Why operational AI is where the ROI really is
The value of AI shows up in operations: faster processing, fewer errors, better service, freed-up time. But moving from a promising idea to a use case that runs reliably every day — with quality control and clear metrics — is where most efforts stall.
I focus squarely there: choosing operational use cases with real payoff, deploying them robustly, controlling quality and risk, and measuring impact. AI becomes a dependable part of how the work gets done, with numbers to prove it.
Production over proof-of-concept: operational AI is judged on reliability and measured impact, daily.