Four futures of AI: Oil and gas and chemicals

The four futures of AI in the oil and gas and chemicals industries

Explore the transformative potential of AI across the energy system

Artificial intelligence (AI) adoption in the oil and gas and chemicals sectors is fragmented. While some companies or functions within companies are focused on standing up the data foundations and governance necessary to properly deploy AI, others are piloting use cases and a more advanced set is driving toward widespread AI adoption at scale. Obstacles persist, including high costs and a cyclical market, public skepticism and potential regulatory hurdles and siloed legacy systems and platforms. Yet widespread adoption could drive profitability and performance across the industry, enabling real-time decision-making and differential operations.

High-profile AI failures among oil and gas and chemicals companies lead to stringent regulatory measures and public backlash globally. Governments worldwide scramble to implement region-specific AI regulations, while organizations adopt cautious AI strategies and innovation continues at a measured pace. As regulations tighten and the potential for cybersecurity failures looms, the popularity of open-source large language models (LLMs) wanes, with only a few trusted and certified LLM providers dominating the market. Investment shifts toward safer, lower-risk applications like inspections, while operational use cases see slower adoption. The industry’s significant risks delay implementation of AI initiatives as unwise investment decisions lead to financial ruin or operational failures for companies.

Drivers of change

In constrained scenarios, the oil and gas and chemicals sectors grapple with high costs and complexities in AI implementation, leading to a cautious approach among companies. Public skepticism and distrust arise from potential AI failures as stakeholders worry about the reliability of these technologies in critical operations. Integration challenges with legacy systems and data silos further complicate progress, making it difficult for organizations to harness the full potential of AI. Stricter regulations and compliance burdens add to the difficulties, creating an environment where companies prioritize risk management over innovation, ultimately stifling growth and limiting the adoption of transformative technologies.

Data becomes a monetizable asset class that, when combined with AI, fuels decision-making for oil and gas and chemicals leaders. Every asset is mirrored by a dynamic digital twin, continuously fed by high-fidelity data streams. This empowers AI agents to orchestrate capital allocation, optimize field development and simulate operational scenarios in real time, significantly shrinking planning cycles and enabling proactive, risk-adjusted decisions. Unified data platforms break down silos across exploration, production and commercial operations, enabling predictive maintenance, autonomous procurement and real-time trading optimization. Standardized operations across global assets allow AI agents to scale leading practices instantly, driving consistency in safety, efficiency and compliance. In this closed-loop system, every molecule, decision and dollar is optimized through AI but is only as powerful as the underlying data. The winners – those who invest in governance, integration and continuous learning to unlock compounding value – operate with fewer people, faster cycles, lower risk and better capital outcomes.

Drivers of change

Growth scenarios highlight an increased demand for efficient energy production, prompting market pressure to maximize productivity and operational excellence. Companies are investing heavily in renewable energy and AI infrastructure, recognizing the need to adapt to changing market dynamics. This investment fosters collaboration within partner ecosystems, enabling organizations to leverage AI solutions that enhance operational efficiency and drive innovation in energy production. As smaller agile firms emerge, they challenge traditional players by adopting advanced technologies, creating a more competitive landscape that prioritizes sustainability and responsiveness to consumer needs.

Leading energy companies are now fully AI-integrated enterprises. Advanced AI systems coordinate both field operations and corporate functions. Autonomous field operations are standard, with robotic systems handling inspections and repairs remotely. In parallel, AI manages enterprise workflows like reporting, forecasting and regulatory submissions. These systems are connected through secure, standardized data interfaces that protect intellectual property while enabling seamless collaboration. Companies now license their AI infrastructure to adjacent industries like manufacturing, transport and utilities, creating new revenue streams. Human teams focus on strategy, ethics and stakeholder engagement, while AI delivers speed, precision and scale.

Drivers of change

In transformative futures, AI adoption becomes prevalent across key field and back-office operations, fundamentally reshaping the industry landscape. Trust, explainability and auditability in AI systems become standard, fostering confidence among stakeholders and encouraging broader acceptance of these technologies. Organizations shift toward AI-native structures where closed-loop AI systems drive real-time decision-making capabilities, enhancing responsiveness and operational agility. This integration allows companies to optimize resource allocation, reduce waste and improve safety, ultimately leading to a more sustainable and efficient energy industry that meets the demands of a rapidly changing world.

By 2030, oil and gas has collapsed into a small club of supermajors, national oil companies and a few dominant tech platforms that control everything from exploration to fuel retail – and the AI that runs it. Their closed systems set the practical rules for safety, emissions, data use and payments, so a policy change or outage at one giant can disrupt drilling schedules, shipping and gas stations worldwide. Getting in is prohibitively expensive: top-tier computing for subsurface imaging and real-time drilling comes only through long costly contracts; the best geology and supply data sit behind restrictive licenses; new mandates – independent safety reviews, insurance, methane monitoring, digital traceability and AI model certification – add large fixed costs; market access often requires strict platform compliance and revenue sharing. New AI classes do appear – edge models on rigs, reservoir and maintenance “foundations,” agent dispatchers, synthetic planning tools and emissions and safety control planes – but gatekeepers control access and monetization. The sector becomes efficient, costly and brittle.

Drivers of change

In a collapsed market, AI capability, compute and premium data are concentrated in a handful of supermajors, national oil companies (NOCs) and dominant tech platforms. This concentration raises costs, hardens gatekeeping and makes the system efficient yet brittle — where a single platform policy or outage reverberates across exploration, production, logistics and retail.