How NOV is moving from FOMO to calculated scaling

For decades, the industrial sector has operated on the simple mantra to live by automation, die by automation. In the oil and gas industry, where precision is measured in millimeters and safety in lives, automation is a necessity, not just nice to have. But as gen AI sweeps through the enterprise, a new challenge has emerged in how a global leader in energy services should transition from experimental chatbots to industrial-grade AI without compromising safety or security.

Here, Alex Philips, CIO of NOV, formerly National Oilwell Varco, discusses implementing OpenAI and securing it with zero trust for 25,000 employees, and why the next phase of agentic AI requires a fundamental shift in how to view human expertise and digital safeguards.

From FOMO to ROI

Like many global companies, NOV’s initial move into gen AI was driven by executive pressure fueled by fear of missing out. Philips remembers the early talks with his CEO about the investment.

“I said we have this opportunity, and it costs this much,” he says. “He asked about the ROI and I replied that’s something I couldn’t calculate, nor what it’d replace or what it’d displace in cost, but I couldn’t say any of that for email either.”

Just as no modern business can function without email, even without a direct line-item ROI, Philips argues that LLMs will soon become the standard for employee productivity. Currently, NOV reports about 50% of its workforce actively use the tool to enhance productivity.

The results, though qualitative, are profound. Philips says that response times for urgent customer requests, for instance, have plummeted, language barriers are crumbling, and employees are tackling complex analyses once considered out of reach.

The six-month validation lesson

One example Philips details involves an engineer who spent six months mastering a highly specialized skill. With ChatGPT, the engineer was able to replicate that six-month learning process in just 10 minutes.

And while his initial response was to think he wasted six months of his life, the response was to show him he spent six months to validate what the AI told him. “This is a great example of why humans are still needed in the AI loop,” says Philips. “AI execution without human validation can lead to errors that cost companies significant time and money.”

This underscores the crucial pillar of NOV’s AI strategy of human accountability because in an industrial setting, AI dictating terms is never an acceptable excuse. Whether designing a drill bit or automating a workflow, the end user remains responsible for the output.

Securing the Wild West of shadow AI

As AI becomes more widespread, shadow AI poses a significant security risk. To address this, NOV uses Zscaler to route all traffic, and ensure visibility and control. And by doing so, the company can:

• Redirect users: If an employee tries to use a non-approved LLM, they’re redirected to a page that explains NOV’s policy, and directed to the approved enterprise OpenAI instance.
• Monitor SaaS evolution: Many authorized SaaS applications are now adding agentic features during contract periods. Zscaler provides the visibility needed to identify these changes before sensitive IP is fed into an unvetted model.
• Enforce data privacy: Preventing intellectual property from leaking into public training sets is the first step in any industrial AI deployment.

The shift to agentic AI

In software development, NOV already benefits from AI-assisted coding, where AI works alongside developers who accept about 32% of AI suggestions. “We’re now beginning to explore the next evolution of full agentic coding,” says Philips, adding that this next stage truly supercharges teams, enabling them to move faster and better meet customer demand for innovation.

However, this efficiency feeds the dilemma of a widening talent gap. The challenge moving forward is if all the low-level, entry-level tasks can be automated, and what’s the best way to develop skilled workers. “I don’t know how we’ll adapt to it, but we’ll figure it out,” he says.

Safety first

In the oil field, some processes are too critical to be left entirely to a black-box algorithm. Philips is adamant that for safety issues, AI remains an advisor, not a decider. NOV uses AI-powered vision to monitor red zones, or dangerous areas on a drilling rig. If the AI detects a person in a restricted area, it can trigger an emergency stop. However, for actual drilling operations, the final call remains with an onsite human operator. “You can’t have a hallucination,” he says. “You can’t say it’s right 90% of the time. It has to be all the time.”

NOV’s journey shows that transitioning to industrial-grade AI isn’t just about choosing the best model but building a framework of trust, transparency, and responsibility. By using Zscaler for governance and GitHub Advanced Security for code validation, NOV is moving toward a future where AI becomes more essential to the oil industry.

“Development teams should produce twice the output with half the people in half the time,” he says. “The only remaining question is how do we train the next generation of developer experts to control the machines that do the work.”