Five Strategic Levers to Boost ADAS Profitability
Advanced driver-assistance systems (ADAS) are poised to deliver substantial safety benefits, with McKinsey estimating a potential 15% reduction in accidents across Europe by 2030. Yet, despite this promise, the sector’s profitability remains under pressure as investment requirements outpace returns for many OEMs, Tier 1 suppliers, and startups. At The Autonomous 2024 conference in Vienna, McKinsey convened industry leaders to dissect the core challenges—system complexity, lack of standardization, extended development timelines, and rising materials costs—and to explore actionable strategies.
McKinsey’s 2023 global autonomous driving survey, released in early 2024, underscores ADAS as a decisive purchasing factor. Martin Kellner, partner at McKinsey, noted, “We see that in China and also in Europe, every fifth customer would switch to another OEM if this other OEM had better ADAS and autonomous driving features.” Despite strong consumer demand, the path to profitability is constrained by high costs, product liability concerns, and regulatory uncertainty.
Design architecture emerged as a pivotal lever. Survey data revealed three prevailing views: 16% of participants anticipate a modular approach, integrating the best available software and hardware elements—a vision contingent on robust industry standards. Another 36% foresee continued hardware-software co-design, with integrated systems developed by a select group of players. The largest share, 48%, expect eventual separation of hardware and software, enabling reuse of architectures and potentially lowering design costs. Kellner emphasized the importance of aligning SAE Level targets with market needs, cautioning that not every OEM must pursue Level 3 capabilities immediately. Decisions on whether to evolve existing stacks or rebuild from scratch with modern tools and algorithms will shape cost structures and competitive positioning.
Collaboration was identified as another profitability driver. Kellner stated, “There’s a clear agreement that if the industry worked more closely together, having more standards and some kind of open source reference software, this would reduce development costs.” Yet aligning diverse ambitions and proprietary architectures remains a challenge. Workshop discussions suggested focusing joint efforts on non-differentiating technologies to accelerate time-to-market while preserving competitive advantages.
Strategic make-or-buy frameworks also play a critical role. Organizations must determine which components are core to their differentiation and which can be outsourced. Outsourcing non-core elements can enhance capabilities and shorten development cycles, provided procurement processes ensure quality, reliability, and cost control. Kellner observed, “Today, we see, especially for the higher level of ADAS, a tendency to do in-house, because there is not a fully functional, ready-to-deploy L3 stack available… but based on the discussions, this could change as there are more systems available.”
Generative AI is emerging as a transformative tool for efficiency. Beyond summarizing regulations and enhancing RFIs and RFQs, GenAI models are being deployed for data annotation, tagging, and synthetic data generation. Kellner highlighted that these capabilities “can reduce the cost to go out and collect real-world data.” Potential savings extend into software development, testing, integration, and validation, offering a pathway to compress timelines and reduce expenditure.
Finally, project excellence was discussed as an overarching goal. Kellner defined it as meeting predefined scope, time, quality, and budget targets, with particular emphasis on software development efficiency. “Project excellence includes software development excellence, not by developing an ADAS system in ten different locations, but by being smart and really trying to improve software development processes,” he said. Standardized tool chains across projects and global key performance indicators were proposed as mechanisms to measure and sustain improvement.
These five levers—design choices, collaboration, make-or-buy strategy, generative AI adoption, and project excellence—form a cohesive framework for addressing the profitability challenge in ADAS development. Each requires disciplined execution, informed by market data and aligned with evolving regulatory landscapes.
