Shankar Narayanan

Shankar B. Narayanan is a recognized leader in machine reliability, control systems, and AI-driven condition monitoring, with over 15 years of experience advancing asset performance and operational efficiency in the energy and industrial sectors. At General Electric (GE), Shankar led pivotal initiatives to modernize control systems for gas, steam, and wind turbines, introducing predictive maintenance frameworks that enhanced fault detection and reduced downtime. His leadership in condition monitoring projects improved failure diagnostics, extended equipment life, and optimized maintenance schedules. Shankar also spearheaded the integration of machine learning models to detect early warning signs of mechanical degradation, enabling a shift from reactive to proactive maintenance strategies. In his role at Amazon Web Services (AWS), Shankar has furthered the application of AI, ML, and generative AI in machine reliability, enabling clients to deploy real-time anomaly detection, automate diagnostics, and implement scalable predictive maintenance solutions. By leveraging cloud-native technologies, he has helped organizations improve decision-making and reduce unplanned outages. Shankar's ability to bridge technical depth with strategic foresight has positioned him as a trusted advisor for enterprises seeking to implement next-generation reliability solutions. His contributions to the industry include thought leadership at global forums and mentorship of cross-disciplinary teams driving innovation in asset management. A strong advocate for sustainable and efficient operations, Shankar holds a master’s degree in mechanical engineering from Texas A&M University and continues to lead advancements in digital transformation, making machine reliability smarter and more resilient through AI-driven insights.