A comprehensive guide to AI business due diligence, including model validation, data quality assessment, and technical infrastructure evaluation for successful AI exits.

**Key Topics:**

– AI model validation and performance testing

– Data quality, bias, and fairness assessments

– Technical infrastructure and security audits

– Intellectual property verification

– Regulatory compliance reviews

– Team expertise and knowledge transfer

– Integration complexity and timeline assessment