2025汽车行业的人工智能（AI）机遇研究报告_The_AI_opportunity_in_Automotive（英文）_18页_3mb

报告摘要

The AI Opportunity in Automotive Summary

Core Content

This document explores the transformative potential of artificial intelligence (AI) in the automotive industry, focusing on how it can enhance both top-line and bottom-line performance. It outlines a strategic framework for leveraging AI across the value chain, emphasizing the importance of a holistic approach to AI integration and governance.

Key Hypotheses on AI in Automotive

AI is evolving from generative to agentic AI, but the realization of bottom-line benefits is taking longer than anticipated.
AI will boost key transformation areas such as software-defined vehicles, autonomous driving, and electric vehicles.
AI use cases are emerging across the value chain, with the highest short-term impact in customer experience, software development, and selected corporate functions.
AI can bring significant bottom-line opportunities if effectively coordinated across the company, potentially leading to a 40-60% margin uplift.
Partnerships are crucial for scaling AI beyond traditional "build" approaches, with winners focusing on foundational AI work and building an ecosystem.

AI Impact Across the Value Chain

Research and Development: Generative design, R&D project prioritization, and performance improvement.
Production and Supply Chain: Automated visual factory control, predictive maintenance, and end-to-end supply chain optimization.
Sales, Marketing, and Aftersales: Automated marketing content, personalized vehicle configuration, and fleet sales co-pilot.
Mobility and Financial Services: Battery state of health estimation, residual value calculation, and adaptive fleet management.
Connected and Automated Services: In-vehicle personal assistants, smart navigation, and driver care.

Corporate Functions and AI Use Cases

Strategy and Planning: Vision and targets, operating model, and portfolio management.
Data Management: Data processing, regulation, and compliance.
Capabilities and Culture: Awareness, training, and community building.
Technology: Tools, platforms, and infrastructure.

AI Governance Models

Centralized Model: Hub manages strategy, compliance, and infrastructure; spokes handle use case prioritization and implementation.
Decentralized Model: Each business unit manages its own AI initiatives with minimal coordination.
Hub and Spoke Model: A balanced approach where the hub sets targets and provides infrastructure, while spokes focus on domain-specific execution.

Success Factors for AI Implementation

Recalibrate Data and AI Activities: Align with (Gen)AI capabilities and adjust use case portfolios.
Establish Trust Frameworks: Address ethical and regulatory challenges through comprehensive monitoring.
Collaborate with External Partners: Leverage expertise without creating dependencies.
Tailor AI Setup: Recognize that each use case may require a different AI configuration.
Foster a Culture of AI Adoption: Encourage learning and experimentation among employees.

AI Development Stages

The document outlines stages from AI foundation to scaling big-ticket items, highlighting the need for a structured and accelerated AI journey.

Immediate Actions for AI Success

Assess AI Maturity: Use a free online test to evaluate current AI capabilities.
Explore Use Cases: Get inspired by a library of industry-specific AI use cases.
Engage in Workshops: Understand AI value potential through joint workshops.

Contact Information

Jonas Seyfferth – Director, AI/technology-driven innovation in mobility
Email: jonas.seyfferth@pwc.com
Jörg Krings – Partner, Auto Practice Lead
Moritz Wächter – Director, Technology/IT architecture and transformation
Florian Stürmer – Partner, Data.AI/IT strategy in industrial manufacturing
Tobias Kasseroler – Senior Associate, Digital/data.AI strategy and business model innovation

Additional Resources

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Disclaimer

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