AI Driven Operational Dashboards for Realtime Monitoringand Crisis Decision Support in IT Systems
DOI:
https://doi.org/10.14419/qnk7fn90Published
19-05-2026Keywords:
AI Driven Dashboards; Real Time Monitoring; Predictive Analytics; Machine Learning; AIOpsAbstract
In today’s digital first landscape, enterprise IT systems form the backbone of mission critical operations, demanding resilience, reliability, and rapid crisis response. The complexity of distributed, cloudnative, and IoTdriven environments has rendered traditional monitoring tools inadequate for ensuring uninterrupted services. AI driven operational dashboards represent a paradigm shift by combining realtime data visualization with advanced machine learning, natural language processing, and predictive analytics. Unlike conventional dashboards that passively display metrics, these intelligent platforms actively detect anomalies, forecast failures, and recommend or even automate remedial actions. This reduces mean time to detect (MTTD) and mean time to resolve (MTTR), providing enterprises with faster, more informed decisionmaking during crises such as outages, cyberattacks, or traffic surges.Case studies of prominent industries show quantifiable enhancements in anomaly detection accuracy, alert noise reduction, and operational efficiency. There are obstacles, however, including data complexity of integration, explanation holes, model shift, and organizational resistance. The review integrates existing technological bricks, experimentation findings, and industrial practices alongside identifying research gaps and limitations. In delineating a theoretical framework and future research directions, it substantiates the imperative of AI driven dashboards as adaptive, reliable, and scalable solutions for enterprise IT resilience in a more dynamic world of operation.
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