Sahil Yadav
Head of Software and AI, AOI| Former TelemeTrak, Cisco, GE, IBM
San Francisco, California, United States
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Sahil Yadav is Head of Software and AI at AOI, building agentic AI-based self-healing and autonomous network solutions. Formerly CPO at TelemeTrak, he launched AI products at Cisco, GE, and Guardhat, used by Fortune 500 firms and mission-critical operations. With 14+ years in AI product management, he specializes in edge/cloud AI, enterprise adoption, and AI trust. A Senior IEEE member and frequent speaker, his expertise spans Trust in AI, Enterprise Data Platforms, and Industrial IoT.
Area of Expertise
Topics
How to Build AI Products When Half the Stack Is Behind the Customer's Firewall
Building AI products is hard—but building them when half of the architecture lives behind a customer’s firewall is a different game altogether. In this 18-minute talk, I’ll unpack lessons from designing and scaling hybrid AI systems in heavily regulated, edge-driven industries like telecom, manufacturing, and public safety.
I’ll cover:
Design patterns for AI in hybrid environments (on-prem + cloud)
Trade-offs in data access, latency, and model explainability
Privacy-aware ML pipelines, RBAC enforcement, and air-gapped fallback strategies
Real-world examples of deploying AI in zero-trust, high-compliance environments
This talk is ideal for AI architects, infra engineers, and product leaders navigating the challenges of bringing modern AI to legacy, siloed systems without compromising trust or performance.
How to Build AI Products When Half the Stack Is Behind the Customer's Firewall
Building AI products is hard, but building them when half of the architecture lives behind a customer’s firewall is a different game altogether. In this talk, I’ll unpack lessons from designing and scaling hybrid AI systems in heavily regulated, edge-driven industries like telecom, manufacturing, and public safety.
I’ll cover:
- Design patterns for AI in hybrid environments (on-prem + cloud)
- Trade-offs in data access, latency, and model explainability
- Privacy-aware ML pipelines, RBAC enforcement, and air-gapped fallback strategies
- Real-world examples of deploying AI in zero-trust, high-compliance environments
This talk is ideal for AI architects, infra engineers, and product leaders navigating the challenges of bringing modern AI to legacy, siloed systems without compromising trust or performance.
CIOs and Industry Leaders: Do You Trust Your AI’s Inferences?
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
CIOs and Industry Leaders: Do You Trust Your AI’s Inferences?
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
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