/pm-case-study

name: pm-case-study
description: "Generate end-to-end PM case studies from real AI product launches, pivots, and decisions. Analyzes what happened, why, what the PM likely decided, trade-offs made, and lessons learned."
argument-hint: "[product launch, feature, or company decision]"

PM Case Study Skill

Generate a detailed PM case study from a real AI product launch, pivot, or strategic decision — reconstructing the PM thinking behind it.

When to Use

• User asks "Write a case study on [AI product launch/decision]"
• User wants to understand PM decisions behind a real product
• User says `/pm-case-study` followed by a topic
• Great for: ChatGPT launch, Claude's Constitutional AI, Gemini's multimodal strategy, GitHub Copilot pricing, Perplexity's search bet, Midjourney's Discord-first strategy, etc.

Framework: PM Case Study (8 Sections)

Section 1: Executive Summary

• **What happened**: One paragraph summary of the product decision/launch
• **When**: Timeline of key events
• **Who**: Key people and teams involved
• **Outcome**: How it played out (success, failure, mixed)

Section 2: Context & Background

• **Company situation**: Where was the company at this point? Stage, funding, competitive position.
• **Market context**: What was happening in the broader market?
• **Technical context**: What capabilities existed? What was newly possible?
• **User context**: What were users doing before this product? What pain existed?

Section 3: The Decision

• **What was decided**: Specific product/strategy decision
• **Alternatives considered**: What other paths were likely on the table?
• **Key trade-offs**: What did they give up by choosing this path?
• **Stakeholder dynamics**: Who likely championed this? Who likely opposed it?

Section 4: Execution Analysis

• **Go-to-market strategy**: How was it launched? Distribution channel?
• **Phasing**: Was it a big bang launch or phased rollout?
• **Pricing**: How was it priced? Why that model?
• **Technical execution**: What was the technical approach? Shortcuts taken?

Section 5: What Went Right

• Identify 3-5 specific decisions that contributed to success
• For each: What was the decision, why it mattered, what would have happened otherwise
• Be specific — reference actual features, timelines, or metrics where available

Section 6: What Went Wrong (or Could Have Been Better)

• Identify 2-3 mistakes, misses, or areas for improvement
• For each: What happened, what the impact was, what could have been done differently
• Be fair — hindsight bias is easy, focus on what was knowable at the time

Section 7: Metrics & Outcomes

• **Growth metrics**: Users, revenue, market share (use real numbers where available)
• **Product metrics**: Engagement, retention, satisfaction
• **Strategic outcomes**: Market position, competitive response, ecosystem effects
• **Unexpected outcomes**: Things that happened that nobody predicted

Section 8: Key Takeaways

Extract 3-5 lessons for product managers:

• **Lesson**: Clear statement of the principle
• **Application**: How to apply this in product sense/strategy decisions
• **Example question**: A product question where this lesson is directly relevant

Case Study Categories

Product Launches

• ChatGPT's launch (Nov 2022) — fastest growing consumer app ever
• Claude's positioning as the "safe" alternative
• Perplexity's answer engine vs. Google Search
• Midjourney's Discord-native strategy
• Cursor's bet on AI-native IDE

Strategic Pivots

• An AI lab's shift from nonprofit to capped-profit
• A safety lab's pivot from pure research to product company
• A big tech company's emergency response to ChatGPT
• An open-source LLM strategy from a major tech company

Feature Decisions

• ChatGPT Plugins → GPTs → the pivot to actions/agents
• GitHub Copilot's pricing model ($10/month individual)
• Claude's Artifacts feature
• Gemini's multimodal-first approach
• NotebookLM's audio overview feature

Pricing & Business Model

• LLM API pricing evolution (the race to the bottom)
• ChatGPT Plus ($20/month) → Team → Enterprise tiers
• The free tier strategy across AI companies
• Usage-based vs. seat-based pricing in AI

Output Format

Write as a business school case study — structured, analytical, and with clear takeaways. Use real data where available, clearly mark estimates or speculation. Aim for ~2500 words.

Research-First Workflow (CRITICAL)

This skill requires real data: 1. **Research extensively** — Do 10-15 web searches for: launch details, user growth data, pricing history, company blog posts, founder interviews, analyst reports, and competitor responses. 2. **Cite everything** — Include `[linked source](url)` inline for all factual claims. 3. **Date awareness** — Note what was known at the time of the decision vs. what we know now. 4. **Display** the complete case study.

What Good Looks Like

• Demonstrates deep knowledge of the AI product landscape
• Shows you can analyze real product decisions with nuance
• Provides concrete examples and data points for product discussions
• Builds pattern recognition across multiple product launches
• Reveals your product judgment when you evaluate decisions