Worksheet

AI Scoping & Use‑Case Prioritization — Worksheet

An interactive worksheet to evaluate and prioritize AI use-cases by scoring business value, technical feasibility, data readiness, risk, and operational change. Includes guidance, scoring formula, thresholds, governance flags, and a sample filled example to help teams move from idea to prototype-ready candidates.

Interactive Tool

AI Scoping & Use‑Case Prioritization — Worksheet

Welcome

This worksheet helps teams surface high-impact, feasible AI opportunities and create a defensible pipeline for prototyping. Use clear, evidence-based judgments about value, feasibility, data readiness, risk, and operational change so stakeholders can agree where to invest.

How to use

  • Fill the descriptive fields so anyone can understand the use-case.
  • Score the numeric dimensions (1 = low, 5 = high) based on evidence, not hope.
  • Use the scoring formula below to compute a priority score (0–100).
  • Check safety/privacy/regulatory flags early — these influence governance and timeline.

Scoring formula & thresholds

Priority score (0–100) guideline:
Score = (BusinessValue*0.40 + TechnicalFeasibility*0.25 + DataReadiness*0.20 + (5 - OperationalChange)*0.10) * 20

Interpreting the score:

  • 70–100: Strong candidate — proceed to prototype/POC with a clear success metric.
  • 50–69: Candidate for research or small pilot — address data or integration blockers first.
  • 0–49: Defer or do further discovery; likely significant gaps or low value.

Governance note

If any safety, privacy, regulatory, or ethical flags are checked, involve relevant compliance, legal, or safety teams before prototyping.

Describe the user problem, desired outcome, and context in 2–5 sentences.
Person or role accountable for value and adoption.
List measurable KPIs this use-case affects (e.g., handle time, conversion rate, cost per unit).
Estimated annual benefit or expected percent improvement. Be explicit about units (e.g., $/year or % reduction).
Judged importance: revenue, cost savings, customer experience, strategic value.
1.0 10.0
Likelihood a technical solution can be built given current systems and skills.
1.0 10.0
Availability, quality, and access of required data (coverage, labels, freshness, lineage).
1.0 10.0
Estimate engineering work to integrate the solution into production systems.
Select the closest current state of ML capabilities for this domain.
Check all that apply. These require early governance and stakeholder involvement.
Amount of process, training, or organizational change needed to adopt the solution.
1.0 10.0
List key risks, dependencies, or missing approvals that could block progress.
Compute using the formula in the introduction, or leave blank and compute centrally after submission. (Guideline: >=70 prototype, 50–69 research/pilot, <50 defer).
Suggested next step based on score, risks, and governance flags.
Example: Customer-support auto-triage: reduces average handle time by 20% (expected $120k/yr). Data: 24 months of support transcripts accessible but unlabelled. Business value 4, feasibility 4, data readiness 3, integration medium, ML maturity prototype, flags: PII, operational change 3, computed priority 72 => Recommendation: prototype/POC with privacy controls and label effort.
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