ADNOC revolutionizes energy sector with ENERGYai
ADNOC, in collaboration with Microsoft and other partners, launched ENERGYai, a highly advanced agentic AI solution built for the energy industry. It leverages large language models and agentic AI to improve seismic analysis, increase energy efficiency, and enable real-time process monitoring within ADNOC's operations. Utilizing Microsoft's AI technology, the solution effectively integrates subsurface data frameworks and OpenAI models, facilitating predictive maintenance and stronger alignment with ADNOC's sustainability goals.
- Organization
- ADNOC
- Industry
- Energy & Utilities
- Location
- United Arab Emirates
- Published
- November 2024
Reported outcomes
75%
timeTime & speed
Strategic outcomes
Catalog median for time & speed deployments: +80% across 203 reported metrics. Compare benchmarks →
Primary read
Use case focus
Showing 3 of 3
- 1predictive maintenance
- 2energy efficiency
- 3seismic analysis
- Energy sector operations involve complex, data-heavy processes.
- Need for more efficient geological modeling to reduce planning time.
- Increasing energy efficiency while maintaining sustainability.
- Reducing emissions and environmental impact.
- Launched ENERGYai, a solution leveraging agentic AI tailored to energy industry tasks.
- Integrates Microsoft's AI technologies and OpenAI models.
- Focuses on predictive maintenance, seismic analysis, and real-time process monitoring.
- Utilizes subsurface data frameworks for better alignment with existing ADNOC systems.
- Accelerated geological modeling by 75%, reducing planning time.
- Significant reduction in emissions.
- Improved operational efficiency in critical areas.
Architecture
ENERGYai integrates subsurface data using Microsoft's cloud platforms and AI technologies for predictive analysis.
Implementation partners1
Sources & evidence1
The same organization appears in newer AI deployment evidence.
- Same organization re-documented as recently as 2025.
- Cited source last checked Jun 12, 2026 — ok (0/1 broken).
Measures whether this deployment's public evidence persists — not whether the system is still in production.
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