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How to measure an AI deployment's return — and defend it.

Publication date unverified · 6 min read

By DeftSpan team · Editorial perspective with illustrative patterns; not a client case study.

An agreed baseline and acceptance definition help evaluate a deployment. Their absence makes results harder to assess; no prevalence or cost claim is made here.

Four numbers to agree up front

  • Baseline: hours, error rate and cycle time for the workflow today, measured, not estimated.
  • Target: a range, not a promise, for what the system should remove.
  • Floor: the result below which you stop or change approach.
  • Cost per run: model, infrastructure and human review, per case handled.

How to test it

Build a representative evaluation set sized to the workflow and risks, using authorised redacted or synthetic cases. Include normal, edge, ambiguous and high-risk situations. Have accountable reviewers label outcomes and evaluate correctness, escalation, tool choice and cost on relevant changes.

If you can't say when you'd stop, you don't have a business case. You have a hope.

Next step

Bring the decision this note is about.

If the topic is live for you right now, a working session gets you further than an article will.