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From Words to Workflows: Automating Business Processes

arXiv 2412.03446 ↗First posted 4 Dec 2024cs.AIcited by 0

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Abstract (arXiv)

As businesses increasingly rely on automation to streamline operations, the limitations of Robotic Process Automation (RPA) have become apparent, particularly its dependence on expert knowledge and inability to handle complex decision-making tasks. Recent advancements in Artificial Intelligence (AI), particularly Generative AI (GenAI) and Large Language Models (LLMs), have paved the way for Intelligent Automation (IA), which integrates cognitive capabilities to overcome the shortcomings of RPA. This paper introduces Text2Workflow, a novel method that automatically generates workflows from natural language user requests. Unlike traditional automation approaches, Text2Workflow offers a generalized solution for automating any business process, translating user inputs into a sequence of executable steps represented in JavaScript Object Notation (JSON) format. Leveraging the decision-making and instruction-following capabilities of LLMs, this method provides a scalable, adaptable framework that enables users to visualize and execute workflows with minimal manual intervention. This research outlines the Text2Workflow methodology and its broader implications for automating complex business processes.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 45 of its 96 references that are in the map. First posted on arXiv 4 Dec 2024.

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45 of 96 references.

References · 45

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