Private Equity AI Due Diligence: The $15M Mistake You Can't Afford

Why AI Due Diligence Is Different

Traditional tech due diligence focuses on code quality, infrastructure, and security. That's not enough for AI systems.

AI models have hidden liabilities that don't show up in a code review:

• Bias: Systematic discrimination against protected groups
• Brittleness: Models that fail catastrophically on edge cases
• Data dependencies: Models that break if data sources change
• Regulatory risk: Non-compliance with AI regulations (EU AI Act, NIST AI RMF, etc.)
• Reputational risk: Models that generate offensive or dangerous outputs

The Complete AI Due Diligence Checklist

Phase 1: Discovery (Week 1)

1.1 AI System Inventory

Get a complete list of all AI/ML systems:

• What AI models are in production?
• What business functions do they support?
• What's the revenue/cost impact of each model?
• What data sources feed each model?
• Who built the models (internal vs. vendor)?

1.2 Regulatory Exposure Assessment

• Are any models subject to FDA/FAA/FCC oversight?
• Do models make decisions about protected classes (lending, hiring, housing)?
• Is the company subject to GDPR, EU AI Act, or state AI laws?
• Have regulators ever reviewed these models?

1.3 Documentation Review

• Does model documentation exist? (Model cards, datasheets, validation reports)
• Is there a model development lifecycle policy?
• Are there incident response procedures for model failures?
• Is there a model inventory with risk tiers?

Phase 2: Technical Deep Dive (Week 2-3)

2.1 Model Architecture Review

• Model type: Traditional ML (XGBoost, etc.) or deep learning?
• Complexity: Can the model be explained to non-technical stakeholders?
• Interpretability: Are there tools to explain individual predictions?
• Versioning: Is there proper model version control and rollback capability?

2.2 Training Data Assessment

• How much training data exists? Is it sufficient for the task?
• Where did the training data come from?
• Is the data representative of deployment conditions?
• Are there data rights issues (licensing, PII, copyright)?
• Is there demographic/geographic coverage in the data?

2.3 Bias & Fairness Testing

Critical: This is where most hidden liabilities are found.

• Demographic parity: Do approval/rejection rates differ by group?
• Equal opportunity: Do true positive rates differ by group?
• Calibration: Are predicted probabilities accurate for all groups?
• Intersectionality: Test combinations (e.g., Black women vs. white men)

2.4 Adversarial Robustness Testing

• Input perturbations: Can small input changes flip predictions?
• Out-of-distribution detection: How does the model handle unusual inputs?
• Prompt injection (for LLMs): Can users extract training data or bypass safety filters?
• Evasion attacks: Can adversaries game the system?

2.5 Performance Validation

• Test/train splits: Are they properly isolated (no data leakage)?
• Backtest vs. live performance: How much degradation in production?
• Error analysis: What types of errors does the model make?
• Edge case testing: Performance on rare but critical scenarios

Phase 3: Operational & Compliance Review (Week 3-4)

3.1 Model Governance

• Who owns model risk management?
• Is there a model risk committee or AI governance board?
• Are there independent validators (separate from developers)?
• How are model changes approved and deployed?

3.2 Monitoring & Alerting

• Are models monitored for drift (data, concept, prediction)?
• Are there automated alerts for performance degradation?
• Is there a dashboard for model health metrics?
• How quickly can the team detect and respond to model failures?

3.3 Incident History

• Have there been model failures or near-misses?
• Were they documented and analyzed?
• What was the financial/reputational impact?
• Were root causes addressed?

3.4 Vendor Dependencies

• Are models built on third-party APIs (OpenAI, AWS, etc.)?
• What happens if vendor pricing changes or service is discontinued?
• Are there vendor lock-in risks?
• Are SLAs in place for critical vendor services?

Phase 4: Financial Impact Analysis (Week 4)

4.1 Technical Debt Quantification

Estimate the cost to remediate identified issues:

• Bias remediation: $200K - $2M (retrain model with fairness constraints)
• Documentation gap closure: $50K - $300K
• Monitoring infrastructure: $100K - $500K
• Regulatory compliance: $300K - $3M (if non-compliant)

4.2 Risk-Adjusted Valuation

Adjust acquisition price based on:

• Remediation costs: Direct dollar-for-dollar reduction
• Delayed go-live: Opportunity cost if models can't be deployed immediately
• Regulatory holdups: Potential 6-18 month delay if compliance gaps exist
• Reputational risk: Insurance/indemnity for undiscovered bias

Common Hidden Liabilities We've Found

Deal Structure Options Based on Findings

Option 1: Price Reduction (Most Common)

Reduce acquisition price by estimated remediation cost × 2 (to account for execution risk and opportunity cost).

Example: $2M in identified issues → $4M price reduction

Option 2: Earnout Tied to Remediation

Defer portion of purchase price until AI issues are resolved and validated by independent auditor.

Example: $5M holdback for 12 months until bias metrics meet thresholds

Option 3: Indemnification & Insurance

Seller provides indemnity for AI-related claims within first 18-24 months post-close.

Example: $10M indemnity cap for regulatory fines or bias-related lawsuits

Option 4: Walk Away

If AI issues are existential (e.g., model fundamentally biased and can't be fixed), consider terminating the deal.

Red flags for walking:

• Active regulatory investigation
• Bias so severe it requires complete model rebuild (12-24 months)
• Critical data rights issues that can't be resolved
• Seller unwilling to provide any reps/warranties on AI systems

Post-Close Integration Checklist

If you proceed with the deal, here's what to do in the first 90 days:

1. Day 1: Freeze model deployments until validation complete
2. Week 1: Implement monitoring for all production models
3. Week 2: Begin bias remediation for high-priority models
4. Month 1: Complete documentation for regulatory-facing models
5. Month 2: Independent validation of all Tier 1 models
6. Month 3: Board-level AI risk report with remediation roadmap

Cost-Benefit Analysis

How to Get Started

If you're evaluating an AI/ML company for acquisition:

1. Request AI system inventory from target (2-3 days)
2. Engage independent AI validator (us or similar firm)
3. Allow 4-6 weeks for thorough technical due diligence
4. Budget $50K-$200K depending on complexity