Key AI features to support scrum software engineeringFujs, Damjan (Avtor) Kochovski, Petar (Avtor) Stankovski, Vlado (Avtor) Vavpotič, Damjan (Avtor) scrumproject managementsoftware developmentKano methodagilerequirementAI featuresindustry practiceSoftware engineering involves more than coding. It encompasses planning, development, communication, and process management. Scrum, the most widely adopted agile methodology, helps teams deliver value iteratively, yet practitioners often struggle with challenges such as maintaining requirement clarity, reducing cognitive load, and managing communication overhead. As artificial intelligence (AI) becomes increasingly integrated into the software engineering lifecycle, its potential to improve productivity, quality, and decision-making is gaining significant attention. Moreover, Scrum offers a structured yet flexible framework, but it remains unclear which AI features can most effectively support its practices in real-world settings. Therefore, this study addresses that gap by identifying and prioritizing key Scrum AI Support Features (SAISFs) based on industry needs. A two-phase research approach was used. First, a focus group with five software engineering industry experts identified 18 relevant SAISFs. Second, a survey using the Kano methodology was conducted with 344 experienced Scrum practitioners to evaluate and prioritize these features. The results were analyzed across three Scrum team size groups: small ( < = 6), medium (7–10), and large (11+), and four functional SAISF groups: Requirements Support (R), Development Support (D), Communication Support (C), and Scrum Process Support (S). The research also provides prioritization of SAISFs according to Scrum roles. Our findings offer actionable insights for designing AI-enhanced tools tailored to Scrum teams, highlighting the importance of considering team size and Scrum roles when prioritizing AI features. This study contributes to the agile software engineering literature by offering a practitioner-informed foundation for integrating AI into Scrum-based project environments. Future Scrum tools may become adaptive and context-aware, automatically tailoring workflows, predicting bottlenecks, and optimizing team communication and performance.20262026-05-14 13:25:20Članek v reviji182518sl