How AI Is Reshaping Shutdown and Turnaround Operations - with Raghu Ahobilam of NOV

**Daniel Faggella** (0:15)
Welcome everyone to the Emerge AI in Business Podcast. Today's guest is Raghu Ahobilam, Global Director of Inventory and Assets at NOV. Raghu joins Emerge CEO and Head of Research Daniel Faggella to break down how energy enterprises are finally creating a unified, data-driven VOP assets, moving from fragmented legacy systems to KPI-aligned AI-ready workflows. Their conversation centers on how scorecards, predictive insights and integrated dashboards are reshaping maintenance and inventory decisions across global fleets, improving utilization, reducing downtime and giving operations, supply chain and manufacturing leaders clearer ROI signals for modernization. In AI, we see a lot of skepticism. And for good reason. The challenge isn't excitement, it's execution. Data readiness, integration complexity, security concerns, proving ROI, it all must align. Our sponsor, SHI, has been a major global IT solutions provider for over 35 years, helping 17,000-plus organizations navigate exactly these challenges. SHI guides enterprise leaders through their proven framework. Imagine the right AI strategy for your business context. Experiment in their AI cyber labs to validate solutions with your actual data and workloads. And adopt solutions that deliver measurable outcomes in production. If you're looking for guidance on moving from AI ambition to real results, visit shi.com. That's shi.com. Look for the AI and cyber labs or schedule a consultation with the team. Here's the conversation with Raghu and Daniel. Just a quick note for our audience that the views expressed by Raghu on today's program do not reflect that of NOV or its leadership.

**Daniel Faggella** (2:03)
So Raghu, welcome to the program.

**Raghu Ahobilam** (2:05)
Hey, thank you, Dan, for being here.

**Daniel Faggella** (2:07)
Yes, glad to be able to dive in to some useful topics in the energy space. We've seen a lot more traction in terms of AI and energy broadly, lots more kinds of adoption than we would have seen a couple years back. I want to open with kind of your world around sort of inventory and kind of maintenance operation stuff, and sort of ask, you know, when managing kind of a global inventory and maintenance operation, what do you think separates those that stay ahead of distributions from those that fall behind? So maybe give us context on like, I guess, how you see excellence being defined in this function here.

**Raghu Ahobilam** (2:37)
Sure. Yeah, I think everything starts with the overall metrics and objectives, like what exactly are overarching goalies as part of the organization, and how do we go about achieving them? So yeah, I think as long as we have a clear definition and a clear understanding as and visibility of metrics, of our goals and scorecard, if you may, I think that really triggers all the required actions and kind of sums up what really needs to happen, step by step in order to achieve without missing those targets.

**Daniel Faggella** (3:19)
And what does it look like, I guess, to set those? Because I'm thinking about, hey, we've got to have the right dashboard to look at, to know if we're kind of winning or losing here. And I think almost everybody would agree. They would say, well, yeah, sure, I want a dashboard that goes green when it's good and red when it's bad. And I know how to steer the ship. How do you decide what to measure specifically and where to draw those lines? Like what's the process of sort of building that kind of dashboard of excellence that gives you the confidence you need to make decisions?

**Raghu Ahobilam** (3:48)
Well, I think it all really starts with the overall vision of the company, why we are in this business and what we are trying to achieve and how we are trying to be different. I mean, at my level, there is a lot of different ways of doing it. I mean, one of the major strategic planning tool that I've used in the past is called Hoshin-Kanri.
It's a major lean six sigma methodology. Basically, it really starts with, okay, what does the company want to achieve? And based on the vision of the company or the objective, the overarching objective, the metrics or the actions that drive the metrics are derived from the setup that we have. So it really depends on how do we want to define the vision or how do we want to define the overarching goal in a way that can be quantified and it can be measured. And it makes sense to what we are doing on a day-to-day basis. Basically, just breaking down one huge goal into small bite-sized pieces at different levels of the organization.

**Daniel Faggella** (5:02)
Yes, so kind of defining success, what are the pillars of that success and, okay, what would be the specific things we need to measure under those individual pillars? And this clearly, it's sort of like you're kind of defining excellence as those who really have a good grasp of their data. Of course, in order to make these decisions off of the dashboards you're talking about, of course, we have to be looking at numbers that we can trust. Are you seeing places now where that data is starting to wake up? I mean, a good first step of getting our data in order is having it clean enough where we can have dashboards, like traditional kind of business intelligence type dashboards to be able to look at. Obviously, there's a lot of possibilities starting to emerge around predictive and other sorts of ways where we can train models on data to kind of add value. Is some of that finding its way into kind of maintenance operations and measurement, or do you see kind of a near term place where that stuff could fit in? In other words, where the data can wake up even beyond traditional BI?