Manufacturers Must Take the Wheel to Scale SDV Success
The automotive industry is moving rapidly toward Software-Defined Vehicles (SDVs), where cars are as much about software as they are about mechanics. The race to SDV is driven by progress in electrification, connectivity, autonomous driving technologies, and innovative ownership models. Unlike traditional cars, SDVs are designed to be as adaptable as smartphones, with software driving features like performance, safety, and personalization. This shift is already underway, with projections showing that SDVs could make up an astounding 90% of vehicle production—a massive leap from just 3.4% in 2021. This marks a powerful transformation, setting the stage for cars to become smarter, safer, and more personalized over time.
But scaling SDV production is no small feat. Integrating complex software with traditional vehicle hardware, managing constant updates, and ensuring quality across evolving models bring new challenges. For example, GM, Volkswagen, and Volvo have all faced major roadblocks in transitioning to SDV despite their desire to capture market opportunity. While these complexities have notably stalled automaker innovation efforts, manufacturers have a golden opportunity to support integration and quality management as we navigate the shift to digital. To truly scale SDVs, manufacturers must take a balanced approach to stay ahead of the curve, overcome roadblocks, and drive the future of SDVs with confidence. Here’s how they can succeed:
Focus on Core Competencies
Traditionally, vehicle differentiation was defined by mechanical and physical characteristics - combustion engines, mechanical transmissions and aerodynamics. Today, it's increasingly about software capabilities, like remote connectivity, customizable features, and advanced driver-assistance systems (ADAS). However, scaling SDVs comes with new engineering and architectural demands. According to Deloitte, connected vehicle data is expected to grow from 20 exabytes in 2022 to 117 exabytes by 2027. This means manufacturers will need to manage massive volumes of data, analyzing and securing it in real time. At the same time, integrating advanced software into hardware systems requires expertise that many traditional manufacturers are still building. Core competencies like design, engineering, architecture and integrating technology stacks from multiple suppliers are essential for creating next-generation vehicles. Without these key skills, production delays, rising costs, and compromised SDV quality could quickly erode consumer trust.
It’s crucial to ensure that ambition doesn’t outpace practicality. We can learn from GM, which announced plans to transition to “100 percent virtual design, development and validation by 2025” in a presentation earlier this year. However, the automaker rightly clarified that they don’t plan to entirely shift to virtual processes just yet. Instead, GM is focusing on adopting a range of tools and innovations to enhance its development capabilities and improve productivity. This recalibrated approach aligns with the need to build core competencies first, striking a balance between setting bold goals and ensuring practical execution as manufacturers navigate the complexities of scaling SDVs.
For their part, manufacturers need to invest in highly skilled development and engineering teams with deep expertise in software integration and vehicle electronics. These teams can bridge the gap between hardware and software, ensuring systems work seamlessly together—a critical factor for SDV success. However, manufacturers don’t have to take on everything themselves. Partnering with external experts to handle non-core tasks, such as maintaining older vehicle generations, can free up valuable resources to focus on developing future versions of the vehicle technology stack and advanced SDV platforms with next-generation features. This division of labor lets manufacturers maintain control over critical areas like design, engineering, and architecture while external experts take charge of validation, verification, and lifecycle management. This strategic approach almost guarantees innovation without overextending teams and positions manufacturers to lead in the competitive SDV market.
Strategize Software and Data Monetization
Monetizing SDVs is one of the biggest opportunities for manufacturers to redefine how vehicles generate value. Unlike traditional cars, SDVs allow for ongoing revenue streams—think subscription-based features, over-the-air (OTA) updates, and data-driven services. For example, manufacturers can offer upgrades like enhanced vehicle acceleration through in-vehicle subscriptions, which could bring in as much as $900 a year per customer, according to Deloitte.
However, poor monetization strategies can hinder profitability and stifle innovation, leaving manufacturers to miss out on these lucrative recurring revenue opportunities. The key lies in building a strategy that creates sustainable value. Features like autonomous driving (AD) capabilities have moved away from futuristic concepts and are actively sought after by today’s consumers. In fact, McKinsey reports that the automotive software market is projected to more than double, reaching $80 billion by 2030, with consumer demand fueling much of this growth in AD technology. This surge in interest presents a significant opportunity for manufacturers to deliver lasting value while aligning with evolving customer expectations.
To fully capitalize on this potential, manufacturers must build modular software systems that simplify updates and enable scalable, customizable features. For instance, offering drivers the option to temporarily upgrade performance on demand or unlock advanced navigation tools can provide more flexibility that resonates with modern customers. Usage-based insurance is another example, where drivers pay premiums based on their actual behavior, meeting market demand while driving steady revenue. These consumer-centric approaches will derive new strategies for income that complement the broader business model strategy.
Integrate Quality Management Across the Lifecycle
Reliability is the foundation of any successful SDV and depends on rigorous quality management throughout the entire vehicle lifecycle. Poor quality management can lead to deferred product launches, increased costs, and missed revenue opportunities. Software-related recalls have skyrocketed, impacting nearly 10 million vehicles in 2022 compared to just 600,000 in 2012. These aren’t minor inconveniences either - failing to comply with quality assurance requirements can lead to penalties exceeding $165 million, not to mention the reputational damage.
SDVs have many components like infotainment systems, temperature controls, and autonomous driving features, all sourced from different suppliers and each with its own tech stack. Edge platforms can deliver real-time, context-aware services like personalized navigation and proactive maintenance alerts. Integrating these disparate elements into a seamless vehicle experience demands robust verification and validation processes. Done right, this ensures reliability, safety, and consumer confidence.
To mitigate risks, manufacturers must implement quality checks and validation processes at every touch point, ensuring consistent performance that meets all regulatory standards. One effective strategy is adopting outcome-based engagements, where vendors are held accountable for delivering high-quality results. This approach helps manage defect leakage and aligns vendor performance with manufacturers’ goals, upholding production standards across SDV lines.
At its core, quality management is about earning trust, especially in an industry where reputation is everything. Manufacturers should think of software development as an enterprise-scale operation, akin to a supply chain. Establishing software factories and leveraging specialized vendors can ensure scalable, secure, and maintainable software solutions for SDVs. When manufacturers commit to rigorous standards, they can deliver vehicles that meet regulatory demands, perform reliably, and provide peace of mind to drivers.
Leverage AI to Accelerate SDV Success
Scaling SDVs means dealing with constant updates and complex software integrations, creating an overwhelming workload for traditional testing methods. AI-powered tools help manufacturers create a fresh engineering approach that prioritizes scalability and efficiency.
AI-driven testing can automate essential processes such as test case generation, test coverage, and test data creation. This automation ensures comprehensive testing, reduces errors, and saves valuable development time. Generative AI takes these capabilities even further by creating synthetic test data that mirrors real-world conditions. It is particularly valuable when gathering real-world data is logistically or financially difficult. For example, AI can simulate challenging scenarios—like adverse weather conditions or traffic congestion—allowing manufacturers to evaluate performance without relying solely on physical testing.
Predictive maintenance is another critical application of AI that can help vehicles stay on the road longer. By analyzing vast amounts of vehicle data in real time, AI can identify wear-and-tear patterns and flag potential issues before they escalate into failures. Additionally, advanced algorithms can validate that software updates meet legal requirements while ensuring compatibility across multiple components and systems. For manufacturers, this translates to fewer recalls, lower costs, and a stronger reputation for reliability.
Manufacturers must plan to support vehicles for the next 15 to 20 years, making it essential to scale SDVs effectively. By embracing the right strategies, manufacturers can transition to a software-defined operation smoothly. Remember, when it comes to the scalability of SDVs, manufacturers are in the driver’s seat.
