29/05/2026
Enterprise AI infrastructure has long scaled in familiar ways: more workloads, more racks, larger campuses, and bigger cloud commitments.
That model still works when the goal is broad compute accumulation. It becomes less reliable when AI moves from pilots to live operational dependency.
As production AI spreads into sectors such as industrial automation, healthcare diagnostics, logistics, surveillance and remote operations, infrastructure planning has to account for more than capacity. Compute concentration risk, terrestrial buildout delays, latency limits and foreign cloud dependency are becoming board-level issues.
Model oversight without compute-location oversight leaves a strategic blind spot.
Ahead of MIT Sloan Management Review India’s AI Research Forum in Bengaluru, Shivani Tiwari spoke with Narendra Sen, Founder and Chief Executive of RackBank® and NeevCloud®, and Saraniya P., Vehicle Director, Agnibaan SOrTeD, AgniKul Cosmos, on the future of AI infrastructure across sectors.
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