Hariharan Ganesan

Hariharan Ganesan is a supply-chain and enterprise AI leader specializing in explainable demand forecasting, inventory optimization, and trustworthy automated decisions. His work addresses a practical problem: sophisticated forecasting and planning systems become operational liabilities when the people accountable for their recommendations cannot understand, challenge, or correct them.

Over more than 15 years, Ganesan has worked across automotive, retail, manufacturing, and consumer hardware, with experience at o9 Solutions, Google, Wayfair, Cognizant, and TVS Group. His training combines mechanical engineering, an MBA from Great Lakes Institute of Management, and a specialized supply-chain management master’s degree from the University of Michigan’s Ross School of Business. He also contributes to professional education in supply-chain AI.

His published work follows the evolution of AI-assisted planning: organizational readiness for AI forecasting, explainability for demand-planning decisions, agentic AI in supply-chain planning, and practical implementation of AI demand planning. The progression moves from preparing organizations and interpreting predictions toward autonomous execution grounded in workable adoption practices.

• Explainability as an operational requirement: Consequential recommendations should include plain-language reasoning that planners, auditors, and executives can act on without specialist interpretation.
• Adaptive guardrails and human escalation: Controls should respond to changing data and operational context, slowing risky decisions or routing them to the appropriate expert with sufficient context.
• XTOps for trustworthy AI operations: Ganesan describes an extension of conventional MLOps incorporating signed provenance, context-sensitive policies, leadership-facing trust dashboards, and clearly assigned accountability throughout the model lifecycle. His proposed mean time to resolve explainable errors measures how quickly teams can understand and correct unexpected behavior.
• Predictive industrial safety: Research coauthored with Sahil Yadav considers how connected sensors, wearable devices, predictive analytics, and human oversight can improve safety in hazardous industrial environments.

Ganesan and Yadav appeared together at AI Engineer World’s Fair 2025, where Ganesan’s conference affiliation was Telemetrak.