ADNOC has awarded AIQ, a subsidiary of Presight, a three-year, $340 million contract to deploy ENERGYai across its upstream operations, marking one of the largest deployments of agentic AI in the energy sector. The agreement follows the successful completion of a proof-of-concept phase and paves the way for a comprehensive rollout designed to optimize ADNOC’s upstream processes. ENERGYai integrates large language models with advanced agentic AI to automate workflows throughout ADNOC’s upstream value chain, spanning activities from seismic analysis to real-time process monitoring. This initiative is positioned as a pivotal step in advancing ADNOC’s AI strategy and digital transformation, reinforcing the company’s ambition to become the world’s most AI-enabled energy firm.
Musabbeh Al Kaabi, ADNOC’s Upstream CEO, framed the project as a strategic alignment with the company’s broader objectives. He noted that the collaboration aims to scale ENERGYai across the upstream business, strengthening ADNOC’s position as a reliable and responsible energy supplier to global markets. The deployment is designed to empower engineers and other oilfield professionals by enabling them to interact with ADNOC’s proprietary data through advanced language models, thereby boosting efficiency and unlocking new insights. By accelerating processes and lowering operating costs, the AI-driven system supports ADNOC’s broader digital transformation and sustainability ambitions, marking a milestone in the company’s journey toward a more data-driven and intelligent operating model.
Magzhan Kenesbai, acting managing director of AIQ, described the contract as a defining moment for the company. He emphasized that the partnership with ADNOC has enabled the development of a world-first agentic AI solution that is scalable across the energy value chain and holds the potential to transform the industry. He added that the deployment would unlock unprecedented efficiencies while supporting ADNOC’s sustainability goals. The collaboration involves inputs from ADNOC experts and is conducted in collaboration with strategic partners G42 and Microsoft. ENERGYai leverages Azure cloud infrastructure, the Open Subsurface Data Universe (OSDU) framework, and OpenAI models, illustrating a tightly integrated tech stack designed for scalability, security, and performance in demanding upstream environments.
Thomas Pramotedham, CEO of Presight and AIQ’s major shareholder, underscored the project’s significance in advancing AI integration within energy. He framed agentic AI as a future-facing development in the AI landscape and asserted that Presight, AIQ, and ADNOC are collectively shaping the energy sector’s trajectory through applied intelligence. His remarks positioned ENERGYai as a driver of practical, field-ready AI capabilities that translate advanced research into tangible operational gains for one of the world’s leading energy producers.
The first operational version of ENERGYai is slated for release by mid-2025, with an initial focus on five AI agents dedicated to subsurface operations. This early version will undergo test deployments across multiple ADNOC upstream assets before expanding to more than 28 producing fields, including some of the world’s largest and lowest-carbon oilfields. The rollout strategy reflects a staged approach to integration, validating performance in controlled settings before broader adoption across ADNOC’s upstream portfolio.
Readiness for broader deployment is anchored in a robust, collaborative development process. The project brings together ADNOC’s in-house domain expertise, AIQ’s technology platform, and the capabilities of G42 and Microsoft to deliver an integrated solution anchored in cloud-native architectures and industry-standard data frameworks. By combining LLM-driven interfaces with agentic capabilities, ENERGYai is designed to automate repetitive tasks, optimize complex workflows, and provide actionable insights in real time—capabilities that are particularly valuable in exploration, appraisal, development, and production phases of upstream operations.
Energy industry observers have highlighted the significance of such a deployment given the scale and complexity of ADNOC’s upstream footprint. The integration of LLMs with agentic AI supports more dynamic decision-making, tighter coordination across engineering disciplines, and faster response times in the face of subsurface uncertainties, operational variances, and market volatility. As ADNOC advances its digital transformation trajectory, ENERGYai represents a concrete manifestation of how AI can be embedded in physical processes to improve efficiency, safety, and environmental performance.
In sum, the ADNOC-AIQ ENERGYai agreement embodies a strategic push toward scalable, AI-enabled operations in the energy sector. It signals a broader industry shift toward agentic AI applications that can manage, interpret, and act upon complex data streams across the upstream value chain. The collaboration’s emphasis on data interoperability, cloud-based scalability, and close alignment with sustainability goals positions ENERGYai as a potential benchmark for similar initiatives in oil, gas, and broader energy markets.
Technology framework and implementation approach
ENERGYai is designed to fuse large language models with agentic AI to create a workflow-automation platform capable of handling the upstream value chain’s diverse needs. The technology stack integrates LLMs with proactive agents that can reason, plan, and operate within predefined safety and governance constraints. The platform is built to interface with ADNOC’s proprietary datasets, enabling engineers and operators to query, interpret, and act on information using natural language and guided actions, thereby shortening the time from insight to action.
The deployment leverages Azure cloud infrastructure, which provides scalable compute, secure storage, and enterprise-grade governance. The choice of Azure aligns with industry standards for reliability, resilience, and security, while enabling seamless integration with existing ADNOC digital ecosystems. ENERGYai also utilizes the Open Subsurface Data Universe (OSDU) framework, a standardized data model designed to unify subsurface data from disparate sources. The OSDU framework supports data interoperability, enabling Energyai to access seismic data, reservoir models, well logs, production data, and related subsurface information in a consistent, queryable format. The integration with OSDU is intended to reduce data silos and accelerate data-driven workflows across exploration, appraisal, development, and production activities.
OpenAI models constitute a core component of the ENERGYai platform’s reasoning and natural language capabilities. By wrapping OpenAI’s robust language understanding and generation features within controlled, enterprise-grade workflows, ENERGYai can translate complex technical information into actionable guidance for engineers and operators. The platform’s design emphasizes reliability, explainability, and traceability, ensuring that model outputs can be validated and audited in the context of critical upstream operations.
In addition to LLM-based reasoning, ENERGYai embeds agentic AI that can autonomously execute tasks within defined boundaries. These agents are designed to perform routine or complex tasks, such as data ingestion, pattern recognition, anomaly detection, workflow orchestration, and real-time process monitoring. The agentic capabilities are intended to complement human expertise by handling repetitive or data-intensive activities, freeing engineers to focus on analysis, interpretation, and decision-making in high-stakes situations.
The collaboration with G42 brings advanced AI capabilities and regional expertise to the project, while Microsoft’s involvement ensures alignment with enterprise cloud strategies, security frameworks, and developer tooling. This ecosystem approach is intended to deliver a robust and scalable platform that can adapt to evolving upstream requirements, support rapid iteration, and maintain rigorous governance standards.
The platform’s architecture is designed to support a staged deployment, starting with a first operational version capable of handling five subsurface AI agents. This initial set is chosen to mirror core subsurface workflows and to validate the platform’s ability to reason across seismic interpretation, reservoir assessment, and subsurface model updates. The mid-2025 target for the first operational release reflects a careful balance between development velocity, safety, and domain validation, with subsequent expansion to cover additional assets and a broader set of AI agents.
From an operational perspective, ENERGYai emphasizes seamless data flow between upstream assets and the platform. Data sources—ranging from seismic datasets and well logs to production metrics—are ingested into the system and indexed within the OSDU-enabled environment. The AI agents can then access these data streams to perform analyses, generate recommendations, and trigger automated actions that align with ADNOC’s standard operating procedures. The platform’s automation layer is designed to respond to events in real time, providing timely insights and the potential to reduce cycle times, minimize manual interventions, and improve consistency across sites.
Engineers using ENERGYai will interact with ADNOC’s proprietary data through intuitive language interfaces enhanced by the platform’s LLM capabilities. This approach enables users to pose questions in natural language, receive synthesized interpretations of complex datasets, and authorize or supervise automated actions where appropriate. The goal is to create a workflow where data-driven insights translate into concrete operational improvements—such as improved seismic interpretation workflows, more accurate reservoir models, optimized well placement, and refined production optimization strategies.
Security and governance are central to ENERGYai’s design. The architecture incorporates enterprise-grade security controls, access management, audit trails, and compliance with relevant industry standards. The system is engineered to maintain data integrity, ensure traceability of model decisions, and enable ongoing oversight by ADNOC’s governance teams. By combining cutting-edge AI technology with rigorous governance, ENERGYai aims to deliver reliable performance in high-stakes upstream environments while maintaining transparency and accountability.
The project’s success hinges on close collaboration among ADNOC, AIQ, G42, and Microsoft. ADNOC contributes domain expertise, data governance, and operational context, ensuring that ENERGYai aligns with industry best practices and regulatory requirements. AIQ provides the core platform and AI capabilities, with G42 and Microsoft enabling the broader AI ecosystem, cloud infrastructure, and enterprise-grade security. The synergy among these partners is designed to accelerate deployment, enhance technical robustness, and support scalable adoption across ADNOC’s global upstream portfolio.
In practical terms, the ENERGYai rollout involves iterative cycles of development, validation, and deployment. Each iteration adds new capabilities, expands the number of AI agents, and broadens the range of subsurface workflows covered by the platform. The initial phase focuses on five subsurface agents and core data sources; subsequent phases will integrate additional agents and expand coverage to more assets. The approach emphasizes risk-mitigated deployment, rigorous testing, and continuous improvement to ensure that the platform meets ADNOC’s operational standards and safety requirements.
Overall, the technology framework for ENERGYai is designed to deliver a robust, scalable, and secure platform that can translate advanced AI capabilities into tangible benefits for ADNOC’s upstream operations. By leveraging a combination of LLMs, agentic AI, standardized data frameworks, and cloud-based infrastructure, the project seeks to unlock higher efficiency, improved decision-making, and sustained progress toward ADNOC’s digital transformation and sustainability objectives.
Subsections: key components and workflow integration
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LLM-enabled interfaces: engineers interact with data and models through natural language, enabling more intuitive querying and decision support.
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Agentic AI capabilities: autonomous agents execute tasks, coordinate workflows, and monitor processes within predefined safety boundaries.
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Data foundation: OSDU-based data architecture ensures consistent data access across seismic, reservoir, and production datasets.
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Cloud and security: Azure provides scalable compute and governance, with strong security controls and compliance.
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Collaborative ecosystem: G42 and Microsoft bring complementary strengths to the platform’s development, deployment, and operations.
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Phased rollout: a staged deployment starts with five subsurface agents, then expands to 28 producing fields, ensuring controlled validation at each step.
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Governance and audit: robust oversight mechanisms ensure traceability and accountability across model decisions and automated actions.
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Sustainability alignment: the platform is positioned to support ADNOC’s environmental performance objectives through optimized workflows and reduced operational waste.
Deployment roadmap and operations
The ENERGYai deployment follows a structured, phased approach designed to validate capabilities, scale across assets, and ensure safe, reliable operations. The first operational version’s target date is mid-2025, with five AI agents dedicated to subsurface operations. The initial deployment will be test-rolled out across several ADNOC upstream assets, providing a controlled environment to validate performance, reliability, and safety before broader expansion. The expansion plan envisions deploying ENERGYai across more than 28 producing fields, including some of the world’s largest and lowest-carbon oilfields, reflecting ADNOC’s commitment to efficiency, innovation, and responsible resource management.
The phased rollout is designed to balance speed with rigorous validation. In the early stage, the five subsurface AI agents will operate within well-defined workflows, enabling the platform to ingest seismic data, interpret subsurface features, update reservoir models, and support decision-making for well planning and production optimization. As the system demonstrates stability and value, additional assets will be brought online, and the number of agents will increase to cover a broader spectrum of subsurface and production workflows. This approach aims to maximize learning, minimize risk, and ensure that the platform delivers measurable improvements in performance, safety, and sustainability.
Test deployments across multiple ADNOC upstream assets will serve as real-world pilots, enabling engineers to evaluate the system’s practical impact on daily operations. These pilots are expected to generate insights into how ENERGYai can streamline collaboration across multidisciplinary teams, reduce downtime, and accelerate data-to-insight cycles. The lessons learned from these pilots will inform subsequent scaling decisions, including data integration strategies, model governance, and operational control frameworks.
The roadmap also emphasizes data quality, interoperability, and governance. By leveraging the OSDU framework, ENERGYai seeks to standardize data access and ensure that models and agents can reliably retrieve, process, and reason over seismic, reservoir, and production data. This standardization is critical for maintaining consistency across fields and for supporting cross-site analytics that can reveal best practices and optimization opportunities. As deployment progresses, ADNOC plans to monitor performance metrics such as cycle time reductions, decision accuracy, and operational cost savings, using these metrics to guide further enhancements and expansions.
Sustainability considerations are integral to the rollout. By optimizing energy production workflows, reducing unnecessary trips to the field, and improving reservoir management, ENERGYai has the potential to contribute to lower emissions intensity and more efficient resource usage. The deployment plan acknowledges ADNOC’s sustainability ambitions and seeks to align AI-driven improvements with environmental targets, including potential reductions in flaring, energy consumption, and non-productive time. The roadmap therefore reflects a convergence of operational efficiency, safety, and environmental stewardship, with a clear path toward a more sustainable upstream portfolio.
From a governance perspective, the deployment will incorporate comprehensive risk management, safety protocols, and regulatory compliance measures. The project will implement robust access controls, data lineage tracking, and explainability features to ensure that model outputs can be understood, verified, and audited. This governance framework is essential for maintaining trust in AI-driven decision-making in critical upstream operations, where reliability and accountability are paramount.
In summary, ENERGYai’s deployment roadmap is designed to deliver controlled, measurable value across ADNOC’s upstream operations. By starting with a five-agent subsurface focus, validating performance through real-world pilots, and then expanding to a broader field footprint, ADNOC and the AIQ-led ecosystem are aiming to demonstrate the practical benefits of agentic AI in one of the world’s most challenging and data-rich oil and gas environments. The approach emphasizes safety, governance, interoperability, and sustainability as core pillars guiding every stage of the rollout.
Subsections: anticipated outcomes and success metrics
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Increased operational efficiency: automation of routine workflows and faster data-to-insight cycles.
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Improved subsurface interpretation: enhanced seismic analysis and reservoir characterization through AI-driven reasoning.
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Real-time monitoring and control: continuous oversight of processes with proactive anomaly detection.
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Cost optimization: reductions in operating expenses through streamlined workflows and optimized asset management.
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Sustainability outcomes: potential reductions in energy use and emissions intensity as production processes are optimized.
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Safety and governance: strong emphasis on traceability, auditability, and compliance with safety standards.
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Field-wide scale: progressive expansion from initial assets to the broader portfolio, with consistent performance improvements.
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Learnings and refinements: ongoing iteration to refine AI agents, workflows, and governance policies.
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Stakeholder alignment: alignment with ADNOC’s strategic priorities, including growth, efficiency, and sustainability.
Strategic implications for ADNOC’s AI strategy and the energy industry
The ENERGYai deployment represents a concrete step in ADNOC’s ambition to become the world’s most AI-enabled energy company. By integrating agentic AI with enterprise-grade cloud infrastructure and standardized data frameworks, the project aims to create a scalable, repeatable model for AI adoption across upstream operations. The collaboration also signals a broader industry move toward embedded AI capabilities that can autonomously execute workflows, interpret complex data, and accelerate decision-making under real-world constraints.
The partnership brings together ADNOC’s domain expertise and data assets with AIQ’s platform capabilities and the technical prowess of G42 and Microsoft. This ecosystem approach is designed to accelerate time-to-value, improve data interoperability, and ensure that the AI system can operate safely and effectively within the demanding context of upstream oil and gas. If successful, ENERGYai could establish a benchmark for future AI deployments in energy, potentially inspiring similar initiatives across global operators and creating a blueprint for how agentic AI can be integrated into large-scale industrial operations.
From a competitive and strategic standpoint, the project underscores the importance of secure, scalable AI platforms that can handle sensitive data and high-stakes decision-making. The use of OSDU ensures data standardization and interoperability, reducing fragmentation and enabling cross-field analytics. The cloud-first approach, supported by Azure’s governance and security capabilities, is intended to balance innovation with risk management—an essential consideration for operators navigating the regulatory and safety requirements of upstream environments.
The involvement of Microsoft, G42, and Presight signals a regional and global collaboration that leverages both established technology ecosystems and local expertise. This mix of partners is well-positioned to drive not only operational improvements but also capabilities development, talent cultivation, and knowledge transfer across ADNOC’s broader digital transformation agenda. The expected outcome is a more responsive, data-driven organization capable of translating sophisticated AI research into practical tools that enhance production efficiency, asset integrity, and environmental performance.
In the broader energy landscape, ADNOC’s ENERGYai rollout may influence industry norms regarding AI governance, data sharing, and the use of agentic systems in critical operations. If the project demonstrates robust performance, reliability, and cost benefits, it could catalyze similar investments by other regional players and international operators seeking to modernize their upstream activities. The emphasis on sustainable development, coupled with added efficiencies, aligns with the ongoing energy transition and the growing push toward more intelligent, low-carbon oil and gas production.
Overall, ADNOC’s AI strategy stands to gain from ENERGYai through enhanced decision support, improved operational coordination, and stronger capabilities to manage subsurface risk. The project exemplifies how large-scale AI integration—grounded in standardized data practices, cloud infrastructure, and cross-organization collaboration—can reshape upstream workflows, drive tangible improvements, and contribute to the company’s long-term goals of efficiency, resilience, and environmental responsibility.
Leadership perspectives and industry outlook
Musabbeh Al Kaabi, ADNOC’s Upstream CEO, framed the ENERGYai initiative as a strategic alignment with the company’s aspiration to become the world’s most AI-enabled energy firm. He highlighted the opportunity to scale ENERGYai across ADNOC’s upstream operations, reinforcing the company’s role as a responsible and reliable energy supplier to global markets. His remarks emphasize a vision of leveraging advanced AI to enhance operational efficiency, decision-making speed, and data-driven performance across ADNOC’s upstream ecosystem.
Magzhan Kenesbai, acting managing director of AIQ, characterized the contract as a defining moment for the company. He cited the collaboration with ADNOC as a catalyst for developing a world-first agentic AI solution that is scalable across the energy value chain and capable of transforming the industry. He noted that the deployment would unlock unprecedented efficiencies and support ADNOC’s sustainability ambitions, underscoring the ambition to integrate AI deeply into core upstream workflows and generate measurable, sustainable gains.
Thomas Pramotedham, CEO of Presight, AIQ’s major shareholder, emphasized the project’s significance in advancing AI integration within the energy sector. He described agentic AI as a widely recognized future direction in AI development and suggested that the collaboration among Presight, AIQ, and ADNOC is shaping the future of energy through applied intelligence. His perspective highlights the strategic value of combining AI research with industry expertise to deliver practical benefits that advance both efficiency and environmental stewardship.
The collaboration’s emphasis on cloud infrastructure, standardized data models, and vetted AI models reflects a broader industry trend toward scalable, governed AI deployments in critical operations. In the context of upstream energy, such deployments can influence how data is accessed, interpreted, and acted upon, potentially transforming the way engineers, geoscientists, and operations teams collaborate to optimize production and maintain asset integrity. The project’s leadership narrative positions ENERGYai as a real-world testbed for agentic AI that can operate within the rigorous constraints of the energy sector, providing valuable lessons for future deployments across the industry.
Market implications extend beyond ADNOC’s operations. If ENERGYai demonstrates tangible improvements in efficiency, safety, and sustainability, it may encourage other energy majors and regional players to explore analogous AI-driven transformations. The combination of LLMs, agentic AI, standardized data frameworks, and cloud-native deployment offers a compelling blueprint for how advanced AI can be integrated into complex, data-rich industrial environments. The broader industry could see accelerated adoption of AI-enabled workflows, greater emphasis on data interoperability, and heightened focus on governance, security, and ethical considerations as AI becomes an increasingly central component of upstream strategy.
Conclusion
ADNOC’s decision to award AIQ a substantial, multi-year contract to deploy ENERGYai marks a significant milestone in the adoption of agentic AI within the energy sector. The project brings together a powerful alliance of ADNOC, AIQ, G42, and Microsoft to deliver a cloud-enabled, data-driven solution built on the OSDU framework and OpenAI models. By embedding five subsurface AI agents in a staged roadmap to scale across more than 28 producing fields, the initiative aims to deliver measurable improvements in efficiency, decision-making, and sustainability while advancing ADNOC’s digital transformation and AI strategy.
The ENERGYai platform is designed to empower engineers to interact with proprietary data through advanced language models, enabling more effective interpretation, faster workflows, and enhanced insight generation across seismic analysis, reservoir modeling, well planning, real-time process monitoring, and production optimization. The collaboration’s emphasis on governance, security, and interoperability underscores a prudent approach to deploying cutting-edge AI in high-stakes upstream environments.
As ADNOC positions ENERGYai at the core of its upstream operations, the project is likely to influence industry norms around AI adoption, data standardization, and the practical deployment of agentic AI at scale. The coming mid-2025 milestone for the first operational version, followed by a broader rollout, will serve as a critical proof point for the viability and value of enterprise-grade agentic AI in energy. The unfolding deployment embodies a concerted effort to merge advanced AI capabilities with disciplined operations, offering a compelling vision for the future of AI-enabled energy production.