Kairo

AI Use-Case Scoring Framework

Use this framework to move from a long list of AI ideas to a short, defensible set of opportunities that can become real business workflows.

How to use this framework

Score each candidate workflow from 1 to 5 against the six criteria below. Multiply the score by the weighting, compare the total score, then use the result to decide whether to move, prepare, hold, or stop.

Scoring criteria

25%

Business value

Will the use case improve revenue, cost, speed, quality, risk, customer experience, or leadership decision-making?

  • High: Clear measurable value and a visible sponsor.
  • Low: Interesting idea but weak business consequence.

20%

Feasibility

Can the workflow be described, scoped, piloted, and supported with current tools or a practical build path?

  • High: Inputs, outputs, process steps, and users are clear.
  • Low: The workflow is ambiguous or depends on major system changes first.

20%

Data readiness

Are the documents, data sources, permissions, quality standards, and ownership clear enough for a pilot?

  • High: Sources are known, accessible, and trusted enough for a controlled pilot.
  • Low: Data is scattered, duplicated, sensitive, or missing clear ownership.

15%

Risk and governance

Can the team define acceptable use, human review, exception handling, privacy, and escalation boundaries?

  • High: Risk boundaries and accountable owners are clear.
  • Low: The use case could affect sensitive decisions without defined controls.

10%

Adoption effort

Can the target users realistically adopt the new workflow with training, templates, and manager support?

  • High: Users feel the pain and can see how the workflow improves their day.
  • Low: Adoption depends on behavior change with no clear support model.

10%

Operating ownership

Is there a business owner who will sponsor, test, approve, measure, and improve the workflow?

  • High: Named owner, decision rights, and success measures exist.
  • Low: The idea has interest but no accountable operator.

Score interpretation

ScoreDecisionGuidance
80-100Move firstStrong candidate for discovery-to-pilot. Define success metrics, data requirements, risk boundaries, and a build path.
60-79Prepare then pilotPromising but needs cleanup, tighter scope, stakeholder alignment, or data foundations before implementation.
40-59Hold for laterUseful idea, but not the best first use case. Revisit after higher-readiness workflows have moved.
0-39Do not prioritizeLow readiness, weak value, unclear owner, or high governance burden. Document the idea but do not fund it now.

Recommended scoring process

  1. List the candidate workflows, not just AI ideas.
  2. Score each workflow against the six criteria using a 1-5 scale.
  3. Apply the weightings to calculate a first-pass score.
  4. Discuss disagreements between business, technology, risk, and user groups.
  5. Select one first-move workflow and write a short pilot brief.
  6. Define the build path: assisted workflow, shared AI workspace, or API workflow pipeline.

Next step

If the scorecard reveals one strong candidate, convert it into a pilot brief with a workflow owner, success measure, data requirement, risk boundary, and build path.

FAQ

AI use-case scoring questions

These answers help teams use the scorecard as a decision tool rather than a generic ranking exercise.

How many AI use cases should we score at once?

A practical first pass usually scores 10 to 20 candidate workflows. The goal is not to catalog every possible AI idea; it is to identify the first few opportunities worth shaping into pilot briefs.

Who should participate in scoring?

Include the business owner, a technology or data representative, a risk or control stakeholder where relevant, and users who understand the workflow. AI prioritization is weaker when it is done by one function alone.

What score makes a use case ready to build?

A high score suggests the use case deserves a pilot brief, not an automatic build. The team should still define success metrics, inputs, outputs, governance, and adoption requirements before implementation.

What happens when a valuable use case has poor data readiness?

That use case should usually move into a data foundations sprint before a build. High value with poor readiness is often a sign that the opportunity matters but the organization needs to prepare the source material first.