凯捷-加速汽车的AI转型（英文）-2019.3-36页_3mb

Summary of "Accelerating automotive's AI transformation"

Core Content

This report explores the current state and future potential of artificial intelligence (AI) in the automotive industry. It highlights that while AI is critical for autonomous vehicles, its impact extends to multiple areas including engineering, production, supply chain, customer experience, and mobility services. The study is based on a survey of 500 automotive executives across eight countries and in-depth interviews with industry experts.

Main Views

• AI's Broad Impact: AI is not limited to autonomous driving but is transforming various aspects of the automotive industry, from product design to customer engagement and supply chain operations.
• Modest Progress: Despite growing interest, only 10% of automotive companies have scaled AI implementation enterprise-wide, up from 7% in 2017. Selective implementations remain at 24%, showing limited progress in widespread adoption.
• OEMs Lead in AI Adoption: Original Equipment Manufacturers (OEMs) are more advanced in AI implementation compared to suppliers and dealers, with 43% of OEMs implementing AI at scale or selectively.
• Regional Leadership: The US, UK, and Germany are leading in AI implementation, while China has shown the most significant growth in scaled AI deployment, nearly doubling its share from 5% to 9%.
• Startups as a Key Partner: Automotive companies are increasingly investing in AI startups to fill capability and skills gaps, especially in customer experience and mobility services. Over $11.2 billion has been invested in AI startups since 2014.
• High-Benefit Use Cases: AI has high potential in all functions, with notable benefits in R&D, supply chain, manufacturing, and customer experience.

Key Information

AI Implementation Status

• Scaled AI: Only 10% of automotive companies have implemented AI at enterprise scale.
• Selective AI: 24% of companies are implementing AI at multiple sites but not enterprise-wide.
• Pilots: 41% of companies are experimenting with AI pilots, down from 2017.

AI Use Cases in Action

AI Investment Trends

• Startups: Automotive companies are investing heavily in AI startups, particularly in mobility services and customer experience, with over $10 billion invested since 2014.
• Top Startups:
  • HERE Global BV (Netherlands) – Mapping, simulation, image recognition
  • Cruise Automation (US) – Full-fledged automation system
  • GrabTaxi Holdings (Singapore) – Ride-hailing
  • Lyft (US) – Ride-hailing
  • Matternet (US) – Autonomous drone delivery
  • Uber (US) – Ride-hailing
  • nuTonomy (US) – Robo-taxis
  • Gett (Israel) – Robo-taxis
• Investment Growth: Investment in AI startups by OEMs increased by 60% in 2018.
• Regional Focus: US, Netherlands, and Singapore startups are attracting the most interest, while Chinese startups are also receiving significant capital.

Financial Benefits of Scaled AI

• Operating Profit Increase: Large OEMs can boost their pre-tax operating profit by up to 16% through scaled AI implementation.
• Conservative Scenario: 5% increase in operating profit, up to $232 million.
• Optimistic Scenario: 16% increase in operating profit, up to $764 million.
• Cost Reduction: AI can reduce operating costs by 0.2%, leading to significant financial gains when scaled across the enterprise.

Challenges in AI Adoption

• Legacy Systems: Integration with existing IT systems remains a major obstacle.
• Data Availability: Lack of quality and quantity of data hinders AI scalability.
• Skills Gap: Limited expertise in AI technologies is a barrier for many organizations.
• Inflexible Structures: Traditional organizational structures and processes limit AI experimentation and adoption.

Recommendations for Scaling AI

• Focus on High-Benefit Use Cases: Prioritize use cases that offer the most value and align with business goals.
• Invest in Startups: Collaborate with AI startups to accelerate innovation and fill skill gaps.
• Build Enterprise-Wide AI Strategy: Ensure AI is integrated across all functions and sites for maximum impact.
• Enhance Training and Awareness: Promote AI understanding and training at all managerial levels to drive adoption.
• Leverage Open Platforms: Consider open-source AI platforms to foster collaboration and reduce development time.

Conclusion

The automotive industry is beginning to recognize the transformative potential of AI, but widespread adoption is still in its early stages. While OEMs are leading in AI implementation, the industry as a whole faces challenges in scaling AI across all functions and regions. Strategic investments in AI startups and a focus on high-benefit use cases are essential for achieving enterprise-wide AI transformation and realizing significant financial and operational gains.