AI-Driven Operations and Automated Workflows on Azure
Keywords:
Predictive intelligence, cloud ecosystem, AIOps, AIOps maturity, AIOps value, Azure workflow automation.Abstract
Cloud environments are becoming increasingly enterprise-wide, with advanced features and capabilities being added to the three service models commonly found in these platforms (IaaS, PaaS, and SaaS). These services are often integrated into organizations' environments to enable predictive (anomaly detection, predictive analytics) and automated intelligence (automated decision-making and actions). The new areas of AIOps and workflow automation are gaining popularity due to their promise of improved business resilience and lower total cost of ownership, but interest remains low. A predictive intelligence framework for AIOps and workflow automation in Azure is presented, providing research support for its adoption.
Although relations between derivates of cloud technology and AIOps or workflow automation are apparent, few studies propose a mapping of Azure services to AIOps capability areas. Also lacking is a cloud-native view of AIOps scalability and performance challenges or a forward-looking perspective on emergent trends. Addressing these gaps aids understanding of how cloud environments empower predictive and automated intelligence. Proposed direction reinforces the notion that the Azure platform is not solely for resource hosting but comprises a continuously evolving toolbox for business management.
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