AUTOMATING AGILE DECISIONS: A COMPARATIVE ANALYSIS OF AI-POWERED REQUIREMENT PRIORITIZATION TOOLS

Abstract

Requirement prioritization is a cornerstone of Agile software engineering, yet traditional methods such as MoSCoW and the Analytical Hierarchy Process (AHP) often struggle with scalability, subjectivity, and stakeholder bias. Recent advances in Artificial Intelligence (AI), including natural language processing (NLP), machine learning (ML), and sentiment analysis, offer opportunities to automate and enhance this process.

This study presents a comparative analysis of prominent AI-powered requirement prioritization tools, evaluating them across four dimensions: AI capabilities, cost structures, integration with Agile ecosystems, and usability. Data was drawn from product documentation, user reviews, academic literature, and expert insights. Tools such as Airfocus, Craft.io, ClickUp AI, ReqSuite RM, Kanoah Tests, Zepel, and Aha! Roadmaps were systematically assessed.

Findings reveal that no single tool is universally optimal; instead, suitability depends on organizational scale, resource availability, and integration needs. While some platforms excel in predictive analytics and compliance, others emphasize affordability or ease of onboarding. However, gaps remain in transparency and explainability, limiting stakeholder trust in AI-driven outputs.

This paper contributes by offering both a practical guide for Agile teams and a research-oriented discussion of adoption barriers, ethical considerations, and future directions. As AI-driven prioritization matures, the integration of explainable AI (XAI), hybrid human-AI decision-making, and emerging techniques such as large language models (LLMs) will be critical for advancing requirement engineering practices.

Index Terms— Requirement prioritization, Artificial intelligence (AI), Agile software engineering, Requirements engineering (RE), Natural language processing (NLP), Explainable AI (XAI), Tool comparison, Machine learning (ML), Human-AI collaboration, Cost-benefit analysis.