N10-10: AI Due Diligence Report Template
The complete framework for delivering a board-ready AI due diligence report.
🎯 What You'll Learn
- ✓ Structure due diligence findings
- ✓ Present risk-adjusted valuations
- ✓ Build recommendation frameworks
- ✓ Deliver board-ready reports
Lesson 1: The 7-Section DD Report
The AI due diligence report has 7 sections: (1) Executive Summary — 1-page go/no-go with confidence level, (2) Revenue Quality — real AI revenue vs hype, (3) Technology Assessment — moat, infrastructure, model quality, (4) Team Assessment — talent depth, key-person risk, (5) Financial Model — unit economics and margin projections, (6) Risk Register — legal, technical, regulatory, and (7) Recommendation — buy/pass with conditions.
One page: go/no-go, confidence level (high/medium/low), 3 key findings.
Composite score: 1-5 based on AI attribution, NRR, and revenue durability.
Standard multiple adjusted for tech debt, key-person risk, and regulatory exposure.
Draft the executive summary of your AI due diligence report. Include go/no-go, confidence level, and 3 key findings.
Lesson 2: Risk-Adjusted Valuation Methodology
Start with comparable company multiples. Adjust down for: tech debt (PDI score discount), key-person risk (talent concentration discount), regulatory exposure (compliance cost discount), data moat weakness (defensibility discount). The risk-adjusted valuation is typically 15-40% below naive comparable valuations.
Tech debt PDI >100: apply 1-2x EBITDA multiple reduction.
If >50% of AI value concentrated in <3 people: apply 5-10% valuation discount.
Sum all risk discounts to arrive at the risk-adjusted valuation.
Apply the risk-adjusted valuation methodology to a target AI company. Show the walk from base valuation to risk-adjusted.
Lesson 3: Conditional Recommendation Framework
Rarely is the recommendation a clean "buy" or "pass." Use conditional recommendations: "Buy if: (1) retention bonuses are funded at $X, (2) SOC2 compliance is achieved within 12 months, (3) key-person risk is mitigated by hiring 2 additional ML engineers." Conditions make the recommendation actionable.
Specific, measurable conditions that must be met for the acquisition to succeed.
Findings that would change the recommendation to "pass."
If conditions can't be guaranteed pre-close, structure an earnout.
Draft a conditional recommendation for an AI acquisition. Include 3 buy conditions and 2 walk-away triggers.
Continue Learning: Track 10 — AI Due Diligence
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Module Syllabus
Lesson 1: Lesson 1: The 7-Section DD Report
The AI due diligence report has 7 sections: (1) Executive Summary — 1-page go/no-go with confidence level, (2) Revenue Quality — real AI revenue vs hype, (3) Technology Assessment — moat, infrastructure, model quality, (4) Team Assessment — talent depth, key-person risk, (5) Financial Model — unit economics and margin projections, (6) Risk Register — legal, technical, regulatory, and (7) Recommendation — buy/pass with conditions.
Lesson 2: Lesson 2: Risk-Adjusted Valuation Methodology
Start with comparable company multiples. Adjust down for: tech debt (PDI score discount), key-person risk (talent concentration discount), regulatory exposure (compliance cost discount), data moat weakness (defensibility discount). The risk-adjusted valuation is typically 15-40% below naive comparable valuations.
Lesson 3: Lesson 3: Conditional Recommendation Framework
Rarely is the recommendation a clean "buy" or "pass." Use conditional recommendations: "Buy if: (1) retention bonuses are funded at $X, (2) SOC2 compliance is achieved within 12 months, (3) key-person risk is mitigated by hiring 2 additional ML engineers." Conditions make the recommendation actionable.