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?