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.

翻译：随着企业日益依赖自动化来简化运营，机器人流程自动化（RPA）的局限性日益凸显，尤其是其依赖专家知识且无法处理复杂决策任务。人工智能（AI）的最新进展，特别是生成式人工智能（GenAI）和大型语言模型（LLMs），为智能自动化（IA）铺平了道路，后者通过整合认知能力来克服RPA的不足。本文介绍了Text2Workflow，一种从自然语言用户请求自动生成工作流的新方法。与传统自动化方法不同，Text2Workflow为自动化任何业务流程提供了一种通用解决方案，将用户输入转化为一系列以JavaScript对象表示法（JSON）格式表示的可执行步骤。该方法利用LLMs的决策和指令遵循能力，提供了一个可扩展、适应性强的框架，使用户能够以最少的人工干预可视化和执行工作流。本研究概述了Text2Workflow方法及其对自动化复杂业务流程的更广泛意义。