Tool

AI & Automation Use-Case Assessment Matrix

Interactive assessment, scoring rules, vendor checklist, and success-criteria template to evaluate, prioritize, and pilot AI & automation opportunities. Collects structured answers, preserves repeatable comparisons, and helps teams pick sensible first pilots while recording rationale and acceptance criteria.

Interactive Tool

AI & Automation Use-Case Assessment Matrix

This interactive assessment captures the key facts, scores, and success criteria needed to evaluate AI or automation opportunities. Use it to create repeatable, comparable case records you can save and share. Follow the scoring guidance below and fill in the success criteria so pilots are measurable.

Scoring guidance (suggested)

  • Business impact: how much measurable benefit (revenue, cost, time savings, guest satisfaction) do you expect? Score 1 (tiny) to 5 (transformative).
  • Data readiness: availability and quality of the required data. Score 1 (no usable data) to 5 (clean, accessible historical data).
  • Implementation complexity: engineering, integration, and process change required. Score 1 (very easy) to 5 (very complex). Lower scores are better for complexity.
  • Human oversight & safety/regulatory: potential for harm, legal/regulatory exposure, or need for continuous human review. Score 1 (low risk/low oversight) to 5 (high risk/high oversight).

Suggested weighted priority formula (for guidance only): Priority = (BusinessImpact*0.35) + (DataReadiness*0.25) - (ImplementationComplexity*0.20) - (SafetyRisk*0.20). Record your manual computed priority below and proposed next step. Saved responses go to the project dataset for later review.

Who filled in this assessment? Name and role help future reviewers.
Use YYYY-MM-DD or a readable date. Will be used for tracking over time.
Optional: e.g., Kitchen Ops, FOH, Catering, Multi-site.
A concise descriptive name, e.g.,
Describe what the AI/automation will do in plain language.
Quantify where possible: weekly time saved, % food waste reduction, $/month, improved CSAT points, etc.
If you can estimate dollars/month, enter it. Leave blank if unknown.
How important is this to the business if successful? Consider measurable outcomes.
1.0 10.0
Higher frequency increases potential value and operational impact.
Is the required data already collected and accessible?
1.0 10.0
Are data fields reliable, consistent, and well-defined?
1.0 10.0
Historical volume matters for ML models and reliable baselines.
Estimate build, integration, testing, and deployment effort.
1.0 10.0
New servers, real-time pipelines, third-party services, or can it run on existing systems?
1.0 10.0
Will staff workflows, training, or policies need major change?
1.0 10.0
How much continuous human review is required to keep the system safe and reliable?
1.0 10.0
Does the use case expose the business to food safety, health, legal, or regulatory risk?
1.0 10.0
Yes/No.
Use the checklist when evaluating vendors for pilots.
List 3–5 measurable acceptance criteria for a pilot: e.g., reduce food waste by X%, reduce prep time by Y minutes, achieve >Z% accuracy, payback in <N months.
Which KPIs, reports, and data sources will you use to prove success?
Be specific: threshold values, evaluation period, and responsible decision-maker.
Compute using your chosen formula (suggested formula in intro). Record the value you compute for sorting/prioritization.
Select a sensible next step based on scores and risk.
Document hard dependencies (data owners, system access), known blockers, or mitigation ideas.
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