From Digital Twins to Decision Twins – Operationalizing Al for Real-Time Energy Workflow
Real-time energy workflows demand more than static models or visual dashboards. A Balakrishnan, CTO of ANB Systems, a DNV company, looks at how digital twins are evolving into decision twins that support assurance, engineering judgement, and operational trust in day-to-day work.
The presentation reframes the digital twin as a lifecycle decision-support system, combining integrated models, data, prediction, traceability, and risk awareness to guide operational action. DNV shows how AI can help move complex process information beyond screen-based constraints and into more collaborative, spatially aware environments where teams can reason around systems together.
Practical examples include smart mooring systems that predict offshore risk, AI-supported pipeline data alignment, NDT digital twins that transform inspection images into trusted integrity evidence, and BPM digital twins that connect workflows with operational intelligence. Across these examples, DNV highlights a consistent principle: AI should surface risks, align evidence, and support recommendations while keeping expert confirmation at the centre of critical decisions.
The session points toward a future where digital twins are no longer passive representations of assets, but active decision environments that strengthen confidence, improve traceability, and help energy teams act faster with greater operational assurance.
