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Overview

Where AI strategy drives automotive performance

The 2026 Global AI Report for automotive reveals how AI leaders translate strategy into measurable impact across software-defined vehicles, engineering and intelligent manufacturing. Based on global executive research, the findings show that disciplined governance, operating model reinvention and end-to-end integration separate leaders from laggards. Automotive organizations that embed AI across the value chain are widening the performance gap at scale.

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Highlights

FAQ

What defines an AI leader in automotive?

AI leaders in automotive have a well-defined AI strategy, mature or evolved AI adoption and measurable operational impact. They move beyond isolated pilots, embedding AI into engineering, manufacturing and software-defined vehicle platforms at scale.
Why is AI investment accelerating among automotive leaders?
83.8% of AI leaders in automotive are increasing AI investment, reflecting confidence built through early operational wins. Leaders convert proof points into disciplined reinvestment that compounds performance gains across the value chain.
What separates AI leaders from laggards in automotive?
Leaders rebuild core systems with embedded AI, centralize governance and redesign operating models end to end. Laggards rely on fragmented pilots that limit SDV integration and ecosystem coordination.
Why is governance critical in automotive AI?
67.6% of AI leaders follow centralized governance models. In safety-critical environments like automotive, structured oversight enables faster scaling across engineering, manufacturing and OTA systems without increasing risk.
Where is AI creating the most value in automotive?
93.2% of AI leaders embed AI directly into operational workflows. In automotive, that includes engineering, intelligent manufacturing, OTA decision systems and customer experience optimization.

Key findings

  • Automotive leaders combine decisive execution with disciplined AI governance.
  • Centralized oversight enables AI to scale safely across software-defined vehicles and plant systems.
  • AI leaders embed intelligence into engineering, manufacturing and in-vehicle experiences.
  • Workflows are redesigned end to end to transform SDV from concept to customer experience.
  • 83.8% of AI leaders in manufacturing are increasing AI investment.
  • 38.6% are rebuilding core systems with embedded AI.
  • 67.6% follow centralized AI governance to scale with control.
  • 93.2% embed AI directly into operational workflows.

In automotive, AI is redefining the operating model itself. The leaders pulling ahead are embedding intelligence into software-defined vehicles, engineering and intelligent manufacturing, governing it with discipline and scaling it across the value chain with confidence."

Ralf Malter
Global Automotive Leader, NTT DATA
Insights

Additional insights for business leaders

  1. Strategic alignment
    Automotive AI leaders align AI initiatives tightly with SDV strategy and value-chain priorities.

  2. Operating model transformation
    Leaders redesign engineering and manufacturing workflows around AI-native decision systems.

  3. Governed for scale
    Centralized oversight enables faster deployment while reducing operational and safety risk.

  4. Customer experience advantage
    AI connects plant systems, OTA updates and in-vehicle intelligence to enhance driver experience.

  5. Enterprise momentum
    Investment success fuels reinvestment, widening the performance gap.

  6. Actionable next steps
    Download the report to benchmark your AI maturity and scaling strategy.
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