How AI Could Unlock $230bn For Oil & Gas, OFSE Cos — McKinsey
According to a recent insight from McKinsey, reported by Rigzone, which was penned by Bill Ambrose, a partner in McKinsey’s Houston, Texas, office, Georgio Bresciani, a senior partner based in the company’s London, UK, office, Spandan, another partner in McKinsey’s Houston office, and Priyank Singh, an engagement manager at the business. “The challenge is where to concentrate, how to scale, and how to share value when efficiency reduces the activity that many contracts reward. “AI is beginning to provide an answer although its value is not uniformly distributed,” the McKinsey representatives stated in the insight.
It noted that a bottomup sizing of AI use cases across value chain stages estimates that AI can unlock approximately $65 billion in annual recurring value across upstream oil and gas in the near term with today’s technology. It revealed that there is a credible path to $230 billion at full potential as the technology matures and autonomous operating modes become more common. “In addition, AI-driven improvements in exploration success could unlock over $35 billion annually in balance sheet value through reserves accretion,” the McKinsey representatives said.
They outlined in the statement that their estimates were built from more than 550 use cases “across key domains in the upstream life cycle.” “Yet, AI is not free. These estimates are net of annual implementation costs that include compute, specialised talent, data infrastructure, and software, totaling over $30 billion,” they highlighted. The representatives went on to state that AI in upstream is a “concentration play, not a ‘thousand flowers bloom’ opportunity”.
“The top 10 use cases drive nearly half of the identified value, the top 20 drive roughly two-thirds, and the top 60 capture approximately 95 percent of identified value. “The implication is that the companies that are likely to succeed in capturing value from AI will be those that focus relentlessly on the handful of use cases where potential value is greatest – those with large value pools, real-time data, physics-based feedback, and measurable operating KPIs,” they said.
Citing a report late last week by Morningstar DBRS, Rigzone quoted analysts at the company, including senior vice president, European Financial Institution Ratings, Arnaud Journois, as saying the emergence of AI “has triggered an unprecedented surge in demand for digital infrastructure, particularly data centers.” The analysts noted that “once viewed as a niche segment within real estate and infrastructure markets, these facilities have rapidly evolved into a distinct asset class,” “As AI models grow in scale and complexity, their computational requirements increase exponentially, making data centers essential to supporting this technological transformation.”
Noting that sustaining AI expansion “requires significant investment,” the analysts highlighted in the report that in the US, AI-related spending was estimated at $430 billion in 2025. They pointed out that this could more than double by 2029, “reflecting both rapid innovation and the capital-intensive demands of computing, storage, and energy infrastructure. “To support this growth, technology companies have raised around $300 billion from U.S. investors and increasingly relied on bank financing for AI and data-center development.
“Banks have become deeply involved in this ecosystem, not only in the U.S. but also in Europe and the UK through large-scale financing, syndicated lending and securitisation. As a result, the banking sector is increasingly embedded in the expansion of AI-driven digital infrastructure. The analysts noted in the report that, according to Morningstar DBRS, this creates “significant business opportunities for banks whilst also increasing sector concentration risks, prompting active exposure management.
“However, whilst datacenter lending has been one of the fastest-growing areas of bank credit in recent years, exposures generally remain a modest share of overall loan portfolios. “Furthermore, banks employ a range of established risk-management tools, including syndication, concentration limits, exposure caps, portfolio monitoring, and risk-transfer mechanisms, to manage sector-specific risks and limit excessive concentration.”
