The term ‘roughneck’ conjures up images of rough-looking, greasy oilfield workers, sort of an iconic visual depiction, like those of cowboys of the old West. In those days, long past, the progress of operations carried out on an oilfield location was determined solely by the grit and tenacity of the workers involved, the roughnecks. Today, the industry is undergoing a transformation driven by artificial intelligence and advanced technologies. Times are uncertain for those old-school roughnecks that still go to work every day in the patch; their numbers are much smaller in size as operations move at the speed of light due to efficiency; new workers come in with new skills.

Efficiency Displaces Workers

First came significant gains in efficiency, making operations that would historically take months now trimmed down to just days or weeks. The new speed of these operations resulted in a significant reduction in the number of workers required. One example is the state of Oklahoma, which has lost nearly a third of its oil and gas workforce since 2019; much of that workforce was lost during a time of prosperity and record profits. Twenty percent of Oklahoma’s workforce is in oil and gas, so they have some challenges ahead when it comes to retraining workers who this shift in the industry will displace.

Another efficiency gain in the industry is reducing unplanned downtime costs. By utilizing technology that can significantly reduce human error, companies can save millions of dollars annually. According to Aberdeen Research, a company will spend thirty-eight million dollars a year on unplanned downtime costs, primarily due to human error ( Ryan Arsenault, 2016). AI predictive maintenance is changing this by catching equipment failures before they occur.

Advanced Technology Requires More Skilled Workers

Traditionally, most oil and gas workers could start their careers with no formal college education. These were manual labor positions that could only be learned through on-the-job training. Today, robotics and drones are used to inspect pipelines and rigs. At the same time, autonomous drilling systems utilize artificial intelligence (AI) to adjust parameters in real time, thereby reducing the nee human oversight. This shift means that companies are now seeking AI specialists, data scientists, and robotics engineers. There is a growing demand for hybrid-type roles that combine petroleum engineering with AI expertise. This shift in technology and streamlined corporate structures where oilfield service companies transition from owning more expensive assets to being technology focused is also creating better profit margins without the need to increase capex (Linnane, 2025).

Fortunately, the very thing that is reshaping the workforce in the oil and gas industry is also the thing that can train the workers. As the oil and gas industry faces a workforce crisis, where forty percent of skilled workers are expected to retire by 2030, it is AI itself that can bridge the gap and equip existing workers with the skills they need moving forward. Augmented Reality training teaches workers to use AI-guided simulators instead of shadowing veterans. Digital twins, combined with AI, can recreate a digital version of any project or job to train workers on a step-by-step basis of how to perform tasks.

Tech Is Replacing the Roughneck

The oil and gas workforce of today looks significantly different from what it was just a decade ago, as the industry relies less on instinct and grit and more on data and technology. Many existing jobs will be replaced as part of this change, while new ones will emerge. One thing is sure: this is creating a much safer workplace than ever before, as we utilize technology to remove people from the most hazardous situations. Shell was able to achieve a forty percent reduction in equipment failures, and a thirty-five percent reduction in unplanned downtime (Insights Global, 2025). Reducing equipment failures and downtime also reduced the amount of time that employees are exposed to hazardous situations. This is particularly beneficial for oil and gas workers who labor through the boom-and-bust cycle of the industry, as these new skills will provide them with access to more opportunities and jobs outside of the industry during those downturns.

I Was recently invited to participate in an analyst panel at PlatformCon 25 in New York City. The conference was not huge, but still delivered impact and featured a mix of vendor booths ranging from industry giants like Google to ambitious startups. The audience was a blend of platform professionals from industries as diverse as healthcare, professional sports, and video gaming. The featured guest speaker was engineer Kelsey Hightower. 

Here are my key takeaways from the event: 

Don’t Be Afraid To Look Under The Hood of AI 

Hightower kicked off the day with a compelling talk which challenged attendees to critically evaluate AI and its capabilities. He emphasized the importance of viewing AI as another piece of technology —there’s nothing mystical about it. Hightower encouraged the audience to dig into the details and not simply buy into the hype. 

Hightower also touched on how the rise of technologies such as Anthropic’s Model Context Protocol has shifted corporate attitudes. For decades, companies maintained strict control over their internal resources, but now many are rushing to API-enable their entire ecosystems with little caution. He posed a thought-provoking question: “Imagine if they had done this 10 years ago—what could have been accomplished?” 

If you’re curious to learn more about AI, MCP and its implications, refer to this blog. 

The Tension Between Developers, Operations, And Platform Teams Is Real

One of the liveliest discussions at the conference centered around the persistent struggles between developers, operations teams, and platform engineers. During the Developer Productivity roundtable, which I had the honor of joining alongside other fellow industry analysts, this tension was laid bare. 

Far from a dry technical discussion, the session felt more like group therapy for platform leaders. Many attendees shared candid stories about the tug-of-war between developers seeking speed and agility, and platform engineers urging patience and structure. It’s clear that the question of whether platform engineering can fully resolve this dynamic is still open. 

Several actionable strategies emerged during the conversation: 

• Adopt a “platform as a product” approach. Treat your platform as a product designed to serve your internal stakeholders. Read this report for more insights. 

• Set clear expectations. When building a platform, align all stakeholders from the outset. Refer to my report for practical guidance. 

• Define common goals based on value streams. Establish shared objectives to bridge the gap between teams. Check out this webinar for actionable advice. 

The tension may never fully disappear, but fostering collaboration and setting shared goals can help mitigate the friction. 

Final Thoughts 

PlatformCon 25 offered a unique window into the evolving world of platform engineering set against the backdrop of AI’s growing influence.  

Whether you’re a developer, an operations leader, or a platform engineer, one thing is clear: the platform landscape is shifting rapidly, and AI is playing a central role.  

Clients of Forrester that have questions on developer platforms or portals are welcome to request an inquiry or guidance session with me.