TLDR: The research paper introduces DTchatbot, an LLM-powered chatbot designed to help organizations identify their digital transformation needs. It addresses challenges of traditional methods like interviews and questionnaires by offering a structured, interactive, multilingual, and multimodal approach. The chatbot uses a workflow-based interview process, provides real-time clarifications, and generates summary reports. Preliminary studies with experts and SMEs show it effectively reduces effort and raises awareness of transformation gaps, though further enhancements for deeper analysis and system integration are suggested.
Digital transformation is a crucial journey for many organizations aiming to boost efficiency, reduce manual tasks, and optimize processes through automation and digital tools. However, understanding an organization’s unique needs for this transformation has traditionally faced hurdles. Methods like expert interviews, while effective, often come with scheduling conflicts, resource limitations, and inconsistencies. Online questionnaires offer 24/7 access but often suffer from a lack of interactivity, leading to survey fatigue, especially with open-ended questions that require significant effort to answer and analyze.
Addressing these challenges, a new research paper introduces the Digital Transformation Chatbot (DTchatbot), an innovative solution that leverages Large Language Models (LLMs) to acquire an organization’s digital transformation needs. This chatbot acts as a virtual expert, conducting interviews through a workflow-based instruction system combined with the LLM’s planning and reasoning capabilities.
How DTchatbot Works
The DTchatbot is designed as a fully web-based platform, built with open-source components for flexibility and adaptability. It features two main components: an interface and a backend.
The interface is user-friendly and supports both multilingual and multimodal inputs, meaning users can interact using text or speech. It integrates a Speech-to-Text model, like OpenAI’s Whisper, to transcribe audio responses, making interactions natural and intuitive. The interface also records client information such as company name, industry, and job title, allowing for tailored consulting workflows and the ability to resume unfinished conversations.
The backend is structured to guide conversations through a predefined workflow of questions. These questions, based on the D3A model developed by Bogazici University, are designed to assess digital transformation capabilities across five key domains: corporate governance, customer and market management, research and development, supply chain, and production management. The chatbot dynamically presents targeted questions based on the user’s priorities.
A significant feature of the DTchatbot is its ability to provide real-time support and guidance. If a user needs clarification on technical terms, like the differences between cloud models, the chatbot can offer clear explanations. It can also guide users on how to effectively answer interview questions, providing examples or prompts to encourage deeper reflection. Upon completion, the chatbot automatically generates a comprehensive summary report of current practices, challenges, and strategic goals.
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Preliminary Evaluation and Feedback
A preliminary user study involved two digital transformation experts and representatives from two Small and Medium-sized Enterprises (SMEs). The findings were encouraging:
- Expert Opinions: Both experts agreed that DTchatbot effectively reduces effort and supports digital transformation initiatives. They praised its user-friendliness and logical question sequence, seeing it as a good starting point for identifying gaps. Suggestions for improvement included dynamic question sequencing, selection boxes for common responses, visual flowcharts for suggested processes, and integrating analysis of diverse data types for more detailed insights.
- Client Reflections: SME participants found the chatbot beneficial for raising awareness about their digital transformation gaps and guiding decision-making. They particularly appreciated the voice transcription and quick response times. While valuing the chatbot’s ability to maintain meaningful conversations and provide technical clarifications, they suggested features like answer auto-completion and the ability to accept diverse inputs beyond text, such as workflows or structured data entries. A key limitation noted was that while good for initial awareness, the chatbot might not be sufficient for a complete digital transformation process without further system integration.
The DTchatbot represents a promising step forward in making digital transformation needs assessment more accessible, scalable, and interactive. While challenges like data privacy and the occasional inaccuracy of LLMs remain, future work aims to incorporate more data input formats and extend functionality for detailed report generation. You can learn more about this research in the full paper available at arXiv:2511.02842.
