September 29, 2026
Learn how Intel is using agentic AI-supported workflows in Seeq to transform maintenance operations across its global semiconductor manufacturing footprint. With thousands of critical assets across multiple sites, Intel is projecting 30% faster anomaly detection in ultrapure water systems, a 15-20% reduction in technician workload through autonomous work order generation, and more than 120,000 labor hours saved annually.
These results are powered by a shift from time-based and alarm-driven maintenance to condition-based operations. Intel leverages Seeq to continuously monitor asset health, detect abnormal conditions earlier, forecast maintenance needs, generate work orders, and provide engineers with actionable context. By codifying the expertise of its global engineering and operations teams into scalable workflows, Intel is extending institutional knowledge across facilities, improving consistency, and accelerating action. With human oversight built into every step, Intel is on a practical, governed journey toward semi-autonomous maintenance operations.
This session will explore real-world applications in ultrapure water and compressor maintenance and share practical lessons for implementing agentic AI at scale, including governance, repeatability, and scaling AI-driven reliability programs across a global manufacturing enterprise.
Time
9:00 am - 10:00 am PDT
Location
United States
Featured Speakers
Subhadra Sampathkumar
Subhadra Sampathkumaran, M.S. in Computer Science, is a Software Engineering Manager at Intel Corporation with over 20 years of experience leading digital transformation, data engineering, and automation across global semiconductor fabs. She has built and scaled solutions spanning software architecture, advanced analytics, manufacturing operations, big
data integration, IoT, workflow automation, and agentic AI, helping improve efficiency, reduce costs, enhance operational resilience, strengthen compliance, and enable predictive maintenance, lights-out manufacturing, and real-time decision support. Passionate about orchestrating intelligence across facilities and sub-fabs, she envisions a future where maintenance and operations evolve from reactive, time-based models into adaptive, anticipatory ecosystems that redefine reliability engineering.
Sean Tropsa
Sean Tropsa began his career as an engineer in the semiconductor manufacturing industry, where he developed expertise in large-scale data analysis, root cause analysis, and process optimization. As Head of Technical Solutions, Growth Markets at Seeq, he helps semiconductor customers use analytics and AI to turn data into business value, supporting sustainability goals while improving reliability, yield, and quality. Sean earned BS and MS degrees in Chemical Engineering from Arizona State University.