AI Use Case Prioritisation Matrix
Not sure which AI initiative to tackle first? Score up to three use cases across five strategic dimensions and get a ranked implementation sequence, calculated from your inputs, not generic advice.
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Calculate Your Ranked AI Roadmap
Name each AI initiative you are considering, then rate it on the five dimensions below. Scores update in real time.
What each dimension measures and why it matters.
Strategic Fit
Does this use case move a needle that your leadership cares about? A score of 1–3 means you are solving a problem nobody is measuring. A 9–10 means this directly enables a board-level priority. The highest-scoring use cases on this dimension are the ones that will get funded, resourced, and sustained past the pilot.
Data Readiness
AI without data is a framework without content. Score this dimension based on whether the data exists, is accessible, is clean, and is sufficient in volume for the model you intend to deploy. A score below 5 indicates data preparation work that will extend your timeline and cost significantly, often by 3–6× the AI development cost itself.
Ease of Implementation
Technical complexity, integration requirements, change management load, and vendor dependency all affect this score. A 9–10 means an off-the-shelf solution with minimal integration. A 1–3 means custom model development, complex data pipelines, and significant organisational change, a category that requires a dedicated implementation team and realistic 12–18 month timelines.
Expected ROI
Consider both direct financial return (cost saved, revenue enabled) and indirect value (risk reduced, quality improved, capacity freed). Use your AI ROI Calculator to generate a defensible financial estimate before scoring this dimension. A 9–10 means a measurable, verifiable return within 12 months. A 1–3 means the value is theoretical, delayed, or difficult to attribute.
Speed to Value
How quickly can you demonstrate a result? This is not the same as ease of implementation: a use case can be technically simple but require 12 months of data collection before the model can make accurate predictions. Score 9–10 if you can deliver a measurable outcome in 4–8 weeks. Score 1–3 if the baseline measurement, training data collection, and model validation add up to over 12 months before any business value is visible.