Key AI features to support scrum software engineering : practitioners’ perspective

Fujs, Damjan (author), Kochovski, Petar (author), Stankovski, Vlado (author), Vavpotič, Damjan (author)

Software 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.

• Language: English
• Keywords: scrum, project management, software development, Kano method, agile, requirement, AI features, industry practice
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2026
• Number of pages: 36 str.
• Numbering: Vol. 31, iss. 5, article no. 138
• PID: 20.500.12556/RUL-182518
• UDC: 004.8:004.4
• ISSN on article: 1382-3256
• DOI: 10.1007/s10664-026-10876-6
• COBISS.SI-ID: 278073603
• Publication date in RUL: 14.05.2026

Record is a part of a journal

• Title: Empirical software engineering
• Publisher: Springer Nature
• ISSN: 1382-3256
• COBISS.SI-ID: 16845317

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: scrum, upravljanje projektov, razvoj programske opreme, metoda Kano, agilno, zahteve, AI funkcionalnosti, stališče industrije

Projects

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: P2-0426-2022
• Name: Digitalna preobrazba za pametno javno upravljanje