AI Due Diligence: Complete Methodology for Investment Funds in 2025
As AI investments surpass $200B globally, standard due diligence frameworks are failing. This guide delivers a battle-tested, 7-pillar methodology with quantitative scoring, red flags, and case studies from $50M–$2B AI deals.
Executive Summary
The global AI investment landscape has undergone a seismic transformation. Venture capital and private equity firms deployed an estimated $91.9 billion into AI startups in 2024 alone — a 62% increase over 2023, according to PitchBook data. Yet the failure rate among AI-native companies within 36 months of a Series A remains stubbornly high at 43%, compared to 31% for non-AI software companies in the same period (CB Insights, 2024).
The gap between capital deployed and value created has one primary cause: standard due diligence frameworks were not built for AI. Traditional SaaS checklists miss the three defining risks of AI businesses — model degradation, data moat erosion, and inference cost inflation — while overweighting conventional metrics like ARR multiples and net revenue retention.