AI-Enhanced Scrum: A Literature Review on the Impact of Artificial Intelligence on Scrum Processes
Abstract
As artificial intelligence (AI) continues to evolve, its role in software development becomes increasinglyrelevant. In agile environments, particularly within the Scrum framework, AI shows the potential toimprove accuracy, productivity, and overall efficiency. Several Scrum related challenges, such as effortestimation or resource allocation, could be solved by applying AI. Despite the potential, manyorganizations still struggle with successful AI adoption, mainly due to limited knowledge about AIcapabilities and challenges in selecting appropriate AI techniques. This study explores the current stateof AI integration in Scrum processes through a literature review. A literature search identified 305records, of which 18 were included in review. The study examines:(a) how much AI has impacted Scrum,(b) which AI techniques have been used to enhance Scrum processes, and (c) how AI affects automation,assistance, and enhancement within Scrum. Data extraction and coding were conducted using a contentanalysis, while the synthesized findings were interpreted and discussed using a narrative synthesisapproach. The analysis identified seven types of AI techniques (regression, classification, clustering,natural language processing, neural networks, search algorithms, and topic modeling) applied acrossseven out of nineteen Scrum processes. Most AI support was observed in the Plan and Estimate phase,particularly in effort estimation, user story creation, and release planning. In contrast, no applicationswere identified for processes in the Implement or Review and Retrospect phases. The impact of AI wascategorized into enhancement, assistance, and automation, with a balanced distribution across thecategories. The findings suggest that current AI applications in Scrum are concentrated on structured,data-rich processes.References
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