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AI Outcome Measurement Scorecard

Define what better means before AI-supported work moves from pilot to business case.

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PDF · A4 · 4 pagesv1.230 minEN + FR available

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The exact sections inside the download.

30 min EN / FR
01

Time saved

Which cycle time, handoff, or preparation step should improve?

02

Quality improved

Which error, completeness, decision, or customer outcome should improve?

03

Review burden

Does AI reduce, shift, or increase human review work?

04

Cost-to-serve

How does model, tool, and operator cost compare with workflow value?

05

Confidence signal

What would prove the system is helping rather than creating hidden rework?

06

Primary KPI

Which single metric should leadership watch first?

01 / 03

Frame the decision

Name the real operating need before designing a solution.

Name the AI-supported workflow, operating loop, or decision.

Capture time, cost, quality, error, or throughput before AI support.

Name who owns the outcome, not just the system.

How to use it

Start with one real decision.

Complete the canvas with the workflow owner, then use the blank areas to expose missing context and controls.

Need a starting point?

Place this workflow in your architecture.

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