Energy producers must balance safety, regulatory compliance and efficiency while operating ageing infrastructure in a rapidly changing market. Traditional inspection methods depend heavily on human judgement, require plant shutdowns and can miss subtle faults, leading to failures and costly downtime. Advances in artificial intelligence (AI) provide a pathway to transform these inspection processes. This work describes an applied AI platform for inspections in the upstream energy sector. The platform combines computer vision to detect anomalies in inspection images, natural language processing to transcribe and analyse voice‑captured field notes, and automated comparison of inspection results over time. Drawing on published case studies and industry surveys, the paper discusses how computer vision pipelines automate detection of cracks and corrosion, how drones and robotics are used to collect inspection data and detect defects in distribution equipment, and how AI‑enabled inspection tools are being integrated with asset management systems to support predictive maintenance and risk‑informed decision making (Dastidar 2024; Optelos 2024; Joshi 2026). A proposed system architecture is presented to illustrate the flow from data capture to decision support, demonstrating how AI can provide early warning of risks, reduce downtime and improve regulatory compliance. The paper concludes with discussion on human oversight, data availability, and lessons learned from early adopters. By grounding the discussion in real‑world examples rather than theory, the extended abstract highlights how AI‑powered inspections can help energy producers operate more safely and efficiently while adapting to evolving regulatory and market requirements.
Naaman Shibi (Wed,) studied this question.
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