Industrial leaders are shifting artificial intelligence from isolated use cases to enterprise transformation, focusing on measurable value, resilience, and competitiveness. This shift is driven by boards that want to see tangible results from AI investments.
At the Industrial Leadership Summit in Pune, technology and business leaders discussed the challenges of scaling industrial AI and demonstrating its business value. Manpreet Singh Ahuja, chief clients and industries officer at PwC India, argued that companies need to rethink their operating models to fully leverage AI.
Scaling Industrial AI
Ahuja emphasized that AI requires companies to rethink their operating models, rather than simply adding AI to existing processes. PwC research indicates that 50% of industrial leaders expect highly automated processes by 2030, compared to 18% today.
This shift is driven by the need for companies to stay competitive and resilient. Deepak N G, managing director of Dassault Systèmes in India, noted that Indian companies are thinking seriously about how to scale AI and explain its value to boards and CEOs.
The discussion highlighted the importance of governance in AI adoption. As AI systems move from providing information to making recommendations and taking action, companies face the challenge of ensuring accountability when AI-driven decisions go wrong.
Ahuja described the progression from information to recommendations, autonomous action, and eventually self-optimizing systems. He stressed that governance must be built into the technology from the outset, including audit trails, data lineage, and decision ownership.
Enterprise-Wide AI Adoption
A focused-group discussion examined how AI could reshape the industrial value chain, from customer feedback and engineering to sourcing, logistics, and inventory. Use cases discussed included analyzing warranty and product-usage data, AI-assisted coding and simulation, and supplier discovery.
The common thread was a feedback loop connecting customer and operational data to decisions, action, and business outcomes. Neerav Mehta, head of Digital & AI at L&T Energy, presented the company’s digital engineering journey, which aimed to connect design, engineering, and project information across the lifecycle.
The lesson from these case studies was straightforward: AI cannot scale on disconnected data and fragmented systems. Technology and data foundations are only part of the equation, and sustained technology adoption also depends on the broader environment in which companies operate.
Samson Khaou, executive vice-president of Dassault Systèmes, noted that confidence is driven not just by short-term economic cycles, but by the consistency of government policy and the pace of technological upgrading. For industrial CIOs, the mandate is changing from selecting AI tools to redesigning processes, connecting digital foundations, and embedding governance.
As companies move towards enterprise-wide AI adoption, they need to consider the competitive cost of doing nothing. Arun Kumar Malhotra, auto industry expert and former Nissan India managing director, framed the boardroom dilemma, stating that companies need to consider not just the return from adopting AI, but the cost of not adopting it, and Adani Group is already using AI models to forecast component availability for large solar projects.
Implementing AI at Scale
The CEO panel discussed the importance of starting AI investments with a business problem, rather than the technology itself. Anil Pawar cited Adani Group’s use of AI models to forecast component availability for large solar projects.
Pramod Mundra emphasized that CIOs, CDIOs, and CTOs must become business technology people, rather than just technologists. This shift in mindset is necessary for successful AI adoption.
