizing AI’s full potential
The difference between companies that capture the AI opportunity and those stuck in the AI paradox is not technology but execution. In our experience, six actions are key:
- End-to-end process rethinking. AI creates the most value when processes are redesigned around its capabilities, not when it is layered onto existing workflows. For agentic AI, this means restructuring knowledge workflows from intake to decision and eliminating handovers designed for human coordination. For physical AI, it means rethinking task sequences, material flows, and quality architectures rather than automating them in place.
- Skills, talent, and people readiness. Capturing value from AI demands new capabilities across organizations, from frontline workers to functional leaders and technical teams. But beyond skills, it requires deliberate attention to people readiness, especially addressing concerns about displacement, building the change capacity to sustain adoption, and creating the conditions for human–AI and human–robot collaboration.
- New technology operating models. Shifting from building and maintaining systems could enable continuous AI deployment for IT teams. Agentic AI requires agent-readable enterprise data, API-based system access, and auditable workflow logic, while physical AI requires sensor infrastructure, simulation environments, digital twins, and integration with manufacturing systems. A strong digital core and composable, modular architecture (shared rather than siloed) are prerequisites for both.
- Partnering for pace. No commercial-vehicle player can build all the capabilities required at the speed the market sets. The strategic question is what to own versus what to access through partnership. Proprietary data, customer and operational models, and deployment know-how are assets that should remain in-house; foundation models, robotics platforms, simulation environments, and deployment tooling are areas in which partnerships can accelerate progress without creating dependency.
- Clear value tracking. Measured outcomes, rather than deployment milestones, should drive scaling decisions. Companies need a value-tracking framework linking each AI deployment to specific financial and operational KPIs such as EBIT impact, cycle-time reduction, quality outcomes, and service attachment rates—with clear accountability for delivery.
- Senior-leader ownership. The companies realizing AI’s full potential share a common pattern: A senior leader owns the agenda, sets EBIT targets, removes organizational barriers, and holds the organization accountable. AI transformation at scale requires visible, sustained commitment from the top rather than delegation to a technology team.
Choosing a practical starting point
The right starting point may not be the most technically interesting use case, or the one most easily automated. For example, commercial-vehicle companies could identify a focused set of high-impact areas across the full value chain—spanning both knowledge workflows and physical operations—guided by a consistent set of criteria. While the criteria for agentic AI and physical AI differ, the discipline would be the same: to prioritize by business value and execution feasibility, not by technological novelty or demonstration potential (exhibit).
[[Exhibit 11]Agentic AI is transforming knowledge work, and physical AI is transforming how physical work is executed. The next competitive frontier for commercial-vehicle players is the ability to rewire work across the full value chain. The path from ambition to impact is well-defined, starting with identifying a handful of areas across the value chain in which agentic or physical AI can generate the greatest business impact and then building an execution plan around those areas with clear ownership, measurable targets, and deliberate sequencing. That focus, applied with the right organizational foundations, separates companies capturing measurable EBIT impact from those still experimenting at the margins.
