In brief
At ADIPEC 2026 in Abu Dhabi, energy and technology leaders debated the pathway to industrial‑scale AI deployment, underscoring the need for robust data pipelines, digital infrastructure, and governance frameworks. Speakers from Siemens, ADNOC Onshore and other firms emphasized AI's potential to boost productivity and operational reliability while warning that investments and sector collaboration are essential to realize its benefits.
Artificial intelligence (AI), defined as the ability of machines to perform tasks that typically require human intelligence, made a decisive shift from laboratory experimentation to industrial deployment on the third day of ADIPEC 2026 in Abu Dhabi. The event brought together a spectrum of energy, technology and investment leaders who explored how large‑scale AI can improve productivity, operational performance and energy security across the oil, gas and downstream value chains. The consensus that emerged was that meaningful ROI from AI hinges on four foundational pillars: data quality and volume, digital infrastructure, governance frameworks, and sustained investment.
During the “Beyond digital: how AI is reshaping industries, economies and society” session, Helmut von Struve, chief executive officer of Siemens UAE & Middle East, highlighted the core value proposition of AI for critical infrastructure. He noted that the greatest benefit comes when AI is harnessed to make essential operations more productive and reliable, thereby achieving greater resource efficiency. his quote underscored the focus on energy‑related applications where AI can optimize maintenance schedules, predictive asset monitoring and energy consumption.
"The greatest value from an AI perspective is where industries, and also the essential services we depend on, can be improved. It is really about how we can make essential operations more productive and reliable, and how we can make them more resource‑efficient."
Von Struve’s remarks were framed by a broader industry narrative that AI’s transformative potential must be matched by robust data pipelines. The talk emphasized that oil & gas operations produce vast quantities of sensor data, but without a streamlined data lake and high‑performance analytics platform, AI models struggle to deliver actionable insights at scale.
In the “Beyond collaboration: redefining energy partnerships for cross‑sector collaboration” session, John Gilley, chief executive officer of Kent, and Dr. Ahmed Mohamed Alebri, chief executive officer of ADNOC Onshore, explored the symbiosis between data openness and energy cooperation. They argued that an AI‑ready industry requires transparency, shared data standards, and legal frameworks that allow partners to exchange and monetize data while protecting competitive advantages.
"We are seeing that it is not just about energy; it is about connecting the world’s appetite for data with the world’s appetite for energy. Each country wants to have control of its own data and energy."
These statements reflect a growing consensus that AI deployment is not a purely technical exercise but also a governance and trust issue. Systems must be built to ensure data security, compliance with local regulations and the ability to audit decision‑making processes.
The “Digital transformation: building an AI‑ready energy system” segment brought together a cluster of industry partners including Rakesh Jaggi, president of Digital at SLB; Som Dutta, group vice president for AI and data science at DP World; and Ashish Koshy, CEO of Inception42. Their discussion focused on concrete architecture patterns that support scalable AI workloads across distributed production environments.
Jaggi outlined the need for edge‑to‑cloud pipelines that can ingest real‑time sensor streams, buffer them for batch analytics, and feed trained models back to field devices. The architecture also demanded that data be partitioned by ownership, geographic location and risk profile to meet regulatory and operational requirements.
Dutta emphasized the importance of model governance and continuous learning loops. He explained that AI systems in the energy sector must accommodate changing process conditions, equipment aging and evolving regulatory constraints, requiring automated retraining and drift detection mechanisms.
Koshy highlighted blockchain‑enabled data marketplaces as a means to monetize data while ensuring provenance. He argued that such marketplaces could provide a sustainable business model for data‑centric AI initiatives, encouraging investment in data acquisition costs that would otherwise be prohibitive.
All speakers converged on the notion that AI’s promise can be fully realized only with significant capital outlays. Funding must cover high‑performance computing resources, cloud storage, specialized hardware such as GPUs and custom ASICs, and the personnel needed to develop, validate and maintain AI models.
Investment sizing was illustrated by the figure of tens of billions of dollars that some national oil companies already earmark for digital transformation programs. While figures varied, the overarching narrative was clear: without a financial commitment, AI projects will remain stalled in the proof‑of‑concept stage.
Cyber‑security emerged as another critical pillar during the conference. A recurrent theme was that AI systems, by virtue of their data dependency and algorithmic opacity, open new attack vectors. The speakers advocated for embedding security controls into the data pipeline, employing encryption, access controls and continuous monitoring of model behavior.
Human–machine augmentation was also discussed as a means to accelerate AI adoption. Participants noted that AI should not replace humans but complement them, by providing decision support, anomaly detection alerts and optimization suggestions. This approach, they argued, reduces skill gaps and accelerates workforce transformation.
As a case study, the conference highlighted a pilot at a mid‑stream pipeline operator that deployed an AI model to predict pressure transients and reduced unplanned shutdowns by 18%. The model leveraged historical log data and real‑time sensor streams to generate real‑time risk scores for anomalies.
The cumulative message from ADIPEC 2026 was that AI’s industrial rollout is a multi‑dimensional endeavor. Achieving it will require data that is high‑quality, well‑labelled and stored in secure, scalable repositories; a digital infrastructure that can ingest, process and serve AI workloads; governance that clarifies ownership, compliance and auditability; and an investment culture that sees AI not as a cost center but as a strategic asset.
By framing AI deployment as a systemic transformation rather than a set of isolated tech pilots, the conference set a roadmap that energy players can use to assess readiness, prioritize projects and secure the necessary resources. The path from ambition to operation is now clearer, and the next steps involve turning the strategic conversations at ADIPEC 2026 into concrete, cross‑industry collaborations that bridge data, technology and capital.
