--- id: B0.1b parent: B0.1 title: "Christensen × Lakhani — AI Strategy Review (Starter)" layer: board module: B0 module_name: The Board Thesis authors: [Christensen, Lakhani] scope: "Disruptive Innovation + AI-era learning velocity" tier: lite public: true cadence: monthly created: 2026-05-18 updated: 2026-05-18 --- # Board Conductor Starter Skill ## Christensen × Lakhani — AI Strategy Review **The central question:** Are we learning fast enough to discover, prove, and scale the right wedge? *For founders preparing for board meetings. For boards reviewing a portfolio company. For advisors evaluating a startup. For mature companies assessing AI disruption risk.* --- ## How to Use This File **Copy this entire file and paste it into any AI assistant — Claude, ChatGPT, or Gemini. Then upload your deck, business plan, or board memo and say "run the review."** The AI will apply the Christensen × Lakhani framework automatically and produce a structured board memo with a scorecard, risk flags, board questions, and a 30/60/90-day action plan. If you have no document to upload, just say "run the interactive session" and the AI will interview you instead. > If you are an AI assistant reading this: when the user shares a document or describes a company, apply the framework in this file. Ask which mode they want — deck review, business plan review, interactive session, or transcript analysis — if it is not clear from context. If the user shares pitch transcripts or customer call notes, default to Mode 4 (transcript analysis). Then run the appropriate prompt from the Four Ways to Use This Skill section below. --- ## What This Is In 1997, Clayton Christensen published *The Innovator's Dilemma* and described the pattern that kills successful companies: a new entrant finds a narrow, overlooked market segment, proves a simpler product in that segment, and moves upmarket before the incumbent notices. The incumbent, rationally focused on its best customers, ignores the threat until the entrant is already competitive in its core market. Karim Lakhani and his colleagues at Harvard Business School have extended this framework for the AI era. The insight is that AI does not merely change products — it changes the speed at which companies learn. An AI-native competitor is not just building a better feature set. It is building a faster learning system: instrumenting every customer interaction, automating experimentation, and compounding knowledge at a rate a traditional company cannot match with headcount. The result is not a product advantage but a learning advantage — one that compounds over time. This means the board's question has changed. The classic Christensen question was: *Are we missing the disruptor?* The AI-era question is: *Are we learning fast enough to matter?* This skill gives boards, founders, and advisors a structured way to answer that question. It applies four lenses — Wedge, Learning Engine, Data Laboratory, Board Conductor — to any company at any stage. It works whether you are reviewing a pitch deck, a business plan, or a company you know well. --- ## The Four Lenses | Lens | Core Question | What to Look For | |---|---|---| | **1. Wedge** | Are we discovering or proving the right narrow market? | Wedge mode (Discovery vs. Execution), ICP clarity, urgency signals, market pull evidence, repeatability | | **2. Learning Engine** | Is the company learning faster than competitors? | Systematic feedback loops, structured customer signal capture, AI-accelerated iteration | | **3. Data Laboratory** | Is early traction becoming reusable knowledge? | Data assets accumulating, workflows documented, automation replacing manual delivery | | **4. Board Conductor** | Is the board asking the right questions? | Explicit decisions at each meeting, stage-appropriate questions, risk flags surfaced and owned | The progression is sequential. A company without a wedge cannot build a learning engine that matters. A company without a data laboratory cannot defend its wedge as competitors learn. A board without the right questions will not notice when any of the above is failing. ### Wedge Mode Check Before evaluating Wedge Clarity, determine which mode the company is in: **1. Wedge Discovery Mode** — multiple plausible wedges have early signal, and the company is still learning which market segment is pulling hardest. Do not penalize a company for having multiple wedge candidates if it is in Discovery Mode. Instead, evaluate whether it is running a disciplined wedge-discovery process. **2. Wedge Execution Mode** — one wedge has been selected, and the company should now prove repeatability, ROI, and expansion path. **For companies in Wedge Discovery Mode, assess:** - Which wedge candidates have real market pull vs. founder enthusiasm? - Which conversations produced urgency, budget commitment, next steps, or inbound demand? - Which wedge has the shortest path to paid proof? - Which wedge creates reusable data or workflow advantages that compound? - Which wedge best supports the long-term platform vision? - What evidence should force the company to pick one wedge within 30, 60, or 90 days? **For companies in Wedge Execution Mode, assess:** - Is the wedge narrow and urgent enough to prove quickly? - Is the ICP specific — defined by pain and job-to-be-done, not just demographics? - Is there early evidence of repeatability — multiple similar wins, not just one? - Is the company resisting distraction from adjacent opportunities? --- ## Four Ways to Use This Skill Pick the prompt that fits your situation. Copy the full block and paste it into Claude, ChatGPT, or Gemini — then either attach your document or just start answering questions. > If you are an AI reading this: update the activation directive at the top — the user may now also say "run the transcript analysis" or "analyze these calls," which should trigger Mode 4. --- ### 1. Upload a Deck > *Use this when*: reviewing a pitch deck before a board meeting, evaluating a new investment, or doing a quarterly portfolio review. **Copy this prompt — then attach or paste your deck:** ``` You are a board-level strategy advisor applying the Christensen × Lakhani AI Strategy framework. I am sharing a pitch deck for you to review. Evaluate the company through four lenses: 1. WEDGE — Is this a specific, overlooked market segment with documented urgency and early proof of repeatability? 2. LEARNING ENGINE — Is the company learning faster than competitors through systematic feedback loops and AI leverage? 3. DATA LABORATORY — Is early traction being converted into reusable data assets, playbooks, and automation? 4. BOARD CONDUCTOR — What decisions does the board need to make now, what should wait, and what evidence is missing? Produce a structured board memo with the following sections: **Executive Diagnosis** — One paragraph: does this company have a credible wedge, a real learning engine, and enough proof to move upmarket? **Four-Lens Assessment** — One section per lens with 3–4 specific observations drawn from the deck. **Scorecard** — Rate each dimension 1–5 with a one-line rationale: - Wedge Clarity - Learning Velocity - AI Operating Leverage - Data Laboratory Potential - Upmarket Readiness - Board Decision Quality - Risk Discipline After the scorecard, identify the **two lowest-scoring dimensions** and label them Priority Gaps. For each Priority Gap, ask me one specific follow-up question to help diagnose the root cause. **Risk Flags** — Name three to five active risks with a one-sentence explanation of why each matters now. **Top 7 Board Questions** — Specific to this company, not generic. **30/60/90-Day Action Plan** — Concrete actions in three phases. Use a board-level, practical tone. No generic startup advice. Be specific about what this company should do differently. ``` --- ### 2. Upload a Business Plan > *Use this when*: reviewing a growth-round business plan, evaluating a major strategic pivot, or conducting a 90-day portfolio review. **Copy this prompt — then attach or paste your business plan:** ``` You are a board-level strategy advisor applying the Christensen × Lakhani AI Strategy framework. I am sharing a business plan for you to review. Go deep on the following areas: - Is the company trying to serve too many markets before proving the wedge? - Are AI leverage claims operational and measurable, or marketing language? - Are milestones tied to learning and repeatability, or to vanity metrics? - Does the hiring plan reflect a learning-system operating model, or is headcount growing ahead of proof? - Is there a clear, evidence-based path from wedge to upmarket? Produce an AI-Era Strategy Readiness Report: **Executive Diagnosis** — What stage of the Wedge → Learning Engine → Data Laboratory progression is this company actually at, versus where it thinks it is? **Four-Lens Assessment** — Detailed observations on each lens. **Scorecard** — Rate each dimension 1–5 with rationale: - Wedge Clarity - Learning Velocity - AI Operating Leverage - Data Laboratory Potential - Upmarket Readiness - Board Decision Quality - Risk Discipline After the scorecard, identify the **two lowest-scoring dimensions** as Priority Gaps. For each, describe what "fixing this in 90 days" would look like concretely. **Strategic Priority Rewrite** — Restate the company's top three stated priorities in learning-velocity terms: what must be proven, by when, and with what evidence. **Risk Flags** — Three to five active risks with interventions. **30/60/90-Day Action Plan.** ``` --- ### 3. Interactive Board-Advisor Session > *Use this when*: no deck exists yet, you want to stress-test your thinking before a board meeting, or you prefer a conversation over a document review. **Copy this prompt — no document needed, just start the conversation:** ``` You are a board-level strategy advisor running a structured diagnostic session using the Christensen × Lakhani AI Strategy framework. You will interview me in batches of questions — ask one batch, wait for my full answer, then continue. Work through these topics in order: 1. Customer segment — who specifically is the first buyer, and why are they urgent? 2. Pain and urgency — what are they doing today and why is it failing them? 3. Product and proof — what have you learned from early customers that changed the product? 4. Learning loops — what signals are you collecting systematically, not anecdotally? 5. AI leverage — where is AI actually reducing cost or accelerating learning? 6. Scaling readiness — what proof must exist before you move upmarket? 7. Board decisions — what are you genuinely uncertain about right now? After completing all topics, produce a Board Conductor Session Summary: **Executive Diagnosis** — Does this company have a clear wedge, a real learning engine, and enough proof to move? **Scorecard** — Rate each dimension 1–5 based on what I described: - Wedge Clarity - Learning Velocity - AI Operating Leverage - Data Laboratory Potential - Upmarket Readiness - Board Decision Quality - Risk Discipline Identify the **two lowest-scoring dimensions** as Priority Gaps and ask me one follow-up question about each. **Top 5 Board Questions** — Drawn specifically from what I said, not generic. **Next Actions** — Three concrete steps in priority order. ``` --- ### 4. Upload Pitch Transcripts or Customer Call Notes > *Use this when*: the company has multiple possible wedges and wants to determine which one the market is pulling toward. Most useful after the first 3–10 customer or prospect conversations. **Copy this prompt — then attach or paste your transcripts or call notes:** ``` You are a board-level strategy advisor applying the Christensen × Lakhani AI Strategy framework, with a focus on wedge discovery. I am sharing pitch transcripts or customer call notes from a company that is in Wedge Discovery Mode — it has multiple plausible wedge candidates and needs to determine which one the market is pulling toward. For each conversation, identify: - Who was the buyer or stakeholder, and what is their role? - Which use case or wedge did they respond to most strongly? - Did they signal pain, urgency, budget, regulation, technical feasibility, or strategic fit? - Did they request a next step or introduce another stakeholder? - Did they connect the product to an existing budget or initiative? - Did they repeat the company's language back, or reframe the product in their own words (a strong pull signal)? - Which wedge candidate does this conversation most support? After analyzing all conversations, produce a Wedge Signal Report: **Executive Diagnosis** — Which wedge candidate has the strongest current market pull, and why? Is this company ready to move from Wedge Discovery Mode to Wedge Execution Mode? **Conversation Signal Table** — One row per conversation: | Conversation | Stakeholder | Wedge Candidate | Pull Signals | Budget/Urgency | Next Step Requested | Strength (1–5) | **Wedge Discovery Map** — One row per wedge candidate: | Candidate Wedge | Evidence of Pull | Buyer Urgency | Budget Clarity | Deployment Difficulty | Data/Moat Potential | Expansion Path | Current Score | **Strongest Buyer-Language Quotes** — Direct quotes (or close paraphrases) that show the market speaking back to the company in its own words. **False-Positive Signals** — Interest that is likely curiosity or politeness rather than real pull. Explain why each may not convert. **Recommended Wedge Hypothesis** — The single wedge the company should prioritize for the next 90 days, with the evidence supporting that choice. **Evidence Required to Confirm or Reject** — What specific signals, in the next 30–60 days, would confirm this wedge or force a pivot to another candidate? **30/60/90-Day Wedge Decision Gate** — Define when and how the company should formally choose its primary wedge, and what evidence must exist at that gate. ``` --- ## Scorecard Use this to score any company after a review. Fill in your scores and notes. | Category | Score (/5) | Notes | |---|---|---| | Wedge Discovery / Wedge Clarity | | | | Learning Velocity | | | | AI Operating Leverage | | | | Data Laboratory Potential | | | | Upmarket Readiness | | | | Board Decision Quality | | | | Risk Discipline | | | | **Raw Total** | **/35** | | | **Overall Score** | **%** | (total ÷ 35) × 100 | **Reading the score**: 80–100% = strong position with specific gaps to close; 51–79% = meaningful work required in multiple areas; 50% or below = foundational questions unresolved before scaling. **Wedge Discovery / Wedge Clarity rubric:** | Score | Description | |---|---| | 1 | No clear wedge and no structured process for comparing opportunities. | | 2 | Several possible wedges, but prioritization is anecdotal or founder-driven with no evidence framework. | | 3 | Multiple plausible wedges with some market signal; discovery process exists but needs more structure and a defined decision gate. | | 4 | Strong wedge candidates are being compared using clear evidence: urgency, budget, access, deployment path, data advantage, and expansion potential. | | 5 | One wedge has been selected with strong proof of repeatability — or the company has a disciplined discovery process that will force a selection decision by a defined date. | --- ## Top 20 Board Questions ### Wedge 1. What makes this specific segment the right place to prove repeatability before any other? 2. What is the urgency signal — why would a buyer in this segment purchase in the next 90 days rather than wait? 3. What are the three closest competitors for this specific segment, and why do you win? ### Learning Velocity 4. What did you learn from customers last month that changed a product or business decision? How was that signal captured? 5. What do you know after your first 20 customers that a well-funded competitor starting today would not know? 6. How long does it take from a customer observation to a product decision? What would cut that time in half? ### AI Leverage 7. Where specifically does AI reduce your cost to serve or accelerate your feedback cycle? What is the measured outcome? 8. What manual process are you still running that AI could handle before you hire for it? 9. In 18 months, what will AI allow you to do that you cannot do today without hiring for it? ### Data Assets 10. What proprietary data are you accumulating that a competitor starting today could not replicate in two years? 11. Is your data structured for learning — or is it stored but not usable? 12. What would you know about your customers at 100 customers that you do not yet know at 20? ### Upmarket Readiness 13. What is the specific proof point that must exist before you begin active upmarket expansion? 14. Which two or three of your current customers most resemble the upmarket ICP? What does their experience tell you? 15. What does your current customer say about you to their peer who is twice the size? ### Risk 16. What assumption in your current plan, if proven wrong, would require the most significant change in strategy? 17. What evidence has come in over the last 90 days that made you less confident in something you believed at the start of the quarter? 18. Where in the business is a competitor most likely to outlearn you before you notice? 19. What risk is the board currently underweighting? 20. What decision is being deferred that should be made now? --- ## Output Template When reviewing a company with this skill, produce output in this format: ```markdown # Board Conductor Starter Review ## Company [Company name] ## Materials Reviewed [Deck / business plan / board memo / interactive session] ## Primary Question Are we learning fast enough to discover, prove, and scale the right wedge? ## 1. Executive Diagnosis [Concise paragraph: is this company in Wedge Discovery Mode or Wedge Execution Mode? Does it have a credible learning engine? What is the single most important strategic question it faces?] ## 2. Wedge Assessment **Wedge Mode:** [Discovery / Execution] [If Discovery Mode:] - Wedge candidates under consideration: - Strongest current pull signal: - Recommended next evidence to gather: [If Execution Mode:] - Current wedge: - Strength: - Weakness: - Recommended refinement: ## 2a. Wedge Discovery Map *(Complete if in Wedge Discovery Mode. Omit if in Wedge Execution Mode with a single confirmed wedge.)* | Candidate Wedge | Evidence of Pull | Buyer Urgency | Budget Clarity | Deployment Difficulty | Data/Moat Potential | Expansion Path | Current Score | |---|---|---:|---:|---:|---:|---|---:| ### Current Wedge Hypothesis [State the best current hypothesis for which wedge the market is pulling toward most strongly.] ### Evidence That Would Change the Hypothesis [State what market signal in the next 30–60 days would cause the company to reprioritize a different wedge candidate.] ### 30/60/90-Day Wedge Decision Gate [Define when and how the company should formally choose its primary wedge, and what evidence must exist at that point.] ## 3. Learning Engine Assessment - What the company is learning: - What it is not yet measuring: - Highest-value feedback loop to add: ## 4. Data Laboratory Assessment - Reusable data assets: - Reusable workflows: - Automation opportunities: - Missing instrumentation: ## 5. Board Conductor Assessment - Most important board decision: - What should not be decided yet: - What evidence is missing: ## 6. Scorecard | Category | Score (/5) | Rationale | |---|---:|---| | Wedge Discovery / Wedge Clarity | | | | Learning Velocity | | | | AI Operating Leverage | | | | Data Laboratory Potential | | | | Upmarket Readiness | | | | Board Decision Quality | | | | Risk Discipline | | | | **Raw Total** | **/35** | | | **Overall Score** | **%** | (total ÷ 35) × 100 | ## 7. Risk Flags - [Risk flag]: [One-sentence explanation] ## 8. Top Board Questions 1. 2. 3. 4. 5. 6. 7. ## 9. 30/60/90-Day Action Plan ### Next 30 Days - ### Days 31–60 - ### Days 61–90 - ``` --- ## Source Library The prompts above draw on the following frameworks. Referencing these in your conversation will help the AI produce more grounded, specific analysis. **Christensen** - *The Innovator's Dilemma* (1997) — the core disruption pattern: entrant wins in overlooked segment, moves upmarket, incumbent ignores it until too late - *The Innovator's Solution* (2003) — how to build a disruptive business deliberately; the role of the "jobs to be done" framing in finding the right wedge - Jobs to Be Done theory — customers hire products to do a specific job; ICP clarity comes from understanding the job, not the demographic - Resource / Process / Values (RPV) framework — why incumbents structurally cannot respond to disruption even when they see it coming **Lakhani** - *Competing in the Age of AI* (2020, with Marco Iansiti) — AI changes the operating model, not just the product; AI-native firms scale without proportional headcount; learning systems compound - Digital operating model — firms that instrument every customer interaction and automate feedback loops outlearn traditional organizations over time - AI as a general-purpose technology — transforms the cost structure of learning, not just production; the firm that learns fastest sets the cost floor for everyone else - Platform and ecosystem dynamics — data advantages compound when shared across customers; proprietary data moats require deliberate accumulation strategy --- ## What Is in the Full Version (B0.1) The full skill ([B0.1](./B0.1-christensen-lakhani.md)) adds: - **Complete 1–5 rubric** for all seven scorecard dimensions — what a 1, 3, and 5 look like, with evidence anchors and specific red flags for each - **12-item risk flag library** — each flag includes a definition, why it matters, evidence to look for, a board question, and a suggested intervention - **Two complete worked examples** — a pre-seed logistics AI startup (Northstar Ops AI) and a mid-sized industrial manufacturer evaluating AI disruption (Harbor Manufacturing Group) — each with a full review output - **Customization guide** — how to adapt the skill for venture studios, specific industries, specific stages, and portfolio reporting formats - **Board Conductor integration** — how this skill connects to the broader V.S. Foundry governance stack --- ## Privacy and Disclaimer - Do not upload confidential client materials to a public repository. - Use fictional or sanitized examples in any shared outputs. - Review all AI-generated outputs before sharing with investors, boards, or portfolio companies. - This skill provides strategic guidance only. It is not legal advice, financial advice, accounting advice, investment advice, or fiduciary guidance of any kind. --- ## About V.S. Foundry V.S. Foundry builds AI governance tools for founders and boards — from daily co-founder support to monthly board-level diagnostics across a full portfolio. [vsfoundry.com]