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AI in Energy Applications Forum Agenda
November 3rd, 2026

November 3rd, 2026
As part of Next-Gen AI for Energy Summit 2026 (AI4E2026), the AI in Energy Applications Forum brings together senior decision-makers from across oil & gas, power and energy infrastructure to examine what it really takes to move AI from experimentation to scaled, real-world impact across the energy industry. Positioned as an application and implementation-focused extension of AI4E2026, the Forum explores not only where AI can create value, but why many promising applications remain stuck between pilot and production.
As energy companies accelerate AI adoption, the challenge is increasingly shifting from technology capability to execution. Discussions will examine the critical gaps shaping AI deployment—from data quality and trust to workflow integration, operational adoption and workforce readiness. Leaders will explore whether companies need perfect data before scaling AI, how AI can move beyond insights and dashboards to become embedded in everyday workflows, and whether organizations should hire specialized AI talent or build AI fluency across their existing workforce.
The Forum provides a high-level platform for industry leaders to challenge conventional approaches, share real-world experiences and debate what is required to scale AI across mission-critical energy operations. Through candid discussions on data, workflows, talent and human-AI collaboration, the Forum aims to identify the practical pathways from AI pilots to trusted, embedded and increasingly autonomous energy operations.
14:00 - 14:20
(20 mins)
[Opening Keynote] Beyond AI Pilots: What Will It Take to Build the Intelligent Energy Enterprise?
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Why has AI adoption in energy remained largely at the pilot and experimentation stage, despite growing investment and technological maturity?
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What does it really take to move from AI pilots to production-scale, enterprise-wide deployment?
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How can energy companies turn AI-generated insights into real operational decisions and measurable business outcomes?
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How should energy leaders prioritize AI investments between quick wins and longer-term transformation?
14:20 - 15:10
(50 mins)
[Panel] Scaling AI in Energy: Data, Trust and the Road to Production
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How much does data quality and accuracy really matter when deploying AI in mission-critical energy operations?
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Does energy AI truly require “perfect data”, or can companies start with “good enough” data and improve it continuously?
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What are the biggest data challenges holding back AI adoption: data quality, fragmented systems, data silos, governance or accessibility?
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How can energy companies establish trust in AI outputs when underlying data is incomplete, inconsistent or outdated?
15:10 - 15:30
(20 mins)
Case Study- Reserved for Sponsor
Coffee Break
16:00 - 16:50
(50 mins)
[Panel] Is the Workflow Holding AI Back? From Insight to Action in Energy
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Why do many AI solutions generate valuable insights but fail to translate them into measurable operational impact?
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How can energy companies embed AI directly into day-to-day operational workflows and decision-making processes?
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What needs to change in existing workflows for AI to move from a standalone tool to an integrated operational capability?
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How can predictive and prescriptive AI be embedded into workflows such as maintenance, production optimization, power generation and grid operations?

John Stretton
Acceleration, Transformation & Project Management Director
EDP Renewables
16:50 - 17:10
(20 mins)
Case Study- Reserved for Sponsor
17:10 - 18:00
(50 mins)
[Panel] From Predicting Failure to Preventing Downtime: Can AI Deliver Real ROI?
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How accurate does AI need to be before failure prediction translates into meaningful operational value?
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Can predictive AI actually prevent unplanned downtime, or does it simply provide earlier warning?
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How can companies turn an AI-generated failure prediction into timely maintenance action?
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How can predictive maintenance be integrated into existing EAM, CMMS, SCADA and maintenance workflows?

Pradheep Kileti
Vice President, Asset Management and Engineering
National Grid
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