This study presents an objective, evidence-based examination of AI-enabled workflow automation and predictive analytics for enterprise operations management, with rigorous analysis and formal structure. Intelligent systems, capable of automating decisions and actions in enterprise processes and workflows across business functions, have often been seen as a futuristic promise, yet they are now within reach. It is now feasible to develop, test and deploy systems capable of automating large swathes of decision-and-data-driven processes, or supporting individual operators and managers with predictions and decision support.
Central to workflow automation and predictive analytics are data and intelligent models trained on historical data. A comprehensive data strategy for operations data should include data quality, lineage and stewardship, a data platform to support sourcing and loading, and, where needed, sufficient storage and compute capacity to support machine learning model development, training and validation. Enterprise operations leaders should assess their readiness for AI-based automation, and identify deployment patterns and best-known practices for the operations functions
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