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Abstract This paper introduces a GenAI-powered digital platform designed to support supervisors across the carbon Capture, Transport, Utilization and Storage (CCUS) value chain. The objective is to enhance operational visibility, accelerate decision-making, and enable predictive maintenance through real-time monitoring, intelligent alerting, and conversational AI interfaces. The solution empowers field and control room personnel with actionable insights and proactive recommendations to ensure safe, efficient, and sustainable CCUS operations. The platform integrates real-time data from key CCUS assets like capture plants, compressors, pipelines, wells, and storage sites, tracking parameters such as pressure, temperature, flow rate, and volume. When anomalies are detected, alerts are automatically triggered and correlated across interconnected systems to identify root causes. Supervisors interact with the system via GenAI-powered chat and voice bots, which provide contextual insights, troubleshooting guidance, and predictive maintenance suggestions. These bots leverage large language models fine-tuned on CCUS operational data and maintenance logs, enabling natural, role-specific interactions that streamline issue resolution and enhance cross-functional collaboration. The deployment of this GenAI-enhanced platform has significantly improved operational efficiency and responsiveness across the CCUS value chain. Supervisors now receive real-time alerts enriched with cross-asset correlations, enabling faster root-cause identification and resolution. For instance, a pressure anomaly in a storage well is automatically linked to upstream pipeline fluctuations, reducing diagnostic time and preventing unnecessary interventions. The GenAI bots have proven especially valuable in field operations, allowing supervisors to query system status, receive step-by-step troubleshooting instructions, and access historical trends all through natural language. Predictive maintenance capabilities, powered by machine learning models trained on historical failure patterns, have enabled early detection of potential equipment issues, reducing unplanned downtime by up to 35%. The system also facilitates seamless escalation to relevant teams, ensuring timely and coordinated responses. Overall, the platform enhances situational awareness, reduces cognitive load, and supports data-driven decision-making, contributing to safer, more reliable, and more sustainable CCUS operations. This solution uniquely integrates GenAI-powered conversational interfaces with real-time monitoring and predictive analytics tailored for CCUS operations. By combining natural language interaction with intelligent alert correlation and maintenance forecasting, it transforms supervisory workflows, bridging the gap between data, decision-making, and action in a highly complex, distributed environment