Learn how Entergy is investing in Artificial Intelligence at scale and the lessons it has learned in the rapid development and expansion of their AI function. In this insightful case study Energy’s Chief AI Officer Andy Quick will share details on the journey including:

● Leveraging AI with advanced metering infrastructure to predict when distribution transformers are likely to fail ● Enabling proactive maintenance that prevents unplanned outages

● Building out and defining the AI function, beginning in 2023, and how AI is positioned internally today

● Developing an enterprise AI strategy that balances value, capability, and risk

● Determining which of the use cases across the value chain to pursue - and which not to

● Approaching the conundrum of centralised or decentralised model, CoE or federated, in-house or outsourcing

Andy Quick

Chief AI Officer
Entergy

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This session will feature lightening talks (10 minutes), from industry leaders who have been through the process of scaling an AI project. Designed to share a project challenge, insights into the technology that helped solve it, and the lessons learnt along the way, you’ll leave with practical insights into how to move beyond pilot purgatory with your projects. 

TALK 1: Scaling Time Series Forecasting Across the Enterprise

TALK 2: How Digital Technology Can Be Used to Accelerate Decision-Making in the Field

Meenakshi Mishra

Principal Data Scientist
ExxonMobil

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Dr. Yue Hu

Asset Management Specialist
bp

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When approaching an AI project, there are many paths that can be taking to get to the desire endpoint. Vendors are innovating and creating valuable solutions but understanding who and which solution to use can be a real headache. Moreover, novel use cases on unique assets may require novel, self-built, solutions. Our panelists will help you through this process, discussing:

● Balancing cost with control: Buy to cut development time by 40%? Build for increased control over customization and long-term cost savings? ● Dissecting the impacts on speed to operationalization

● Do talent gaps in the face of AI-specific developer shortages make consultants and vendors a necessity?

● How to strike the hybrid sweet spot

Paolo Morra

Director of Analytics
DTE Energy

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Jason Gee

Data Science Manager
ExxonMobil

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Mark Bohn

Vice President, Transformation and Enterprise Portfolio Management
Western Midstream

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● Exploring the data models driving automated maintenance management at Noble Corp.

● Examining the sensor data which is driving asset reliability

● Navigating implementation challenges

● Collaborating with AI/ML and data model providers

Christopher McQuillin

Senior Director, Technical Maintenance Center
Noble Corporation

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Kamil Poplawski

Manager of Reliability and Condition Based Maintenance
Noble Corporation

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Susan discusses specific cases of how AI, both generative AI with Large Language Models, and Machine Learning have been used to detect and eliminate methane and other greenhouse gas emissions. The first example involves evaluating a service company's software package that uses historical and current measurements of oil and gas operations to analyze and forecast emissions. We will examine Chevron's blueprint for reducing methane emissions in the Permian Basin and discuss areas where Machine Learning and Deep Learning have been used, and where generative AI is being used for repurposing data sets for future needs.  

Dr Susan Nash

Director of Innovation & Emerging Science and Technology
AAPG

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• Discussing how Causal AI methods are emerging as a means to go beyond diagnosis and prediction to prognostication, enabling decision-makers to objectively analyze “what-if” scenarios
• Exploring how by aiding in the discovery of relationships in data and creating a means to integrate SME knowledge into predictive models, causal AI-based applications can help operating companies to optimize supply chains, assess safety risk, perform root cause analysis, and execute production plans
• Analyzing the opportunity presented by this emerging technology, and the requirements for its development and implementation to generate value in oil and gas

Giang Mai

Digital Science Principal - Digital Science and Engineering Team
bp

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Maya D'Souza

Digital Science Associate
bp

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• Understand the potential of AI within the industry and why most initiatives fail 
• Explore EDP’s four step transformation method: 

1. Setting goals upfront by understanding the business problem and future state and identifying quick wins to drive early momentum 
2. Analyze the process, examining work pattern (repetitive versus unstructured) with the goal to leverage technology effectively 
3. Select the right technology that allows for speed and flexibility to empower employees and accelerate innovation 
4. Engage with employees with the goal to foster a culture of continuous improvement through agile practices like MVPs, rapid adaptation, and actionable metrics 

Stephan Blasilli

Head of Business Process Excellence
EDP

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• Exploring the key elements of building an organizational culture that supports AI adoption, from leadership buy-in to front line operations team engagement 
• Learning strategies to overcome resistance to AI, addressing fears around job displacement and fostering collaboration between humans and machines 
• Discovering how to upskill your workforce, providing them with the tools and knowledge they need to support increased productivity 
• Understand the importance of transparency and ethical AI practices in gaining trust and ensuring successful, sustainable AI integration 

Julie Thyne

Global Improvement Director
Dow

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Laura Tibodeau

Chief Technology Officer
DAI Global, Inc.

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As the energy sector increasingly embraces AI, the pressure to ensure long-term ROI will grow. Gartner estimates over 50% of AI initiatives fail to deliver sustained value, primarily due to poor scalability, governance issues, and misalignment with evolving business needs. In this session our speakers will discuss strategies you can deploy today to ensure you future-proof your investments for tomorrow. Key topics include: 

• Building AI systems that can scale across operations and adapt to new data, use cases, and emerging technologies without constant rework 
• Implementing governance frameworks that mitigate compliance and ethics risk in a rapidly changing regulatory landscape 
• Ensuring collaboration between data scientists, engineers, and operations teams to maintain AI models, adjust them to operational changes, and sustain value 
• Embedding a culture of AI-centricity, data-centricity, and of continuous learning and development  

Erin Boyd

Chief Digital Commercial Transformation Officer
AES Corporation

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Julie Thyne

Global Improvement Director
Dow

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75% Interactive for Maximum Learning

Take a break from the PowerPoints with this interactive session and further your conference takeaways by actively participating and learning realistic ways to adapt your AI strategy long after the summit ends. This workshop will help you map your vision – reflecting on what you’ve heard over the past two days and work on key takeaways you can communicate to your executive team tomorrow. The opportunity to learn from your peers will provide critical and insightful industry perspectives.

The Interactive Workshop Agenda: 

1. Introductory overview 
2. Attendees divided into teams and provided a case study assignment 
3. Using the provided prompts, teams will brainstorm and develop their ideas 
4. Team leads to present ideas to the wider group, providing key takeaways

Building and Sustaining a Centralized AI Capability

Operators are increasingly looking to leverage AI across their operations by developing out a centralized AI capability. Perhaps the summit has inspired you to start this process, or perhaps you’re looking to develop your current organizational capability. This workshop will help you navigate the challenges and various paths which can be taken in not only building out a centralized AI capability but building out a capability which is sustainable and consistently delivers value to your organization.

The Workshop will focus on:

● Opportunities and challenges with adopting a centralized functional model for AI

● Hype vs reality in AI and positioning a data science team for long-term success

● Approaches on deciding whether to re-use, rent, build or buy an AI solution

● Experiences in development & change management for predictive AI models that improve performance in commercial & supply chain operations

● Developing credibility of recommendations and sustaining long-term performance of AI models

Jason Gee

Data Science Manager
ExxonMobil

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