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…
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