Accelerating the SDV Journey: Insights from AEK 2025

At the Automobil-Elektronik Kongress 2025, Anup Sable, CTO of KPIT Technologies, delivered a keynote that reframed how the industry should approach Software-Defined Vehicles (SDVs). His central message: Speed of Change is the defining metric for SDV success—not just in feature delivery, but in how OEMs validate, integrate, and scale software across vehicle platforms

The Reality Behind the SDV Promise

While SDVs were expected to deliver faster innovation and richer user experiences, the industry has encountered significant friction:

• Fragmented architectures and underestimation of integration complexity.
• Constraints unique to each OEM’s platform, limiting scalability.
• A mismatch between software flexibility and system rigidity

As Anup Sable noted, “The promise of software has been overestimated in terms of ease. Integration with constrained systems is the real challenge”

Four Critical Areas for Transformation

Drawing from real-world implementations, the presentation outlined four areas where OEMs can accelerate SDV programs—even mid-cycle:

1. Platform Validation
A foundational yet often overlooked step, platform validation ensures that middleware, operating systems, ECUs, and networks are robust before application layers are added. KPIT has demonstrated that validating 10,000–35,000 test cases with full automation can dramatically improve release quality and speed

2. Virtual Validation
Virtual benches are not replacements for physical ones—they’re complementary. They enable scalable, nightly regression testing and fault simulation across car lines. As Anup Sable emphasized, “Virtual infrastructure is essential when testing every build, every test case, every night”

3. Subsystem Bench Strategy
Modern vehicles contain 7–15 subsystems. Building virtual and physical benches for these allows early issue detection—before problems escalate in fleet vehicles. This approach is underutilized but proven effective

4. Observability
Capturing system behavior in real-world fleets and feeding it back into subsystem benches is key to diagnosing and resolving issues. Observability bridges the gap between validation and root cause analysis

AI as a Catalyst for Process Disruption

Beyond validation, the presentation explored how generative AI can transform automotive engineering workflows:

• KPIT’s AI workbench is used by 5,500+ engineers across 50+ projects, supported by 15+ agents
• AI automates defect triaging, code patching, and regression testing.
• Success depends on domain-specific ontologies and structured knowledge bases—not just generic AI tools.

As Anup Sable explained, “The real power of AI emerges when you disrupt existing workflows—not just automate them”

Organizational Shift Required

To fully leverage AI and validation strategies, OEMs must rethink their departmental structures. AI implementation requires collaboration between domain experts, process owners, and technical teams. KPIT advocates for outcome-based engagement models over traditional time-and-material approaches

Looking Ahead

The presentation concluded with a provocative question: Can the industry converge on a common SDV architecture by 2030? While the answer remains open, the path forward is clear—platform strength, validation rigor, and AI-led transformation are essential to delivering on the SDV promise.
For additional perspective on the broader industry context, see the article published by ADT Media:

Global OEMs are under pressure.