Impacts of Generative AI on Agile Teams' Productivity: A Multi-Case Longitudinal Study
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866912904468496384 |
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| author | Tomaz, Rafael Guenes, Paloma Araújo, Allysson Allex Baldassarre, Maria Teresa Kalinowski, Marcos |
| author_facet | Tomaz, Rafael Guenes, Paloma Araújo, Allysson Allex Baldassarre, Maria Teresa Kalinowski, Marcos |
| contents | Context: Generative Artificial Intelligence (GenAI) tools, such as GitHub Copilot and GPT tools, represent a paradigm shift in software engineering. While their impact is clear, most studies are short-term, focused on individual experiments. The sustained, team-level effects on productivity within industrial agile environments remain largely uncharacterized. Goal: This study aims to provide a longitudinal evaluation of GenAI's impact on agile software teams. We characterize its effect on developers' productivity by applying the multi-dimensional SPACE framework. Method: We conducted a multi-case longitudinal study involving 3 agile teams at a large technology consulting firm for around 13 months. We collected and compared quantitative telemetry (Jira, SonarQube, Git) and qualitative survey data from historical (pre-adoption) and research (post-adoption) sprints. Conclusion: GenAI tools can significantly improve team performance and well-being. Our key finding is a sharp increase in Performance and perceived Efficiency concurrent with flat developer Activity. This suggests GenAI increases the value density of development work, not its volume. This finding validates the necessity of multi-dimensional frameworks like SPACE to capture the true, nuanced impact of GenAI in situ, which would be invisible to studies measuring Activity alone. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_13766 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Impacts of Generative AI on Agile Teams' Productivity: A Multi-Case Longitudinal Study Tomaz, Rafael Guenes, Paloma Araújo, Allysson Allex Baldassarre, Maria Teresa Kalinowski, Marcos Software Engineering Context: Generative Artificial Intelligence (GenAI) tools, such as GitHub Copilot and GPT tools, represent a paradigm shift in software engineering. While their impact is clear, most studies are short-term, focused on individual experiments. The sustained, team-level effects on productivity within industrial agile environments remain largely uncharacterized. Goal: This study aims to provide a longitudinal evaluation of GenAI's impact on agile software teams. We characterize its effect on developers' productivity by applying the multi-dimensional SPACE framework. Method: We conducted a multi-case longitudinal study involving 3 agile teams at a large technology consulting firm for around 13 months. We collected and compared quantitative telemetry (Jira, SonarQube, Git) and qualitative survey data from historical (pre-adoption) and research (post-adoption) sprints. Conclusion: GenAI tools can significantly improve team performance and well-being. Our key finding is a sharp increase in Performance and perceived Efficiency concurrent with flat developer Activity. This suggests GenAI increases the value density of development work, not its volume. This finding validates the necessity of multi-dimensional frameworks like SPACE to capture the true, nuanced impact of GenAI in situ, which would be invisible to studies measuring Activity alone. |
| title | Impacts of Generative AI on Agile Teams' Productivity: A Multi-Case Longitudinal Study |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2602.13766 |