Industry Insights

Join 300+ energy, utilities, and industrial leaders from March 1-4, 2027, in Houston, Texas, for AI in Energy Week, the premier gathering for organizations looking to accelerate AI adoption and deliver measurable business value.

Discover how leading energy companies are moving beyond AI pilots to achieve real-world results. Gain actionable strategies to improve operational performance, empower your workforce, strengthen decision-making, and scale AI initiatives with confidence through operator-led case studies, workshops, keynotes, peer discussions, and networking opportunities.

Take a look at the practical approaches, lessons learned, and transformation roadmaps helping organizations succeed across six critical areas:

• Scaling and delivering AI across the enterprise
• Using data to drive faster, smarter decisions
• Building organizational readiness for AI adoption
• Improving asset reliability and performance
• Augmenting and empowering the workforce
• Enhancing operational intelligence and visibility

Explore the 2027 Event Guide, featuring insights from senior executives at AES Corporation, Aker BP, RWE, NOV, Delek US, Celanese, Dow, Phillips 66, energyRe, Duke Energy, ExxonMobil, EDP Renewables, and more.

After years of experimentation, the energy sector has reached a turning point. AI is no longer about pilots. It’s about operational scale, workforce readiness and real commercial impact.

Drawing on insights shared by 131 senior energy leaders across 34 sessions at AI in Energy Week 2026, this report reveals what's actually driving AI success across the energy sector and where organisations continue to fall short.

While AI investment continues to accelerate, speakers repeatedly highlighted the same reality: technology alone isn't determining success. Organizational readiness, data foundations and operational integration remain the defining factors.

Inside the report, you’ll discover:

• Why 95% of AI initiatives still fail to deliver value and why the problem often has little to do with the technology itself
• The 3 non‑negotiable foundations of scaled AI success: data governance, workflow integration and workforce readiness
• Where energy companies are already achieving measurable returns through predictive, prescriptive and preventive maintenance
• The common barriers preventing organizations from moving beyond pilot projects
• Lessons learned from energy leaders actively deploying AI at scale

What prevents AI initiatives from successfully scaling across the enterprise?

To understand the realities of moving AI from proof of concept to business-wide deployment, we sat down with Adam Pryor, Manager, Strategic Analytics at Murphy Oil.

In this exclusive interview preview, Adam shares his perspective on the barriers that continue to slow AI adoption, the risks of creating technical debt in a rapidly evolving technology landscape, and how organisations can identify the initiatives truly worth scaling.

Read as Adam discusses:

• The most persistent barriers to scaling AI beyond proof of concept
• Why data availability and consistency remain critical challenges
• The impact of rapid technology development on long-term AI strategy
• How to avoid creating unnecessary technical debt
• The difference between being technology-first and problem-first
• How to determine which AI initiatives are genuinely worth scaling

Exclusive Interview Highlight: 

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."

"The second barrier is the pace of change in the technology itself. We've had instances where we develop and deploy something, and there's already a better tool on the market. You don't want to develop that technical debt."

Download the full interview to discover how Murphy Oil is navigating the realities of AI adoption and tackling the challenges of scaling AI across the energy sector.

How do energy companies invest in AI today without creating tomorrow's legacy systems?

Moving from successful pilots to enterprise-scale adoption remains one of the biggest challenges facing energy organisations today.

In this exclusive interview, Marcus Johansson, Director, Technology Services, Application Delivery at Xcel Energy, shares his experience navigating the realities of AI deployment, revealing the barriers that continue to slow progress and the foundations required to scale successfully across the enterprise.

Read as Marcus explores:

• The most common barriers preventing AI initiatives from scaling beyond proof of concept
• Whether organisations should prioritise hiring specialist AI talent or upskilling existing teams
• How to identify which AI use cases are genuinely worth scaling
• The metrics and business outcomes leaders should use to measure success
• What energy companies need to do today to remain competitive tomorrow

With more than 20 years of experience leading strategy, transformation and technology initiatives across Fortune 500 organisations, Marcus offers a practical perspective on moving AI from experimentation to measurable business value.

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.