Main Conference Day 1 - February 24, 2026

View the schedule for Day one of the AI in Energy Summit, Houston's leading energy conference and learn how to augment your workforce, maximize asset performance and power intelligent operations

7:00 am - 7:55 am Registration and Networking Breakfast

7:15 am - 7:45 am Meet the Expert Breakfast: Mapping the Field: Building Data Foundations Across Multi-Operator Assets

Raymond Mitten - Vice President, Advanced Digital Technologies, Business Transformation, Imperative Chemical Partners

In this session, we explore the challenges and opportunities of applying AI in a multi-operator environment, where data from thousands of operators must be securely separated, yet intelligently connected. Raymond will share how his team is building a cloud-based data lake and partnership ecosystem to support scalable, secure, and actionable insights across regions like the Permian.

Attendees will learn how generative AI and machine learning can be used to extract patterns from diverse datasets, ranging from chemical usage and fluid dynamics to production volumes and field conditions, to create a unified view of asset behavior across operators. The session will also highlight the roadmap for real-time data sharing, the importance of foundational systems and data cleaning, and how regional modelling can unlock efficiencies that individual operators may not see alone. 

Limited to 20 places, book your spot now!  

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Raymond Mitten

Vice President, Advanced Digital Technologies, Business Transformation
Imperative Chemical Partners

7:55 am - 8:00 am Event Directors Welcome

8:00 am - 8:10 am Chair’s Opening Remarks

As AI matures from experimentation to enterprise-wide deployment, leaders are under pressure to define strategies that will deliver measurable value. This keynote panel explores what a future-ready AI strategy looks like and what leadership must do now, to get there.

  • Understanding the operational efficiency drivers that will justify a move from POC to enterprise-wide deployment
  • Determining the information that is vital for securing buy-in, from clear ROI projections to alignment with operational and business goals
  • Bridging the gap between data science teams and frontline operators
  • Identifying and prioritizing ethical AI use cases that are technically feasible, socially responsible, secure, and aligned with regulatory expectations 

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Ido Biger

Executive Vice President, Chief Information and Data Officer
Delek US Holdings

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Larry Bekkedahl

Senior Vice President, Advanced Energy Delivery
Portland General Electric

8:40 am - 9:10 am Unlocking Operational Autonomy: Deploying Agentic AI in the Energy Sector

  • 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 

9:10 am - 9:40 am Keynote Case Study: Mission-Driven Data: Governance, Stewardship and the Future of AI in Energy

  • Learn how data governance models can unify data practices across complex operational environments while maintaining flexibility and control
  • Explore strategies that prioritize data quality, discoverability, and trustworthiness, critical foundations for AI-driven decision-making in energy operations
  • Understand how to align data architecture, stewardship, and security controls to support scalable analytics, cross-functional collaboration, and mission-critical outcomes 
  • Defining a strategic AI roadmap that aligns with evolving business needs, operational realities, and sustainability goals, while also ensuring the right technological foundation is in place
  • Evaluating build vs. buy decisions by weighing speed, cost, talent, and long-term control
  • Empowering frontline teams and aligning operational timelines with AI deployment goals
  • Leveraging partnerships and hybrid models to overcome talent shortages, accelerate innovation, and balance scalability with ownership 
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Ted Furlong

Executive Director, Data Science
Baker Hughes

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Arundhati Biswas

Senior Director, IT Strategy and Business Operations
National Grid

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Richard Stinson

Senior Manager of Predictive Insights
TC Energy

10:10 am - 10:40 am Morning Networking Break

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 

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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

Advanced Analytics

11:10 am - 11:40 am Operational Clarity: Turning Unstructured Data into Actionable Intelligence

In energy operations, critical knowledge is often buried in unstructured text, maintenance logs, field reports, technical manuals, and supplier documents. In this session, we will demonstrate how Natural Language Processing (NLP) can unlock that information and turn it into real-time operational insight. Discover how frontline teams are using NLP to reduce downtime, improve compliance, and make faster, more informed decisions, without needing to sift through thousands of documents. 

Advanced Analytics

11:40 am - 12:10 pm Case Study: Laying the Groundwork: Identifying Pain Points & Building a Data Architecture for Scalable AI

As digital transformation accelerates, the ability to scale AI across operations hinges on a solid data foundation with modernized data architecture. Yet many still struggle to move beyond siloed analytics and pilot projects due to fragmented systems, unclear ownership, and inconsistent data quality. This session explores how to identify high-impact pain points and design data architectures that support scalable, secure, and AI-ready analytics environments.

  • Identifying operational pain points that are ripe for AI enablement, whether in maintenance, emissions tracking or production optimization
  • Best practices for building a modern data architecture, including semantic layers, unified data pipelines, and governance frameworks that support cross-functional analytics
  • Lessons learned on overcoming legacy system constraints and preparing data for AI 

Agentic AI

10:40 am - 11:10 am Panel Discussion: Agentic AI in Action: Optimizing Operations Through Autonomous Decision-Making
David Moore - Global Wells Digital Advisor, Program Manager and AI Digital Delivery Lead, Shell

As energy systems grow more complex and data-rich, the next frontier is not just prediction, but action. Agentic AI introduces autonomous, goal-driven agents capable of making decisions, coordinating tasks, and adapting to changing conditions in real time. This panel brings together operators who are exploring or deploying agentic AI to streamline operations, reduce manual intervention, and accelerate response times across asset-heavy environments.

  • What agentic AI means for energy operations, from single-agent task automation to multi-agent orchestration across workflows
  • How autonomous agents can optimize frontline decisions, such as dispatching maintenance crews, adjusting load forecasts, or managing emissions thresholds
  • Implementation challenges including data integration, trust, oversight and aligning agent behavior with business goals
  • Strategies for human-AI collaboration
  • Lessons from early deployments, how agentic AI can reduce downtime, improve safety, and enhance operational agility 

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David Moore

Global Wells Digital Advisor, Program Manager and AI Digital Delivery Lead
Shell

Agentic AI

11:10 am - 11:40 am Autonomous Operations: Driving Decision-Making and Coordination Across Enterprises
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. 


Agentic AI

11:40 am - 12:10 pm Case Study: Architecting Autonomous Operations: Hierarchical Multi-Agent Systems for Scalable Energy Automation

As operations grow in complexity, the next frontier lies in orchestrating autonomous decision-making across entire business units. This session explores the theoretical and emerging practical applications of hierarchical multi-agent systems, a layered architecture of AI agents that collaborate, communicate, and self-organize to optimize workflows across drilling, subsurface, completions, and abandonment. 

Intelligent Vision

10:40 am - 11:10 am Panel Discussion: No Disconnects: Proving the Value of Machine Learning for Scalable Predictive Maintenance
Amit Kumar - Technical Project Manager, Data Science, Unconventional Business Line, ExxonMobil
Sriram Ramaganesan - Director, APC, Blending and Optimization, Phillips 66

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 

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Amit Kumar

Technical Project Manager, Data Science, Unconventional Business Line
ExxonMobil

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Sriram Ramaganesan

Director, APC, Blending and Optimization
Phillips 66

Intelligent Vision

11:10 am - 11:40 am No Blind Spots: Remote Monitoring with Visual AI

Monitoring remote and offshore oil fields is a critical challenge. By replacing manual inspections and sensor-heavy setups with real-time video analytics, site surveillance can be digitized, improving uptime, reducing maintenance delays, and increasing productivity. The result: scalable, cost-effective field visibility without the need for constant human presence or complex sensor networks.  

Intelligent Vision

11:40 am - 12:10 pm Case Study: Computer Vision in Action: Scaling AI for Grid Asset Intelligence and Operational Efficiency
Zefan Tang - Senior Data Scientist, Eversource Energy

As utilities modernize their infrastructure, the ability to extract actionable insights from visual and operational data is becoming a game-changer. In this session, Eversource Energy, New England’s largest utility, shares how they are deploying computer vision and machine learning to assist asset inspection, predictive maintenance, and internal engineering workflows for transmission and distribution systems.

From drone-based inspections of transmission lines to dashcam-enabled video analysis of distribution infrastructure, learn how Eversource is accelerating anomaly detection and reducing manual review time. Key challenges such as data collection, model retraining, and cross-team deployment will be addressed, offering practical insights into building scalable, secure AI pipelines that deliver real operational value. 

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Zefan Tang

Senior Data Scientist
Eversource Energy

12:10 pm - 1:10 pm Networking Lunch


12:20 pm - 1:00 pm Lunch and Learn: Reimagining Talent Strategy with AI: Building a Resilient Workforce

Lisa Williams - Senior Director Operations Talent Strategy and Employee Experience, Dow

In today’s volatile landscape, traditional workforce planning is no longer enough. Lisa shares how Dow is rethinking talent strategy from within operations, not HR, by identifying the urgent need for AI-powered models that can forecast, modernize, and redeploy talent across global sites. This session will explore how to build a resilient, data-informed talent strategy that meets the demands of a rapidly evolving marketplace.

  • Understand why legacy job structures and outdated data systems are no longer fit for purpose and how AI can help organizations forecast talent gaps, model workforce scenarios, and make smarter, faster decisions in times of disruption
  • Explore how Dow is laying the groundwork for transformation by capturing raw skills data from 22,000 employees, creating a foundation for modernizing roles, identifying risk areas and targeting upskilling efforts with efficiency
  • Analyze how AI models could help visualize workforce dynamics across geographies, identifying where critical skills are lacking and where surplus talent could be redeployed enabling smarter, more agile workforce planning
  • Gain insight into why Lisa is calling for industry-wide collaboration, encouraging peers and competitors alike to articulate shared needs so that technology providers can build scalable, cross-sector solutions that benefit the entire ecosystem 

Limited to 25 places, book your spot now! 

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Lisa Williams

Senior Director Operations Talent Strategy and Employee Experience
Dow

AI in Action Track A

1:10 pm - 1:40 pm Case Study: Seeing the Unseen: AI-Powered Leak Detection for Safer, Smarter Pipeline Monitoring
Philippe Daroux - Operations Technology Product Line Manager, Chevron

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.

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Philippe Daroux

Operations Technology Product Line Manager
Chevron

AI in Action Track A

1:40 pm - 2:10 pm Smarter Assets, Better Outcomes: Enabling Intelligent Decisions with AI & Analytics

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 

AI in Action Track A

2:10 pm - 2:40 pm Case Study: Optimizing Production in End-of-Life Wells with Machine Learning at Ovintiv
Yousef Hamedi Shokrlu - Advisor, Data Science, Ovintiv

As wells reach the end of their productive life, maximizing output becomes increasingly complex, especially for artificial lift systems like plunger lift and gas-assisted plunger wells. This session explores how machine learning is being used to optimize production by identifying the ideal shut in and flow times, gas injection rates, and equipment configurations. After a successful pilot, Ovintiv is moving toward full automation, allowing models to recommend optimal settings and prioritize wells for intervention.

With limited engineering resources and hundreds of wells to manage, this data-driven approach helps surface underperforming assets and simulate production outcomes under various scenarios. Yousef will also explore the challenges of working with sparse data, the trade-offs between global and well-specific models, and the importance of communicating model reliability and limitations to operational teams. 

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Yousef Hamedi Shokrlu

Advisor, Data Science
Ovintiv

AI in Action Track A

2:40 pm - 3:10 pm Harnessing Computer Vision to Drive Reliability and Safety: Connected Worker & Asset Intelligence

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. 

AI in Action Track B

1:10 pm - 1:40 pm Case Study: Predictive Maintenance: Anomaly Detection for Smarter Refinery Operations at Marathon
Tim Sandford - Refining PI Coordinator, Marathon Petroleum

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. 

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Tim Sandford

Refining PI Coordinator
Marathon Petroleum

AI in Action Track B

1:40 pm - 2:10 pm AI Driven Smart Compliance & Reporting: Unlocking Efficiency Through Intelligent Automation

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  

AI in Action Track B

2:10 pm - 2:40 pm Case Study: Generative AI for Root Cause Analysis of Customer Quality Complaint Data
Swee-Teng Chin - Statistics Technical Leader, Dow

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 will illustrate how preliminary results demonstrate improved diagnostic accuracy and actionable insights, positioning Generative AI as a transformative tool for quality assurance and customer satisfaction across industries. 

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Swee-Teng Chin

Statistics Technical Leader
Dow

AI in Action Track B

2:40 pm - 3:10 pm Voice and Wearable AI for Hands-Free Operations

Learn how AI voice-enabled assistants and smart wearables are empowering field workers with instant access to data, procedures, and remote support, boosting productivity while keeping hands and eyes on the job. 

AI in Action Track C

1:10 pm - 1:40 pm Case Study: Hybrid AI Strategies to Reduce Downtime and Expenses for Energy Industry
Ivan Castillo - Senior Data Scientist, Dow

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. 

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Ivan Castillo

Senior Data Scientist
Dow

AI in Action Track C

1:40 pm - 2:10 pm Asset Health Management through Simulation

Powerful simulation tools provide the opportunity to increase the reliability, resilience and productivity of your asset portfolio. By creating models of your assets your organization can model, predict and optimize performance. From understanding the likelihood of equipment failures to drive predictive maintenance, to understanding how your assets will behave in different operating conditions to drive performance optimization, forecasting the health of your asset is key to retaining safe and efficient operations. 

AI in Action Track C

2:10 pm - 2:40 pm Case Study: From Manual to Machine: How ExxonMobil Built a Smarter Way to Work with Documents through Generative AI
Jason Gee - Manager, Data Science, ExxonMobil

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.


However, building a scalable natural language processing capability came with various challenges. Accuracy was imperative, but the off the shelf large language model required rigorous fine tuning to reach a 95% accuracy rate. Furthermore, to realize business value the team had to anticipate and mitigate organizational bottlenecks that could hinder user adoption, ensuring human-led design was at the forefront. Future-proofing the solution was also non-negotiable. The system needed to evolve with the rapid pace of innovation in generative AI, enabling intelligent contract analysis, cost-saving opportunity detection, and real-time negotiation support. In this session Jason will walk through ExxonMobil’s phased deployment strategy for this transformative generative AI tool. 

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Jason Gee

Manager, Data Science
ExxonMobil

AI in Action Track C

2:40 pm - 3:10 pm The Intersection of Human Intelligence and Advanced Analytics

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. 

3:10 pm - 3:50 pm AI Spotlight Session

Looking to discover the latest AI technologies transforming energy operations? The AI Spotlight Session is your opportunity to get up close with the innovators shaping the industry. During this high-energy networking break, you'll rotate through vendor booths in rapid 5-minute intervals, giving you focused time to explore each solution, ask specific questions, and understand exactly how these technologies can address your challenges. Whether you're evaluating a new tool, seeking a strategic partner, or just want to stay ahead of the curve, this session is designed to deliver maximum value in minimal time. Connect directly with solution providers, gather actionable insights, and walk away with ideas you can take back to your team and start implementing right away. 

3:50 pm - 4:20 pm Case Study: Building AI-Ready Operations Across Midstream: Inside Marathon Petroleum’s Maintenance Transformation

Daniel Byrne - Senior Director, Digital Transformation, Marathon Petroleum

Marathon Petroleum is laying the groundwork for AI powered technology with a transformational program designed to standardize maintenance operations across its midstream business. With a focus on data quality, system integration, and process consistency, this initiative is enabling scalable AI use cases across the full IPSEC lifecycle (Identify, Plan, Schedule, Execute, Close). As Dan shares, this foundational work is critical, especially in light of a recent MIT study showing that 95% of AI initiatives deliver little to no measurable value. This session explores how Marathon is building the digital and data infrastructure needed to ensure AI delivers real operational impact and how they’re actively exploring AI use cases that support predictive maintenance, intelligent scheduling, and frontline decision-making.

  • Explore how Marathon is unifying maintenance processes across pipelines, terminals, and logistics, creating a consistent data layer that supports AI integration and long-term operational visibility
  • Understand how AI agents are being designed to support complex planning and scheduling decisions, factoring in asset priority, workforce availability, geospatial constraints, and material readiness
  • Learn how Marathon is preparing for future use cases, including predictive modelling for asset health, computer vision for leak detection, and intelligent agents that assist with troubleshooting and parts ordering
  • Gain insight into how this transformation is generating a rich dataset across the full maintenance lifecycle, enabling continuous improvement and reshaping how operational excellence is measured and achieved 

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Daniel Byrne

Senior Director, Digital Transformation
Marathon Petroleum

4:20 pm - 4:50 pm Seeing is Empowering: Unlocking the Power of Computer Vision

Computer vision technology is driving productivity and safety improvements across operations, helping to detect anomalies, identify safety issues and automate inspection processes. This session we will explore the key applications of computer vision and how to implement them.

  • Understanding how computer vision systems can seamlessly integrate with existing machinery and software
  • Combining data from computer vision systems with other operational data sources for comprehensive analysis
  • Examining initial costs during the implementation phase and the challenges faced when deploying in complex operating environments
  • Exploring the benefits that can be achieved through enhanced quality control, automating repetitive tasks and continuous monitoring of operations to detect safety hazards
  • Leveraging data driven insights and advanced analytics to optimize production processes and reduce costs 

4:50 pm - 5:20 pm Keynote Case Study: Building the Future of Energy with AI at AES

Erin Boyd - Chief Digital Commercial Transformation Officer, AES

As AES shifts from a global digital model to a hub-and-spoke structure, it’s reimagining how innovation scales across the enterprise. At the heart of this transformation is a new Digital Center of Excellence, led by Erin, who is tasked with building future-ready platforms that not only keep pace with AI’s rapid evolution but also empower teams to explore what’s possible. With a bold mandate to triple energy capacity without increasing headcount, AES is betting on AI to drive both cultural and operational transformation.

  • Discover how AES is empowering creativity at scale by building an ecosystem that enables teams across all business units to experiment and innovate with AI tools, driving individual productivity and enterprise-wide impact
  • Explore how platform-agnostic experimentation allows AES to test a wide range of AI tools to find the best fit for specific use cases, while managing the complexity of evaluating and integrating rapidly evolving technologies
  • Learn how AES is scaling with guardrails by defining clear parameters for enterprise-wide deployment of successfully piloted tools, balancing innovation with the need for robust data governance and access control 
  • Understand how partnering for agility enables AES to accelerate AI adoption through strategic collaborations with startups, while addressing the challenge of ensuring long-term value and seamless integration in a fast-moving market 

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Erin Boyd

Chief Digital Commercial Transformation Officer
AES

5:20 pm - 5:30 pm Chair’s Closing Remarks

Where are you on your AI journey, and who else is walking the same path? Grab a drink and join this dynamic networking session to find and connect with peers at a similar stage of AI readiness. During sign in, attendees are requested to select a badge that identifies their AI readiness level from:

    Attendees are encouraged to stand up, walk around, and talk! The room will have conversational zones including “Biggest Barriers”, “Quick Wins,” “Tech Stack Talk”, “AI Workforce Readiness” and “Data Foundations & Architecture” to help people self-organize around topics. Plus, our facilitators will roam the room to help spark conversation with light prompts or introduce people with shared interests. Finally, every attendee gets a simple “AI Readiness Reflection Card” to help them jot down one idea, one contact, and one next step. 

    • Exploring – Just starting to learn
    • Piloting – Running small-scale experiments
    • Scaling – Expanding successful use cases
    • Optimizing – AI is embedded and evolving
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    Jim Parker

    Senior Program Manager
    Tennessee Valley Authority

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    Ron Norris

    Former Director of Innovation and Founder
    Georgia-Pacific and Advanced Innovation Management