AI and its Application in the Commercial Vehicles Market, Global, 2024-2029

AI is Driving Transformational Growth in Commercial Vehicles

This study examines the development prospects that artificial intelligence (AI) offers the commercial vehicle (CV) industry, focusing on both the revolutionary potential of AI and the difficulties businesses face in fostering growth, including complicated regulations, high capital expenditure, and challenges in incorporating new technology into pre-existing systems as the industry becomes more competitive. Owing to these obstacles, businesses are challenged to scale and maintain growth. In such a scenario, AI is a potential facilitator, providing solutions to boost safety, optimize operations, and improve customer experiences-all of which eventually promote expansion in an industry that is changing quickly.

The study starts by outlining AI in terms of its use throughout the CV life cycle. AI is defined, and several subsets of technologies are examined, including robotics, machine learning, and natural language processing, all of which can be applied in CVs. These technologies improve the efficiency and performance of commercial fleets across several critical fleet activities, including autonomous driving, ADAS and driver behavior, predictive maintenance, and real-time decision-making. From enhancing car design to revolutionizing supply chain operations, AI's influence spans the entire CV life cycle, highlighting its widespread applicability and promise in this field.

The study also discusses how AI is used in design, sales, operations, and in-vehicle features. Each life cycle stage's key ecosystems are examined, and a case study is used to show how AI is impacting the industry. The study includes real-world examples of how businesses are successfully incorporating AI into their operations for each ecosystem and its key fleet applications. Leaders in AI adoption include Dassault Systemes for its ongoing innovation in software-generated designs, FourKites, which uses AI to track vehicle data and monitor fleet performance, and Samsara, which employs AI to monitor fleet performance. These case studies highlight the advantages AI offers CV operations, including increased productivity, reduced expenses, and better service.

The study then explores the major global trends of AI in the CV industry, including work order automation, prognostics, emotional intelligence, and autonomous driving. While emotional intelligence improves user-vehicle connections and makes cars safer and more proactive, autonomous driving technology is predicted to transform transportation by decreasing human intervention and boosting efficiency. Work order automation improves overall efficiency by streamlining operations and decreasing administrative burdens, while prognostics-the capacity to anticipate vehicle breakdowns before they happen-helps businesses save maintenance costs.

With an emphasis on the major business models propelling AI adoption, the study also discusses the competitive landscape in the AI-driven CV space. The primary business models for the CV industry to acquire revenue traction are hardware-integrated solutions, software-as-a-service (SaaS) models, and subscription-based services. In addition, the business models are dissected ecosystem- and fleet-operation-wise, and an AI-based revenue estimate for the entire CV industry is calculated. Furthermore, the study compares global regions using criteria that have a significant impact on the regional development of AI and important areas of AI's rapid expansion in the CV industry.

The study concludes by highlighting several significant potential prospects in the AI-driven CV space. As AI develops, it will play a crucial role in fostering innovation and expansion in the CV industry and assisting businesses in streamlining processes, cutting expenses, and maintaining their competitiveness in a world that is becoming increasingly automated. By adopting AI, the CV industry can open new growth prospects and revolutionize the international transportation of products and services.

Scope

• Market Dynamics
  • Analysis of current trends and market forces impacting the commercial vehicle sector.
• Technology Trends
  • Examination of advancements in AI technologies relevant to commercial vehicles.
• Competitive Landscape
  • Overview of key players and their strategic initiatives.

The Impact of the Top 3 Strategic Imperatives of AI in the CV Industry

Transformative Megatrends

• Why: From vehicle design and manufacturing to sales, operations, and safety, AI is revolutionizing the CV industry and generating value throughout the ecosystem. AI is essential for the seamless integration of electric and autonomous vehicles, leading to a shift that drives the sector toward sustainable efficiency.
• Frost Perspective: AI will play a key role in major transformative trends across the entire life cycle of CVs. With exponential data generation, AI is crucial for enhancing efficiency and establishing standardization across all operations.

Disruptive Technologies

• Why: The CV industry is being disrupted by faster, more efficient, and aesthetically pleasing vehicle designs enabled by AI, facilitating the production of personalized and efficient automobiles. Telematics and logistics are key areas where AI is driving rapid disruption by integrating prognostics and freight visibility, respectively, altering market dynamics and the interaction between fleet operators and their fleets.
• Frost Perspective: AI will be pivotal in disrupting conventional vehicle management and operational practices. Industry demands are continually escalating, characterized by shorter delivery times, new and efficient designs, safer vehicles, and personalized cabin experiences. AI will be vital in addressing these demands. In addition, maintenance and telematics are experiencing AI disruptions, where it is being leveraged to minimize total operational costs.

Customer Value Chain Compression

• Why: In CVs, AI shortens the customer value chain by simultaneously reducing maintenance costs and enhancing operational efficiency. In-cabin AI features improve the overall driving experience with voice assistants and ADAS, while also reducing accidents, leading to increased profitability.
• Frost Perspective: With millions of terabytes (TB) of data generated every second, AI, with its high processing power, cuts through multiple layers of conventional decision-making with faster and more efficient decisions, generating value. AI is and will continue to actively contribute to value chain compression by minimizing total operational costs through increased operational efficiency.

Competitive Environment

• Number of Competitors
  • >25
• Competitive Factors
  • Technology, accuracy, partnerships, cost, performance, support, reliability, ease of integration, customer relationships
• Key End-user Industry Verticals
  • CVs, passenger vehicles (PVs), two wheelers (2Ws)
• Leading Competitors
  • Amazon, Apple, Baidu, Nvidia, Meta, Microsoft, IBM, Uber
• Other Notable Competitors
  • Adobe, Dell, Intel, AMD, Salesforce
• Distribution Structure
  • Technology companies, data science companies, OEMs, fleet managers
• Notable Mergers and Acquisitions
  • Microsoft acquired OpenAI; Google acquired Waymo

Growth Drivers

• Growth of telematics and connected vehicles
• Growing logistics and eCommerce sectors
• Increasing demand for efficiency
• Safety improvements
• Competitive advantages

Growth Restraints

• Regulatory restrictions
• False positives
• Data privacy and security concerns
• High initial costs
• User acceptance

Key Competitors

• Siemens
• Dasault Systems
• Ulpath
• UBTech
• Autodesk
• Blue Prism
• Verizon
• Samsara
• Tusimple
• Aurora
• Geotab
• Pretekt
• Pitstop
• Project 44
• Transplace
• Four Kites
• Amazon, Meta
• Salesforce
• Imaginovate
• Light
• Pearl Auto
• Phantom
• In-cabing assistance
• Safety & ADAS
• Marketing & dynamic pricing
• Robotics automation
• Design software
• Telematic