Assess whether your product work is AI-first or AI-shaped. Use when evaluating AI maturity and choosing the next team capability to build.

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. Input Works best with: A description of how your team currently uses AI in its product work — even 'barely' is a valid answer. Also useful: Team size, product domain, and which of the 5 competencies you suspect is weakest. Anything supplied with the invocation itself — text after the skill name, a pasted context dump, or an appended ARGUMENTS: line — counts as answers already given. Use it and skip whatever it covers; don't re ask. Arriving empty handed? That works too. The advisor opens by asking how AI currently shows up in your team's day to day product work. Example invocation: Assess my team: 6 PMs, we use ChatGPT for PRD drafts and meeting summaries but nothing in our discovery or delivery process has changed. 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 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 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" Anti Patterns (What This Is NOT) Not about AI tools: Using Claude vs. ChatGPT doesn't matter. Redesigning workflows matters. Not about speed: Writing PRDs 2x faster isn't strategic if PRDs weren't the bottleneck. Not about automation: Automating bad processes just scales the bad. Not about replacing humans: AI shaped orgs augment judgment, not eliminate it. When to Use This Skill ✅ Use this when: You're using AI tools but not seeing strategic advantage You suspect you're "AI first" (efficiency) but want to be "AI shaped" (transformation) You need to prioritize which AI capability to build next Leadership asks "How are we using AI?" and you're not sure how to answer strategically You want to assess team readiness for AI powered product work ❌ Don't use this when: You haven't started using AI at all (start with basic tools first) You're looking for tool recommendations (this is about organizational design, not tooling) You need tactical "how to write a prompt" guidance (use skills for that) Facilitation Source of Truth Use [ workshop facilitation ](../workshop facilitation/SKILL.md) as the default interaction protocol for this skill. It defines: session heads up + entry mode (Guided, Context dump, Best guess) one question turns with plain language prompts progress labels (for example, Context Qx/8 and Scoring Qx/5) interruption handling and pause/resume behavior numbered recommendations at decision points quick select numbered response options for regular questions (include Other (specify) when useful) This file defines the domain specific assessment content. If there is a conflict, follow this file's domain logic. Application This interactive skill uses adaptive questioning to assess your maturity across 5 competencies, then recommends which to prioritize. Facilitation Protocol (Mandatory) 1. Ask exactly one question per turn . 2. Wait for the user's answer before asking the next question. 3. Use plain language questions (no shorthand labels as the primary question). If needed, include an example response format. 4. Show progress on every turn using user facing labels: Context Qx/8 during context gathering Scoring Qx/5 during maturity scoring Include "questions remaining" when practical. 5. Do not use internal phase labels (like "Step 0") in user facing prompts unless the user asks for internal structure details. 6. For maturity scoring questions, present concise 1 4 choices first; share full rubric details only if requested. 7. For context questions, offer concise numbered quick select options when practical, plus Other (specify) for open ended answers. Accept multi select replies like 1,3 or 1 and 3 . 8. Give numbered recommendations only at decision points , not after every answer. 9. Decision points include: After the full context summary After the 5 dimension maturity profile During priority selection and action plan path selection 10. When recommendations are shown, enumerate clearly ( 1. , 2. , 3. ) and accept selections like 1 , 1 , 1 and 3 , 1,3 , or custom text. 11. If multiple options are selected, synthesize a combined path and continue. 12. If custom text is provided, map it to the closest valid path and continue without forcing re entry. 13. Interruption handling is mandatory: if the user asks a meta question ("how many left?", "why this label?", "pause"), answer directly first, then restate current progress and resume with the pending question. 14. If the user says to stop or pause, halt the assessment immediately and wait for explicit resume. 15. If the user asks for "one question at a time," keep that mode for the rest of the session unless they explicitly opt out. 16. Before any assessment question, give a short heads up on time/length and let the user choose an entry mode. Session Start: Heads Up + Entry Mode (Mandatory) Agent opening prompt (use this first): "Quick heads up before we start: this usually takes about 7 10 minutes and up to 13 questions total (8 context + 5 scoring). How do you want to do this? 1. Guided mode: I’ll ask one question at a time. 2. Context dump: you paste what you already know, and I’ll skip anything redundant. 3. Best guess mode: I’ll make reasonable assumptions where details are missing, label them, and keep moving." Accept selections as 1 , 1 , 1 and 3 , 1,3 , or custom text. Mode behavior: If Guided mode: Run Step 0 as written, then scoring. If Context dump: Ask for pasted context once, summarize it, identify gaps, and: Skip any context questions already answered. Ask only the minimum missing context needed (0 2 clarifying questions). Move to scoring as soon as context is sufficient. If Best guess mode: Ask for the smallest viable starting input (role/team + primary goal), then: Infer missing details using reasonable defaults. Label each inferred item as Assumption . Include confidence tags ( High , Medium , Low ) for each assumption. Continue without blocking on unknowns. At the final summary, include an Assumptions to Validate section when context dump or best guess mode was used. Step 0: Gather Context Agent asks: Collect context using this exact sequence, one question at a time: 1. "Which AI tools are you using today?" 2. "How does your team usually use AI today: one off prompts, reusable templates, or multi step workflows?" 3. "Who uses AI consistently today: just you, PMs,

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