Template

Applied AI Use-Case Prioritization Canvas (Interactive)

An interactive prioritization canvas to capture, score, and track AI pilot opportunities by expected value, feasibility, and data readiness. Saves entries for later review and helps teams pick small, testable pilots with clear success criteria.

Interactive Tool

Applied AI Use-Case Prioritization Canvas

This interactive canvas helps teams capture an AI opportunity, estimate its value, assess feasibility and data readiness, and define clear pilot success criteria. Use the scoring fields to compare ideas consistently. Aim to identify small, testable pilots that deliver measurable operational value while managing safety, privacy, and regulatory risks.

Scoring rubric (1–5)

  • Value: How big and measurable is the expected benefit? (time saved, waste reduced, margin uplift, guest or safety impact)
  • Feasibility: How easy is it to implement given the org's engineering resources, vendor options, and operational fit?
  • Data readiness: Do you have reliable, accessible historical data of sufficient volume and quality for a pilot?

Suggested weighting for a simple prioritization score: Value 40%, Feasibility 30%, Data Readiness 30%. Use the recommended next step at the end to decide whether to pilot, collect more data, or re-scope.

A concise name for this AI opportunity (e.g., 'Lunch rush scheduling assistant').
Describe the operational problem or opportunity this pilot will address. Be concrete about what is happening today and why it matters.
Who will be accountable for the pilot and outcome? Include role or name.
Choose the primary operational area this pilot targets.
Describe the expected benefits in practical terms (time saved, fewer remakes, reduced waste, revenue uplift, improved guest satisfaction).
Estimate how many staff-hours per week the pilot could save (if applicable).
Percent reduction in relevant waste or spoilage (if applicable).
Estimated percentage impact on margin or contribution for the affected items/shift.
Select all data sources you can access for a pilot.
Enter approximate months of usable historical data per key source (e.g., 12).
Note known quality issues, missing fields, inconsistent timestamps, duplicate records, or known gaps.
1 = no usable data, 5 = rich, well-structured, accessible data.
1.0 10.0
Technical and operational feasibility given current tools, staff, and timeline.
1.0 10.0
Describe required engineering work, integrations, UI changes, or ops changes that would be needed.
Rough order of magnitude for initial pilot development (e.g., 80).
List potential compliance, privacy, or safety concerns and controls required.
Risks to service, food safety, guest experience, or staff workflows and how you'd mitigate them.
Define 2–4 measurable outcomes that will determine pilot success (e.g., reduce plate waste by X kg/week, improve forecasting MAPE to Y%).
How long should the pilot run to gather reliable evidence?
Estimated one-time or incremental pilot cost (development, tooling, monitoring).
Is this likely to be low-effort and high-impact?
If you use a custom weighted formula, enter the computed priority here for quick sorting.
Free-form notes, links to docs, or tags for grouping (e.g., 'forecasting, summer-menu').
You can explore this tool now. Sign in or create an account to save your responses and return to them later.
Sign In to Save Create an Account
Make this tool part of your work

Save a personal copy, bring it to your team, or tailor the questions and workflow to fit what you are hungry to improve.

Member customization and team collaboration are coming soon.
Discuss Ask the Librarian

Discussion

Comments and conversation will live here.