Why AI that works in the lab fails on the factory floor: A conversation with Prof. Roop Mahajan, Virginia Tech
Every CIO has seen the same pattern: an AI model posts impressive accuracy in the pilot, wins internal buy-in, gets funded for scale-up — and then quietly underperforms once it hits real production data. The instinct is to blame the vendor, the data team, or the model itself. Prof. Roop Mahajan, Director, Institute for Critical Technology and Applied Science, Virginia Tech says the real problem is more fundamental: most industrial AI is built to win in the lab, not to survive on the floor.
Mahajan has spent three decades on both sides of that gap — running AI-driven process control at Bell Labs, where a fractional drift in temperature or contamination can wipe out an entire production run, and later building his own neural network software, CU-ANN, because the commercial tools of the time couldn’t hold up under real manufacturing conditions. His diagnosis is blunt: academic and vendor benchmarks reward...
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