Conseiller en préparation basé sur l'IA
/SKILLDéterminez si votre travail sur le produit est axé sur l'IA ou influencé par l'IA. Utilisez cette approche pour évaluer la maturité en matière d'IA et choisir la prochaine compétence à développer au
name: ai-shaped-readiness-advisor
description: Assess whether your product work is AI-first or AI-shaped. Use when evaluating AI maturity and choosing the next team capability to build.
intent: >-
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.
type: interactive
theme: ai-agents
best_for:
- "Assessing whether your team is AI-first or genuinely AI-shaped"
- "Identifying which of the 5 AI competencies to build next"
- "Understanding your product org's AI maturity honestly"
scenarios:
- "My team uses AI tools but I'm not sure if we're working differently or just automating the same tasks"
- "I want to assess my product org's AI maturity and prioritize where to invest next quarter"
estimated_time: "15-20 min"
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:
- What specific decision does this support?
- Can retrieval replace persistence?
- Who owns the context boundary?
- What fails if we exclude this?
- 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, gener
