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).

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### 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 files, evidence databases, constraint registries AI agents reference; two-layer memory architecture; Research→Plan→Reset→Implement cycle to prevent context rot

**Deep Dive:** See [`context-engineering-advisor`](../context-engineering-advisor/SKILL.md) for detailed guidance on diagnosing context stuffing and implementing memory architecture.

---

#### 2. **Agent Orchestration**
Creating repeatable, traceable AI workflows (not one-off prompts).

**What it includes:**
- Defined workflow loops: research → synthesis → critique → decision → log rationale
- Each step shows its work (traceable reasoning)
- Workflows run consistently (same inputs = predictable process)
- Version-controlled prompts and agents

**Key Principle:** One-off prompts are tactical. Orchestrated workflows are strategic.

**AI-first version:** "Ask ChatGPT to analyze this user feedback"
**AI-shaped version:** Automated workflow that ingests feedback, tags themes, generates hypotheses, flags contradictions, logs decisions

---

#### 3. **Outcome Acceleration**
Using AI to compress **learning cycles** (not just speed up tasks).

**What it includes:**
- Eliminate validation lag (PoL probes run in days, not weeks)
- Remove approval delays (AI pre-validates against constraints)
- Cut meeting overhead (async AI synthesis replaces status meetings)

**Key Principle:** Do less, purposefully. AI removes bottlenecks, not generates more work.

**AI-first version:** "AI writes user stories faster"
**AI-shaped version:** "AI runs feasibility checks overnight, eliminating 2 weeks of technical discovery"

---

#### 4. **Team-AI Facilitation**
Redesigning team systems so AI operates as **co-intelligence**, not an accountability shield.

**What it includes:**
- Review norms (who checks AI outputs, when, how)
- Evidence standards (AI must cite sources, not hallucinate)
- Decision authority (AI recommends, humans decide—clear boundaries)
- Psychological safety (team can challenge AI without feeling "dumb")

**Key Principle:** AI amplifies judgment, doesn't replace accountability.

**AI-first version:** "I used AI" as excuse for bad outputs
**AI-shaped version:** Clear review protocols; AI outputs treated as drafts requiring human validation

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#### 5. **Strategic Differentiation**
Moving beyond efficiency to create **defensible competitive advantages**.

**What it includes:**
- New customer capabilities (what can users do now that they couldn't before?)
- Workflow rewiring (processes competitors can't replicate without full redesign)
- Economics competitors can't match (10x cost advantage through AI)

**Key Principle:** *"If a competitor can copy it by throwing bodies at it, it's not differentiation."*

**AI-first version:** "We use AI to write better docs"
**AI-shaped version:** "We validate product hypotheses in 2 days vs. industry standard 3 weeks—ship 6x more validated features per quarter"

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### Anti-Patterns (What This Is NOT)

- **Not about AI tools:** Using Claude vs. ChatGPT doesn'