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AI PM Decision System

Planned

AI-assisted framework for evaluating build vs. kill decisions with structured analysis.

FastAPI OpenAI API HTMX SQLite Data Visualization
GitHub Live Demo
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The Problem

PMs face binary decisions daily: Build this feature? Kill this initiative? Most decisions rely on gut feel, incomplete data, or vocal stakeholders—not rigorous analysis. Without a framework, decisions feel reactive rather than strategic.

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The Approach

Build a structured decision system that takes a feature/product idea and analyzes it across multiple dimensions: market fit, feasibility, resource trade-offs, strategic alignment, and risk. Provide AI-powered reasoning for the recommendation.

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The Solution

An interactive tool where PMs input an idea and get a comprehensive decision analysis: estimated impact, risk assessment, resource requirements, and a clear build/kill/shelve recommendation with reasoning.

What

An AI-powered decision system that helps PMs evaluate whether to build, kill, or shelve a feature or product idea.

Input: A product idea or feature request with: - Description and user problem it solves - Estimated effort (small/medium/large) - Strategic alignment rating - Market opportunity (size, urgency) - Current constraints (team, budget, time)

Output: A structured decision analysis including: - Build/Kill/Shelve Recommendation — Clear recommendation with confidence level - Impact Score (1-10) — Market impact vs. effort - Risk Assessment — Technical, market, and strategic risks - Resource Trade-offs — What else won’t get built if we do this? - Strategic Alignment — How does this fit the roadmap? - Reasoning — Why this recommendation (AI-generated but explainable) - Alternative Paths — What if we built this differently?

History: Save past decisions, see how they aged, learn from patterns

Why

The Problem: Most feature decisions get made in 10-minute meetings based on: - “The loudest voice wins” - “The CEO asked for it” - “It sounds cool” - Incomplete ROI analysis

This creates feature bloat, misaligned teams, and opportunity cost blindness.

Why This Tool Matters: - Rigor: Frameworks force structured thinking, not gut feel - Transparency: Everyone can see the reasoning, not just the decision - Learning: Track decisions over time, see what worked/didn’t - Buy-in: Teams accept decisions more when they understand the logic - Speed: Go from idea to decision in 5 minutes vs. 2 weeks of debate

Who Needs This: - PMs who want to make defensible decisions - Teams tired of unclear trade-off discussions - Founders who need to say “no” to good ideas to focus - You, demonstrating decision rigor + data thinking

How

Architecture:

User inputs idea + context
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FastAPI validates and enriches data
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OpenAI analyzes across 6 dimensions:
  1. Market fit (TAM, urgency, competition)
  2. Technical feasibility (effort, complexity, risk)
  3. Strategic alignment (roadmap fit, company goals)
  4. Resource impact (what gets sacrificed?)
  5. Team capacity (can we actually do this?)
  6. Risk profile (failure modes, mitigations)
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AI synthesizes into recommendation
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Renders decision dashboard + reasoning
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User can accept, disagree, or drill deeper
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Save to history + learn from patterns

Key Features:

  1. Multi-Dimensional Analysis: Not just “effort vs. impact”. Consider:
  2. Market (does anyone want this?)
  3. Strategy (does this move us toward our goals?)
  4. Resources (what’s the real cost?)

  5. Risk (what could go wrong?)

  6. Confidence Levels: “Build this 9/10” vs “Shelve this 6/10” (clear vs. unclear decisions get different recommendations)

  7. Alternative Paths: AI suggests “What if we built a cheaper version?” or “What if we bought vs. built?”

  8. Explanation: Users see the reasoning, not just the recommendation (so they learn + can argue back if needed)

  9. Decision History: Track decisions over time:

  10. Which ideas actually delivered ROI?
  11. Which “kills” do we regret?
  12. What patterns emerge?

Build Path:

  • Phase 1 (Week 1): Core decision engine

  • Input form → OpenAI analysis → recommendation dashboard

  • Phase 2 (Week 2): Decision history + learning

  • SQLite storage → trending analysis → “decision quality” metrics

  • Phase 3 (Week 3): Polish + visualization

  • Decision matrix visualization
  • Risk heatmap
  • ROI estimation over time
  • Comparison view (2 ideas side-by-side)

Technical Stack

  • Backend: FastAPI (structured decision logic)
  • AI: OpenAI API (gpt-4o for reasoning depth)
  • Frontend: HTMX + D3.js (visualization of trade-offs)
  • Storage: SQLite (decision history)
  • Analysis: Pandas (trending, pattern detection)
  • Export: PDF reports of decisions

Why Build This Project

  1. Demonstrates PM Rigor — Not just visionary thinking. Systematic, defensible decision-making.

  2. Shows Multi-Dimensional Thinking — Real product decisions aren’t binary. You consider market, feasibility, strategy, AND team capacity simultaneously.

  3. Immediately Useful — You’ll use this for your own product planning. Teams will demand access.

  4. Differentiator — Most PMs decide intuitively. You decide systematically AND communicate why.

Next Steps

See strategy/05_AI_PM_DECISION_SYSTEM.md for detailed specification.

Expected Timeline: 2-3 weeks for full build Complexity: Medium (decision logic + visualization) Impact: High (improves team alignment and decision quality)

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