Assessment

AI Readiness Assessment & Prioritization Tool

An interactive assessment that helps teams score and prioritize AI pilot opportunities by combining business value, data readiness, technical feasibility, operational maturity, staff readiness and governance risk. Includes guided scoring guidance, a weighted-prioritization method, and space to capture estimates and next steps.

Interactive Tool

AI Readiness Assessment

Use this assessment to evaluate and prioritize AI pilot opportunities. Score each dimension honestly using the guidance below. The form collects inputs you can save and return to. A recommended weighted scoring method is provided to help rank opportunities consistently across projects.

Scoring guidance: Use scales where 1 = low readiness / low value / high risk and 5 = high readiness / high value / low risk unless otherwise noted. Later you will combine scaled values into a weighted priority score (example and weights are shown).

Example calculation: Convert each 1–5 scale to 0–100 (e.g., 1→0, 2→25, 3→50, 4→75, 5→100). Apply weights: Business Value 30%, Data Readiness 25%, Technical Feasibility 15%, Operational Readiness 20%, Risk/Governance 10% (where higher risk lowers the score — see field help). Sum to a 0–100 priority score. Use the priority score together with cost and time-to-value to pick pilots.

A short name that identifies the use case (e.g., Forecasting — weekly demand for perishables).
What problem does this solve? Who benefits? What decisions will the output inform?
1 = small operational improvement, 5 = transformative (large revenue increase or major cost reduction).
1.0 10.0
Best estimate of yearly benefit in USD (use for ROI thinking).
1 = requires new sensors or substantial engineering; 5 = straightforward using existing systems and standard ML approaches.
1.0 10.0
1 = no historical data or inaccessible sources; 5 = rich, labeled historical data available and accessible.
1.0 10.0
1 = many gaps, inconsistent or poor labels; 5 = clean, representative, and labeled (or easily labeled) data.
1.0 10.0
1 = many siloed systems requiring manual ETL; 5 = consolidated data platform and APIs available.
1.0 10.0
1 = ad hoc, undocumented processes; 5 = documented standard work and reliable handoffs.
1.0 10.0
1 = no analytics/AI skills; 5 = team includes data engineers, ML practitioners, and data-literate operators.
1.0 10.0
1 = little executive support or training capacity; 5 = strong sponsorship and plan for adoption and training.
1.0 10.0
1 = high regulatory / safety constraints (e.g., health, food-safety-critical decisions); 5 = low sensitivity. Note: for the weighted priority calculation, higher sensitivity reduces priority — you will invert this score (see instructions).
1.0 10.0
High complexity often needs cross-functional integration and longer timelines. Consider breaking into a smaller MVP if complexity is high.
How long until the pilot produces measurable business impact. Shorter times to value favor earlier pilots.
Include data work, engineering, cloud, vendor, and validation costs.
Select controls and governance elements already in place. Missing items increase pilot risk.
Compute the weighted score manually or in a spreadsheet using these weights: Business Value 30%, Data Readiness 25% (average of availability, quality, integration), Technical Feasibility 15%, Operational Readiness 20% (average of process, staff, change), Risk/Governance 10% (use 100 - regulatory_sensitivity_converted so higher risk lowers score). Convert 1–5 scales to 0–100 first (1→0, 2→25, 3→50, 4→75, 5→100). Example: Score = 0.30*BV + 0.25*Data + 0.15*Tech + 0.20*Ops + 0.10*(100 - Risk).
Choose the most appropriate immediate action given readiness and value.
Capture assumptions, known data owners, potential vendors, and key risks to monitor.
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