Enterprise Workflow Intelligence Through Generative AI and Retrieval-Augmented Generation: A Framework for Adaptive Process Automation

Abstract

Enterprise workflows are usually automated as fixed paths. Every case follows the same steps, and problems are noticed only when a deadline is missed or a customer complains. Real cases differ, however. Some are at risk of delay, some need extra checks, and some would benefit from a different next step. Predictive process monitoring can forecast how a running case will unfold, including its outcome, next activity, and remaining time. Generative artificial intelligence (AI) with retrieval-augmented generation (RAG) can turn those forecasts into grounded, explained recommendations for action. This article proposes a framework for workflow intelligence: the ability of an automated workflow to understand each running case, anticipate problems, and adapt its handling. The framework combines event-driven sensing of running cases, predictive models, retrieval of policies and similar past cases, an LLM-based agent that proposes next best actions, guardrails that bound what the agent may do, a configurable workflow engine that executes actions, and scalable services that deliver these capabilities. Using a design-oriented approach grounded in ten studies published between 2017 and 2024, the article maps the evidence base and presents the framework and its case-level intelligence loop. It defines prediction targets, four levels of case autonomy matched to risk, and case-level guardrails. It illustrates the framework with hospital discharge planning. It argues that adaptive automation is safe and effective when predictions are grounded, recommendations are explained, and autonomy is matched to the risk of each action.