How Physics-Based Platforms Turn Operational Data into Real-Time Insights That Drive Efficiency, Reliability, and Enterprise-Wide Digital Transformation

Rohan Sheth is a Forward Deployed Engineer at Geminus AI, where he works directly with energy and industrial organizations to implement AI-driven analytics and optimization solutions. With a background spanning product management and engineering, he has led the development and deployment of data-driven technologies at companies including GE Vernova, Kava Labs, Imubit, and Marathon Petroleum. Rohan brings strong expertise in industrial analytics, process optimization, and automation, supported by an MBA from Rice University and advanced degrees in data science and chemical engineering from the University of Texas at Austin.
 

In his session, Rohan will discuss how physics-based AI platforms can transform fragmented operational data into real-time, actionable insights. He will highlight why many digital transformation initiatives struggle to scale beyond pilot stages, and how combining physics-based models with small-data AI enables enterprise-wide deployment across complex industrial environments. The session will also explore how organizations can improve production, reliability, and emissions performance without requiring major infrastructure changes, while demonstrating what real-world digital transformation looks like in heavy industry.
 

1. Challenges in scaling digital transformation beyond pilot projects
2. Converting fragmented data into real-time operational insights
3. Using physics-based AI for enterprise-wide deployment
4. Improving efficiency, reliability, and emissions performance
5. Enabling measurable ROI through real-world digital transformation