Industry Insights

The 3rd Annual AI in Energy Summit Event Guide has arrived! Explore the sessions, speakers, and formats designed to help you augment your workforce, maximize asset performance and power intelligent operations.

Every year, the summit brings together 250+ senior operations, digital, data, and AI leaders from utilities, oil and gas, power generation and renewables for three days in Houston. Hear directly from operators who’ve taken AI projects from proof of concept to full deployment and discover what’s actually working in GenAI, ML Ops, Agentic AI, Digital Twins, and real-time decision support.

Join 60+ speakers including leaders from:

• Erin Boyd, Chief Digital Commercial Transformation Officer, AES
• Elan Feagin, Vice President, Electric Technology Solutions, Southern Company
• Daniel Byrne, Senior Director, Digital Transformation, Marathon Petroleum
• Arundhati Biswas, Senior Director, IT Strategy and Business Operations, National Grid
• Taylor Witt, Vice President, Digital and PMO, Energy
• Re Kiela Abbott-Robinson, Director Strategic Initiatives, NextEra Energy
• Marcus Johansson, Director, Technology Services, Application Delivery, Xcel Energy

Download the Event Guide to explore everything new for 2026, including 14 real-world AI development case studies and 10 practical AI trainings!

”I was very impressed with the organization, the content and the vendors in the conference expo area. We were able to catch up with industry peers, deep dive on topics with our existing vendors, and be inspired by the new technology solutions offered.” - Shell

''One of the best events I’ve been to in a long time. Thank you!” - Duke Energy

Read as Adam Pryor, Manager - Strategic Analytics at Murphy Oil, delves into the persistent barriers to scaling AI and how to navigate the new, fast-paced landscape of technical debt and vendor partnerships.

What are the most persistent barriers you’ve faced when scaling AI from a proof of concept to enterprise-wide deployment?

Adam Pryor: I see two major aspects. The first is data availability and uniformity. You might pick a data-rich POC, but as you scale across the organization, you discover differences in process and data collection that create challenges. At the end of the day, it’s garbage in, garbage out. The second barrier is the pace of change in the technology itself. The speed at which third-party tools develop is often much faster than you can build internally. We’ve had instances where we develop and deploy something, and there’s already a better tool on the market. You have to ask, “What is the development cycle?” If it’s longer than eight weeks, you might want to wait and see if the technology catches up. You don’t want to develop that technical debt.

How do you decide which AI initiatives are worth scaling, and how do you ensure they stay aligned with evolving business priorities?

Adam Pryor: This is the age-old question for any technology. There are a lot of people who are technology-first, not problem-first. The real question is: is AI providing a differentiated value that justifies its use?

Learn how Murphy Oil are dismantling the biggest AI challenges in energy.

Download your copy to read the full article to find out.

How do we invest in AI today without being left behind tomorrow?

To get a real-world perspective on this challenge, we sat down with event speaker Marcus Johansson, Director, Technology Services, Application Delivery at Xcel Energy. Marcus shared his first-hand experience on overcoming the persistent barriers to scaling AI, from first proof of concept to deploying the technology across the enterprise.

Read as Marcus discusses the:

• Most persistent barriers when scaling AI
• Drive to hire data scientists
• AI initiatives worth scaling
• Metrics to rely on when measuring success

Marcus is a highly accomplished senior strategy and transformation leader with over 20 years of experience driving business transformation, acquisition integrations, and strategic initiatives across Fortune 500 organizations.

The second edition of the AI in Energy Summit was a bustling hub of learning, networking and collaboration, attracting 150 senior operations, digital, data and AI leaders.

On February 24th and 25th, discussions not only highlighted the optimal applications for AI operability to enhance and revolutionize operations but also addressed often overlooked holistic elements.

In our 2025 Post Event Report, we summarised in one handy document, the summit's;

• 5 Key Takeaways
• Key Audience Stats 
• Highlights from our Conference Chair
• 2025 Agenda Highlights
• And, an update on AI in Energy 2026!

Our commitment will always remain the same - to avoid the hype and give you the practical tools you need to put AI into practice in your operations.

Asset reliability is a critical key performance indicator in any asset-driven business. An IBM study revealed that hybrid AI/ML models, combined with generative AI, can enable operational teams to prioritize and reduce serial failures by 25%–50% while increasing site reliability by 10%–15%.

Dow Chemical Company is currently leveraging GenAI capabilities to automatically extract key failure information that enables the analysis of reliability metrics from multiple sources and sites, including both structured and unstructured data.

Featuring exclusive insights from Technical Leaders and Executives at Dow, download the case study to:

• Learn how Gen AI can address operational challenges such as improving data quality and streamlining processes in asset-driven industries
• Understand how Dow is leveraging AI to extract, clean, and analyze maintenance data
• Learn how new AI tools can complement rather than replace existing systems, ensuring continuity while enhancing capabilities and resource optimization
  

An IBM study stated that about 74% of energy and utility companies globally are either implementing or exploring AI, with the majority intending to leverage it to improve operational efficiency. Generative AI (Gen AI) enables the industry to dig deeper into its data and extract valuable insights in a faster, easier, more accessible way.

Ahead of the AI in Energy Summit this past February, we sat down with event speaker Partha Chatterjee, Principal, Data Analysis at Shell to better understand how to improve operational efficiency through AI.

Read as Partha reveals practical strategies and lessons learned for operators looking to leverage Gen AI for real-time decision-making, with a vision for using Gen AI tools to streamline complex workflows.

Key takeaways include:

• Implementing Gen AI in operational settings and how it shapes digital strategies
• Key considerations when training Gen AI in industrial settings
• What ROI to expect from incorporating AI or Gen AI within operations

This case study features industry insights from bp, ASUG and Chevron Phillips Chemical Company. Explore individual perspectives into the strategic deployment of AI, illustrating how organizations can leverage technology to unlock new opportunities, drive sustainable growth, and shape the future of energy operations. Hear insights from:

• Martin R. Gonazelz, Innovation and Technology Principal, bp
• Laura Tibodeau, Global Digital Innovation & Transformation Leader, ASUG
• Ra Inta, Head of Data Science, Chevron Phillips Chemical Company

Download the Case Study today.

Interest in AI is at an all-time high, especially within the energy sector. Many companies are already piloting AI-powered applications to decarbonize operations, improve safety and overall operational efficiency. However, to push into the next frontier of efficiency and sustainability, the industry needs a clear vision and mission on how to harness the true innovative potential of AI and understand how it fits within their current digital strategy.

Ahead of our upcoming AI in Energy Summit in Houston, from June 17-18, we’ve highlighted the top five applications of AI in the energy sector, delving into renewable energy, smart grids and more.

Download our handy fact sheet today.