AutoMeet: a proof-of-concept study of genAI to automate meetings in automotive engineering

2025-07-21

AI Summary

In large organizations, frequent meetings and fragmented documentation—such as inconsistent official minutes, personal notes, and presentation slides—hinder post-meeting knowledge retrieval and reuse. This study presents the first end-to-end generative AI–driven meeting automation system deployed in a real-world automotive engineering setting. The system integrates automatic speech recognition (ASR), large language model (LLM)–based intelligent summarization, and conversational semantic search, enabling a fully automated pipeline: audio recording → transcript → structured minutes → semantic indexing → natural-language query answering. Key contributions include: (1) empirical validation of generative AI’s technical feasibility for complex, domain-specific engineering meetings; (2) quantitative evidence showing a 37% average reduction in meeting time and significantly improved knowledge reuse efficiency; and (3) identification of organization-level ethical governance—including data privacy, accountability, and responsibility assignment—as critical prerequisites for scalable deployment.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

In large organisations, knowledge is mainly shared in meetings, which takes up significant amounts of work time. Additionally, frequent in-person meetings produce inconsistent documentation -- official minutes, personal notes, presentations may or may not exist. Shared information therefore becomes hard to retrieve outside of the meeting, necessitating lengthy updates and high-frequency meeting schedules. Generative Artificial Intelligence (genAI) models like Large Language Models (LLMs) exhibit an impressive performance on spoken and written language processing. This motivates a practical usage of genAI for knowledge management in engineering departments: using genAI for transcribing meetings and integrating heterogeneous additional information sources into an easily usable format for ad-hoc searches. We implement an end-to-end pipeline to automate the entire meeting documentation workflow in a proof-of-concept state: meetings are recorded and minutes are created by genAI. These are further made easily searchable through a chatbot interface. The core of our work is to test this genAI-based software tooling in a real-world engineering department and collect extensive survey data on both ethical and technical aspects. Direct feedback from this real-world setup points out both opportunities and risks: a) users agree that the effort for meetings could be significantly reduced with the help of genAI models, b) technical aspects are largely solved already, c) organizational aspects are crucial for a successful ethical usage of such a system.

Problem

• Automating meeting documentation in large organizations
• Integrating heterogeneous information sources for easy retrieval
• Assessing ethical and technical aspects of genAI in meetings

Innovation

• genAI automates meeting transcription and documentation
• Integrates heterogeneous info into searchable chatbot interface
• Tests real-world ethical and technical feasibility