From Words to Workflows: Automating Business Processes

TL;DR

This paper presents Text2Workflow, a novel AI-driven method that automatically converts natural language requests into executable business workflows, overcoming RPA limitations by leveraging Large Language Models for scalable automation.

Contribution

Introduction of Text2Workflow, a generalized AI-based approach that translates natural language into executable workflows, enabling automation of complex business processes with minimal manual effort.

Findings

Successfully generates workflows from natural language inputs

Uses LLMs to interpret and structure user requests

Provides a scalable and adaptable automation framework

Abstract

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.