An AI-Driven Decision Support System for Product Feature Prioritization in Software Startups

Product feature prioritization is widely recognized as a critical challenge for software startups. Fast-paced environments, extreme uncertainty, and limited resources often force these companies to make trade-offs between competing ideas. Our study investigates the potential of an AI-based decision support system for feature prioritization in the context of startups. We used a design science research methodology to design, develop, and evaluate a proof of concept (PoC) artifact for AI-based product feature prioritization. The artifact uses AI to make decisions regarding product features using criteria such as return on investment, confidence, and time-to-value. We demonstrated and evaluated this PoC artifact using semi-structured interviews with seven software entrepreneurs. We analyzed the evaluation data using thematic analysis. The evaluation results indicate that startups perceive the potential to use AI to support the prioritization process. However, adoption depends on improving transparency, explainability, interoperability, and usability in these tools. The study contributes to the literature by providing early insights into how AI-based tools for product feature prioritization are perceived by startups.