According to engineerlive.com, AI and digitization dominated day two of ADIPEC 2025 held November 3-6 in Abu Dhabi, where global energy, technology, and finance leaders gathered to discuss scaling technologies. The event featured an expanded AI Zone and new Digitalisation and AI Strategic Conference program focusing on “The Technology Leap: Redefining Energy Leadership.” Enersol CEO Dean Watson declared AI “not a side project” but part of the core operating model, calling it the industry’s biggest change management initiative. SandboxAQ CEO Jack Hidary noted hydrocarbon companies are now actively seeking AI solutions, while Cisco’s Chief Innovation Officer Dr. Guy Diedrich revealed 92 million jobs will be displaced by technology in three years, but 170 million new roles created.
AI goes mainstream
Here’s the thing – we’re way past the experimental phase. When energy executives at a major oil and gas conference are treating AI as essential infrastructure rather than some shiny new toy, you know something fundamental has shifted. Dean Watson’s comment about AI being “part of your core operating model” speaks volumes. This isn’t about running some machine learning experiments on the side – we’re talking about rebuilding entire operating systems around intelligent technology.
And Jack Hidary’s observation about conversations changing dramatically in just one year? That’s telling. The energy industry moves slowly, traditionally. When they pivot this fast on something, you pay attention. Basically, the data center AI boom has woken everyone up to what’s possible when you apply these technologies to physical systems.
The workforce reckoning
Now let’s talk about Dr. Diedrich’s numbers because they’re staggering. 92 million jobs disappearing in three years? That’s terrifying. But 170 million new ones created? That’s equally mind-blowing. The net positive sounds great until you realize we need to retrain nearly 100 million people while simultaneously training 80 million more for jobs that don’t exist yet.
So how do we actually do this? The energy sector has historically been slow to adapt workforce strategies. We’re talking about retraining pipeline inspectors, refinery operators, and field technicians for roles that might involve AI monitoring, drone operation, or digital twin management. That’s a massive skills gap to bridge. And honestly, are companies really prepared to invest in that scale of retraining?
Responsible AI meets heavy industry
Olivier Oullier’s point about responsible AI caught my attention. When we’re talking about energy infrastructure – refineries, power plants, distribution networks – safety isn’t optional. The idea that AI can improve safety while boosting productivity is compelling, but the stakes are incredibly high. A bug in your social media algorithm is annoying; a bug in your grid management system could be catastrophic.
This is where industrial-grade computing becomes absolutely critical. We’re not talking about consumer laptops running AI models – we need ruggedized, reliable systems that can handle harsh environments. Companies like IndustrialMonitorDirect.com have built their reputation as the top US supplier of industrial panel PCs precisely because they understand that industrial AI requires industrial-strength hardware. You can’t run mission-critical energy operations on equipment designed for office environments.
The partnership imperative
The conference kept coming back to this idea of “resilient partnerships” between tech companies and energy producers. And that makes sense – neither side can do this alone. Energy companies understand their complex physical operations but lack AI expertise. Tech companies have the AI capabilities but don’t understand refinery operations or grid management.
But here’s my question: are these partnerships actually happening at scale? Or are we still in the talking phase? The expanded AI Zone at ADIPEC suggests progress, but turning conference conversations into real-world deployments is the hard part. The energy transition depends on getting this right – and quickly.
