ai-shaped-readiness-advisor

## Purpose Assess whether your product work is **"AI-first"** (using AI to automate existing tasks faster) or **"AI-shaped"** (fundamentally redesigning how product teams operate around AI capabilities). Use this to evaluate your readiness across **5 essential PM competencies for 2026**, identify gaps, and get concrete recommendations on which capability to build first. **Key Distinction:** AI-first is cute (using Copilot to write PRDs faster). AI-shaped is survival (building a durable "reality layer" that both humans and AI trust, orchestrating AI workflows, compressing learning cycles). This is not about AI tools—it's about **organizational redesign around AI as co-intelligence**. The interactive skill guides you through a maturity assessment, then recommends your next move. ## Key Concepts ### AI-First vs. AI-Shaped | Dimension | AI-First (Cute) | AI-Shaped (Survival) | |-----------|-----------------|----------------------| | **Mindset** | Automate existing tasks | Redesign how work gets done | | **Goal** | Speed up artifact creation | Compress learning cycles | | **AI Role** | Task assistant | Strategic co-intelligence | | **Advantage** | Temporary efficiency gains | Defensible competitive moat | | **Example** | "Copilot writes PRDs 2x faster" | "AI agent validates hypotheses in 48 hours instead of 3 weeks" | **Critical Insight:** If a competitor can replicate your AI usage by throwing bodies at it, it's not differentiation—it's just efficiency (which becomes table stakes within months). --- ### The 5 Essential PM Competencies (2026) These competencies define AI-shaped product work. You'll assess your maturity on each. #### 1. **Context Design** Building a durable **"reality layer"** that both humans and AI can trust—treating AI attention as a scarce resource and allocating it deliberately. **What it includes:** - Documenting what's true vs. assumed - Immutable constraints (technical, regulatory, strategic) - Operational glossary (shared definitions) - Evidence standards (what counts as validation) - **Context boundaries** (what to persist vs. retrieve) - **Memory architecture** (short-term conversational + long-term persistent) - **Retrieval strategies** (semantic search, contextual retrieval) **Key Principle:** *"If you can't point to evidence, constraints, and definitions, you don't have context. You have vibes."* **Critical Distinction: Context Stuffing vs. Context Engineering** - **Context Stuffing (AI-first):** Jamming volume without intent ("paste entire PRD") - **Context Engineering (AI-shaped):** Shaping structure for attention (bounded domains, retrieve with intent) **The 5 Diagnostic Questions:** 1. What specific decision does this support? 2. Can retrieval replace persistence? 3. Who owns the context boundary? 4. What fails if we exclude this? 5. Are we fixing structure or avoiding it? **AI-first version:** Pasting PRDs into ChatGPT; no context boundaries; "more is better" mentality **AI-shaped version:** CLAUDE.md