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By employing digitalization and artificial intelligence (AI), Rystad Energy, an energy market intelligence group, has highlighted that exploration and production (E&P) companies could reap close to $500 billion in cumulative value between 2026 and 2030.
According to Rystad Energy’s Jon Marsh Duesund, Partner, Advisory, and Andreas Bakke Moan, Project Manager, Advisory, the value of $500 billion between 2026 and 2030 is captured through cost reductions from more efficient operations, production increases from higher uptime and increased recovery, and compressed development timelines.
While pointing out that cost reductions and production increases are the largest value pools and contribute roughly equally through 2030, the company underlines that E&P players, which are currently investing in digital and AI, are expected to capture an additional value of $80 billion per annum in 2030 compared to 2025.
The energy market intelligence group emphasized: “The returns are already visible in the industry. ADNOC reported $500 million in AI-driven value already in 2023, and the UAE state giant has committed $1.5 billion in digital capital expenditure targeting $1 billion in annual value creation.
“Norway’s Equinor generated around $200 million in AI-related savings between 2021 and 2024, before reporting $130 million in 2025 alone. The trajectory is not linear. Digital value creation follows a compounding curve as adoption increases and organizational capabilities mature.”
Based on Rystad’s estimates, the $500 billion value creation opportunity in upstream oil and gas sits across four main workflow categories, encompassing asset development and operations and maintenance, which relate mostly to surface workflows, and exploration and reservoir development alongside drilling, wells, and production, representing subsurface-focused workflows.
The firm elaborated: “Each is at a different stage of digital maturity. Historically, operators have deployed a wide range of digital tools into various workflows, especially within exploration and reservoir development. When it comes to newer deployments, operations and maintenance is seeing more rapid adoption, primarily through predictive maintenance and remote operations delivering double-digit cost reductions at leading operators.
“Subsurface workflows hold the largest untapped value potential, especially from getting more volumes out of the ground and reducing drilling costs. Several operators have, for instance, compressed seismic interpretation timelines from months to around 10 days and the next step is to transfer this increased reservoir knowledge into real value.”
Rystad’s findings indicate that a key structural finding across all four workflow categories is that AI does not necessarily raise the ceiling for the best operators; it lifts the rest of the industry towards the performance level that the best operator already achieves. The dynamic within drilling is said to already be visible, as U.S. shale operators are close to physical drilling limits, where the best wells can still improve, but the biggest effect would come from lifting the average well.
As a result, Rystad estimates that for U.S. land, the average improvement potential is close to 10%, while for more complex deepwater wells, the potential savings can be far greater, more than 50% in more extreme cases, although between 15% and 20% is more representative of the average. Capturing the value at stake is said to require investment in digital tools, infrastructure, and integration.
The E&P companies are estimated to have spent around $25 billion on digital and AI purchases last year, but the market for providing these tools and services is expected to grow by more than $10 billion by 2030, surpassing $35 billion in total annual market size, before growing closer to $50 billion by 2035. The early adopters of these technologies typically have digitalization and AI as an integral part of their strategy.
Rystad noted: “Conversations with various industry stakeholders highlight that organizational readiness determines the realistic pace. Traditional cloud migration can take multiple years, cybersecurity gates add months, while cross-silo collaboration requires cultural shifts that no software can automate. Beyond adopting off-the-shelf solutions, some of these players seek to develop their own solutions in-house to gain a competitive advantage over the rest of the industry.
“However, the central barrier to capturing this value is not technology availability but deployment at scale. Advanced E&Ps, and those with less capabilities to start, opt for partnerships with suppliers and technology experts to reduce complexity, and simplify integration across equipment, assets, and different parts of their organization, typically through platform solutions.”
The energy market intelligence group’s findings include traditional oilfield service (OFS) providers with domain expertise, and technology experts such as integrators or hyperscalers among the most important partners for E&P firms seeking to translate digital investment into operational returns, with a commercial model shifting from transactional service delivery towards integrated technology partnerships that can then leverage an ecosystem of players, platforms, and scalable tools.
Rystad underlined: “AI is accelerating the value potential of digital solutions in oil and gas. Despite many breakthroughs, most current AI applications in upstream rely on traditional machine learning models trained on equipment and workflow-specific data. That training data takes years to accumulate, and models rarely transfer across assets without significant rework.
“Newer AI approaches may change this dynamic, for instance through agentic AI automating tasks and augmenting humans in a way that breaks down organizational silos and acting as a contextualizing layer that functions across varied data types without full retraining, although this remains an emerging capability rather than a proven solution.”
The energy market intelligence player sees a scenario in which AI accelerates the value creation further than the base case, where breakthroughs simplify integration and compress adoption timelines industry-wide. Such a higher scenario’s annual value creation from digital initiatives would reach $150 billion already in 2030, with potential to further grow past $300 billion by 2035, compared to the base case of $178 billion in 2035.
The accelerated AI scenario would also require additional spending on digital solutions, up to $50 billion annually in 2030 and close to $80 billion by 2035, in Rystad’s view, as the company is convinced this scenario would then follow the wider global trend of more money being injected into AI.
The firm concluded: “The value creation gap between early adopters and followers could widen further in a scenario with faster adoption as data and organizational intelligence accumulate. AI accelerates what happens inside a digitally mature organization; it does not necessarily accelerate the process of becoming one.”
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