Measurement & Value Realization¶
Why most AI programs can't prove they worked — and how to instrument, attribute, and use measurement to reprioritize investment.
In this section¶
| Page | Last updated |
|---|---|
| Measurement & Value Realization Framework Why most AI programs can't prove they worked, the instrumentation-before-deployment principle, leading vs. lagging indicators, and the measurement-to-reprioritization feedback loop that compounds returns. |
Updated 2026-06-18 |
| Attribution Methodology How to isolate AI's impact from confounding variables, choose direct vs. indirect attribution models, and build a defensible investment case from measurement data. |
Updated 2026-06-18 |
| Practitioner Guide: Standing Up AI Measurement How to instrument AI initiatives before deployment, define success metrics per use case, build measurement infrastructure, set a reporting cadence, and use measurement to drive roadmap decisions. |
Updated 2026-06-18 |
| Assessment: Measurement Maturity Scoring A scoring assessment for measurement maturity across instrumentation coverage, attribution capability, and feedback-loop effectiveness, with level definitions and a remediation path. |
Updated 2026-06-18 |