1 More Paper.

Abstract Generative artificial intelligence (GenAI) is a rapidly evolving field that enables novel and valuable information systems. However, developing GenAI-based information systems poses unique challenges that need to be addressed by researchers and practitioners. This paper proposes a method that supports organizations in the responsible engineering…

Abstract Generative AI is embedded in everyday knowledge work, but we know little about how workers preserve human agency when large language models (LLM) output enters real deliverables. Drawing on 15 interviews in two early-adopting German tech firms and analysis of handbooks and workflow artifacts, we examine how professionals keep control and…

This study investigated the utility of Large Language Models (LLMs) in supporting human experts in the maritime industry by generating draft replies to stakeholder inquiries. Using a mixed-methods approach, observations, interviews, a survey, and text similarity analysis, we examined how human experts perceive LLM drafts, how drafts align with final…

How do product teams evaluate LLM-powered products? As or- ganizations integrate large language models (LLMs) into digital products, their unpredictable nature makes traditional evaluation approaches inadequate, yet little is known about how practitioners navigate this challenge. Through interviews with nineteen practi- tioners across diverse sectors, we…

Generative AI (GenAI) presents significant opportunities, particularly for creative work like visual design. GenAI can effectively address creative challenges like the “blank page” problem by enabling rapid visual conceptualisation, thereby enhancing productivity in visual design tasks. However, organisations face significant challenges in integrating…

Generative Artificial Intelligence (GenAI) is increas-ingly integrated into software products to enable new features and user capabilities, from early exploration to operational deployment. GenAI adoption as a component within a soft-ware system introduces quality risks because GenAI outputs are probabilistic, prompt-sensitive, and may drift after…

“Vibe coding” — the practice of developing software through iteratively conversing with a large language model (LLM) — has exploded in popularity within the last year. However, de- velopers report key limitations including the accumulation of technical debt, security issues, and code churn to achieve satisfactory results. We argue that these pitfalls…

Abstract We study the staggered introduction of a generative AI–based conversational assistant using data from 5,172 customer-support agents. Access to AI assistance increases worker productivity, as measured by issues resolved per hour, by 15% on average, with substantial heterogeneity across workers. The effects vary significantly across different…