The dual AI governance challenge for PE
Private equity firms using AI in their own operations — deal sourcing algorithms, AI-assisted due diligence, portfolio monitoring systems, fund accounting AI — face direct AI governance obligations. These obligations include the same regulatory requirements that apply to any enterprise using AI: data protection law compliance, anti-discrimination obligations for AI used in employment decisions, and sector-specific requirements if the firm is FCA-authorised, SEC-registered, or otherwise regulated. The operational AI governance requirements for PE firms are real and largely unmet — most PE firms have not conducted AI inventories of their own systems and have not assessed the regulatory risk of their AI deployments.
The larger and more complex AI governance challenge for PE is at the portfolio level. Portfolio companies across the PE firm's holdings may have material AI governance gaps — gaps that create regulatory liability, reputational risk, and enterprise value impairment. Managing this risk requires a portfolio-level AI governance framework that goes beyond deal-by-deal due diligence to create systematic governance across all holdings.
AI governance in the deal lifecycle
Due diligence: AI governance has become a standard component of technology due diligence and increasingly of legal and commercial due diligence for AI-enabled businesses. The AI due diligence questions that matter most — training data provenance, bias testing history, regulatory compliance status, incident history — should be standard in your due diligence framework. AI governance failures discovered post-close are expensive to remediate and often create disclosed or undisclosed liabilities. The 100-day plan: for new portfolio companies with material AI, establish baseline AI governance within the first 100 days of ownership. This means: AI inventory, risk classification of the AI portfolio, assessment of regulatory compliance for high-risk AI, and appointment of an accountable AI governance lead. Portfolio monitoring: quarterly AI governance reporting should be part of the management information package for portfolio companies with material AI — covering AI inventory changes, incidents, regulatory developments, and governance programme status. Exit preparation: AI governance documentation should be prepared as part of exit readiness 12-18 months before target exit. This includes an AI due diligence data room, response to likely buyer AI governance questions, and remediation of identified governance gaps.
