Between Policy and Practice: GenAI Adoption in Agile Software Development Teams
- URL: http://arxiv.org/abs/2601.07051v1
- Date: Sun, 11 Jan 2026 20:04:56 GMT
- Title: Between Policy and Practice: GenAI Adoption in Agile Software Development Teams
- Authors: Michael Neumann, Lasse Bischof, Nic Elias Hinz, Luca Stockmann, Dennis Schrader, Ana Carolina Ahaus, Erim Can Demirci, Benjamin Gabel, Maria Rauschenberger, Philipp Diebold, Henning Fritzemeier, Adam Przybylek,
- Abstract summary: generative AI (GenAI) tools have begun to reshape various software engineering activities.<n>This study investigates how agile practitioners adopt GenAI tools in real-world organizational contexts.
- Score: 3.4768202202649783
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Context: The rapid emergence of generative AI (GenAI) tools has begun to reshape various software engineering activities. Yet, their adoption within agile environments remains underexplored. Objective: This study investigates how agile practitioners adopt GenAI tools in real-world organizational contexts, focusing on regulatory conditions, use cases, benefits, and barriers. Method: An exploratory multiple case study was conducted in three German organizations, involving 17 semi-structured interviews and document analysis. A cross-case thematic analysis was applied to identify GenAI adoption patterns. Results: Findings reveal that GenAI is primarily used for creative tasks, documentation, and code assistance. Benefits include efficiency gains and enhanced creativity, while barriers relate to data privacy, validation effort, and lack of governance. Using the Technology-Organization-Environment (TOE) framework, we find that these barriers stem from misalignments across the three dimensions. Regulatory pressures are often translated into policies without accounting for actual technological usage patterns or organizational constraints. This leads to systematic gaps between policy and practice. Conclusion: GenAI offers significant potential to augment agile roles but requires alignment across TOE dimensions, including clear policies, data protection measures, and user training to ensure responsible and effective integration.
Related papers
- Impacts of Generative AI on Agile Teams' Productivity: A Multi-Case Longitudinal Study [5.9568322124195845]
Generative Artificial Intelligence (GenAI) tools represent a paradigm shift in software engineering.<n>This study aims to provide a longitudinal evaluation of GenAI's impact on agile software teams.
arXiv Detail & Related papers (2026-02-14T13:26:16Z) - Adoption of Generative Artificial Intelligence in the German Software Engineering Industry: An Empirical Study [9.442926409509038]
Generative artificial intelligence (GenAI) tools have seen rapid adoption among software developers.<n>While adoption rates in the industry are rising, the underlying factors influencing the effective use of these tools have not been thoroughly investigated.<n>This issue is particularly relevant in environments with stringent regulatory requirements, such as Germany.<n>No empirical study has systematically examined the adoption dynamics of GenAI tools within the German context.
arXiv Detail & Related papers (2026-01-23T12:42:33Z) - An Empirical Study of Generative AI Adoption in Software Engineering [2.3419132746983236]
GenAI tools are being increasingly adopted by practitioners in SE.<n>Despite increasing adoption, we still lack empirical evidence on how GenAI is used in practice.
arXiv Detail & Related papers (2025-12-29T09:24:52Z) - AssurAI: Experience with Constructing Korean Socio-cultural Datasets to Discover Potential Risks of Generative AI [50.802995291689086]
We introduce AssurAI, a new quality-controlled Korean multimodal dataset for evaluating the safety of generative AI.<n>We define a taxonomy of 35 distinct AI risk factors, adapted from established frameworks to cover both universal harms and relevance to the Korean socio-cultural context.<n>AssurAI is a large-scale Korean multimodal dataset comprising 11,480 instances across text, image, video, and audio.
arXiv Detail & Related papers (2025-11-20T13:59:42Z) - Enterprise Architecture as a Dynamic Capability for Scalable and Sustainable Generative AI adoption: Bridging Innovation and Governance in Large Organisations [55.2480439325792]
Generative Artificial Intelligence is a powerful new technology with the potential to boost innovation and reshape governance in many industries.<n>However, organisations face major challenges in scaling GenAI, including technology complexity, governance gaps and resource misalignments.<n>This study explores how Enterprise Architecture Management can meet the complex requirements of GenAI adoption within large enterprises.
arXiv Detail & Related papers (2025-05-09T07:41:33Z) - Computational Safety for Generative AI: A Signal Processing Perspective [65.268245109828]
computational safety is a mathematical framework that enables the quantitative assessment, formulation, and study of safety challenges in GenAI.<n>We show how sensitivity analysis and loss landscape analysis can be used to detect malicious prompts with jailbreak attempts.<n>We discuss key open research challenges, opportunities, and the essential role of signal processing in computational AI safety.
arXiv Detail & Related papers (2025-02-18T02:26:50Z) - Generative Artificial Intelligence-Supported Pentesting: A Comparison between Claude Opus, GPT-4, and Copilot [42.558423984270135]
GenAI can be applied across numerous fields, with particular relevance in cybersecurity.<n>In this paper, we have analyzed the potential of leading generic-purpose GenAI tools.<n>Claude Opus, GPT-4 from ChatGPT, and Copilot-in augmenting the penetration testing process as defined by the Penetration Testing Execution Standard (PTES)
arXiv Detail & Related papers (2025-01-12T22:48:37Z) - Hey GPT, Can You be More Racist? Analysis from Crowdsourced Attempts to Elicit Biased Content from Generative AI [41.96102438774773]
This work presents the findings from a university-level competition, which challenged participants to design prompts for eliciting biased outputs from GenAI tools.
We quantitatively and qualitatively analyze the competition submissions and identify a diverse set of biases in GenAI and strategies employed by participants to induce bias in GenAI.
arXiv Detail & Related papers (2024-10-20T18:44:45Z) - Securing the Future of GenAI: Policy and Technology [50.586585729683776]
Governments globally are grappling with the challenge of regulating GenAI, balancing innovation against safety.
A workshop co-organized by Google, University of Wisconsin, Madison, and Stanford University aimed to bridge this gap between GenAI policy and technology.
This paper summarizes the discussions during the workshop which addressed questions, such as: How regulation can be designed without hindering technological progress?
arXiv Detail & Related papers (2024-05-21T20:30:01Z) - Governance of Generative Artificial Intelligence for Companies [1.2818275315985972]
Despite extensive debates on GenAI's transformative nature and regulatory measures, limited research addresses organizational governance.<n>Our review paper fills this gap by surveying recent works with the purpose of better understanding fundamental characteristics of GenAI.<n>Our framework outlines the scope, objectives, and governance mechanisms tailored to harness business opportunities.
arXiv Detail & Related papers (2024-02-05T14:20:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.