A major strategic deal is unfolding as AIQ, a Presight subsidiary, secures a substantial contract with ADNOC to deploy the ENERGYai platform and related AI solutions across ADNOC’s upstream operations. Valued at $340 million and set for a three-year horizon, the agreement follows a successful proof-of-concept phase and outlines a phased rollout designed to optimize ADNOC’s upstream processes. ENERGYai is poised to weave together large language models with advanced agentic AI, enabling comprehensive workflow automation that spans the entire upstream value chain—from seismic interpretation to real-time process monitoring. This deployment marks a pivotal step in accelerating ADNOC’s digital transformation and strengthening its AI-enabled capabilities within the energy sector.
Overview of the ADNOC-ENERGYai Deal
The deal represents a landmark engagement in which AIQ will bring ENERGYai to life across ADNOC’s upstream operations over a three-year period. The contract’s value, at $340 million, underscores the scale of the initiative and its potential to redefine how ADNOC leverages artificial intelligence to optimize exploration, development, and production activities. The contract is the culmination of a formal proof-of-concept phase that demonstrated ENERGYai’s capacity to integrate with ADNOC’s data ecosystem and deliver tangible improvements in efficiency and decision-making. The implementation aims to standardize and accelerate processes that have historically been labor-intensive and data-rich, translating vast datasets into actionable insights.
Key objectives of the deal include deploying ENERGYai to automate workflows across the upstream value chain. By combining large language models with agentic AI, ENERGYai enables engineers and operators to interact more effectively with ADNOC’s proprietary data, enabling quicker analysis, better forecasting, and streamlined operations. The platform’s architecture is designed to support end-to-end automation—from data ingestion and preprocessing to decision support and autonomous task execution—thereby reducing manual intervention, minimizing error rates, and lowering operating costs. In this sense, the ADNOC-ENERGYai agreement is not merely a technology purchase but a strategic integration aimed at elevating ADNOC’s operational efficiency, resilience, and competitiveness in global energy markets.
This collaboration aligns with ADNOC’s ambition to become the world’s most AI-enabled energy company. As a statement from Musabbeh Al Kaabi, ADNOC’s Upstream CEO, underscored, the initiative is a critical step in scaling ENERGYai across the upstream business and reinforcing ADNOC’s standing as a responsible and reliable energy supplier to global markets. The deployment is framed as an enabler of more efficient, data-driven decision-making that supports ADNOC’s broader strategic goals, including sustainability and cost optimization. The AI-driven enhancements are expected to accelerate processes and reduce operational costs, making a meaningful contribution to ADNOC’s digital transformation journey and its sustainability commitments.
The partnership is described as a milestone for AIQ. Magzhan Kenesbai, acting managing director of AIQ, characterized the contract as a defining moment for the company, highlighting the collaboration’s potential to deliver a world-leading, scalable agentic AI solution across the entire energy value chain. The deployment is anticipated to unlock unprecedented efficiencies and reinforce ADNOC’s sustainability objectives, signaling a new era of AI-enabled energy operations. This project has benefited from the close involvement of ADNOC experts and collaborative input from key technology partners, signaling a holistic approach to AI integration within the energy sector.
The initiative embodies a broad ecosystem approach. ENERGYai has been developed with input from ADNOC subject-matter experts and in collaboration with strategic partners, including G42 and Microsoft. The technical stack leverages the Azure cloud environment and the Open Subsurface Data Universe (OSDU) framework, alongside OpenAI models. The collaboration highlights a convergence of cloud infrastructure, AI model capabilities, and industry-specific data standards designed to enable scalable, secure, and compliant AI operations in energy contexts. This ecosystem orientation emphasizes interoperability, data governance, and the ability to deploy advanced AI capabilities across multiple asset classes and geographies.
In the corporate leadership narrative, Thomas Pramotedham, CEO of Presight and AIQ’s major shareholder, emphasized the project’s significance for advancing AI integration at scale. He framed agentic AI as the future of AI development and indicated that the collaboration with AIQ and ADNOC would help shape the energy sector’s trajectory through applied intelligence. The emphasis on applied intelligence reflects a strategic commitment to translating cutting-edge AI research into practical, industry-ready solutions that deliver measurable outcomes in operations, safety, and environmental performance.
The first operational version of ENERGYai is anticipated by mid-2025. The initial rollout will cover five AI agents focused on subsurface operations, enabling targeted automation and advanced analytics in subsurface workflows. Following this initial phase, the deployment will be test-deployed across multiple ADNOC upstream assets and then expanded to more than 28 producing fields. This expansion includes some of the world’s largest and lowest-carbon oilfields, illustrating how the technology is expected to contribute to ADNOC’s low-carbon and sustainability objectives while enhancing productivity and efficiency across diverse asset types.
This deal sits within a broader trend of digital transformation in the energy sector, where major operators are increasingly turning to AI-driven platforms to unlock value from complex data landscapes. The ADNOC-ENERGYai initiative is positioned as a showcase of how agentic AI, integrated with robust data standards and cloud-based infrastructure, can deliver end-to-end improvements across exploration, development, and production activities. The project also signals a strategic emphasis on safety, reliability, and environmental stewardship as integral outcomes of AI-enabled operations.
ENERGYai Platform: What It Is and How It Works
ENERGYai represents a combination of large language models (LLMs) and agentic AI capabilities designed to automate and optimize upstream workflows. The platform is built to interface with ADNOC’s proprietary datasets, enabling engineers and operators to query, analyze, and act upon information in a streamlined, intuitive manner. At its core, ENERGYai integrates advanced AI models with domain-specific agents that can carry out tasks autonomously or semi-autonomously, guided by human input and governance protocols. This approach blends the flexibility of LLM-driven dialogue with the precision and reliability of agentic automation, creating a versatile toolset that can adapt to the nuanced requirements of upstream oil and gas operations.
From seismic interpretation to real-time process monitoring, ENERGYai is designed to support a wide spectrum of activities. The platform enables seamless interaction with complex subsurface data, allowing geoscientists to extract insights more rapidly and with greater accuracy. It supports operations planning, execution, and optimization by providing decision-ready outputs, alerts, and actionable recommendations that engineers can act upon within established safety and regulatory frameworks. The integration of LLMs with agentic AI stands to reduce manual data wrangling and analytical overhead, permitting teams to focus on higher-value tasks such as interpretation, risk assessment, and strategic planning.
The architecture emphasizes scalable deployment, governance, and security. By leveraging the Azure cloud, ENERGYai benefits from robust cloud-native capabilities, including scalable compute resources, data protection, and enterprise-grade security controls. The use of the Open Subsurface Data Universe framework ensures standardized data interchange and interoperability across different subsurface domains, enabling consistent data modeling and analysis. The platform’s compatibility with widely used AI models, including OpenAI technologies, ensures access to cutting-edge AI capabilities while maintaining alignment with ADNOC’s data governance policies and security requirements.
The energy sector’s distinctive challenges—heterogeneous data, high-stakes decision-making, and the need for safety-by-design—are addressed through ENERGYai’s integrated approach. The agentic AI components can automate repetitive workflows, monitor process parameters in real time, and trigger appropriate actions in response to evolving conditions. This capability supports more proactive maintenance, quicker anomaly detection, and improved operational resilience. The result is a platform that not only analyzes data but also acts on it in a controlled, auditable manner, aligning with ADNOC’s governance and compliance standards.
Technical Architecture and Capabilities
ENERGYai’s technical framework blends cutting-edge AI models with domain-specific agents to deliver end-to-end automation. The convergence of LLMs with agentic systems enables dynamic, context-aware task execution. The platform’s architecture is designed to accommodate ADNOC’s extensive data landscape, integrating data sources from seismic surveys, production telemetry, reservoir simulations, and downstream information streams. This multi-source data integration is essential for delivering a holistic view of upstream operations and empowering operators with timely, accurate insights.
A critical aspect of ENERGYai is its ability to support workflow automation across the upstream value chain. This includes tasks such as data ingestion, preprocessing, model inference, decision support, and autonomous execution of defined actions. The agentic AI components act as intelligent agents that can carry out actions such as running simulations, triggering alerts, initiating optimization loops, or coordinating with edge devices for real-time process control. The system’s design enables a balance between automation and human oversight, with governance mechanisms in place to ensure safety, auditability, and accountability.
The platform’s collaboration with G42 and Microsoft is central to its technical strategy. The Azure cloud environment provides scalable compute, storage, and security features that support large-scale AI workloads. The OS DU framework underpins standardized subsurface data management, enabling consistent data formats and interoperability across workflows. OpenAI models contribute state-of-the-art language understanding and reasoning capabilities, enhancing the system’s ability to interpret complex data and generate actionable recommendations. By combining these technologies, ENERGYai aims to deliver robust performance, reliability, and security across ADNOC’s remote and on-site operations.
In terms of user interaction, ENERGYai emphasizes intuitive engagement for engineers and operators. The use of LLMs enables natural language interfaces, making it easier for personnel to pose questions, request analyses, or issue commands. This can reduce the cognitive load on human operators and accelerate decision-making processes. The agentic components can then translate these inputs into concrete tasks, coordinating with data platforms and operational systems to carry out the requested actions. The overarching goal is to streamline workflows while ensuring traceability, compliance, and safety at every step.
The platform’s subsurface focus is a central pillar of the initial rollout. The first operational version will feature five AI agents dedicated to subsurface operations, reflecting ADNOC’s emphasis on improving subsurface analytics, reservoir management, and related exploration and development activities. As the deployment progresses, the system will be tested across multiple upstream assets and eventually scaled to cover more than 28 producing fields, including some of the petroleum industry’s largest and lowest-carbon oilfields. This phased approach allows for iterative learning, risk management, and continuous optimization as the platform’s capabilities mature.
From an engineering and data science perspective, ENERGYai is designed to support iterative model development and deployment. Engineers can leverage the platform to run simulations, validate model outputs, and refine decision-making processes based on real-world feedback. The integration with ADNOC’s internal data sources ensures that models stay aligned with evolving operational realities, enabling more accurate forecasting and better resource allocation. The platform’s architecture also prioritizes data governance, provenance, and compliance, ensuring that all AI-driven actions are auditable and defensible.
Implementation Pathway, Timeline, and Rollout
The three-year contract provides a structured pathway for implementing ENERGYai across ADNOC’s upstream operations. The initial PoC phase established the feasibility of integrating ENERGYai with ADNOC’s proprietary data and demonstrated the platform’s potential to deliver measurable improvements in efficiency and decision quality. With the PoC completed, the project enters a multi-phase rollout designed to scale the solution across ADNOC’s upstream portfolio, starting with five subsurface-focused agents and expanding to a broader constellation of capabilities over time.
Mid-2025 marks a key milestone with the expected availability of the first operational version. This version will introduce five AI agents focused on subsurface operations, providing a concrete demonstration of ENERGYai’s ability to automate, optimize, and augment critical subsurface workflows. Subsequent deployment will occur across several ADNOC upstream assets in a staged manner, allowing teams to gain hands-on experience, validate performance, and address any integration challenges in a controlled environment.
As the rollout progresses, ENERGYai will be expanded to cover more than 28 producing fields. This expansion includes some of the world’s largest oilfields and those recognized for their relatively low-carbon footprints. The scale of deployment underscores ADNOC’s commitment to leveraging AI to improve efficiency, safety, and environmental performance across its upstream network. The phased approach also enables continuous improvement, as insights gained from early deployments inform subsequent iterations and feature enhancements.
The rollout plan emphasizes collaboration with ADNOC experts and ongoing input from partner organizations. The project’s success hinges on aligning AI capabilities with real-world operational requirements, safety standards, and regulatory considerations. Regular reviews, performance assessments, and governance checks will be integral to ensuring that ENERGYai delivers sustained value while maintaining the highest levels of safety and compliance.
The implementation path also contemplates potential extensions beyond the initial 28 fields, enabling ADNOC to adapt ENERGYai to evolving asset configurations, new exploration opportunities, and future technology advancements. The flexibility of the platform will be tested through ongoing experimentation, domain-specific customizations, and the addition of new AI agents that address emerging use cases across the upstream value chain. This approach reinforces ADNOC’s strategy of remaining at the forefront of digital innovation while ensuring robust risk management and operational reliability.
Strategic Implications for ADNOC and the Energy Sector
The ADNOC-ENERGYai deployment represents a strategic milestone with broad implications for ADNOC’s operations and the wider energy landscape. By embedding advanced AI capabilities into upstream processes, ADNOC aims to achieve higher operational efficiency, faster decision cycles, and improved resilience across its portfolio. The combination of LLMs and agentic AI enables more sophisticated data interpretation, scenario planning, and automated task execution, all of which contribute to reduced cycle times and more precise optimization of resources.
From a strategic perspective, the initiative aligns with ADNOC’s ambition to become the world’s most AI-enabled energy firm. By scaling ENERGYai across its upstream operations, ADNOC seeks to consolidate its position as a reliable energy provider to global markets while simultaneously advancing its sustainability and digital transformation objectives. The integration of AI-driven automation supports safer operations, better asset integrity management, and more consistent performance across a diverse range of assets and geographies.
For the energy sector at large, the deal reflects a growing trend toward deploying agentic AI and advanced analytics to manage complex, data-rich environments. The ability to automate routine tasks, extract insights from large seismic and production datasets, and monitor processes in real time has the potential to reshape how upstream activities are planned, executed, and optimized. As energy companies seek to balance increasing demand with sustainability goals, AI-powered platforms like ENERGYai offer a path toward more efficient resource use, lower emissions, and improved governance.
The partnership also signals the importance of ecosystem collaboration in enterprise AI deployments. Cooperation with G42 and Microsoft, two influential players in AI and cloud services, demonstrates how technology providers, operators, and domain experts can co-create solutions that are tailored to the energy sector’s unique needs. The use of Azure cloud infrastructure, the OSDU framework, and OpenAI models illustrates a concerted effort to combine best-in-class technology with industry-specific data standards, thereby accelerating adoption while maintaining strong governance and security standards.
Leadership perspectives emphasize a shared vision for transforming energy operations through applied intelligence. By viewing agentic AI as a practical, scalable approach to AI development, ADNOC, AIQ, Presight, and their partners aim to deliver tangible outcomes that extend beyond mere technological novelty. The focus remains on delivering real-world benefits—improved efficiency, reduced costs, enhanced safety, and strengthened environmental performance—while ensuring that AI systems operate within robust governance frameworks and ethical considerations.
Ecosystem, Partnerships, and Technology Stack
A crucial element of ENERGYai’s strategy is the collaboration with key ecosystem partners. G42 and Microsoft have played important roles in shaping the platform’s technical foundation, with the Azure cloud providing scalable compute resources and enterprise-grade security. The Open Subsurface Data Universe framework provides the data standardization and interoperability needed to integrate diverse subsurface datasets, enabling consistent and reliable AI-driven analyses. OpenAI models contribute advanced language and reasoning capabilities that empower engineers to interact with complex datasets and extract actionable insights.
This ecosystem-oriented approach emphasizes interoperability, security, and governance. The energy sector’s data ecosystems are often fragmented, with data residing in multiple silos and subject to stringent regulatory requirements. By aligning with established data standards like OSDU and leveraging robust cloud infrastructure, ENERGYai aims to deliver a cohesive, auditable, and scalable solution that can be deployed across multiple assets and geographies. Such an approach reduces data friction, enhances collaboration among teams, and supports consistent decision-making across the enterprise.
The technology stack underpinning ENERGYai is designed to balance cutting-edge AI capabilities with the practical realities of upstream operations. LLMs provide natural language understanding, enabling intuitive interaction with complex datasets. Agentic AI components automate tasks, coordinate workflows, and carry out actions under governance controls. Azure’s cloud services deliver secure, scalable infrastructure, including data storage, compute, and network resources. The OSDU framework ensures standardized data models and interoperable interfaces, facilitating seamless data exchange across subsurface disciplines.
From a business perspective, the collaboration with strategic partners supports a faster, safer, and more reliable journey toward AI-enabled energy operations. The combination of domain expertise, cloud infrastructure, and advanced AI models aims to deliver a robust, enterprise-grade solution that can withstand the rigors of upstream oil and gas environments. This collaborative model reflects a broader industry trend toward multi-party partnerships that blend technology, data science, and domain knowledge to achieve meaningful outcomes.
Leadership Statements and Strategic Vision
Statements from ADNOC leadership emphasize the strategic alignment between ENERGYai and ADNOC’s broader AI ambitions. The Upstream CEO highlighted the initiative as a defining move toward scaling ENERGYai across the upstream business, reinforcing ADNOC’s commitment to being a responsible and reliable energy supplier on the global stage. The emphasis on scale, reliability, and sustainability signals how the company intends to leverage AI to drive transformational outcomes while maintaining the highest standards of corporate responsibility.
AIQ’s leadership has framed the contract as a milestone that demonstrates the potential of agentic AI to transform the energy industry. The acting managing director described the project as a world-first solution that is scalable along the energy value chain, with the potential to unlock efficiencies that were previously unattainable. The leadership narrative centers on delivering measurable impact—improved efficiency, cost reductions, and enhanced sustainability performance—through a scalable, industry-specific AI platform. This perspective aligns with Presight’s broader vision of applying intelligence to unlock value across complex, data-rich sectors.
The Presight group CEO highlighted the project’s significance for advancing AI integration. He underscored agentic AI’s role as a forward-looking direction for AI development and framed the collaboration as a catalyst for shaping the future of energy through applied intelligence. This leadership stance reinforces the belief that practical, deployable AI solutions can drive meaningful improvements in operational performance, safety, and environmental stewardship.
Operational, Safety, and Sustainability Implications
Energy companies are increasingly seeking AI-driven gains that translate into tangible operational benefits. ENERGYai is designed to accelerate processes, improve decision accuracy, and reduce the time required to translate data into action. By automating repetitive or data-intensive tasks, engineers and operators can devote more time to analysis, risk assessment, and strategic planning. The platform’s real-time monitoring and automated decision-support capabilities contribute to proactive maintenance, faster anomaly detection, and more consistent production optimization.
From a safety and governance standpoint, ENERGYai includes safeguards to ensure that AI-driven actions adhere to established safety protocols and regulatory requirements. The architecture emphasizes auditability and traceability, enabling operators to review decisions and actions taken by the AI system. This is particularly important in upstream environments, where decisions can have significant safety and environmental implications. The governance framework is designed to balance automation with human oversight, ensuring that critical decisions remain under human review when necessary.
Sustainability is a central pillar of ADNOC’s strategy, and AI-enabled optimization is positioned as a way to reduce emissions and improve energy efficiency. By streamlining operations, reducing downtime, and optimizing resource use, energy intensity can be lowered, contributing to broader decarbonization goals. The deployment across large, potentially low-carbon oilfields underscores ADNOC’s commitment to leveraging AI to enhance environmental performance while maintaining energy security and reliability for global markets.
The collaboration’s emphasis on sustainability also extends to technology choices and practices. The use of cloud-based infrastructure, standardized data frameworks, and open AI models is paired with rigorous data governance, security, and ethical considerations. This combination aims to deliver responsible AI that respects data privacy, intellectual property, and safety requirements while enabling practical improvements in operations and environmental performance.
Risks, Governance, and Compliance Considerations
As with any large-scale AI deployment in an industrial setting, several risk domains require careful management. Data governance, security, and regulatory compliance are central considerations, given the sensitivity and volume of upstream data. Ensuring proper access controls, data lineage, and model governance will be essential to maintaining trust in ENERGYai’s outputs and actions. The platform’s auditable decision-making processes are critical to ensuring accountability and enabling post-event analysis if needed.
Operational risk is another important area. While agentic AI can automate many tasks, there is a need to monitor for failures, biases, or incorrect actions and to have robust fallback mechanisms. Real-time monitoring, safety overlays, and human-in-the-loop governance help mitigate these risks, ensuring that automated actions can be reviewed and overridden if necessary. The deployment across high-stakes environments requires rigorous validation, continuous testing, and staged rollouts to minimize disruption and ensure reliability.
Cybersecurity risk is also a priority, given the potential exposure of critical assets to digital threats. The integration with cloud platforms and external partners increases the attack surface, so comprehensive security measures, threat detection, incident response plans, and regular security audits are indispensable. Data protection strategies must be aligned with ADNOC’s compliance requirements and applicable international standards to safeguard sensitive information and maintain operational integrity.
Operational governance and policy management will be required to ensure that AI-driven actions respect safety protocols, environmental standards, and regulatory constraints. Establishing clear accountability, documentation, and change-management processes helps ensure that AI-enabled workflows remain under human oversight where appropriate and that any decisions or actions can be traced and explained.
Market Context and Global Trends in Agentic AI for Upstream Energy
The ADNOC-ENERGYai deployment takes place within a broader global movement toward adopting agentic AI and AI-driven digital transformations in the energy sector. Operators across major oil and gas regions are exploring how AI can optimize exploration, drilling, reservoir management, and production optimization. The emphasis on agentic AI—where AI agents autonomously execute tasks under appropriate governance—reflects a maturity phase in industrial AI adoption, moving beyond merely analyzing data to actively shaping operational outcomes.
The collaboration with technology partners such as G42 and Microsoft aligns with a growing trend of alliances between energy operators and technology firms to co-create scalable, secure, and compliant AI solutions. The combination of cloud infrastructure, standardized data frameworks, and advanced AI models provides a recipe for scaling AI across complex, data-intensive environments. This approach also demonstrates the importance of integrating domain expertise with technology platforms to ensure that AI capabilities are effectively tailored to industry-specific challenges.
The project’s emphasis on subsurface operations initially reflects ADNOC’s strategic priorities. Subsurface analytics represent a high-value domain where AI can significantly impact imaging interpretation, reservoir characterization, and predictive maintenance. By starting with five subsurface agents and expanding to a broader field network, ADNOC aims to build confidence, demonstrate value, and iteratively refine the platform to address a wide array of assets and workflows. The trajectory toward covering more than 28 producing fields signals a scalable blueprint that other operators may look to as a model for AI-enabled upstream digital transformation.
In the broader market, the energy sector’s adoption of AI is driven by needs to improve efficiency, reduce costs, enhance safety, and meet sustainability commitments. The performance gains from AI-enabled automation can translate into lower cycle times, better asset integrity management, and more accurate forecasting, all of which contribute to more resilient operations in the face of market volatility and regulatory pressures. The ADNOC deal exemplifies how large, strategic AI deployments can help national oil companies and major operators maintain competitive advantage while advancing environmental and safety goals.
Leadership Vision, Future Outlook, and Closing Thoughts
As this collaboration progresses, the leadership teams envision a future where agentic AI becomes an integral, trusted component of energy operations. The expectation is that ENERGYai will continue to evolve beyond its initial subsurface focus, expanding to additional domains across the upstream value chain and potentially integrating with downstream and midstream processes where appropriate. The long-term outlook emphasizes ongoing improvements in efficiency, safety, and sustainability, driven by continuous learning, model refinement, and expanded asset deployment.
The strategic partnership’s impact on ADNOC’s AI roadmap is expected to be transformative. By embedding ENERGYai into its core operations, ADNOC anticipates accelerating digital maturity, elevating decision quality, and strengthening its ability to respond proactively to operational challenges. The collaboration with AIQ and Presight positions ADNOC at the forefront of AI-enabled energy innovation, reinforcing its commitment to responsible leadership in technology-driven energy production.
For AIQ, the contract represents a validation of the company’s approach to agentic AI and its applicability to complex energy environments. The milestone underscores AIQ’s capacity to deliver scalable, industry-specific AI solutions that can be deployed across a broad asset portfolio. The partnership also reinforces Presight’s strategic emphasis on applied intelligence—turning advanced AI research into practical tools that deliver measurable value in real-world settings.
The first operational version’s anticipated mid-2025 launch, with five subsurface agents, marks a critical inflection point. As the deployment expands to a larger set of fields, the project will serve as a proving ground for the efficiency gains, cost reductions, and environmental benefits that agentic AI can deliver in upstream oil and gas. This evolution will likely inform best practices, governance standards, and deployment methodologies not only for ADNOC but for other operators seeking to adopt similar AI-enabled strategies.
Conclusion
The $340 million, three-year ADNOC-ENERGYai contract represents a watershed moment in the integration of agentic AI into upstream energy operations. By combining ENERGYai’s LLM-enabled interfaces with intelligent agents, ADNOC aims to streamline workflows, accelerate decision-making, and reduce operating costs across its upstream portfolio. The collaboration with G42 and Microsoft, built on Azure, OSDU, and OpenAI models, reflects a forward-looking strategy to leverage cloud infrastructure and standardized data frameworks for scalable, secure AI deployments. The phased rollout, beginning with five subsurface agents and expanding to more than 28 producing fields, demonstrates a thoughtful approach to risk management, governance, and continuous improvement.
Leadership voices from ADNOC, AIQ, and Presight emphasize a shared commitment to applying intelligence to energy challenges, advancing digital transformation, and achieving sustainability goals. The initiative embodies a broader market trend toward agentic AI in energy, signaling a shift toward more automated, data-driven, and resilient operations. As ENERGYai evolves, it is poised to set new benchmarks for efficiency, safety, and environmental stewardship across the upstream landscape, while delivering tangible value to global energy markets.