alexe-ev Frame AI Product Value
Purpose
Help teams move from "we can do this with AI" to "this AI capability solves a real user problem and creates measurable value" — grounding AI feature decisions in outcomes rather than technology.
Skill type
Conceptual skill
Use this skill when
- An AI feature idea needs to be evaluated before investment
- The team is excited about AI capability but unclear on user value
- Stakeholders need a business case for an AI initiative
- An AI feature's value proposition needs to be articulated for users, sales, or leadership
Do not use this skill when
- The AI capability itself needs to be assessed (use assess-model-capabilities)
- A business case needs full financial modeling (use build-business-case)
Required inputs
- AI capability or feature idea
- Target user segment
Optional inputs
- User research on the problem area
- Comparable AI features in the market
- Cost or complexity of building
- Existing baseline (what users do today without AI)
Upstream context
Works best when:
- Product problem is identified
- User research exists
Downstream handoff
Output can feed:
- assess-model-capabilities (value frame → capability requirements)
- design-human-in-loop-workflow (value frame informs where human oversight is needed)
- build-business-case (value frame → business case inputs)
Instructions
- Identify the user problem the AI capability addresses.
- Describe what users do today without the AI (current behavior / workaround).
- Articulate the outcome the AI delivers for users (not the mechanism).
- Identify the business value: how does the user outcome translate to business metrics?
- Identify the risks: where could AI create harm, false confidence, or user distrust?
- Define what success looks like: specific, measurable outcomes.
Output
Provide:
- User problem statement
- Current user behavior without AI
- AI-delivered outcome (user-centric)
- Business value translation
- Risk identification
- Success definition
- Build / don't build recommendation with rationale
Risks / caveats
- "AI can do X" is not a value statement — always connect to user problem and outcome
- AI features that impress in demos but create friction in daily use are not valuable
- Identify risks before building, not after — especially for high-stakes decisions