Critical AI Inference your CIO can Trust — Sahil Yadav, Hariharan Ganesan, Telemetrak
Jul 22, 2025 · 19:04
Sahil Yadav and Hariharan Ganesan of Telemetrak present a three-pillar framework—explainability, adaptive guardrails, and human-in-the-loop on a foundation of traceability—to operationalize trust in enterprise AI inferences, arguing that without these, silent failures can cost millions. They introduce XTOPS as an integrated upgrade to MLOps, with trust-specific dashboards and dynamic guardrails. A case study of GuardHat, a worker safety platform, shows how GPS drift caused 70% false positives; applying XTOPS reduced resolution time from eight months to seven days. They propose metrics MTTRE (mean time to resolve explainable errors) and trust-adjusted risk in dollars, and demonstrate convincing CIOs by quantifying savings, e.g., $500K per site per year in fines avoided.
