From predictive analytics to real-time diagnostics, Ai is transforming frontline operations, enabling workers to troubleshoot and resolve issues, often before they escalate, by processing vast, complex datasets and providing actionable insights. This panel will explore how AI is reshaping frontline workflows, driving operational efficiency, and enhancing decision-making. While considering the practical challenges of implementation, including data fragmentation, legacy systems, and workforce adoption.

• Real-world use cases showcasing the tangible value of AI deployment at the operational frontline
• Strategies to ensure AI decision validity through robust, scalable data foundations and model governance
• How to assess and enhance workforce readiness for AI integration through change management, digital upskilling, and human-centered design    

Stephan Blasilli

Head of Business Process Excellence
EDP Renewables North America

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As AI tools become embedded in day-to-day operations success increasingly depends on how well frontline teams can interpret, question, and act on AI-driven insights. This session explores how to equip your operational workforce not just to use AI tools, but to think critically with them.

• Designing hands-on training programs that go beyond button-clicking to build data reasoning, model awareness, and decision confidence
• Embedding AI into existing workflows in ways that support, not disrupt operator judgment
• Using vendor platforms to create interactive learning environments and build trust in AI outputs 

As industrial facilities become increasingly data-driven, the challenge shifts from collecting time series data to unlocking its full potential. This session explores how PBF Energy deployed a unified analytics platform across six operational sites, integrating generative AI agents to automate reporting, reverse-engineer analytical workflows, and enhance engineering efficiency. Patrick will also explore how engineers are now using coding agents to interact with historical process data, bridging the gap between technical capability and user accessibility.

Patrick Robinson

Senior Director, Operations Technology and Advanced Process Control
PBF Energy

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In today’s energy landscape, operational optimization depends on more than knowing an assets current behavior, it requires understanding how assets perform as part of an interconnected system. This panel explores how organizations are building holistic asset intelligence frameworks that unify data across asset classes, improve situational awareness, and support faster, more informed decision-making.

• Learn how operators are building unified views of asset health by integrating inspection data, engineering reports, and real-time performance metrics
• Explore strategies for improving cross-functional coordination between engineering, operations, and planning teams using intelligent asset insights.
• Understand how modeling techniques, from system-level simulations to probabilistic frameworks, are being used to support strategic decisions and optimize asset portfolios 

Turnarounds are high-stakes, high-cost events and delays can ripple across operations. In this session, discover how AI-powered predictive maintenance is transforming turnaround planning by forecasting equipment degradation timelines using historical logs, work orders, and sensor data. Learn how operators are reducing unnecessary part replacements, improving coordination between maintenance and operations, and minimizing downtime windows.

• Understand how machine learning models identify degradation patterns to inform turnaround schedules
• Explore how predictive insights improve resource planning, inventory management, and contractor coordination
• Learn how AI-driven planning reduces cost overruns, shortens downtime, and enhances operational reliability

National Grid has designed a unique approach to asset intelligence, building a unified Asset Health Index, a mathematical framework powered by generative AI and ensemble modelling, to support long-term portfolio optimization and strategic planning. Rather than predicting when an asset will fail, the index provides probabilistic insights into asset end-of-life scenarios over five-year horizons, enabling smarter budgeting, material procurement, and capital project prioritization.

Zach Price

Data Scientist
National Grid

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As energy organizations modernize their HSE systems, the shift from manual processes to centralized digital platforms is unlocking new opportunities for AI integration. This panel brings together leaders who are digitizing audit workflows, streamlining reporting and testing how AI can be integrated to reduce manual effort and improve safety outcomes. Learn how foundational changes in data capture and system design are paving the way for smarter, faster, and more scalable HSE processes, while maintaining regulatory integrity and frontline usability.

• How AI can support consistent and structured data capture across field operations, creating a reliable foundation for safety insights and future automation
• How AI can support incident classification and decision-making, helping teams reduce ambiguity and streamline workflows
• The importance of preparing data and systems for AI integration, including strategies for improving data quality and consistency
• How digital upgrades can evolve into scalable AI solutions, supporting long-term safety performance and operational resilience 

Joshua West

HSE Manager of Systems, Planning and Performance
Oxy

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In energy operations, the quality of decision-making is shaped by the architecture behind it. Intelligent decision environments, built on robust data systems, bring together people, processes, and technologies to enable autonomous, AI-driven operations. In this session, we’ll explore how selecting and designing the right data architecture unlocks advanced analytics, reduces operational costs, and drives scalable efficiency. Discover how to move beyond automation to autonomy by creating systems that support real-time insights, cross-functional coordination, and resilient decision-making across the enterprise. 

As energy markets evolve, regulatory complexity is rising, especially in commodity trading, where carbon credit obligations vary across products and jurisdictions. This session explores how a custom-built AI solution transformed a fragmented, manual reporting process into a streamlined, intelligent workflow. Faced with inconsistent email confirmations and siloed data, regulatory analysts were spending hours reconciling contract terms and calculating obligations. By embedding process intelligence into the data architecture, Shell enabled real-time insight, reduced compliance risk, and accelerated operational decision-making.

Attendees will gain a practical understanding of how a modular architecture, anchored by a large language model for unstructured data extraction, a Python-based processing pipeline, and storage can be deployed to automate regulatory workflows. Hakim will share lessons learned in designing AI-ready data infrastructure, orchestrating multi-source ingestion, and aligning automation with compliance needs. This session offers a replicable framework for embedding AI into operational processes, empowering energy organizations to unlock speed, precision, and resilience in their data-driven decision-making. 

Hakim Razman

Process Automation Lead
Shell

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Preparing your organization for AI is where change management and technology mix. How can you create a culture that is ready for and embraces AI? Furthermore, how can you engage your employees of all skill levels in the use of AI? This panel will explore the strategic workforce priorities required to maximize the value of AI.

• Establishing standard ways of working with AI through internal connectivity and collaboration
• Building implementation strategies that account for your diverse workforce talents, resources and challenges
• Pathways to maximize buy-in and adoption, how can you create a culture that embraces AI?
• Ensuring AI readiness through change management initiatives and robust workforce training programmes 

Molly Determan

President
Energy Workforce & Technology Council

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AI adoption isn’t just a technical challenge; it’s a human one. In this session, Laura shares how leadership grounded in energy intelligence can accelerate adoption of AI tools, even in complex, change-resistant environments. Drawing on real-world experience from a Houston manufacturing site, the session explores how coaching techniques help engineers move from reactive, fight-or-flight thinking, into higher-energy mindsets where they see opportunity, take ownership, and drive innovation.

Laura Maesaka

Energy and Optimization Manager
TPC Group

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Methanex is focused on driving the integration of intelligent chatbots across its 13 global plants, providing frontline workers the necessary information from different data sources to power intelligent decision making. The next step is an autonomous AI agent that is capable of responding to incidents without human intervention. This evolution promises to save up to 16 man-hours per incident, but the success of this strategy hinges not just on technical capability, but on workforce trust and AI fluency.

Julio Figueroa

Manager of Artificial Intelligence
Methanex

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As the fifth-largest independent power producer in North America, Capital Power is navigating a bold transformation, expanding its thermal portfolio with a $2.1B acquisition in PJM, reshaping its workforce and building a future-ready AI strategy. At the heart of this shift is a pragmatic, use-case-first approach to AI innovation that prioritizes flexibility, measurable business value, and deep user engagement. Rather than betting on individual tools, Capital Power is building a composable architecture that allows the best models to be orchestrated in real time ensuring long-term enterprise impact.

• Discover how Capital Power identifies high-impact AI use cases, such-as predictive maintenance, by starting with operational pain points and defining success through clear, business-aligned metrics
• Explore how generative AI is being used to scale workforce capabilities following the reduction in staff to enable smarter regulatory oversight, environmental compliance, and market visibility across a diverse asset base
• Learn how Capital Power is future-proofing its AI investments by building flexible, model-agnostic architectures that allow for plug-and-play integration of emerging technologies
• Understand why deep user engagement, from ideation to implementation, is the make-or-break factor in AI adoption, and how Capital Power see’s senior leadership as coaches for transformation 

John A. Shannon

Vice President, Data Science and Insights
Capital Power

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As AI becomes deeply embedded the need for ethical oversight has never been more urgent. While 77% of organizations are actively developing AI governance strategies (rising to 90% among those with operational AI), the industry still grapples with a fundamental question: how do we govern AI responsibly in environments here the stakes are high, and the consequences of failure even higher? This session will explore:

• How organizations can move from capability to responsibility when automating tasks with AI, balancing efficiency with ethical considerations, especially in contexts where human judgment remains essential
• How to implement governance frameworks that provide guardrails, not guesswork, evaluating AI outputs for accuracy, mitigating hallucinations, and reducing bias to build trust in systems making increasingly high-impact decisions
• How to identify and address bias and behavior in AI models by uncovering hidden biases, understanding model behavior patterns, and training teams to critically assess AI-generated outputs  

Mathias Klinkby

Senior Performance Specialist
Noble

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Adam Pryor

Manager, Digital Insights and Analytics
Murphy Oil

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Emile-Otto Coetzer

Senior Digital Advisor
Chevron

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EnergyRE is leveraging AI across the enterprise to drive operational efficiency, while placing strategic alignment and responsible governance at the core of its approach. In this session, Taylor will share key milestones, lessons learned, and the future opportunities that lie ahead.

• Gain an update on the implementation journey of deploying EnergyRE’s generalized AI platform tools across the enterprise from initial pilot
• Explore the cruciality of EnergyRE’s AI policy and enterprise-wide training program as part of their enterprise deployment. Understand how clear usage guidelines and embedded platform controls helped build user confidence and prevent data leakage, while navigating the ongoing challenge of evolving legal and ethical grey areas
• Operating in a greenfield, SaaS-native environment, EnergyRE opted to buy rather than build its AI tools. Learn how this accelerated deployment and simplified integration, while recognizing common challenges many operators face when constrained by legacy infrastructure or limited internal development capacity.
• Review the journey of using traditional RPA to streamline tasks like invoice processing to then move to broader automation. With UiPath’s support for agentic automation and multi-model orchestration, more dynamic, goal-based workflows can be achieved however we’ll explore the challenges of identifying scalable use cases
• Gain insight into EnergyRe’s joint venture strategy to develop and externalize proprietary automation tools with commercial potential. This includes leveraging partnerships with agentic automation specialists to co-develop scalable products for the broader energy sector  

Taylor Witt

Vice President, Digital and PMO
EnergyRe

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While the potential of AI is no longer in question, the journey from POC to scalable, enterprise-grade deployment remains complex. A key challenge lies in the disconnect between technology leaders and the operational realities of energy environments, leading to unrealistic expectations around the pace and complexity of scaling AI. In this session we will review the end-to-end lifecycle of AI deployment, from initial PoC, to platform-based implementation within business units and ultimately enterprise-wide integration, shining a spotlight on common pitfalls, success factors and execution strategies.

• Examining how data architecture and standardization can help structure both operational and historical data to ensure AI models interact effectively and reliably at scale
• Considering the role of human-centered design and user interfaces in driving adoption of AI-powered tools, making complex technologies more intuitive, accessible, and impactful for end users
• Real-world use cases that illustrate different implementation strategies by AI type, with a spotlight on how traditional machine learning models and generative AI require distinct approaches to scaling and integration

Sandeep Mukherjee

Petrophysics Advisor
Apache

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Meenakshi Mishra

Principal Data Scientist
ExxonMobil

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Shuxing Cheng

Principal Research Data Scientist
Chevron

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As AI continues to evolve at breakneck speed, the energy sector stands at a pivotal crossroad, balancing the promise of transformative efficiency with the complexity of real-world implementation. The true value of AI lies not in experimentation, but in aligning deployment with strategic business priorities and operational realities. This session explores how energy organizations can move beyond hype to build pragmatic, high-impact AI roadmaps.

• Why targeting business-critical challenges, not just technically interesting problems, is essential for ROI. Learn how to identify and prioritize high-value AI use cases that align with core operational goals
• Is a top-down, enterprise-wide AI rollout always the right approach? For many operators, a more agile strategy which leverages plug-and-play models within specific business units can accelerate time-to-value while minimizing risk. How do you assess if modular or enterprise scale deployment is right for your business?
• What does a robust AI governance framework look like in practice? It’s time to unpack the organizational structures, cross-functional collaboration models, and oversight mechanisms that support sustainable AI adoption  

Harvinder Singh

Platform and Services Manager, Knowledge Management
bp

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Ukeme Essien

Senior Innovation Specialist
Noble

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Gary Dunbar

Head of Business Transformation
Signal Energy

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