Why Software-Defined Vehicle Projects Stall and How Functional Control Restores Confidence
In July 2025, McKinsey published its latest analysis titled "Winning the Automotive Software Development Race," highlighting how traditional OEMs remain years behind agile, software-first entrants such as Tesla, Nio, and BYD. The report clearly outlines that SDV delays are not due to a lack of vision or investment, but due to deeply embedded structural issues, fragmented architectures, poor system coordination, and reactive validation cycles. McKinsey calls out a critical blind spot: lack of visibility and ownership across the entire V-model lifecycle.
At SPREAD.ai, we believe this blind spot stems from one root cause: missing functional control. And it’s exactly what we address.
Why SDV execution gaps persist in OEM environments
Every major OEM has launched multi-billion euro programs to deliver software-first architectures, continuously update vehicles post SOP, and monetize feature delivery over the air. Yet despite the strategic urgency and rising investment, an execution gap remains. Delays are mounting. Variant complexity is exploding. Software features are slipping. Launches are postponed. In many cases, the vision of an SDV-ready organization is undone not by lack of commitment or budget, but by the inability to manage complexity at a functional level.
The assumption has been that better tooling and agile delivery models would close the gap. They have not. Functions are highly interdependent across the architecture, running on many ECUs and SWCs at once. Dependencies are buried inside disconnected legacy tools. Change impacts are unclear. Redundant tests waste capacity. And unexpected late-stage integration errors become the norm.
McKinsey’s data is clear: average OEM software timelines exceed 40 months, with integration and late-stage validation issues among the top three causes of delay. Forrester has similarly noted that system-level traceability is now a prerequisite for reducing software change risk in regulated industries.
The role of functional control in Software-Defined Vehicle programs
SPREAD AI was built to address this precise problem. Our Engineering Intelligence Platform creates a living, navigable model of the system across its domains and variants. It ingests and connects data from architecture models, wiring diagrams, BOMs, test results, requirements, ticketing systems, and more. This creates a shared layer of understanding between product, engineering, integration, and compliance teams.
At the core is the ability to visualize how a function is realized across signals, ECUs, and SWCs. Engineers can trace an issue to its architectural origin, assess downstream change impact by seeing which other components are affected. Requirements changes, architecture updates, and component redesigns can all be scoped proactively and precisely, not reactively. Validation effort is reduced, by testing what needs to be tested.
To understand how this architecture works in practice, consult SPREAD understand the tech document.
SDV Case study: €2B SOP risk mitigated with functional transparency
A leading European automotive OEM deployed SPREAD to secure development timelines for a next-generation vehicle platform. The program was at risk of missing the planned SOP. Teams operated in silos. Functional dependencies and maturity were unclear. Errors surfaced late and consumed weeks to be resolved.
By building a cross-domain product twin, the OEM linked behavior, architecture, and test results in one environment. The SOP timeline stayed on track. More than €20 million in cost were saved through fewer unexpected errors and quicker resolution. Root-cause identification accelerated by sixty percent. The project mitigated over €2 billion in SOP-related risk – all without replacing existing tools.
Read the case study: Mitigating €2B SOP risk at a leading automotive OEM
In a separate case, another OEM used SPREAD to accelerate R&D change management. Manual approval chains and lack of impact visibility had made the process expensive and slow. SPREAD enabled automated mapping of each change to relevant components and engineers. Approvals sped up by 75%. The company saved over €2 million annually and secured their SOP milestones.
Read the case study: Saving >€2M through faster change management at an automotive OEM
Why system intelligence is essential for Software-Defined Vehicle success
These outcomes were not achieved through generic AI or new dashboards. They were made possible by connecting engineering logic and making system relationships explainable. SPREAD complements PLM and ALM by filling the coordination gaps they were never designed to solve.
OEMs succeeding in SDV execution win by orchestrating complexity. Functional control is the foundation. Without it, the complexity is not manageable, where every change creates new risks. With it, programs accelerate, integration works, testing effort decline, and SOP becomes reachable.
OEMs that read McKinsey’s diagnosis and ask, “How do we operationalize this?” will find that functional control is the answer. SPREAD is how they get there.
Ready to steer your SDV ambitions with confidence?
Our experts with OEMs and suppliers to operationalize AI where it matters most – across architecture, validation, and variant complexity. We design scalable workflows grounded in your existing data, systems, and priorities. Talk to an Expert at SPREAD.
