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Critical AI Inference your CIO can Trust — Sahil Yadav, Hariharan Ganesan, Telemetrak
Takeaway
For CIOs to trust AI in critical systems, MLOps needs to evolve into XTOps with explainability, traceability and guardrails plus business-facing metrics like MTRE and trust-adjusted risk.
Summary
- Sahil Yadav and Hariharan Ganesan (Telemetrak) frame trustworthy AI for mission-critical industries: McKinsey says 78% of companies adopt AI but only 11% focus on governance — citing telecom outages costing millions/minute and a gas-sensor misread that risked human lives.
- Propose three pillars of trustworthy AI: explainability ('show your work' in plain English), adaptive control (guardrails that slow down or hand off to humans), and human-in-the-loop role/playbook design — all built on traceability ('SBOM for AI', every data/change digitally signed).
- Introduce XTOps — MLOps with built-in conscience: verifiable traceability from data through training (embedding 'actionable intelligibility'), deployment guardrails, and structured human teaming.
- Propose two governance metrics: MTRE (mean time to resolve explainable errors) and trust-adjusted risk in dollars — citing that serious privacy/bias incidents can escalate to $700M and current MTRE often runs into months.
- Argues XTOps isn't reinventing MLOps; it adds dynamic AI-aware guardrails, trust dashboards for leadership, and 'click-to-fix' human feedback fast lanes.
governancetrustmlops
Original description
Enterprise AI adoption is accelerating, but with it comes a hard question: Do we trust the model’s decisions? In this 18-minute talk, I’ll explore the invisible risks behind automated decision-making in safety-critical and revenue-sensitive environments. Drawing on case studies across manufacturing, telecom, and industrial IoT, I’ll highlight how explainability, traceability, and robust guardrails drive adoption and protect enterprise value. Attendees will walk away with: • A 3-step framework for operationalizing AI trust • Real-world lessons from building guardrails in on-prem and hybrid systems • Tools and techniques for debugging and explaining inferences at scale • A blueprint for building trust between models, engineers, and executive stakeholders ---related links--- https://www.linkedin.com/in/yadavsahil/ https://telemetrak.com