---
id: "question-rigorous-measurement"
type: "open-question"
source_timestamps: ["¶Last"]
tags: ["metrics", "evaluation", "roi"]
related: ["claim-hr-silo-failure"]
resolutionPath: "Developing systematic, experimental, and long-term evaluation frameworks that track post-reskilling productivity, retention, and strategic goal attainment."
sources: ["reskilling"]
sourceVaultSlug: "hbr-seg-reskilling"
originDay: 10
articleStem: "hbr-edu-34-reskilling-in-age-of-ai"
sourceUrl: "https://hbr.org/2023/09/reskilling-in-the-age-of-ai"
sourceTitle: "Reskilling in the Age of AI"
---
# How can organizations rigorously measure reskilling effectiveness?

**Open question.** Current reskilling efforts are heavily hampered by a **lack of rigor in measurement and evaluation** of what actually works. Moving beyond narrow HR metrics like *cost-per-learner* (the failure mode named in [[claim-hr-silo-failure]]) to measure true strategic ROI remains unsolved for most firms.

**Resolution path.** Develop systematic, experimental, and long-term evaluation frameworks that track post-reskilling productivity, retention, and strategic goal attainment. [[entity-year-up|Year Up]]'s rigorous statistical/RCT-style impact studies are a model.

**Enrichment note.** Traditional L&D evaluation frameworks (Kirkpatrick/Phillips: reaction → learning → behavior → results → ROI) are implicitly critiqued as too narrow when reskilling is a strategic change initiative rather than a course.