• What agents change: understanding the impact on power demand, decision speed, and the skills operators need to hire 
• Building connected systems: enabling operational intelligence across generation, pipelines, and the grid 
• Aligning technology spend with operating goals: managing payback cycles for AI deployed in the field 

• Prioritize high-value AI use cases that improve production and reduce operating costs 
• Measure ROI and business impact to shift the conversation from cost avoidance to sustained value 
• Build phased implementation strategies that sequence quick wins alongside longer-term change 
• Balance the pace of innovation against operational risk tolerance and safety requirements 
• Secure board-level sponsorship for enterprise-wide digital transformation programs

• How company moved from an initial 25-user Copilot pilot to enterprise-wide deployment across 6,300 employees 

• Establishing the data governance and access foundations needed before scaling AI 

• Driving 92% adoption through governance, communication, and field-led adoption 

• Building adoption across corporate functions and frontline operations 

• Measuring success through hard-dollar savings, operational efficiency, and board-level reporting

A case study from a fast-growing natural gas producer: unifying data inherited through acquisitions into one governed foundation; running AI at the wellhead through connectivity outages; keeping AI workloads inside operator owned infrastructure; and what autonomy did and did not deliver on the path from pilot to standard practice.

• Define clear ownership for digital initiatives across traditionally separate business functions 
• Break down organizational silos that slow cross-functional alignment and digital success 
• Align competing priorities between technology teams and day-to-day operational demands 
• Create governance models that give every stakeholder a voice in the outcome 

Roundtable leaders will open these highly interactive, peer-driven roundtables with a brief 5-minute introduction and a short overview of the topic, which can include a case study, lessons learned, or an industry update. 

Each roundtable covers a different topic, allowing attendees to choose the discussion most relevant to them. 

This is followed by a 30-minute peer discussion, with the leader facilitating questions, feedback, and practical exchange. The final 5 minutes are dedicated to aligning on five clear industry recommendations which will be included in the post-conference report. 

Choose one topic that aligns with your most urgent priority.  

1. From Back Office Automation to Operational Intelligence: Where AI Creates the Greatest Value 

2. Building AI Guardrails: Enabling Innovation Without Compromising Security 

3. Reskilling for a Converged Workforce: What's Actually Working on the Ground 

4. Vetting AI Vendors: Avoiding Tool Sprawl Across a Growing AI Portfolio 

5. Why Smaller Operators Are Moving Faster: Lessons in AI Agility 

6. AI Across the Utility Value Chain: From Generation to Customer Operations 

• Enable business users to query operations data in plain language without SQL or dependence on analytics teams 

• Identify the failure modes and accuracy challenges that emerge when users begin testing real-world questions 

• Build verification practices and governance guardrails that give users confidence in AI-generated answers 

• Move beyond a successful demo to a production system that business teams can consistently trust and use 

• Breaking down data silos and creating trusted, accessible data foundations for enterprise AI  

• Modernizing historians, SCADA, and legacy OT while maintaining security and operational integrity  

• Establishing governance, access controls, and AI guardrails that enable innovation without increasing risk  

• Balancing data accessibility, cybersecurity, regulatory requirements, and responsible AI adoption  

• Building the foundation needed to move AI from isolated pilots to secure, enterprise-wide deployment 

• Understanding how AI adds value alongside simulation, optimization, and engineering models 
• Using AI and computational methods to support decisions where first-principles understanding is limited 
• Using optimization to improve areas such as produced-water management, energy utilization, and asset operations 
• Translating computational insights into practical recommendations to improve planning and operations 

• Address PJM capacity constraints driven by accelerating data center load growth. 
• Explore behind-the-meter gas generation as a near-term reliability and speed-to-power solution. 
• Balance the speed of deployment against long-term grid stability and safety obligations. 
• Examine what regional utilities and operators are doing differently to keep pace. 

• Applying AI to wellsite optimization and production operations  
• Improving operational efficiency through real-time intelligence  
• Using AI to support faster engineering and production decisions  
• Practical applications across Appalachian natural gas assets 

• Move from reactive maintenance toward predictive, risk-based intervention  

• Use operational data and AI to identify equipment degradation and emerging performance issues earlier  

• Connect predictive maintenance with production optimization, reliability, and day-to-day operational decision-making  

• Integrate AI with existing operational systems and legacy infrastructure without disrupting critical operations  

• Prioritize use cases based on measurable improvements in reliability, productivity, and operating performance 

• Leveraging computer vision to monitor remote operations 
• Detecting safety risks, security threats, and abnormal field events 
• Reducing field visits while maintaining operational visibility 
• Industry-specific AI for smarter, safer energy operations 

• Reskilling employees for an AI-enabled workforce 
• Build training programs that move beyond compliance into genuine day-to-day adoption 
• Balancing innovation with ethics, accountability, and trust 
• Creating a culture that embraces AI-enabled ways of working

• 2:15-2:30 - Everyday AI: Enabling Copilot, Gemini, and Low-Code Agents Across the Enterprise 

• 2:30-2:45 - The Next Frontier: Agentic AI and Autonomous Workflows 

• 2:45-3:00 - Vetting AI Before It Enters the Enterprise: Vendor Risk and Procurement in the AI Era 

• Distinguish digital twins delivering real operational value from those stuck in pilot mode. 
• Apply twin modeling across plant assets, pipelines, and grid substations. 
• Address the data and integration of work required before a twin adds value.  

• Moving beyond machine learning to intelligent autonomous agents 
• Understanding how AI changes competitive advantage and operating models 
• Identifying breakthrough applications with transformative potential 
• Weigh the opportunities and risks as agentic AI moves from concept to deployment.