Trouble is brewing for an energy company at a key offshore site. Operations could grind to a halt there if one of the company’s logistics specialists can’t get a replacement part for a critical piece of drilling equipment to the site quickly. She needs to know ASAP if that part is available in a company warehouse or can be readily sourced from a supplier. So she puts her assistant on the case, and in a matter of seconds, the assistant has good news: Multiple units of that part are available at several company warehouses.
As the conversation continues, the assistant shows the logistics specialist her options, then offers a recommendation for which warehouse to access the part based on an estimated time for it to reach the site where it’s needed. She agrees with the recommendation. Next, her assistant asks if she’d like to formally order the part from that warehouse under the specified timeline. She does, and the assistant promptly places the order, triggering a series of actions that ultimately result in the part arriving at its destination on schedule. Potentially costly disruption averted.
All this occurred in a matter of a few minutes, with no typing out multiple emails and waiting for responses, no bouncing across software systems in search of real-time availability and logistics information, no phone calls to multiple suppliers — and, it turns out, no human-to-human interaction whatsoever. In fact, it all unfolded within a single digital environment of which the assistant, a generative AI-driven companion, is part.
Energy Sector as an AI Leader
Conversations like this are occurring more frequently across the energy industry as companies recognize what artificial intelligence, and genAI in particular, can do for their operations in a range of use cases. Up to now, energy companies have been conservative in their adoption of artificial intelligence. In a 2025 report, KPMG noted that a sizeable proportion of the energy sector — 33% — is still at the proof-of-concept stage of AI experimentation, although among those with active AI use cases, 67% said those applications are achieving business value. Meanwhile, energy sector investment in genAI specifically is projected to grow 3.5x by 2030, according to an Accenture analysis. Just this year, 85% of C-suite leaders said they plan to increase their company’s overall AI investments.
- Knowing where equipment, materials, parts and other field assets are at all times. In the field, the warehouse or the supply chain, learning about the whereabouts of a piece of equipment (rented or owned), the stock level of a certain material, or the availability and delivery status of a key part from a supplier, as well as the optimal pathways for getting any of these from Point A to Point B, can be merely a matter of querying a built-in AI assistant in the system you’re using in plain language, and getting a plain-language response in real time. “How many of this certain valve do we have in stock across our warehouses?” or “What options do I have to get this mobile compressor from one site to the next by tomorrow?” or “Which suppliers in our network can deliver this compressor part by next week?”GenAI can take data from cameras, sensors, satellites and other sources to provide full visibility into the movement of assets and materials, as well as the inbound and outbound status and condition of kits, parts, tools and materials. With all this at their fingertips, logistics specialists gain a clear picture of what needs to go where, when, and in which quantity. The result: better procurement and logistics decisions, faster, with fewer disruptions, shortages, over-orders and penalties for late return of rental equipment.
- Enhanced visibility into equipment condition to support predictive maintenance. By applying intelligent analytics and modeling to historical sensor, maintenance and field data, a genAI assistant can recommend optimal maintenance schedules for all kinds of equipment. This helps companies avoid equipment failures, minimize downtime, reduce costs, and keep the energy molecules flowing. Companies like Equinor and Aker BP that have embraced genAI-driven predictive maintenance planning are seeing measurable benefits as a result. GenAI also can provide timely guidance to maintenance workers when they’re in the field installing a part or making a repair.
- Simulation and modeling for exploration and production. GenAI can take data from reservoir modeling systems, sensors, digital twins and the like to offer recommendations for how to optimize drilling strategies, predict well performance and guide oil well placement with greater accuracy. For example, BP is using large language models embedded within software from Palantir to suggest courses of action based on data from sensors and digital twin models of its oil and gas production systems and sites, resulting in timelier, more on-point decision-making and improved overall performance.
- Production and revenue accounting (PRA). When someone in the finance department needs information on the division of interest for a production site, or about outstanding balances, transfers, checks received, and other PRA data, instead of having to bounce between multiple systems for that information, they can simply ask the system’s virtual assistant to compile and present all that information. This leads to quicker, better-informed decision-making.
- Infrastructure monitoring to detect risks, reduce carbon footprint and improve compliance. GenAI tools can analyze satellite and sensor data to detect risks to infrastructure and equipment, so companies can address issues like terrain instability more proactively. Similarly, genAI can analyze data from satellites and sensors to monitor methane emissions, and to identify and alert teams to leaks.
- Reporting the findings from physical AI. Autonomous, self-learning AI-driven robots are becoming more practical for energy companies to use to perform tasks in hostile and remote environments, such as deep-water drilling. A physical AI robot then can feed data to a genAI assistant that describes in plain language what the robot is seeing, the work it’s performing, the obstacles it’s encountering, and recommendations for next steps.
Measuring GenAI’s ROI
CEOs across industries are reporting tangible impacts from genAI, according to PWC. More than half (56%) said they are seeing efficiency gains in their employees’ time over the last 12 months as a result of using the technology, while one-third are seeing revenue increases. But as with any tech investment, AI requires a solid foundation to deliver ROI. High-quality, comprehensive data has to flow unimpeded across systems, to AI tools. A strong governance program has to be in place to ensure ethical usage, compliance, sovereignty and security. And robust employee training/skilling on AI is a must to ensure they know how to use the tools at hand. With these fundamentals in place, the possibilities are limitless.
Kelly Boyer brings 20 years of oil, gas and energy (OG&E) experience to her role as an industry advisor expert with SAP’s OG&E team, overseeing the complete SAP industry solution portfolio for business data cloud and AI. Prior to joining SAP, Kelly worked as a U.S. land-based engineer at Halliburton and Southwestern Energy (now Expand Energy) in completions, production, and planning roles.
