ai-usecase-prioritiser

name	ai-usecase-prioritiser
description	Identifies and ranks practical AI opportunities based on value, effort, data readiness, and operating risk. Use when: the user asks where AI should be used, which pilot should come first, or how to avoid vague AI strategy work without a realistic starting point.
license	MIT
compatibility	opencode
metadata	{"owner":"arif","type":"read-write","approval":"No"}

ai-usecase-prioritiser

Owner: arif Type: read-write Approval: No

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What I Do

Ranks AI use cases by practical fit so the user can start with a small, testable pilot instead of a broad transformation promise.

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When to Use Me

• "Where should we use AI?"
• "What should we pilot first?"
• A company wants AI ideas ranked by business reality

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Context Requirements

Requires already loaded:
  .crux/workspace/arif/MEMORY.md

Loads during execution (lazy):
  .crux/docs/ai-transformation-principles.md  (generate from agent assets first if missing)

Estimated token cost: ~350 tokens
Unloaded after: task completion

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Inputs

Input	Source	Required
company-context	user	Yes
candidate-processes	user	No
constraints	user / MEMORY.md	No

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Steps

1. Identify the core business bottlenecks or repetitive work
2. Propose candidate AI use cases if the user did not provide them
3. Score each candidate using value, effort, data readiness, adoption friction, and risk
4. Recommend one primary pilot and one secondary option
5. Explain why the top choice is the right starting point
6. Skill complete — unload

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Output

Writes to: no file required by default Format: markdown

# AI Use-Case Priorities

| Use Case | Value | Effort | Risk | Why It Fits |
|---|---|---|---|---|
| ... | ... | ... | ... | ... |

## Recommended First Pilot
- ...

## Why
- ...

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Error Handling

Condition	Action
Company context is too thin	ask for sector, team size, process pain, and data availability
Too many speculative ideas	keep only the top 3 realistic options
Unexpected failure	Stop. Write error to bus. Notify user.