As AI and software-defined vehicles transform automotive engineering, historical decisions are becoming as important as the systems themselves. But what happens when a proven design carries forward assumptions that no longer apply? This presentation examines the growing challenge of interpretive debt and why preserving the context behind waivers, requirements, and engineering decisions is critical for safety, compliance, and innovation. Learn how OEMs can leverage AI to uncover hidden dependencies, maintain governance continuity, and ensure the decisions of yesterday remain valid for the vehicles of tomorrow.

• Why waivers are governance artifacts, not administrative paperwork — and what happens when that distinction is ignored across builds
• How AI systems silently inherit unresolved conditional decisions from prior designs, and why structured governance data must precede ingestion
• The five failure modes that occur when waiver debt goes ungoverned in SDV programs — from OTA scope expansion to audit reconstruction failure
• A practical waiver governance protocol with five mandatory elements any program can implement before the next build
• How to establish the minimum viable governance loop: when a dependency changes, the waiver that relies on it gets reviewed