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Automation opportunity scorecard

Compare a few real opportunities using the same criteria. This worksheet makes assumptions explicit; it is not a validated scoring model or a promise of returns.

By Stolen OrbitRevision

When to use it and what to produce

When several departments propose AI projects and you need to decide what to investigate with limited data, people and budget.

A reasoned comparison, including missing evidence, blocking conditions and a decision about the next experiment.

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1. Document the value

Complete one worksheet per opportunity. Use a consistent observation period and avoid counting the same operational improvement under multiple benefits.

See a worked example

Illustrative example: preparing 200 monthly summaries uses 20 hours. A trial must measure remaining review time; it cannot assume that all 20 hours disappear.

Identify who benefits and which operational behaviour should change.

Record cases per period, observed handling time, known error costs and where each figure came from.

Separate released capacity, actual spending reductions and commercial outcomes that remain unproven.

2. Check feasibility

Check dependencies before comparing scores. An attractive project without access or an available owner is not ready to begin.

See a worked example

Illustrative example: summaries can remain internal drafts. Automatic price updates need approval rules and write controls before a meaningful trial can begin.

List what is already available and what needs authorisation or third-party work.

Describe a plausible harmful action, how it would be detected and whether it can be reversed.

Identify who can supply examples, clarify rules and review the trial.

3. Record the decision

If using a scale, define its values in advance. Keep non-negotiable conditions separate from any combined score.

See a worked example

Illustrative example: trial summaries because usable records and a reviewer exist. Defer automated pricing until its approval rules and recovery process are defined.

Define how every proposal will be compared on value, effort, data reliability and reversibility.

List requirements that must hold before the trial starts, even when its overall score is high.

Choose trial, gather evidence or defer; assign an owner and a review date.

Pitfalls to avoid

  • Treating a numerical average as evidence of feasibility.
  • Rewarding visibility without checking data and ownership.
  • Equating released capacity with cash savings without an operational plan.

Take the next step together.

Use this document to start a discussion about scope, evaluation and project ownership.

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