Commercial and AI Visual Presentation AI02: Transforming remote monitoring and diagnostics dashboards with agentic AI: from data display to intelligent insight

Commercial and AI Visual Presentation AI02 The Oil and Gas industry faces increasingly stringent requirements related to process reliability, operational efficiency, and maintenance cost reduction. To respond effectively, companies are adopting advanced technologies capable of continuously monitoring critical equipment and supporting fast, well-informed decision-making. Agentic AI systems offer a powerful solution by embedding autonomous, goal-oriented intelligence into digital service platforms. These systems interpret and synthesise data from multiple sources, delivering contextualised insights and proactive recommendations through natural language interaction. By accelerating diagnostics, reducing manual data analysis, and minimising unplanned downtime, agentic AI acts as a key enabler of operational excellence and cost-effective maintenance. Conventional Remote Monitoring and Diagnostics solutions for high-performance turbomachinery rely on customer dashboards that provide extensive data visibility. While comprehensive, these interfaces often demand significant effort to translate information into actionable insights. This work proposes the integration of a single-agentic AI system that aggregates, correlates, and summarises heterogeneous data sources within a unified conversational interface. The result is improved usability, more intuitive interaction, and faster decision-making in critical turbomachinery operations. The proposed architecture addresses security and intellectual property concerns by fully decoupling authorisation logic from AI components through deterministic orchestration layers. This approach demonstrates that advanced AI can be safely deployed in sensitive industrial environments, transforming operator interaction from static data consultation to dynamic dialogue while enabling secure AI adoption in regulated monitoring contexts. Permission has not yet been granted to publish this presentation. The full content will be published as soon as possible.