Tracing the Transformation: A Digital Trace Data Perspective on AI-Augmented Information Systems Development
Type: article
Abstract: Information systems development (ISD) is undergoing a fundamental transformation as developers increasingly augment their work with generative AI. While prior research documents productivity gains from AI-assisted coding, the longitudinal effects on ISD processes and outputs in enterprise contexts remain poorly understood. This research-in-progress aims to investigate how ISD processes and outputs change as ISD becomes AI-augmented over time. We conduct an exploratory multiple-case study of two ISD units at a large European enterprise software company, drawing on longitudinal digital trace data from GitHub spanning the periods before and after the introduction of Claude Code in March 2026. Our preliminary analysis reveals two observations: (1) a substantial volume increase in commit frequency and code churn, and (2) divergent trajectories in pull request activity. We contribute to ISD research by examining how AI-augmentation reshapes both processes and outputs longitudinally in real-world enterprise settings.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7222990574