recommendation-canvas

Evaluate AI product ideas across business outcomes, risks, and value justification.

Updated Apr 6, 2026

Search Tags:#risk-assessment #stakeholder-alignment #hypothesis-testing #ai-evaluation #product-canvas #value-justification

Ready to run

npx skills add https://github.com/sicktastic/skill-issue --skill recommendation-canvas-sicktastic

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: recommendation-canvas
Source: https://github.com/sicktastic/skill-issue/tree/main/product-management/recommendation-canvas
Command: npx skills add https://github.com/sicktastic/skill-issue --skill recommendation-canvas-sicktastic

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill forces AI product teams to defend investment decisions by articulating business and customer outcomes, solution hypotheses, risks, and value justification before any engineering commitment is made.

Core Features & Use Cases

Outcome-first evaluation: Prompts you to capture directional business and customer outcomes, ensuring measurable goals anchor every idea.
Structured discovery: Guides problem narratives, solution hypotheses, tiny experiments, proof-of-life criteria, positioning, PESTEL risks, assumptions, and next steps so nothing is overlooked.
Use Case: Pitch a new AI-powered capability to executives by showing validated hypotheses, risk mitigation, differentiated positioning, and SMART success metrics.

Quick Start

Fill in the recommendation canvas template with your AI idea's business outcomes, hypotheses, risks, and success metrics to craft a defensible investment recommendation.

Frequently Asked Questions about recommendation-canvas

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

What is an AI product canvas and when should I use one for investment evaluation?▼

An AI product canvas structures investment evaluation by forcing teams to define business outcomes, customer outcomes, and value justification before engineering commitment. Use it to assess whether an AI solution deserves investment and to align product and data teams.

How do I validate AI solution hypotheses and risks before pitching to stakeholders?▼

Validate AI solution hypotheses by populating a structured template with problem narratives, proof-of-life criteria, PESTEL risks, and assumptions. This process prepares a defensible stakeholder brief that aligns product and data teams on measurable success metrics.

How do I structure an AI product pitch using a recommendation canvas?▼

Structure an AI product pitch by filling the recommendation canvas template with directional business outcomes, solution hypotheses, PESTEL risks, and SMART success metrics. This frames your investment recommendation with validated hypotheses and differentiated positioning.

Can I use this canvas for stakeholder alignment across product and data teams?▼

Yes, you can use the canvas for stakeholder alignment by capturing directional outcomes, assumptions, and next steps. It bridges product and data teams by providing a shared structure for estimating impacts and justifying AI investments.

What is the best way to justify AI investments and estimate business impacts?▼

The best way to justify AI investments is applying a structured canvas that requires value justification, outcome-first evaluation, and PESTEL risk assessment. This ensures every idea is anchored to measurable business and customer outcomes before commitment.

What limitations exist when using a structured canvas for AI product assessment?▼

A limitation of using a structured canvas for AI assessment is that it forces premature articulation of hypotheses and success metrics before engineering begins. Teams must ensure they have enough preliminary data to populate proof-of-life criteria accurately.