AI Impact Measurement

Find out whether AI is improving the work.

Define baselines and measures for outcomes, costs, quality and attributable change.

Outcome baseline: Define baselines and measures for outcomes, costs, quality and attributable change.
Cost and quality: Define the evidence, interfaces and accountable owners for this part of the work.
Attribution: Review findings against agreed decision criteria and record unresolved limitations.

Proposed engagement framework; scope and evidence requirements are agreed for the workflow.

Explore the engagement scope

Define baselines and measures for outcomes, costs, quality and attributable change. Begin with an agreed workflow, accountable owner and representative evidence. The engagement defines its coverage, dependencies and decision criteria before work starts.

AI Impact Measurement Framework

Define what better should mean before scaling an agent. Connect quality, cost, adoption and business outcomes to a baseline your team can revisit.

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Supporting capabilities

Measuring AI impact

Define baselines and measures that connect workflow behaviour to business value.

A little more clarity

Questions worth asking.

Start with the questions that shape a better decision.

What does AI Impact Measurement cover?

Define baselines and measures for outcomes, costs, quality and attributable change. Indicative focus areas include outcome baseline, cost and quality, attribution.

What is needed to scope AI Impact Measurement?

What decision does AI Impact Measurement support?

Find out whether AI is improving the work. Findings and indicative outputs apply to the agreed scope. Your accountable owner retains deployment, investment and risk decisions.

What changed, at what cost, and why?

Bring the workflow, the constraints and the evidence you already have.

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