A Simple Tool for Prioritizing AI Product Features: Balancing Customer Value, Data Readiness, and Implementation Cost

This paper proposes a simple, practical tool for prioritizing AI product features by balancing three critical dimensions: customer value, data readiness, and implementation cost. While many AI roadmaps focus heavily on technical feasibility or market demand alone, teams often struggle to compare features that differ widely in data availability, model complexity, and development effort. To address this, the study introduces a lightweight scoring matrix and prioritization canvas that enables product teams to assess features using consistent criteria and transparent trade-offs. The tool combines qualitative judgment with a structured numeric rubric, producing an interpretable priority score and a clear visual map for decision-making. We demonstrate how the framework can reduce misalignment between product, data, and engineering stakeholders, improve early-stage estimation, and support faster, evidence-informed roadmap decisions. The proposed approach is designed for real-world constraints, making it especially suitable for small to mid-sized teams or organizations early in their AI maturity. By integrating user-centric impact with data and cost realities, this tool helps organizations invest in AI features that are both desirable and deliverable, increasing the likelihood of measurable business outcomes.

Keywords: Supply chain resilience, U.S. manufacturing firms, Post-pandemic strategy, Digital transformation, Operational performance.