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The numbers back this claim. According to a recent Hexagon survey, nearly half( 47 percent) of oil and gas executives plan to add AI capabilities to their digital twins, the highest figure of any industry polled. This is a clear signal that leaders are no longer asking if AI should be part of their operations, but where it can move the needle faster.
From overload to insight
Oil and gas operators are not short on data, but gathering and analyzing it, quickly and accurately, remains one of the industry’ s toughest challenges.
From legacy documents to engineering drawings, datasheets, and 3D scans, the volume and complexity of information generated by industrial facilities often creates more friction than insight. According to IDC, 65 percent of refinery executives rank data cleansing and standardization among their top priorities over the next two years. This is a clear sign that foundational data problems still demand attention.
This is where AI is quietly becoming indispensable. New AI-powered services can now ingest and contextualize vast stores of unstructured legacy data, extracting tags from piping and instrumentation diagrams( P & IDs), technical drawings, and datasheets. At the same time, advances in AI are accelerating the integration of 3D laser scans, creating richly detailed spatial visualizations that would have been impractical to build- let alone maintain- just a few years ago.
The result? Digital twins with a new level of depth, interconnectedness, and operational utility.
One of Europe’ s largest independent oil and gas producers recently put this into practice. In just three months, the company digitized and classified more than five million control documents, enriched with metadata aligned to its asset taxonomy. At the same time, it integrated over 20,000 laser scan points into a unified
visualization environment, surfacing critical information that had long been buried and putting it to work to drive compliance and optimize performance.
AI-driven digital twins deliver real impact
The role of AI in digital twins is evolving fast, from passive data recall to active decision-making that improves performance and safety. One offshore operator offers a compelling example: managing a deepwater platform with a complex legacy, including multiple ownership changes and fragmented safety systems.
By embedding AI across maintenance, optimization, monitoring, and compliance, the operator unlocked new levels of operational control.
For maintenance, machine learning tracks vibration and temperature patterns in critical equipment such as pumps and compressors.
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