Exclusive Interview: Overcoming Barriers to Accelerate AI in Energy with Murphy Oil

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.