Template

AI Opportunity Canvas — Interactive Template

An interactive, saveable AI Opportunity Canvas to help teams capture candidate AI use cases, assess feasibility and risk, prototype responsibly, and prioritize opportunities using a simple scoring rubric.

Interactive Tool

AI Opportunity Canvas

Use this Canvas to capture and evaluate candidate AI use cases

Fill each section with concise, evidence-focused answers. The form records your submission so teams can compare, prioritize, and track progress. Use the quick scoring rubric at the end to surface high-impact, feasible, and lower-risk opportunities.

Scoring guidance

  • Impact (1–5): How large and measurable is the expected benefit to users, operations, revenue, costs, safety, or compliance?
  • Feasibility (1–5): How realistic is building a prototype within available data, tooling, and skills?
  • Data Readiness (1–5): Do you have access to the right data, and is quality sufficient?
  • Risk (1–5, lower is better): Potential safety, privacy, or regulatory concerns. Use this to surface items needing stronger mitigation before scaling.

After scoring, add a short recommendation: proceed to prototype, rethink scope, or deprioritize.

Give this candidate a short name that others will recognize (e.g., 'Invoice OCR for AP', 'Customer Churn Early Warning').
Person or team responsible for next steps (name, role, or email).
Describe the problem and how an AI solution would create measurable value. Be specific about the outcome you expect to change.
Who benefits or which operational process is impacted? Include frequency and scale (e.g., 3k invoices/month, 2000 support chats/week).
List concrete metrics you will use to judge success (e.g., reduce manual review time by 50%, increase conversion by 3%).
Describe data sources, formats, sample size, labeled data availability, known quality issues, and retention windows.
How available and clean is the required data for prototyping?
1.0 10.0
Classify the sensitivity of the data involved.
Choose the primary technical approach you expect to use.
Estimate how feasible a low-risk prototype is given current tooling, skills, and infra.
1.0 10.0
Identify potential harms, bias risks, regulatory issues, or explainability needs. If none, enter 'none noted'.
Actions to reduce risks (e.g., differential privacy, human-in-loop, thresholding, monitoring, legal review).
How much engineering and process change will be needed to integrate a working model into production?
1.0 10.0
Rough order of magnitude to integrate a working prototype into a production-adjacent environment.
Provide a short ROI estimate and expected timeline to measurable benefit (months). Be explicit about assumptions.
Describe the minimum viable prototype scope, key milestones, required datasets, and success criteria for the prototype stage.
Target duration for a low-risk prototype (typical: 2–12 weeks).
List the measurable indicators you'll use to decide whether the prototype demonstrates value (e.g., precision/recall thresholds, time saved).
Clear pass/fail criteria that will determine whether to scale this opportunity after a successful prototype.
How strategically important and impactful is this opportunity?
1.0 10.0
How feasible is it, considering data, skills, and infra?
1.0 10.0
Score the overall risk from safety, privacy, regulatory, or operational impact. Use lower values for lower risk.
1.0 10.0
A short recommendation based on the above inputs.
Anything else the team should know, plus next actions and owners.
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