In the race to unlock AI’s full potential, data is both the fuel and the friction. As companies battle to integrate diverse control systems, legacy platforms, and siloed datasets, the question isn’t just how to centralize data, but how to make it usable, trustworthy, and AI-ready? With Generative AI offering new ways to structure and interpret messy data, is it time to rethink the traditional “clean first, deploy later” mindset? This session challenges conventional data strategies and explores how AI can reshape, not just rely on, your data foundations.

• How generative AI can help you move from data chaos to contextual clarity by structuring unstandardized data streams, reducing the burden of upfront data cleaning, and accelerating time-to-value in AI deployments
• How the “clean as you go” paradigm, based on iterative data refinement powered by real-time AI results, can uncover hidden issues, optimize resource allocation, and reshape how you engage vendors and suppliers
• The complexities of ownership, integration, and trust in data ecosystems, including challenges around data ownership, integration across control systems, and the pursuit of a single source of truth in a fragmented digital landscape 

Shivaprasad Sankesha Narayana

Lead Architect Consultant
bp

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Inderpreet Jalli

Senior Power Trading Analyst
NRG Energy

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Indu Nambiar

Principal, IT Architecture
California ISO

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Data alone doesn’t drive optimal decision making; context does. As organizations integrate diverse data sets across business units, advanced analytics and visualization platforms are enabling a new level of operational clarity. In this session, we’ll explore how combining smart energy analytics with human intelligence unlocks deeper insights, improves forecasting accuracy, and supports smarter resource allocation. Discover how energy leaders are using intuitive dashboards and cross-functional data models to make faster, more reliable decisions, while maintaining the human judgment that keeps operations resilient and adaptive. 

Building a Scalable AI Practice in your business takes talented practitioners and clean data. This session explores how Murphy Oil Corporation is laying the groundwork for scalable AI by transforming legacy data into actionable insights while using that effort to grow AI fluency and experience. Murphy’s goal is to empower human energy and refine data into information, support intelligent decision-making across engineering and asset workflows into the future.

Nathan will walk through Murphy’s phased approach to data architecture. We will start with AI-assisted cleansing and standardization of historical well intervention logs and progress toward the development of a flexible, geospatially indexed knowledge graph. Learn how Murphy is leveraging a data lake to incrementally improve data quality at the source, track intervention patterns, and surface hidden inefficiencies. Attendees will gain practical insights into overcoming legacy constraints, planning their organizational journey, and building a foundation that enables engineers to learn faster, act smarter, and unlock real business value, especially in environments where every well counts. 

Nathan Church

Data Science Lead - Drilling, Completions and Interventions
Murphy Oil

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As the industry seeks to move beyond reactive maintenance, machine learning offers a powerful opportunity to predict failures before they happen, reducing downtime, optimizing asset performance, and improving safety. This panel explores how early-stage pilots can lay the foundation for scalable, enterprise-wide predictive maintenance solutions.

• How to build a business case for machine learning in maintenance by identifying high-value assets, leveraging historical failure data, and demonstrating early wins
• Strategies for assessing data readiness and model adaptability, ensuring that predictive algorithms are trained on relevant, high-quality inputs and evolve with operational complexity
• Stakeholder engagement techniques that drive adoption, from localized decision-making to cross-functional collaboration between data teams and asset owners
• How proof-of-concept projects can be scaled across the enterprise with lessons learned in governance, infrastructure integration, and long-term value tracking 

Amit Kumar

Technical Project Manager, Data Science, Unconventional Business Line
ExxonMobil

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Sriram Ramaganesan

Director, APC, Blending and Optimization
Phillips 66

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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.

Attendees will learn how generative AI and ensemble modelling are used to extract structured data from field reports, visual inspections, and engineer comments, feeding into a system that produces standardized scores and prediction intervals. The framework emphasizes transparency and flexibility, allowing leadership to assess system-wide health across regions and asset classes using a common language of risk.

Rather than aiming to predict maintenance needs, the team deliberately chose not to pursue this path, highlighting the limitations of historical data, regulatory variability, and shifting budget priorities. Instead, the focus is on building a universal language of asset health that informs decisions without overpromising precision, offering a scalable, future-proof model for organizations navigating complex infrastructure and asset planning. 

Zach Price

Data Scientist
National Grid

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When every function across ExxonMobil came knocking with the same request of “We need a smarter way to interact with our documents”, the data science team knew an Enterprise-scale generative AI powered solution was the answer. The vision was to create a solution that empowered employees to ‘speak with their documents’, instantly summarizing complex technical reports, contracts, and operational manuals into actionable insights. The potential to drive productivity enterprise wide and eliminate hours of manual effort was undeniable.

Jason Gee

Manager, Data Science
ExxonMobil

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AI can only operate effectively if it understands your business and workflow processes in granularity, allowing you to pinpoint where the inefficiencies lie. With operational context you can understand where AI deployment will translate into real efficiency gains and business value, allowing you to re-design your processes with a foundation for scalable AI deployment. 

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.

Attendees will gain insight into the strategic rollout of AI tools, including lessons learned from early missteps, adoption trends among early career talent, and the cultural shift required to drive meaningful engagement. The session will also explore the next steps in PBF’s AI journey, offering a glimpse into the future of intelligent operations. 

Patrick Robinson

Senior Director, Operations Technology and Advanced Process Control
PBF Energy

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As operators face increasing pressure to improve safety and reduce downtime, AI-powered leak detection is emerging as a high-impact solution. This use case will illustrate how computer vision, thermal imaging, and edge computing are being combined to monitor pipelines in real time, enabling early identification of leaks without relying solely on traditional sensors.

Philippe Daroux

Operations Technology Product Line Manager
Chevron

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Timely, data-driven decisions are essential for improving reliability, reducing costs, and managing operational risk. This session focuses on how AI and advanced analytics are transforming asset management by enabling predictive maintenance and risk-based decision-making. Learn how to:

• Leverage machine learning models trained on historical and real-time data to detect early signs of equipment degradation, optimize maintenance intervals, and minimize unplanned downtime
• Apply risk-based frameworks to prioritize assets based on criticality and failure probability, ensuring that maintenance and capital investments are aligned with business impact
• Visualize AI-driven insights through integrated dashboards that combine IoT data, risk scores, and KPIs, empowering teams to act faster and with greater confidence 

As experienced engineers retire and operational demands grow, refineries are turning to AI to bridge the gap. In this session, Tim shares how anomaly detection is being used to surface early signs of equipment issues, enabling proactive maintenance, reducing unplanned downtime and supporting a shift towards predictive maintenance.

Using a dynamic risk analyzer, the team used a hands-off AI tool that automates data cleansing, modeling, and real-time monitoring. It flags subtle shifts in process behavior, such as changes in reactor temperature slopes, that may signal emerging equipment issues. By surfacing these early indicators, engineers can plan interventions more effectively and reduce the risk of costly failures.

The session also explores how to simplify model building and analysis, enabling engineers to extract insights from thousands of data points without writing code. While the pilot has shown promising results, human oversight remains critical, with a “trust but verify” approach guiding the path toward enterprise-wide adoption. 

Tim Sandford

Refining PI Coordinator
Marathon Petroleum

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In energy operations, visual data is everywhere, but often underutilized. Computer vision platforms powered by AI are changing that, enabling automated inspections, real-time risk detection and intelligent monitoring of assets. In this session, we’ll explore how computer vision is being used to enhance maintenance programs, improve the safety for frontline workers and deliver continuous visibility into operational environments. Discover how energy companies are leveraging these solutions to reduce manual effort, detect anomalies before they escalate, and drive smarter, safer decisions across the enterprise. 

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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Iqbal Qasim

Global Process Safety and Regulatory Compliance Leader
Baker Hughes

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This session examines methods that combine artificial intelligence with other approaches to improve predictive diagnostics in industrial production, particularly in scenarios with limited data and uncertainty. The discussion includes examples of AI applications for predicting heat exchanger fouling. The case study demonstrates how integrating physics-based models with machine learning can enhance reliability, reduce downtime, and support more cost-effective decision-making in energy management. 

Ivan Castillo

Senior Data Scientist
Dow

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As regulatory frameworks evolve and voluntary reporting commitments expand, organizations face mounting pressure to streamline how they collect, validate, and report operational data. This session explores how AI-powered compliance and reporting systems can dramatically reduce manual effort, enhance data integrity, and empower frontline teams to focus on high-value tasks. By embedding intelligence into reporting workflows, organizations can unlock new levels of agility, transparency, and safety. 

• Automate your data preparation and validation abilities to slash preparation time and gain insights into metrics with minimal intervention
• Consolidate data from various sources into a secure environment with guard rails that support compliance, ESG tracking, and operational transparency
• Gain real-time visibility of metrics, to adapting workflows and adjusting operational activities to meet regulatory requirements and sustainability goals
• Enhance the safety of frontline workers with better reporting and documentation of incents providing intelligent insights that optimize your safety culture  

This session explores the use of Generative AI to automate and improve Root Cause Analysis (RCA) on customer quality complaint data. By combining domain-specific language models with structured and unstructured feedback records, Dow’s system uncovers hidden patterns, links operational issues to customer experiences, and generates coherent RCA summaries. The approach streamlines investigation workflows, enhances consistency in issue resolution, and supports proactive quality improvements.

Swee-Teng Chin

Statistics Technical Leader
Dow

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• 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

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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Strata Clean Energy has devised a multi-year transformation roadmap hinging on implementing AI-powered solutions across every aspect of their business operations. In this session, Shyam will unpack the company’s roadmap for embedding AI at scale, balancing innovation, operational value, and organizational alignment.

• Build vs. Buy: evaluating the trade-offs between building proprietary AI solutions vs leveraging off-the-shelf platforms with a spotlight on prioritizing initiatives that deliver a competitive advantage in the marketplace through intellectual property ownership
• Establishing high-impact AI PoCs, including generative AI pipelines, performance analytics, predictive maintenance, drone-based infrared inspections, and risk forecasting
• Building a GenAI ecosystem, integrating natural language processing to address domain-specific challenges through in-house innovation
• Establishing an integrated AI taskforce, bridging IT, data science, and business users, to drive adoption, foster buy-in and scale prototypes through internal showcases and town halls
• AI deployment that leverages data-driven evaluations of PoC outcomes while avoiding long-term vendor lock-in to maintain flexibility and ROI focus  

Shyam Perugupalli

Chief Information Officer
Strata Clean Energy

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• Closing the gap between data and real-time action: turning insight into execution with minimal human input
• Exploring real-world scenarios where AI autonomously monitors performance, predicts failures, schedules maintenance, and optimizes energy use
• How agentic AI breaks down silos across production, logistics, and maintenance teams, enabling seamless operational coordination
• Implementing governance frameworks to ensure responsible, ethical, and secure deployment
• Amplifying, not replacing, the value of human engineers and operators
• Practical steps and strategic insights to future-proof your workforce and operations for the agentic AI era

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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There is a new kid in town and it’s going fully autonomous! In energy operations, the shift from reactive workflows to autonomous coordination is accelerating. Agentic AI platforms, powered by autonomous agents, are transforming how decisions are made and executed across complex environments. In this session, we’ll explore how agentic AI enables proactive problem-solving, intelligent workflow orchestration, and real-time decision-making with minimal human intervention. Learn how these platforms reduce downtime, streamline asset management and enhance HSE outcomes.  

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 - Strategic Analytics
Murphy Oil

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Emile-Otto Coetzer

Senior Digital Advisor
Chevron

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