Momentum around connected and data-enabled services in commercial vehicles is accelerating. Data-enabled services are critical for new opportunities in charging energy solutions, connected insurance, the automotive aftermarket, advanced financing solutions, and transport as a service [TaaS]. Their potential to solve complex problems for OEMs, automakers, and fleet managers is too enticing to ignore. And those possibilities could yield serious value creation. According to McKinsey research, data-enabled services could represent a more than $3 billion profit pool by 2035.
To get a clearer picture of the current connected-services landscape—and what’s to come—McKinsey’s Florian Garms and Tobias Schneiderbauer spoke with Rupert Stuetzle, general manager of EMEA manufacturing and mobility at Microsoft, and Frank Kaleck, automotive industry expert at Microsoft. Rupert and Frank have been working with commercial vehicle industry players for more than a decade and are experiencing the evolution of connected services firsthand. In this interview, they discuss the evolution of connected services for commercial vehicles, the transition to zero-emission vehicles, generative AI (gen AI) use cases, and why industry players must work together to reap the full value of connected services.
McKinsey: What trends are you seeing related to connected services for commercial vehicles, and what do you anticipate moving forward?
Rupert Stuetzle: Three trends will drive the relevance of connected services. The latest vehicle architecture and connectivity solutions will provide value beyond traditional telematics use cases in the short term. For example, we anticipate increased visibility into real-time logistics and correlating data from multiple domains, such as traffic or weather. Examples of these applications in practice include tracking and fleet management services, intelligent cruise control, eco-driving capabilities, and predictive diagnostics.
We’ve also observed a growing interest in integrated transport system solutions and as-a-service business models. Connected services are a prerequisite for these offerings because they ensure profitable service provisioning for the operators and enable efficient transport management. When we look at full logistics-as-a-service solutions, connected services could support higher-level services beyond road transport. This trend will likely fuel demand for connected services in trucking in the midterm. Virtual-freight-forwarding players are already offering TaaS-like services, which could increase the use of connected services in fleets.
The zero-emission vehicle transition could also heighten demand for connected trucks as various use cases emerge that provide substantial value to customers, such as charge planning. As this transition gathers momentum, we expect a significant uptake of connected services in the mid- to long term. Autonomous trucks will likely make connected services even more relevant, though this is a bit further out, even with assisted driver functionalities integrating into the connected-services landscape.
McKinsey: Frank, what use cases have you observed among key players in the field?
Frank Kaleck: First, we need to distinguish between commercial vehicles and passenger cars, and there is a clear business case for commercial vehicles. In such a margin-critical industry, fleet managers earn money only if a truck is on the street. Therefore, with commercial vehicles, it’s all about reliability and efficiency, which provide significant opportunities for monetization and value creation.
Business-critical services have a higher demand for near real-time capabilities, such as vehicle tracking. Several large retailers already claim they can reduce diesel consumption by up to 8 percent by leveraging a fleet management system. Nearly all the commercial vehicle OEMs that provide guidance on efficient consumption in their vehicles incorporate gamification. For example, OEMs can identify drivers with the lowest diesel consumption on a specific route and generate daily high scores.
However, the most sophisticated use cases for connected services are intelligent driver-assistance systems, which some OEMs have in the market or are announcing. For example, a truck can develop a route plan by incorporating maps, route profiles, road conditions, and traffic data to guide the driver. And if the driver approaches a speed reduction zone, the truck can tell the driver to take their foot off the accelerator.
McKinsey: Those are some impressive use cases. What do you think is next? If we consider TaaS, for example, how will connectivity shape that model?
Frank Kaleck: The TaaS model is meant to reduce logistics companies’ total cost of ownership [TCO]. As we know, TCO is related to vehicle asset management, which is driven by new-vehicle prices, maintenance, insurance, and, eventually, depreciation. By leveraging TaaS offerings, logistics companies pay only for the core purpose of a truck, meaning the transportation of goods and people from A to B.
There isn’t just one flavor of TaaS; we have logistics as a service, mobility as a service, and electric vehicle [EV] charging as a service. For connected fleets, task providers rely heavily on data and advanced analytics to optimize routes, manage the fleets, proactively maintain vehicle health, and match supply with demand. All these examples illustrate the movement toward TaaS models.
When we look at full logistics-as-a-service solutions, connected services could support higher-level services beyond road transport.
Rupert Stuetzle
McKinsey: The biggest transition in commercial vehicles is the shift toward zero-emission vehicles. Depending on the forecast, estimates suggest that by 2030, 20 to 25 percent of new-vehicle sales in the US and 40 percent in Europe might be zero emission. Rupert, you mentioned that the zero-emission transition could help connected services become more relevant and differentiated. Could you elaborate?
Rupert Stuetzle: We see two drivers of connectivity services through zero-emission vehicles: shorter innovation and R&D cycles in product development, and broader use cases for efficient EV operations. In general, the automotive market expects significantly shorter development cycles. Tier-1 suppliers and commercial vehicle OEMs are under pressure to iterate on EV technology within an extremely short period. To do so, they need a fast digital feedback loop. Engineers can use connected services to capture real-time signals from any vehicle module, such as engine control units, a human–machine interface, a battery, or sensors. They can then analyze the information to identify anomalies quickly, make data-driven decisions to solve problems, and optimize performance.
Connectivity can also support efficient operations for zero-emission vehicle truck fleets. Example use cases include route optimization, route charge planning, battery and truck condition monitoring, depot charge planning, and even bidirectional charging. However, achieving efficient zero-emission vehicle truck and fleet operations requires significantly more connected and linked data domains than traditional telematics. Range optimization and charging, or refueling planning, will also be crucial in reaching TCO targets.
McKinsey: What implications do you think the evolution of things such as connected trucks, charging infrastructure, and transport solutions will have on system and business architecture?
Frank Kaleck: The automotive industry has been on a disruptive path over the past two or three decades. As more vehicle capabilities become software-based, the industry is adopting recognized technology patterns from mobile app or cloud application development, such as observability.
However, to effectively monitor, log, and trace data, automakers need a mature, reliable, performance-optimized, and secure vehicle telematics and data analytics platform. Automakers already have machine learning and analytics capabilities to gather insights and extract valuable information from vast data. This approach helps them identify anomalies in vehicle performance and proactively adapt vehicle configurations to reduce unplanned maintenance events.
Automakers must also consider the electrical and electronics [E/E] domain. In addition to cloud and backend capabilities, they need to increase the power of the high-performance computer within the vehicle. Therefore, technology patterns such as cloud computing, hybrid infrastructures, and data processing at the edge and in various domains are becoming more important. Connectivity modules are an essential component of this puzzle.
Another interesting dimension is the impact of connectivity on large fleets. For example, if you look at large fleets, you will see multiple brands and vehicles of different ages. Managers currently rely on retrofitted OEM-agnostic solutions to get a holistic view of their fleet. This situation paves the way for a new digital services and data-related business model. Tech companies are already helping collect fleet data and enriching it with other data sources, such as weather or traffic information. They are also establishing marketplaces so that OEMs, fleet managers, and other players can exchange data, creating a new pathway for data monetization. Of course, this business model is not just for tech companies—it’s an opportunity for all commercial vehicle OEMs.
Rupert Stuetzle: We’re also seeing significant changes in backend infrastructure. From our perspective, a standard harmonization layer can enable data analytics across a fleet with different OEMs and vehicle ages and provide a cost-efficient way for providers and ecosystem partners to participate in connected services without bearing the development cost.
Open-source initiatives and alliances—such as Eclipse SDV and COVESA—are already working to establish data format standards and common ontologies related to vehicle data and signals. Competitors are also teaming up to create a common software-defined vehicle platform and dedicated truck operating system that offers advanced digital features and services to enhance customer efficiency and experience. By pooling resources and expertise, stakeholders can accelerate the development of new platforms and systems, increase scale, and reduce material costs and R&D costs per unit.
There’s huge pressure to improve efficiency in the new-vehicle life cycle, generating interest in gen AI among OEMs and tier ones and leading to rapid adoption.
Frank Kaleck
McKinsey: One technology on everyone’s mind right now is gen AI. Where do you see gen AI operating or evolving in the current vehicle ecosystem?
Frank Kaleck: There’s huge pressure to improve efficiency in the new vehicle life cycle, generating interest in gen AI among OEMs and Tier 1s and leading to rapid adoption. At the same time, the complexity of vehicle engineering is growing exponentially because automakers are producing zero-emission vehicles alongside combustion engines.
So how can AI help drive efficiency in vehicle engineering? One option is to use gen AI to analyze regulatory documents, extract functional specifications, check consistency, and suggest test and validation cases. Automakers are already starting to use gen AI-based tools such as GitHub Copilot, for instance, to generate software code, identify issues, and automate repetitive tasks, such as testing software code, which shows proof of value.
Gen AI can also help with data literacy. In the past, you had two personas in automakers: a data analytics specialist and a domain expert (like an engineer) who knows about E/E mechatronics development. To gather insights from the vast amount of data at their disposal, they had to talk to each other and translate what they needed from each other.
Now, gen AI can help increase data literacy instead of having the engineer explain the question so that it makes sense to a data-literate analyst. Instead, the engineer can ask gen AI questions in natural language and get detailed insights. For example, engineers could tell gen AI to “plot a map where anomaly battery drains happened during the last test drive” and get a figure with all the information. OEMs and tech companies are working to provide similar capabilities to their fleet customers, tapping into another opportunity to monetize new digital services.
Automotive and commercial vehicle market players are deploying Gen AI as an intelligent driver companion or assistant. Some passenger car OEMs already offer ChatGPT-powered assistance, for example, providing a benchmark for commercial vehicle OEMs. These assistants can improve safety by ensuring the driver is not distracted by trying to retrieve information manually. Future applications on the horizon include asking the assistant to solve complex tasks. For instance, you could say “Get me from A to B, but make sure that there’s a parking space with a DC fast charger available.”
McKinsey: How can the industry maximize the value of connected truck services?
Rupert Stuetzle: Automakers, suppliers, and other commercial vehicle and transportation players need to address three critical challenges.
First, we need stronger ecosystem thinking around use cases that create the value customers expect. We receive many requests from traditional commercial vehicle ecosystem players, such as OEMs and fleet operators, on how to operate a competitive business model with appropriate governance, steering, and target setting. These requests indicate a desire to move away from conventional thinking. Interface standardization will be crucial to value generation because connectivity services need to work across various truck models, domains, and data sources. For this to work, however, industry players must be open to using standard APIs and common anthologies rather than closed solutions.
Second, stakeholders should invest in pilot projects and real-world deployments of autonomous-driving technology to gain practical experience and refine their business models. Partnerships with tech companies and regulatory bodies can also accelerate the deployment and integration of autonomous vehicles.
Third, automakers and OEMs must also establish clear policies and frameworks around data ownership, ensuring transparency and trust among all stakeholders. Specifically, they should implement robust data protection measures that comply with regional and international regulations and safeguard vehicle and customer data. Content management systems should allow customers, OEMs, and suppliers to easily control and manage their data-sharing preferences.
And finally, stakeholders should establish a data-driven engineering feedback loop. By sharing real-time vehicle data within a closed feedback loop, stakeholders can get the right information for requirements management and engineering, design, product engineering, production processes, and sales. This approach can accelerate continuous improvement and innovation through advanced analytics that capture and analyze customer feedback and vehicle signals.
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