Digital Transformation Chatbot (DTchatbot): Integrating Large Language Model-based Chatbot in Acquiring Digital Transformation Needs
Jiawei Zheng, Gokcen Yilmaz, Ji Han, Saeema Ahmed-Kristensen
TL;DR
The paper addresses the challenge of efficiently eliciting digital transformation needs from organisations amid time and resource constraints. It introduces DTchatbot, an LLM-powered chatbot with a workflow-driven interview process, multilingual/multimodal interfaces, and a modular question framework using $D3A$ questions. A preliminary study with two SMEs and two experts shows the system can conduct interviews and generate summaries, while identifying areas for improvement. The work demonstrates the potential of LLM-powered chatbots to streamline consulting interviews and scale information gathering, with considerations around data privacy and integration needs.
Abstract
Many organisations pursue digital transformation to enhance operational efficiency, reduce manual efforts, and optimise processes by automation and digital tools. To achieve this, a comprehensive understanding of their unique needs is required. However, traditional methods, such as expert interviews, while effective, face several challenges, including scheduling conflicts, resource constraints, inconsistency, etc. To tackle these issues, we investigate the use of a Large Language Model (LLM)-powered chatbot to acquire organisations' digital transformation needs. Specifically, the chatbot integrates workflow-based instruction with LLM's planning and reasoning capabilities, enabling it to function as a virtual expert and conduct interviews. We detail the chatbot's features and its implementation. Our preliminary evaluation indicates that the chatbot performs as designed, effectively following predefined workflows and supporting user interactions with areas for improvement. We conclude by discussing the implications of employing chatbots to elicit user information, emphasizing their potential and limitations.