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

TL;DR

This study demonstrates a proof-of-concept for using generative AI to automate meeting documentation in automotive engineering, improving efficiency and searchability, while highlighting organizational and ethical considerations.

Contribution

It presents an end-to-end genAI pipeline for automating meeting transcription and documentation in a real-world engineering setting, with extensive user feedback.

Findings

Users see significant effort reduction in meetings.

Technical challenges are largely addressed.

Organizational and ethical factors are critical for success.

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.