Industry experts met at the Automotive Executive Exchange in Delhi to discuss how AI and data will drive automotive transformation [1].
The integration of these technologies is critical as manufacturers shift toward electric, software-defined vehicles. This transition requires a unification of engineering, manufacturing, and data streams to maintain a competitive edge in a rapidly evolving global market.
Siddhartha Sharma of NDTV AutoMate hosted the panel, which included Michele Del Mondo and Upkar Saini [1]. The discussion focused on the necessity of combining data and AI to shape the future of vehicle architecture. The experts said these tools can accelerate the development of electric vehicles by optimizing the relationship between software and hardware.
There is an ongoing debate regarding the financial impact of these technologies. Some reports suggest that AI has become the baseline for profitability in the automotive sector by 2026 [2]. However, other industry observations indicate that the expected monetisation of AI within software-defined vehicles has yet to fully materialise.
Beyond profitability, the industry is grappling with the governance of these systems. Recent discussions on AI and data sovereignty, including reports published on May 14, 2026, highlight the complexities of establishing control over autonomous systems [3]. These regulatory and sovereignty hurdles remain a primary concern for companies attempting to scale AI-driven features across different international borders.
The panel said the path forward depends on the ability of manufacturers to treat software as a core component of the vehicle rather than an add-on feature. By uniting data with traditional manufacturing, the industry aims to create more efficient, and responsive transportation systems [1].
“AI and data will drive automotive transformation.”
The automotive industry is currently in a transition period where the theoretical value of AI is clashing with practical monetisation. While technical integration is accelerating in hubs like Delhi, the gap between AI as a 'profitability baseline' and actual realized revenue suggests that the industry is still refining its business models for software-defined vehicles.
