Srikanth RP

Share 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.

Share For as long as ransomware has existed as a category, it has had a human somewhere in the loop — writing the script, choosing the target, deciding when to pull the trigger on encryption. That assumption just broke.

Share Enterprises are struggling to move generative AI from pilot to production, and the reason is rarely the model. In a wide-ranging conversation, Rajesh Sinha, Founder and Chairman of Fulcrum Digital, argues that the industry has fixated on large language models (LLMs) as the unit of value, when the real bottleneck sits elsewhere — in data architecture, security governance, orchestration, and process redesign.