That argument was echoed across the panel. Samrat Sehgal said AI can help process vast amounts of data, detect risk earlier, improve forecasting, optimize inventory, and generate decision options faster than planners in some cases, but it cannot fix poor data quality. Sunit Mukherji made a similar point from a value-chain angle: without proper data management and transparency across the chain, AI implementation becomes an onerous task. Swaminathan Ramachandran added that organizations need both a clear AI strategy and a realistic assessment of data preparedness before they can build meaningful use cases.

What makes this discussion especially valuable is that it goes beyond the standard “garbage in, garbage out” warning. It points to a broader readiness problem. Pankaj Aggarwal argued that many companies are still missing the fundamentals of a robust, cloud-based, multi-source supply chain. In that environment, AI becomes an expensive overlay on top of weak operating basics. Swaminathan extended the point further by saying leadership teams also need a better understanding of AI’s capabilities and limitations. Vinayaka captured that in one sharp line: AI success starts with leadership literacy, not technology deployment.

There is also a practical proof point in Vinayaka’s example from bigbasket. He described an earlier stage where purchase orders were created in Excel or Word, converted to PDFs, emailed, and stored in Google Drive. Only after the underlying data across channels was cleaned up through a custom ERP did implementing an AI-driven procurement ERP become much easier. That is the real case-study lesson here: companies do not fail with AI because AI is weak. They fail because the data model, process foundation, and leadership understanding are too weak to support it.

This makes the real supply-chain AI question much sharper. It is not “Who is experimenting with AI?” It is “Who has built the data discipline, process clarity, and leadership literacy needed for AI to deliver repeatable value at scale?” The roundtable suggests that this is where the true competitive gap will open.

Why it matters:
In supply chain, AI will not become a durable advantage until companies fix the layer underneath it: clean data, clear use-case strategy, resilient processes, and leaders who understand what the technology can actually do.