---
id: "question-measuring-augmentation-roi"
type: "open-question"
source_timestamps: ["§ A Tale of Two J-Curves", "§ Choosing the Path of Human Potential"]
tags: ["metrics", "roi", "patience"]
related: ["concept-micro-j-curve", "concept-ai-augmentation-strategy"]
resolutionPath: "Develop new KPIs that measure employee AI fluency, workflow redesign progress, and 'pilot' engagement levels rather than immediate output or cost savings."
sources: ["spine"]
sourceVaultSlug: "hbr-seg-spine"
originDay: 1
articleStem: "hbr-ext-19-augmentation-over-automation"
sourceUrl: "https://hbr.org/2026/04/why-companies-that-choose-ai-augmentation-over-automation-may-win-in-the-long-run"
sourceTitle: "Why Companies That Choose AI Augmentation Over Automation May Win in the Long Run"
---
# How to Measure ROI During the Augmentation J-Curve Dip?

**Open question.** [[concept-ai-augmentation-strategy-d1|Augmentation]] requires a longer, deeper dip in [[concept-micro-j-curve|the Micro Productivity J-Curve]], and its compounding advantage is only visible to those who look **"beyond the next quarter."** It remains unclear **what specific leading indicators** organizations should track during this extended dip to justify the investment to shareholders.

**Proposed resolution path.** Develop new KPIs that measure **employee AI fluency, workflow-redesign progress, and [[concept-pilots-vs-passengers|pilot]]-engagement levels** — process and capability metrics — rather than immediate output or cost savings. This is the measurement counterpart to [[action-codevelop-ai-tools|co-developing AI tools with employees]].


## Related across articles
- [[question-measuring-collective-intelligence]]
- [[claim-traditional-roi-fails-ai]]
- [[claim-ai-roi-timeline]]