Into the Fast Lane with AI: Driving the Future of Mobility

1.1 Research Scope

1.2 Research Methodology

1.3 Research Methodology Explained

1.4 Key Findings

2.1 AI Application in the Automotive Industry

2.2 Market Size – Global AI in Automotive Market

2.3 The Global Automotive Production by Region

2.4 AI in Automotive – Technology Value Chain

2.5 AI Use Cases Across Automotive Value Chain

3.1 AI Enables Automotive R&D Teams to Review Data Efficiently, Improve Analysis, and Prioritize the Innovations

3.2 Use Cases – Research & Development

4.1 AI Reduces the Automotive Supply Chain Complexities by Making Logistics Systems Transparent, Lean, and Flexible

4.2 Use Cases – Supply Chain

5.1 AI-based Cobots Work Along with Human Operators, Completing Repetitive Tasks More Quickly and Increasing the Overall Efficiency

5.2 Use Cases – Manufacturing

6.1 Automotive Companies are Continuing to Adopt Programmatic Automation Using AI/ML Capabilities

6.2 AI Systems are helping OEMs to Increase their Revenue through Sales and Service Recommendations to Customers at the Right Time

6.3 AI Use Cases – Marketing and Sales

7.1 Automotive Manufacturers are focusing on Making Interaction with Cars' AI Systems More Intelligent

7.2 AI Use Cases – Enhancing Driver/Customer Experience

8.1 Automotive Companies are Accelerating their Interest in AI-led Startups to Enhance their Capabilities and Stay Competitive

8.2 Automotive Manufacturers are Teaming up to Accelerate their AI Implementation

9.1 US, Singapore and China are Accelerating their Efforts to Make Autonomous Driving a Reality

10.1 The US and China are the Leading Countries in AI-based Automotive Patent Registrations

10.2 Research Initiatives Around AI will Result in Exponential Rise of Patents in the Next 5 years

10.3 Strategic Insights

11.1 Key Contacts

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