As global automotive companies rush towards a more digital future, artificial intelligence (AI) has emerged as a key player in driving innovation, efficiency and profitability.
Despite years of heavy investment and testing of AI initiatives, a significant gap remains between promise and performance. According to a recent report from Boston Consulting Group (BCG), entitled: Where’s the Value in AI?, only 26% of companies across industries have successfully moved beyond the pilot stage to driving tangible value from AI. The automotive sector is not immune to this challenge as it must navigate the complexities of integrating AI into core functions while pursuing measurable returns.
AI leaders are performing better, but most have yet to see gains
Based on a survey of 1,000 senior executives across 59 countries and 10 industries, the BCG report finds that only 4% of companies have achieved advanced AI integration across functions, consistently driving significant value. Meanwhile, 22% have made progress in AI strategy and implementation, achieving significant benefits early on. As many as 74% of companies still struggle to translate AI investments into results.
In the automotive sector, the gap between AI ambitions and outcomes is particularly clear. Automotive companies often want to apply AI in their operations, R&D, sales and customer service. Yet only a few have managed to generate consistent returns.
Characteristics of AI leaders
The report identifies six key characteristics of AI leaders, many of which apply to the automotive industry.
Core business focus: AI’s greatest value lies in transforming core business processes and not just support functions. In the automotive sector, this includes AI-driven improvements in production lines, predictive maintenance, supply chain optimization and connected vehicle solutions. According to BCG, core processes are responsible for 62% of AI value, underscoring the importance of deploying AI where it impacts the business most.
Ambition that goes beyond productivity: Leading automotive companies view AI as a strategic enabler for competitive differentiation, not just a tool for incremental profits. These leaders are investing heavily in AI and digital talent, with expectations of 60% higher AI-driven revenue growth by 2027.
Integrated cost and revenue strategies: The potential of AI extends to both cost reduction and revenue generation in the automotive industry. About 45% of AI leaders are integrating AI into cost efficiency measures, while over a third are using AI to drive revenue growth through innovations such as personalized customer experiences, smarter sales processes and tailored marketing strategies.
Strategic investments in high priority projects: Unlike companies that spread their investments across multiple AI initiatives, leading automakers are focusing on select high-impact opportunities. With advances in autonomous driving, predictive vehicle maintenance, and AI-driven R&D, these companies aim to maximize returns by strategically scaling successful AI pilots.
People and processes first: The success of AI in the automotive sector is more dependent on talent, culture and process improvements than on algorithms and data infrastructure. Industry leaders allocate 70% of AI resources to people and processes, 20% to technology and only 10% to algorithms – a strategy that helps overcome adoption barriers and ensure sustainable value creation.
Rapid Adoption of Generative AI (GenAI): Across industry segments, automakers are accelerating GenAI implementation to improve vehicle design, customer interactions, and supply chain optimization. From qualitative analysis to automated content creation for marketing: GenAI enables automotive companies to innovate quickly while maintaining quality.
The impact of AI on the core functions of the automotive industry
Contrary to popular belief, the value of AI in automotive extends far beyond support functions. According to BCG, more than half of the value comes from core business activities, with AI driving improvements in areas such as operations (23%), sales and marketing (20%) and R&D (13%).
R&D: Automotive leaders are using AI to accelerate the development of new models and optimize vehicle designs. AI-powered simulation tools, digital twins and predictive analytics allow companies to test designs virtually, saving both time and costs.
Operations: AI improves production line efficiency through predictive maintenance, real-time monitoring and defect detection, leading to faster assembly and less downtime.
Sales and marketing: With AI-powered personalization tools, automakers can better understand consumer preferences, create tailored marketing campaigns and improve conversion rates.
Navigating AI Implementation Challenges
While AI offers enormous opportunities, challenges remain. According to BCG research, 70% of AI-related obstacles are related to people and processes, while technology and algorithms account for 20% and 10% respectively. Common barriers in the automotive sector include resistance to change, skills gaps, and ineffective collaboration between AI teams and business units.
To bridge the gap between AI’s potential and results, automakers must emphasize change management, workflow optimization and AI talent development. For example, integrating AI into autonomous vehicle development requires not only technical advancements, but also the coordination of engineering teams, regulators, and customers.
Focus on people, processes
Successful AI implementation in the automotive sector depends on a balanced approach that prioritizes people and processes over purely technical solutions. Companies that spend significant resources on AI training, talent acquisition, and process refinement often achieve better results. In contrast, those who focus disproportionately on algorithms and technical tools struggle to get past the pilot phase.
The way forward
As automotive companies continue to invest in AI, they must focus on scalability, strategic investments and workforce alignment. By emulating AI leaders and emphasizing core capabilities, the industry can unlock significant value and maintain competitiveness in an increasingly digital landscape.
Without decisive action, companies risk falling behind. Embracing the 70-20-10 principle – 70% of resources go to people and processes, 20% to technology and 10% to algorithms – can help players realize the full potential of AI. As the industry races towards an AI-driven future, those who strike the right balance between ambition and execution will emerge as winners in the rapidly evolving mobility ecosystem.
NB: Photo is representative. Courtesy: continental.
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