As global automotive companies race toward 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 piloting AI initiatives, a significant gap remains between promise and performance. Only 26% of companies across industries have successfully moved beyond pilot stages to derive tangible value from AI, according to a recent report by Boston Consulting Group (BCG), titled: Where’s the Value in AI?. The automotive sector is not immune to this challenge, as it navigates the complexities of integrating AI into core functions while pursuing measurable returns.
AI Leaders Outperform, But Most Are Yet To See Gains
Based on a survey of 1,000 senior executives across 59 countries and 10 industries, the BCG report identifies that merely 4% of companies have achieved cutting-edge AI integration across functions, consistently generating significant value. Meanwhile, 22% have made strides in AI strategy and implementation, reaping substantial early benefits. However, a staggering 74% of companies still struggle to translate AI investments into results.
In the automotive sector, the gap between AI ambitions and outcomes is particularly evident. Automotive companies are often eager to apply AI across operations, R&D, sales, and customer service. Yet, only a few have managed to generate consistent returns.
Traits Of AI Leaders
The report identifies six key characteristics of AI leaders, many of which are applicable to the automotive industry.
Core Business Focus: AI’s greatest value lies in transforming core business processes rather than just support functions. In the automotive sector, this includes AI-driven enhancements in production lines, predictive maintenance, supply chain optimisation, and connected vehicle solutions. According to BCG, core processes account for 62% of AI value, underscoring the importance of deploying AI where it impacts the business most significantly.
Ambition Beyond Productivity: Leading automotive firms view AI as a strategic enabler for competitive differentiation rather than merely a tool for incremental gains. These leaders invest heavily in AI and digital talent, with expectations of 60% higher AI-driven revenue growth by 2027.
Integrated Cost and Revenue Strategies: AI’s potential extends to both cost reduction and revenue generation in the automotive industry. Around 45% of AI leaders integrate AI into cost-efficiency measures, while over a third use AI to drive revenue growth through innovations such as personalized customer experiences, smarter sales processes, and tailored marketing strategies.
Strategic Investment in High-Priority Projects: Unlike companies that scatter investments across multiple AI initiatives, leading automakers focus on select, high-impact opportunities. With advancements in autonomous driving, predictive vehicle maintenance, and AI-driven R&D, these firms aim to maximize returns by strategically scaling successful AI pilots.
People and Processes First: AI success in the automotive sector relies more 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 ensures sustainable value creation.
Rapid Adoption of Generative AI (GenAI): Among several industrial segments, automakers are fast-tracking GenAI implementation to enhance vehicle design, customer interactions, and supply chain optimisation. From qualitative analysis to automated content creation for marketing, GenAI enables automotive firms to innovate rapidly while maintaining quality.
AI’s Impact on Core Automotive Functions
Contrary to popular belief, AI’s value in the automotive sector extends well beyond support functions. According to BCG, more than half of the value comes from core business functions, with AI driving improvements in areas like operations (23%), sales and marketing (20%), and R&D (13%).
R&D: Automotive leaders leverage AI to accelerate the development of new models and optimise vehicle designs. AI-driven simulation tools, digital twins, and predictive analytics allow companies to test designs virtually, reducing both time and costs.
Operations: AI enhances production line efficiency through predictive maintenance, real-time monitoring, and defect detection, leading to faster assembly and reduced downtime.
Sales and Marketing: AI-powered personalisation tools enable automakers to better understand consumer preferences, craft tailored marketing campaigns, and improve conversion rates.
Navigating AI Implementation Challenges
While AI presents immense opportunities, challenges persist. According to BCG’s research, 70% of AI-related obstacles are tied to people and processes, while technology and algorithms account for 20% and 10%, respectively. Common hurdles in the automotive sector include resistance to change, skill gaps, and ineffective collaboration between AI teams and business units.
To bridge the gap between AI potential and results, automakers need to emphasise change management, workflow optimisation, and AI talent development. For instance, integrating AI into autonomous vehicle development requires not just technical advancements but also the alignment of engineering teams, regulators, and customers.
Focusing On People, Processes
Successful AI implementation in the automotive sector hinges on a well-balanced approach that prioritises people and processes over purely technical solutions. Companies that allocate significant resources to AI training, talent acquisition, and process refinement often achieve better results. In contrast, those that focus disproportionately on algorithms and technical tools struggle to move beyond the pilot phase.
The Road Ahead
As automotive companies continue to invest in AI, they must focus on scalability, strategic investments, and workforce alignment. By emulating AI leaders and emphasising core functions, the sector 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 on people and processes, 20% on technology, and 10% on algorithms—can help the players harness AI’s full potential. As the industry races toward an AI-driven future, those who strike the right balance between ambition and execution will emerge as winners in the fast-evolving mobility ecosystem.
NB: Photo is representational. Courtesy: Continental.
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