A major step forward in AI-powered energy operations is underway as a new collaboration scales up. AIQ, a subsidiary of Presight, secured a substantial contract to implement ENERGYai across ADNOC’s upstream activities, signaling a landmark deployment of agentic AI within the energy sector. The engagement follows a successful proof-of-concept and unfolds over three years, with ENERGYai designed to streamline complex upstream workflows. The arrangement positions ENERGYai as a core engine for data-driven decision making, operational automation, and enhanced performance across ADNOC’s extensive field operations.
Background and Contract Overview
The agreement ties AIQ to a multi-year deployment aimed at transforming ADNOC’s upstream value chain through advanced artificial intelligence. The contract, valued at 340 million dollars, represents one of the most significant applications of agentic AI in the energy sector to date, underscoring the importance of scalable AI platforms in large, integrated energy companies. After demonstrating proof-of-concept success, the three-year rollout will transition ENERGYai from pilot status to full-scale implementation, covering multiple sites and processes. The objective is to optimize upstream operations by embedding intelligent agents into routine workflows, enabling automation, faster insights, and more consistent outcomes.
This deployment centers on integrating large language models with agentic AI capabilities to automate workflows that traverse ADNOC’s upstream ecosystem. The platform aims to streamline activities from seismic analysis to real-time process monitoring, and it seeks to unify disparate data sources into actionable intelligence. By enabling engineers and operators to interact with ADNOC’s proprietary datasets through natural-language interfaces backed by advanced AI, ENERGYai aspires to boost efficiency, reduce latency, and unlock novel insights that were previously difficult to uncover. The project’s scope includes not only processing and analysis but also orchestration of activities across teams and assets, leveraging automation to harmonize upstream operations at scale.
Industry leaders view this as a milestone for AI adoption within the energy sector, illustrating how agentic AI can move beyond isolated pilots to become a core, scalable capability across a company’s value chain. The contract marks a strategic alignment with ADNOC’s ambition to accelerate digital transformation, embed sustainability into daily operations, and strengthen resilience against market and operational pressures. As ADNOC’s upstream leadership notes, the initiative is part of a broader push to position the company as a world-leading, AI-enabled energy enterprise that can reliably supply energy to global markets while upholding high standards of governance and sustainability. The partnership also signals growing confidence in AIQ’s ability to deliver enterprise-grade AI that can operate across complex, safety-critical environments.
For AIQ, the deal is described as a defining moment that demonstrates the company’s ability to deliver a scalable, end-to-end agentic AI solution for the energy sector. The collaboration with ADNOC is framed as a catalyst for broader adoption of advanced AI across the energy industry, with the potential to unlock unprecedented efficiencies and bolster ADNOC’s sustainability objectives. The leadership at AIQ emphasizes that the deployment is not a one-off pilot but a strategic platform capable of expanding across the entire energy value chain, unlocking deeper integration between data, analytics, and automated decision-making.
Presight’s leadership highlights the broader significance of the project within the company’s portfolio. The CEO of Presight notes that the collaboration with AIQ and ADNOC reflects a shared belief that agentic AI represents a fundamental shift in the development and application of artificial intelligence. The project is portrayed as a concrete step toward shaping the future of energy through applied intelligence, combining cutting-edge technology with real-world operational needs. This perspective frames ENERGYai as more than a tool; it is positioned as a strategic driver of efficiency, safety, and sustainability across the upstream business.
The collaboration is built on a foundation of technical integration that leverages cloud infrastructure and industry standards. ENERGYai is designed to work with Azure as the cloud backbone, integrates the Open Subsurface Data Universe (OSDU) framework for standardized data access, and utilizes OpenAI models to power language understanding and agentic capabilities. This architecture supports secure data handling, scalable compute, and interoperability with ADNOC’s existing digital stack, ensuring that the AI system can operate within established governance and compliance frameworks. The project also reflects a collaborative ecosystem approach, bringing together ADNOC experts, AIQ engineers, and partners to align technology choices with operational realities.
Mid-2025 is the target for delivering the first operational version of ENERGYai. The initial release will feature five specialized AI agents tailored to subsurface operations, providing a focused yet robust introduction to the platform’s capabilities. The rollout plan includes test deployments across multiple ADNOC upstream assets to validate performance, reliability, and safety before broader expansion. The long-term aim is to extend ENERGYai to more than 28 producing fields, including some of the world’s largest and lowest-carbon oilfields, demonstrating the platform’s ability to scale across diverse asset types and geographies. This phased approach seeks to balance rapid capability realization with rigorous safety and operational discipline.
ENERGYai Platform: Architecture, Capabilities, and Use Cases
At its core, ENERGYai blends large language models with agentic AI to enable end-to-end workflow automation across ADNOC’s upstream value chain. The platform is designed to understand complex technical data, translate it into actionable tasks, and autonomously drive activities across teams and assets where appropriate. This architecture supports dynamic decision-making, real-time monitoring, and proactive optimization, creating a more responsive and resilient operational environment. By enabling natural-language interactions with proprietary data, ENERGYai helps engineers access critical insights without requiring deep specialization in every data format or silo.
The platform’s architecture is built to accommodate the needs of a large, integrated oil company operating across multiple geographies and asset types. It integrates with ADNOC’s seismic data, reservoir models, production data, and surface facility information, creating a unified data fabric. The use of the OSDU framework ensures standardized data access and interoperability, which is essential for cross-functional analytics and cross-asset intelligence. The Azure cloud foundation provides scalable compute, secure storage, and enterprise-grade governance, aligning with ADNOC’s risk management and compliance requirements. OpenAI models contribute language understanding, reasoning, and interaction capabilities, enabling agents to interpret complex technical instructions and respond with clear, context-aware actions.
ENERGYai’s agentic layer is designed to orchestrate tasks across the upstream value chain. Agents can perform a variety of roles, from subsurface interpretation and data quality checks to real-time process optimization and anomaly detection. The system is engineered to support workflow automation, enabling repeatable and auditable execution of standard procedures as well as adaptive responses to changing field conditions. The platform is also designed to capture and incorporate human expertise, allowing engineers to guide and refine AI behavior through intuitive interfaces and governance mechanisms. By combining automation with expert oversight, ENERGYai aims to improve both speed and accuracy in critical upstream operations.
In terms of practical use cases, ENERGYai is expected to streamline seismic analysis, reservoir characterization, well planning, and drilling optimization by providing rapid data synthesis and scenario evaluation. The platform is intended to assist operators in monitoring real-time process variables, detecting deviations, and triggering appropriate corrective actions. Subsurface workflows, which traditionally rely on fragmented data and manual interpretation, stand to benefit from a coordinated, AI-assisted approach that reduces cycle times, enhances decision quality, and aligns with safety and regulatory requirements. The energy sector’s emphasis on reliability and risk management makes ENERGYai’s capability for proactive monitoring and automated response particularly valuable.
From a performance perspective, ENERGYai is designed to deliver measurable improvements in efficiency and cost reduction. By accelerating data processing, enabling faster decision cycles, and minimizing manual intervention, the platform has the potential to lower operating expenses and optimize capital allocation. The AI-driven system is also positioned to contribute to ADNOC’s broader digital transformation and sustainability goals by enabling more precise resource management, reducing waste, and supporting more efficient energy production. The platform’s success hinges on robust data governance, model monitoring, and continuous improvement processes that ensure reliability and transparency in its recommendations and automated actions.
Energy efficiency and environmental stewardship are central to ADNOC’s strategy, and ENERGYai is framed as a tool to support those objectives. The platform’s capability to optimize processes and improve operational performance can translate into lower emissions intensity, better energy use efficiency, and improved environmental performance across upstream operations. By enabling more precise field management and optimization, ENERGYai aligns with the industry’s push toward sustainable energy production and responsible resource stewardship. The project thus represents a convergence of advanced AI, data-driven decision making, and sustainable operations, underscored by a commitment to safety and reliability.
To ensure practical implementation, the ENERGYai rollout will be staged to manage risk and maximize learning. The first operational version will provide a controlled environment to validate agent performance, data integrity, and system resilience before broader deployment. This phased approach will facilitate close coordination with ADNOC’s asset teams, allowing for real-time feedback, iterative improvements, and alignment with safety standards. As energy operators gain confidence in the platform, ENERGYai is expected to extend its coverage to additional subsurface workflows and surface facilities, creating a more integrated intelligence layer across upstream activities. The ultimate objective is to deliver a scalable, end-to-end AI-driven operating model that can adapt to evolving field conditions and emerging data sources.
AN ecosystem of collaboration underpins the ENERGYai program. The project’s development benefited from input from ADNOC experts who offered critical domain knowledge, ensuring that the AI solutions address real-world operational challenges. The collaboration with industry partners, including G42 and Microsoft, adds depth to the platform’s capabilities and accelerates innovation through shared research and engineering efforts. The combined expertise from these organizations supports the platform’s ability to handle complex data, maintain rigorous security standards, and scale across diverse operating environments. The integration strategy emphasizes interoperability, governance, and reliability, enabling ADNOC to harness advanced AI while maintaining control over data and workflows.
From a governance standpoint, ENERGYai is designed to operate within ADNOC’s established policies and regulatory expectations. The system’s architecture supports auditable decision logic, traceable actions, and transparent monitoring of AI-driven processes. This transparency is essential for building trust among engineers, operators, and management, and it helps ensure that automated decisions align with safety and compliance requirements. The collaboration also prioritizes data privacy, access controls, and secure data handling practices, reflecting industry norms for managing sensitive upstream information. The end-to-end governance framework aims to balance innovation with accountability, ensuring that the AI system’s impact remains positive and measurable across operations.
Strategic Significance for ADNOC and the Middle East Energy Sector
ADNOC’s Upstream CEO emphasizes that the ENERGYai initiative is a strategic accelerant for the company’s overarching AI strategy. The program aligns with the ambition to become the world’s most AI-enabled energy enterprise, reinforcing ADNOC’s leadership in digital transformation and data-driven operations. By deploying ENERGYai across upstream processes, ADNOC seeks to consolidate its status as a dependable and responsible energy supplier to global markets, while simultaneously pushing for higher efficiency and greater resilience in its operations. This strategic focus reflects a broader industry trend toward integrating advanced AI into core business functions to improve performance and competitiveness.
From a leadership perspective, the project is framed as a collaborative effort designed to deliver scalable, enterprise-grade AI capabilities across the energy value chain. The aim is to create a world-class AI platform that can be deployed across multiple assets, enabling consistent performance, standardized processes, and repeatable success. In addition to efficiency gains, the initiative is expected to strengthen ADNOC’s position as a responsible energy supplier by supporting sustainability goals and reducing environmental footprints. The leadership articulates a clear belief that agentic AI will help ADNOC navigate the complexities of modern energy production, including volatility, regulatory pressures, and the imperative to decarbonize.
For AIQ, the contract represents a milestone that highlights the company’s ability to deliver a cutting-edge, scalable AI solution for the energy sector. The collaboration with ADNOC provides a proving ground for ENERGYai’s capabilities and validates the company’s approach to integrating AI across complex industrial operations. The leadership underscores that the project is not only about technology deployment but also about transforming how upstream teams work, enabling them to leverage AI insights and automated processes to achieve better outcomes. The partnership also positions AIQ as a core partner in ADNOC’s digital journey, reinforcing its role in advancing applied intelligence across the energy industry.
The energy sector’s broader context reveals growing attention to agentic AI as a tool for optimizing exploration, production, and asset management. The deployment aligns with global trends toward digital maturity in oil and gas, where large operators seek to balance performance improvements with safety, reliability, and environmental stewardship. The ENERGYai program demonstrates how cloud-based AI platforms, standardized data frameworks, and strategic collaborations can unlock new levels of efficiency and decision support in upstream activities. The initiative thus contributes to a regional narrative in which Middle Eastern energy players leverage advanced AI to maintain competitiveness while advancing sustainability and governance objectives.
Industry observers note that ADNOC’s approach could become a blueprint for similar large-scale AI deployments in comparable markets. If successful, the ENERGYai rollout could encourage other operators to pursue agentic AI in their upstream portfolios, spurring a wave of digital transformation across the region. The combination of a major national oil company, a trusted AI partner ecosystem, and a robust data framework offers a compelling model for implementing secure, scalable AI in complex, safety-critical industries. The long-term implications extend beyond individual efficiency gains, potentially shaping how energy producers manage data, optimize assets, and balance production with environmental responsibilities.
The collaboration’s emphasis on sustainability is notable. By enabling more precise resource management and optimized operations, ENERGYai has the potential to contribute to lower emissions intensity and more efficient energy production. The project’s alignment with ADNOC’s sustainability goals signals a broader commitment to reducing the environmental impact of upstream activity while maintaining reliable energy supplies to global markets. The energy industry’s transition toward lower-carbon production models is driving demand for AI solutions that can help operators manage emissions, reduce waste, and improve overall environmental performance. In this context, ENERGYai stands as a concrete example of how AI can support both performance and sustainability objectives in parallel.
Collaboration and Ecosystem: AIQ, Presight, G42, and Microsoft
The ENERGYai initiative is anchored by a collaboration network that includes AIQ as the primary integrator and Presight as a major shareholder. This ecosystem brings together a blend of operational expertise, engineering know-how, and AI development capabilities designed to address ADNOC’s upstream needs. The partnership emphasizes co-creation, with AIQ and its collaborators actively engaging ADNOC experts to tailor the platform to real-world field conditions and regulatory requirements. The organizational model prioritizes iterative development, rigorous validation, and close alignment with safety and reliability standards.
G42 and Microsoft play critical roles within the broader ecosystem, contributing to the platform’s AI capabilities, cloud infrastructure, and data analytics capacity. The collaboration leverages G42’s AI strengths and Microsoft’s enterprise cloud solutions to deliver robust performance, scalable compute resources, and strong governance features. The combined expertise supports the platform’s ability to handle sensitive data securely, maintain compliance with industry standards, and provide resilience in edge-case scenarios that may arise in upstream operations. This ecosystem approach is designed to deliver a holistic solution that integrates AI, data, and domain expertise into a coherent operating model for ADNOC.
From a governance and security standpoint, the ecosystem emphasizes rigorous data management, access control, and auditability. The architecture is designed to ensure that AI-driven actions can be traced, reviewed, and adjusted as needed, enabling stakeholders to verify outcomes and maintain accountability. The partnership also prioritizes responsible AI practices, including bias mitigation, model monitoring, and continuous improvement loops to ensure that ENERGYai remains reliable and aligned with ADNOC’s strategic objectives. Such governance considerations are essential in high-stakes environments where autonomous systems interact with complex physical assets and critical processes.
The collaboration’s strategic intent includes knowledge transfer and capability building within ADNOC’s workforce. By involving ADNOC experts in the development and deployment process, the project fosters internal capability and long-term self-sufficiency in managing AI-enabled operations. This approach is expected to strengthen ADNOC’s digital culture, promote data literacy among engineers and operators, and support ongoing optimization initiatives beyond the initial deployment. The knowledge transfer component also helps ensure that the platform evolves in tandem with ADNOC’s evolving needs and the broader energy market’s progress in AI adoption.
Implementation Plan, Timeline, and Deployment Strategy
The implementation plan envisions a phased approach that balances rapid capability realization with careful risk management. The first operational version of ENERGYai is slated for delivery by mid-2025, with a focus on subsurface operations through five specialized AI agents designed to handle critical subsurface workflows. This initial package will act as a proof of concept at scale, validating agent capabilities, integration fidelity, and the end-to-end orchestration of tasks across assets. The phased rollout allows teams to learn from early deployments, refine models, and improve system reliability before expanding to additional functions and fields.
Following the initial release, ENERGYai will be test-deployed across several ADNOC upstream assets. This stage is intended to validate performance in diverse operating environments, verify data governance processes, and confirm that automation aligns with safety protocols and regulatory requirements. The test deployments will inform subsequent expansion, enabling a controlled increase in the platform’s footprint while maintaining rigorous oversight. The strategy prioritizes a balance between speed and thorough validation, ensuring that the system can handle operational variations and edge cases that arise across fields.
As part of the broader expansion plan, ENERGYai is expected to scale to more than 28 producing fields, including some of the world’s largest and lowest-carbon oilfields. This scaling objective reflects ADNOC’s ambition to harness AI across its upstream portfolio, delivering consistent benefits in efficiency, safety, and environmental performance. The approach emphasizes modularity, allowing new agents and capabilities to be added without destabilizing the existing operations. It also highlights the importance of robust data pipelines, secure data habitats, and scalable inference capabilities to support a wide range of field conditions and asset types.
To ensure success, the deployment plan incorporates continuous monitoring and governance. AI-powered decisions and automated actions will be subject to ongoing validation, performance metrics, and human oversight where appropriate. The project’s governance framework will oversee model updates, data quality checks, and operational risk assessments, maintaining alignment with ADNOC’s standards for safety, reliability, and regulatory compliance. The deployment strategy also considers the need for ongoing training and knowledge sharing to keep engineers and operators proficient in using ENERGYai, interpreting its outputs, and managing its recommendations.
In parallel with rollout, the project emphasizes measurable outcomes. Efficiency gains, reduced cycle times, improved decision quality, and minimized operational costs are among the expected benefits. The platform’s ability to accelerate data processing, enable rapid scenario analysis, and automate routine workflows should translate into tangible improvements in productivity and asset utilization. By quantifying these benefits, ADNOC and AIQ aim to demonstrate a compelling return on investment and justify continued investment in AI-driven modernization across upstream operations.
The integration work is designed to be deeply collaborative, with ADNOC, AIQ, and partner teams aligning on technical standards, data governance, and safety requirements. Regular reviews, performance dashboards, and incident reporting mechanisms will support transparent progress tracking and issue resolution. This collaborative cadence will help ensure that ENERGYai remains on track to deliver the promised capabilities while adapting to evolving field conditions and corporate priorities. The plan envisions an iterative process in which insights from early deployments inform enhancements to AI agents, data handling practices, and the platform’s overall architecture.
Impact on Operations, Efficiency, and Sustainability
ADNOC’s upstream operations stand to benefit from substantial improvements in efficiency and process optimization through ENERGYai. By enabling automated workflows and intelligent data interpretation, the platform can shorten decision cycles, reduce manual workloads, and accelerate critical analysis tasks. The automation of routine tasks frees engineers to focus on higher-value activities, potentially improving productivity and reducing the likelihood of human error in complex field environments. These efficiency gains are expected to translate into lower operating costs and more effective use of capital across upstream projects.
Beyond efficiency, ENERGYai is positioned to enhance the accuracy and speed of seismic interpretation, reservoir evaluation, and drilling optimization. The AI-driven system can assimilate multiple data streams, reconcile discrepancies, and propose optimized drilling plans or stimulation strategies. This capability supports more informed decision-making, enabling ADNOC to optimize resource extraction while maintaining safety and environmental stewardship. The platform’s real-time monitoring and anomaly detection capabilities contribute to improved field reliability and reduced unplanned downtime, which in turn strengthens overall production performance and resilience.
Sustainability outcomes are a central consideration in the ENERGYai program. By optimizing processes and reducing unnecessary interventions, the platform can lower emissions intensity associated with upstream activities and improve energy efficiency across facilities. More precise reservoir management leads to better recovery with fewer costly and energy-intensive interventions. The deployment aligns with ADNOC’s broader sustainability agenda, which emphasizes responsible energy production, lower carbon footprints, and tighter governance around environmental performance. The AI-driven approach supports continuous improvement in emissions management, waste reduction, and resource optimization across upstream operations.
ADNOC’s leadership frames ENERGYai as a core enabler of the company’s digital transformation and sustainability ambitions. The technology is expected to drive a cycle of ongoing enhancements to upstream processes, enabling more consistent results and better alignment with global energy market demands. As the platform matures, its integration with existing systems and workflows will become deeper, creating a more seamless, data-informed operating model. This evolution aims to deliver sustainable performance gains over time, anchored in data-driven decision making, automation, and comprehensive governance.
From an operational risk perspective, the ENERGYai deployment requires robust governance, security, and safety controls. The project will rely on strict data access controls, monitoring, and auditing to ensure compliance with regulatory requirements and industry best practices. Safety is a paramount consideration in all upstream activities, and the AI system’s actions will be designed to support human oversight and accountability. The project’s risk management approach includes scenario testing, fail-safe mechanisms, and continuous improvement processes to mitigate potential adverse outcomes while maximizing benefits. The governance framework will help ensure that AI-driven recommendations and automations align with ADNOC’s safety standards and operational protocols.
As ADNOC scales ENERGYai, the potential organizational impact becomes more evident. The platform’s deployment is expected to influence workforce roles, with operators and engineers adapting to AI-enabled workflows and new forms of collaboration. Training and change management will be essential to ensure that staff can effectively leverage ENERGYai’s capabilities while maintaining ownership of critical decisions. This cultural shift toward AI-assisted operation is viewed as a path to sustaining competitiveness in a rapidly evolving energy landscape, where digital technologies increasingly underpin strategic success, risk management, and value creation.
In parallel, the broader energy industry could observe a ripple effect as major operators explore similar agentic AI deployments. The successful integration of ENERGYai with ADNOC’s upstream operations could demonstrate a practical model for translating advanced AI research into real-world industrial applications. The implications extend to data infrastructure, cloud ecosystems, and cross-organizational collaboration, reinforcing the trend toward centralized AI platforms that enable standardized yet adaptable workflows across multiple assets and geographies. The resulting ecosystem would likely foster innovation, efficiency, and resilience across the energy sector, encouraging further investment in AI-enabled modernization.
Economic Implications, Market Positioning, and Competitive Landscape
The ENERGYai deployment positions ADNOC at the forefront of AI-driven transformation within the Middle East’s energy sector. By investing in an enterprise-scale agentic AI platform, ADNOC aims to improve capital efficiency, optimize asset performance, and strengthen its competitive stance in a market shaped by volatile price dynamics and evolving energy policies. The project’s scale and ambition signal a strategic bet on AI as a differentiator in upstream operations, with potential spillover effects for supplier ecosystems and regional AI ecosystems. The anticipated gains in efficiency and decision quality could translate into tangible improvements in production economics and project execution.
The collaboration’s architecture emphasizes interoperability, data governance, and security, features critical to the successful deployment of AI in a high-stakes industrial setting. The use of Azure as the cloud backbone, combined with the OSDU framework, ensures standardized data access and robust integration with existing ADNOC data assets. This approach reduces data silos and fosters a more cohesive analytics environment, enabling the organization to exploit cross-asset insights and achieve greater economies of scale in AI adoption. The inclusion of OpenAI models suggests a commitment to leveraging state-of-the-art natural language processing and reasoning capabilities to unlock intuitive human-AI interactions and advanced automation capabilities.
From a competitive standpoint, the project signals a shift in how energy majors approach digital modernization. By committing to a multi-year, large-scale deployment with a credible AI ecosystem, ADNOC sets a high bar for AI-enabled upstream operations. Other operators in the region and beyond may look to ADNOC’s blueprint as a reference model for implementing agentic AI in complex industrial contexts. The emphasis on collaboration with reputable tech partners and cloud providers further signals a trend toward building robust, multi-party ecosystems to accelerate AI deployment and ensure governance, reliability, and security across large-scale industrial programs.
The economic implications extend to the wider market for AI-enabled energy solutions. The ENERGYai platform’s deployment could drive demand for specialized AI talent, data infrastructure investments, and governance tools tailored to oil and gas environments. As upstream operators seek to translate AI advances into tangible results, market demand for platform-level AI solutions that can scale across fields, integrate with existing data ecosystems, and comply with safety standards may rise. This could stimulate competition among AI vendors to offer robust, enterprise-grade energy AI solutions and to forge strategic partnerships with major energy players.
ADNOC’s leadership has highlighted the strategic importance of cost optimization and productive scaling. By accelerating processes and reducing operational costs, ENERGYai is expected to support ADNOC’s broader digital transformation initiatives and sustainability targets. The deployment could drive long-term cost savings and improved asset utilization, contributing to a more favorable capital cycle and stronger return on investment for upstream projects. As a result, the program has the potential to influence funding priorities, project selection, and strategic planning across ADNOC’s portfolio.
The project’s impact on the Middle East’s energy market may extend beyond ADNOC’s immediate operations. If successful, the ENERGYai deployment could encourage national and regional energy entities to pursue similar AI-driven transformations. Such a wave of modernization could enhance the competitiveness of regional oil and gas sectors by boosting efficiency, safety, and environmental performance, while also reinforcing the appeal of the broader Middle East as a hub for advanced energy technologies and digital innovation. The potential for regional collaboration and knowledge sharing could accelerate AI adoption and drive further advancements in how upstream operations are managed.
Risks, Governance, and Data Security
Operating an agentic AI platform within critical upstream operations requires a rigorous governance and risk management framework. The ENERGYai program integrates formal risk assessment processes, safety protocols, and regulatory compliance measures designed to mitigate potential risks associated with AI-driven automation and decision-making. The project’s governance profile emphasizes transparency, traceability, and accountability for AI actions, ensuring that automated workflows can be reviewed, audited, and adjusted as needed. This approach is essential in maintaining trust among operators, engineers, and management while meeting safety and regulatory expectations.
Data security and privacy are central concerns for any AI deployment involving proprietary upstream information. The ENERGYai architecture employs robust data access controls, encryption, and monitoring mechanisms to ensure that sensitive data remains protected. Access permissions are managed to prevent unauthorized use, and auditing capabilities enable end-to-end traceability of AI-driven actions. The collaboration with Microsoft and other ecosystem partners reinforces a secure foundation, leveraging established enterprise security practices and compliance certifications. This security posture is critical given the sensitivity of reservoir data, well logs, and production information that ENERGYai will handle.
Model governance is another critical element. Continuous monitoring of model performance, drift detection, and validation against safety and reliability criteria are essential to maintaining AI quality over time. The project will implement feedback loops that allow engineers to correct or constrain AI behaviors, ensuring that outputs remain aligned with operational realities and safety standards. The approach must balance innovation with predictability, ensuring that AI recommendations are reliable and explainable, particularly in high-stakes decision contexts such as drilling, well planning, and reservoir management.
Operational resilience is a key concern for large-scale AI deployments. The project must plan for contingencies, including data outages, partial system failures, and network disruptions. Redundancy, disaster recovery, and robust failover procedures will be part of the deployment strategy to minimize downtime and maintain safe operations. In practice, this means designing failure modes that default to safe operational states and ensuring that human oversight remains available to intervene when necessary. The resilience mindset is critical to protecting asset integrity, personnel safety, and the consistency of production outcomes.
Safety remains a non-negotiable priority in any upstream AI initiative. The platform’s automation is designed to enhance safety by reducing manual routines that can lead to errors, while also ensuring that automated actions adhere to established safety protocols. The collaboration emphasizes safety-by-design principles, integrating machine guidance with human supervisory control to maintain a strong safety culture. In complex field environments, this approach helps reduce risk while enabling the organization to capitalize on AI-driven efficiency and precision.
Regulatory compliance is addressed through a combination of governance policies, standardized data handling, and auditable AI behavior. The ENERGYai program aligns with ADNOC’s regulatory obligations and industry best practices, supporting responsible data stewardship and accountability. This alignment is vital for maintaining legitimacy and credibility with stakeholders, including regulators, partners, and the broader public. The project’s compliance framework will evolve with changes in regulations and industry standards, ensuring that the platform remains up-to-date and capable of meeting evolving requirements.
Future Prospects: Scaling ENERGYai Across Upstream Value Chain
As ENERGYai scales beyond subsurface operations, its potential to impact the entire upstream value chain grows substantially. The architecture is designed to accommodate expansion to additional domains within upstream activities, enabling AI-driven optimization for surface facilities, production operations, and field logistics. The scalability plan envisions a seamless integration of new agents and capabilities, allowing ADNOC to extend automation and advanced analytics across multiple stages of the upstream process. This expansion will require careful management of data quality, governance, and alignment with safety protocols, but the underlying framework is built to support such growth.
The broader implications include deeper cross-asset intelligence and more sophisticated decision support. By aggregating data across exploration, drilling, production, and facilities management, ENERGYai could deliver holistic insights that drive better planning, resource allocation, and performance optimization. Cross-asset analytics could reveal patterns and correlations that are not visible when focusing on individual assets, enabling more effective risk management and opportunity identification. This level of integration requires robust data standards, disciplined data stewardship, and governance processes that maintain data integrity and trust in AI-driven recommendations.
The user experience within ADNOC’s workforce is also expected to evolve as ENERGYai expands. As more agents are introduced and capabilities broaden, engineers and operators will interact with AI through intuitive interfaces, receiving concise, actionable guidance tailored to their roles. The platform’s ability to translate complex data into practical actions will be a key factor in adoption, reinforcing the value of AI as a day-to-day companion in the field. Training programs and change management initiatives will support this transition, ensuring that staff can leverage ENERGYai’s capabilities effectively while maintaining a clear sense of control over critical decisions.
From a technology perspective, ongoing enhancements to model performance, data ingestion, and integration with third-party tools will be essential to sustain momentum. The ecosystem’s partners will continue to contribute to research and development, refining algorithms, improving interpretability, and expanding the platform’s capacity to handle diverse data modalities. The ability to incorporate new data types, support advanced analytics, and optimize entire workflows will determine how quickly and effectively ENERGYai can scale across ADNOC’s upstream network. The long-term trajectory envisions a mature platform that acts as a centralized intelligence layer, coordinating activities, accelerating decision cycles, and delivering measurable improvements across the upstream value chain.
The regional and global energy markets could benefit from ADNOC’s leadership in AI-enabled upstream operations. Demonstrating the practicality and value of agentic AI at scale may accelerate adoption by other operators seeking to modernize their own upstream activities. The knowledge generated from ADNOC’s implementation—best practices, governance models, and performance benchmarks—could become a reference for the broader industry. This diffusion of learnings would contribute to a more efficient, data-driven energy sector worldwide, potentially raising the bar for safety, reliability, and environmental performance across the industry.
The potential sustainability impacts are significant as well. Widespread adoption of AI-driven optimization could enable better reservoir management, more efficient production ramp-ups, and lower environmental footprints for upstream projects. The combination of efficiency gains and responsible practices aligns with global expectations for reducing emissions and environmental impact in the energy sector. If ADNOC’s ENERGYai program successfully demonstrates these outcomes, it could strengthen support for continued investment in AI-driven digital transformation as a core driver of sustainable energy production.
The strategic value of the partnership between ADNOC, AIQ, Presight, G42, and Microsoft lies in its ability to deliver a comprehensive, scalable, and secure AI solution for a complex industrial context. The project’s trajectory—from PoC to a full-scale deployment across multiple fields—illustrates the feasibility of agentic AI in high-stakes upstream operations. The collaboration’s success would not only transform ADNOC’s operations but could redefine how the energy sector approaches AI adoption, data governance, and cross-industry partnerships in the years to come.
Conclusion
ADNOC’s decision to commission ENERGYai from AIQ marks a significant milestone in the deployment of agentic AI across the energy sector’s upstream operations. The three-year contract, valued at 340 million dollars, follows a successful proof-of-concept and signals a concerted effort to scale an AI-enabled platform across the organization’s upstream value chain. ENERGYai’s integration of large language models with agentic AI aims to automate workflows, enhance efficiency, and unlock new insights from ADNOC’s proprietary data, spanning the journey from seismic analysis to real-time process monitoring. The platform’s architecture, which combines Azure cloud infrastructure, the OSDU data framework, and OpenAI models, supports a scalable, secure, and interoperable solution designed to operate within ADNOC’s governance and safety requirements.
The initiative aligns with ADNOC’s ambition to become the world’s most AI-enabled energy firm, reinforcing the company’s drive toward digital transformation and sustainable operations. The collaboration with Presight, AIQ, G42, and Microsoft underscores a strategic ecosystem approach that leverages diverse strengths to deliver enterprise-grade AI across upstream operations. The project’s phased rollout—beginning with five subsurface agents projected for mid-2025 and expanding to more than 28 fields—highlights a careful balance between rapid capability development and rigorous validation to ensure safety, reliability, and measurable benefits.
As ENERGYai scales, it promises to reshape how engineers interact with data, how decisions are made under uncertainty, and how automation can reduce costs while supporting sustainability goals. The platform’s potential to streamline seismic interpretation, reservoir management, and drilling optimization, among other subsurface workflows, positions ADNOC to achieve faster cycle times, higher confidence in decisions, and more resilient operations. The broader industry implications point to a growing openness to enterprise AI solutions that integrate data standards, governance, and secure cloud-enabled compute to unlock value across the upstream value chain.
For ADNOC, the ENERGYai deployment represents more than a technology upgrade; it is a strategic investment in a data-driven operating model that can deliver lasting competitive advantages. The initiative embodies a holistic approach to digital transformation, combining advanced AI capabilities, a secure ecosystem, and a strong governance framework to advance safety, efficiency, and sustainability. As the project progresses toward broader deployment and real-world impact, industry observers will watch closely to understand how agentic AI can translate from concept to scalable, responsible, and transformative operations across the energy sector.