ESB Networks launches 5-year AI-powered digital inspection program for 10,000 grid structures

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ESB Networks launches 5-year AI-powered digital inspection program for 10,000 grid structures

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ESB Networks — Ireland's distribution operator serving approximately 2.4 million customers — has launched a five-year program to digitally inspect up to 10,000 grid structures using AI-enabled visual inspection. Near-term outcomes: compressed inspection-to-report timelines, fewer truck rolls and helicopter hours, and a lower carbon footprint. In a direct quote, Oisín Armstrong of ESB Networks' engineering & major projects team said the virtual inspection approach is allowing ESB to "gain efficiency through an end-to-end inspection program, saving time, reducing costs and our carbon footprint, and supporting our mission to achieve zero carbon emissions by 2040." Donald McPhail, VP of market development at eSmart Systems, contributed the case study. ESB Networks is a subsidiary of ESB Group, publicly committed to a net-zero electricity system by 2040. Three pressures shape operations: growing variable renewable generation, electrification of heat and transport, and an aging distribution network where corrosion is a visible condition challenge. Conventional helicopter, foot, and climbing-crew patrols worked when data stayed local; they struggle when condition data must drive capital prioritization or demonstrate decarbonization progress. ESB Networks partnered with eSmart Systems to deploy Grid Vision, an AI-enabled grid inspection platform. The phased five-year program is scoped at network scale. Methodology was formalized into standardized UAV capture protocols, AI defect detection models, and human-in-the-loop validation — every flagged finding is reviewed before action. On the technical architecture: the pipeline runs UAV imagery under standardized protocols → computer-vision models identifying specific asset-condition categories → analyst review of every flagged finding → structured output written to a per-structure asset record with linked image, location, condition assessment, and recommended action. Each cycle grows the longitudinal dataset. Automated workflows have compressed inspection-to-report timelines, reducing truck rolls and helicopter hours. The carbon footprint of the inspection program is dropping as a direct contribution to ESB Group's 2040 commitment. Asset condition data is now captured in a consistent, network-wide format supporting proactive decisions — the precondition for risk-based replacement prioritization and predictive maintenance. The intent is to build inspection into a continuous intelligence layer rather than a discrete periodic activity, integrating findings with broader asset and environmental datasets. The deployment is positioned as replicable for distribution operators across Europe and North America. Key ingredients: phased program-level scope, structured capture protocol prioritizing data consistency, and treating inspection findings as inputs to asset management.