Digitalization and artificial intelligence (AI) will create close to $500 billion in cumulative value for E&P companies between 2026 and 2030, according to Rystad Energy estimates. This value is captured through cost reductions from more efficient operations, production increases from higher uptime and increased recovery, and compressed development timelines. Cost reductions and production increases are the largest value pools and contribute roughly equally through 2030. Exploration and production (E&P) players currently investing in digital and AI are expected to capture an additional value of $80 billion per annum in 2030 compared to 2025.  

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.

The $500 billion value creation opportunity in upstream oil and gas sits across four main workflow categories. The first, asset development, and second, operations and maintenance, relate to mostly surface workflows. The third, exploration and reservoir development, and fourth, drilling, wells and production, represent subsurface-focused workflows. 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.

A key structural finding across all four workflow categories is that AI, in general, 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. In drilling, the dynamic is already visible as leading US 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. We estimate for US 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 requires investment in digital tools, infrastructure and integration, and E&Ps are estimated to have spent around $25 billion on digital and AI purchases last year. 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. 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. Traditional oilfield service (OFS) providers with domain expertise, and technology experts such as integrators or hyperscalers are among the most important partners for E&Ps seeking to translate digital investment into operational returns. These projects see the commercial model shift from transactional service delivery towards integrated technology partnerships that can then leverage an ecosystem of players, platforms and scalable tools.

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.

As such, we see a scenario where AI accelerates the value creation further than the base case, where breakthroughs simplify integration and compress adoption timelines industry wide. In this higher scenario, annual value creation from digital initiatives reaches $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.

This 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. This scenario would then follow the wider global trend of more money being injected into AI. 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.

Author:

Jon Marsh Duesund
Partner, Advisory
jonm@rystadenergy.com

Andreas Bakke Moan
Project Manager, Advisory
andreas.moan@rystadenergy.com