AI Due Diligence: Complete Methodology for Investment Funds in 2025
A rigorous, 7-pillar framework for evaluating AI companies before investment — covering technical depth, data moats, regulatory exposure, and team composition. Backed by quantitative benchmarks and real fund case studies with measurable outcomes.
Executive Summary
The AI investment landscape has never been more complex — or more consequential. Global venture capital deployed into AI startups reached $97.4 billion in 2024, up 62% from $60.1 billion in 2023 (PitchBook, Q4 2024). Yet failure rates remain alarmingly high: 43% of AI startups that raised Series A rounds between 2020 and 2022 have either shut down, pivoted away from AI, or experienced >70% valuation resets by end of 2024 (CB Insights, 2024 AI Startup Mortality Report).
The root cause is rarely market timing. It is due diligence failure — specifically, the absence of a structured methodology capable of distinguishing genuine AI technical differentiation from superficial LLM wrappers, assessing the durability of data moats, and pricing regulatory tail risks that are now materializing in real enforcement actions.