AI Due Diligence for Investors: Ten Questions Before Funding the Theme
A practical framework for evaluating AI-linked companies, from data advantage and workflow integration to unit economics and governance.
AI is now attached to almost every investment story. That makes due diligence more important, not less. Investors need a framework that separates durable AI advantage from generic exposure to a popular theme.
The core question is whether AI changes economics. Does it improve margins, retention, speed, quality, risk management, or market reach? Or does it simply add cost and marketing language?
Ten questions to ask
- What workflow does AI improve, and how often is that workflow used?
- Is the outcome measurable in revenue, margin, speed, quality, or risk?
- Does the company have proprietary data or distribution?
- Can competitors access similar models and tools?
- How much does inference or training cost at scale?
- Who owns governance, security, and compliance?
- Does AI reduce churn or increase switching costs?
- Are employees trained to use the system effectively?
- What happens if model prices fall or open-source alternatives improve?
- Where is AI visible in financial statements?
From narrative to numbers
Strong AI companies should be able to connect capabilities to operating metrics. A software firm may show faster product release cycles. A services company may show higher consultant utilization. An industrial company may show less downtime. A financial platform may show better risk controls or customer personalization.
The best AI diligence turns a theme into testable unit economics.
AI can create enormous value, but investors need to avoid paying for undifferentiated enthusiasm. A rigorous framework makes the opportunity investable.