ADNOC and Gecko Robotics expand AI and robotics across operations with new UAE talent training
ADNOC signed three agreements with Gecko Robotics at the ENACT Majlis in Abu Dhabi to scale AI and robotics across its energy assets and to launch advanced skills training for UAE Nationals. The package includes a multi-year technology rollout for ADNOC Gas, broader deployment of robotics and AI-based analytics across onshore and offshore sites, and a new training track through the ADNOC Technical Academy.
What's in the multi-year deployment
AIQ-ADNOC's joint venture with Presight-will introduce Gecko's Cantilever operating system across ADNOC Gas assets. The platform combines robotic inspection with AI to capture high-fidelity physical data from critical infrastructure, analyze degradation patterns, and feed maintenance decisions faster.
For operations teams, this means earlier fault detection, fewer blind spots in inspection routes, and tighter control over maintenance windows. Expect gains in reliability, safety, and throughput as asset condition data becomes continuous rather than periodic.
Scaling robotics and AI across assets
The second agreement explores wider use of robotics and AI-based analytics across ADNOC's facilities-onshore and offshore. The collaboration also assesses local manufacturing of robotic systems in the UAE and the development of new AI applications aligned to site-level needs.
Practically, this points to standardized inspection methods, consistent data models across plants, and faster feedback loops from field to control room. It also improves spares planning and risk-based inspection by making condition data usable across multiple systems.
Training Emirati talent for new operations roles
The third agreement links Gecko Robotics with the ADNOC Technical Academy to deliver programs for UAE Nationals in robotics operations, AI, and digital maintenance. The goal: build a workforce that can run robotic inspections, interpret condition data, and close the loop into work management systems.
This helps staff shift from manual rounds and reactive fixes to data-driven maintenance. It also creates clear pathways for technicians and engineers moving into reliability, inspection planning, and analytics roles.
Why this matters for operations
- Less unplanned downtime: earlier anomaly detection and prioritized repairs.
- Shorter turnarounds: precise scope definition from quantified damage maps.
- Better asset integrity: consistent, high-resolution inspection across hard-to-reach areas.
- Stronger KPIs: clearer impact on MTBF, MTTR, and OEE through traceable asset data.
- Standardized data: common formats that integrate with CMMS/EAM and historians.
Execution focus areas
- Data quality: define inspection frequencies, coverage, and acceptance criteria before rollout.
- System integration: wire outputs into your CMMS/EAM (work orders, job plans) and analytics stack.
- Asset hierarchy: align tags, P&IDs, and locations to ensure clean aggregation and benchmarking.
- Safety and compliance: confirm procedures for confined spaces, live assets, and cyber controls for connected devices.
- Change management: train technicians, inspectors, and planners on new workflows and decision rights.
Action checklist for ops leaders
- Prioritize high-impact systems: gas compressors, pressure vessels, pipelines, and critical rotating equipment.
- Set measurable targets: unplanned downtime, inspection hours, backlog age, and first-time fix rate.
- Define the workflow: from robotic scan to defect ranking to work order creation and closeout.
- Stand up a data pipeline: secure storage, model governance, and audit trails for regulatory reporting.
- Run a pilot, then scale: start with one asset class, prove value, standardize, and expand.
ADNOC states this move advances its plan to become the most AI-enabled energy company, with Gecko Robotics providing the inspection and analytics stack to make maintenance more predictive and less disruptive. For context on the companies, see ADNOC and Gecko Robotics.
Upskilling your team
If you're building similar capabilities in your own operations function, structured learning helps speed up adoption. Explore role-based AI training options here: Courses by Job and practical certifications here: AI Automation Certification.
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