Indian industrial companies are moving beyond isolated AI experiments toward redesigning entire business operations around AI. At the Industrial Leadership Summit in Pune, executives argued that AI should influence product design, engineering, manufacturing, supply chains and revenue—not simply automate individual factory tasks. PwC research cited at the summit found that 50% of industrial leaders expect highly automated processes by 2030, compared with 18% today.
The key shift is from asking “Where can we use AI?” to “What business problem are we trying to solve?” For example, AI models at Adani Group are being used to forecast component availability for large solar projects, helping identify supply-chain constraints before they require companies to hold additional inventory. Other industrial leaders are focused on shortening manufacturing cycles, bringing products to market faster and reducing costs through digital technologies.
But scaling industrial AI creates a major accountability and governance problem. As systems move from providing information to making recommendations and eventually taking autonomous actions, companies need clear decision ownership, audit trails and data lineage. The summit emphasized that governance cannot be added after deployment; it needs to be built into the system from the beginning. AI also cannot scale effectively when engineering, sourcing, logistics and forecasting data remain fragmented across disconnected systems.
The broader takeaway is that industrial AI is becoming an operating-model transformation rather than another software project. Indian companies have an opportunity to become leaders in areas where they combine industrial expertise with AI, digital twins, connected engineering and automation. For CEOs and boards, the strategic question is increasingly not what AI can do today, but what the company could become if its products, factories and decisions were designed around AI from the start.
