AI in the Energy Sector: Practical and Trustworthy Solutions for Critical Infrastructure
Artificial intelligence is transforming the energy sector and creating new opportunities to improve reliability, efficiency, safety, and decision-making across complex technical systems. At the same time, applying AI in energy can be challenging because these systems often involve critical infrastructure, complex engineering workflows, regulatory considerations, and a strong need for trust, reliability, and explainability.
This talk will provide a practical overview of how AI is being applied in the energy sector, with a brief focus on nuclear energy as an example of a safety-conscious and highly regulated field. It will cover key AI applications, including predictive modeling, operational analytics, anomaly detection, technical document intelligence, engineering decision support, and human-in-the-loop workflows. The session will also discuss why successful AI adoption in energy requires more than model accuracy alone, including validation, traceability, explainability, and responsible implementation. A key theme of the talk will be that AI in energy is not just about building advanced models, but also about solving meaningful problems, supporting expert decision-making, and building reliable systems that people can use and trust. Attendees will leave with a clearer understanding of how AI can support the energy sector and what considerations are needed to apply AI responsibly in critical infrastructure
environments.
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- Starts 10 June 2026 04:00 AM UTC
- Ends 20 June 2026 03:55 AM UTC
- No Admission Charge
Speakers
Sanchari
AI in the Energy Sector: Practical and Trustworthy Solutions for Critical Infrastructure
Artificial intelligence is transforming the energy sector and creating new opportunities to improve reliability, efficiency, safety, and decision-making across complex technical systems. At the same time, applying AI in energy can be challenging because these systems often involve critical infrastructure, complex engineering workflows, regulatory considerations, and a strong need for trust, reliability, and explainability. This talk will provide a practical overview of how AI is being applied in the energy sector, with a brief focus on nuclear energy as an example of a safety-conscious and highly regulated field. It will cover key AI applications, including predictive modeling, operational analytics, anomaly detection, technical document intelligence, engineering decision support, and human-in-the-loop workflows. The session will also discuss why successful AI adoption in energy requires more than model accuracy alone, including validation, traceability, explainability, and responsible implementation. A key theme of the talk will be that AI in energy is not just about building advanced models, but also about solving meaningful problems, supporting expert decision-making, and building reliable systems that people can use and trust. Attendees will leave with a clearer understanding of how AI can support the energy sector and what considerations are needed to apply AI responsibly in critical infrastructure environments.
Biography:
Sanchari Banerjee is a Senior AI Applications Architect at Blue Wave AI Labs, where she works at the intersection of artificial intelligence, engineering, and energy. She holds a Master’s degree in Electrical and Computer Engineering from Purdue University. Her work centers on applying AI and machine learning to complex industry problems, with a focus on building practical, trustworthy AI solutions for engineering and energy-sector applications. Her broader interests include responsible AI adoption, domain-specific AI applications, and the role of emerging technologies in advancing critical infrastructure.
Agenda
1. Understand major AI applications in the energy sector and how they support reliability, efficiency, safety, and decision-making
2. Learn how AI can be applied to use cases such as predictive modeling, operational analytics, anomaly detection, technical document intelligence, and engineering decision support
3. Explore how nuclear energy provides an example of AI adoption in a safety-conscious, highly regulated technical field
4. Recognize why reliability, explainability, traceability, validation, and human oversight are essential for AI in critical infrastructure
5. Understand the importance of building practical and trustworthy AI systems that align with real-world engineering workflows
6. Leave with a clearer view of how AI can contribute to modernization and responsible innovation in the energy sector