What's Happening?
Norton Rose Fulbright, a global law firm, highlights a significant shift in private equity (PE) buy-side transactions, where Artificial Intelligence (AI) due diligence is no longer a niche concern but a critical workstream. This change is driven by the
proliferation of AI laws in the United States and internationally, making AI-related risks relevant even for companies that only use AI in specific business functions. PE buyers are now conducting robust AI due diligence in all transactions involving AI, regardless of whether AI is the target company's primary focus. This includes negotiating AI-specific representations and warranties in purchase agreements. The firm notes that AI diligence now intersects with intellectual property, privacy, cybersecurity, product performance, customer contracting, governance, and sector regulation, moving beyond a simple IT checklist. Buyers are increasingly scrutinizing AI governance policies, user training, monitoring, incident reporting, human-in-the-loop controls, and data governance to address retention, data rights, segregation, bias, and explainability.
Why It's Important?
This heightened focus on AI due diligence by firms like Norton Rose Fulbright signifies a maturing understanding of AI's pervasive impact across industries and its associated legal and operational risks. For U.S. businesses, particularly those involved in mergers and acquisitions, this means a more rigorous evaluation process for AI capabilities and liabilities. Companies that develop or utilize AI must ensure their governance, data handling, and compliance frameworks are robust to avoid potential misrepresentations, indemnification claims, and diminished-value disputes post-acquisition. The emphasis on documented AI governance policies and explainability is crucial, especially for customer-facing or legally regulated functions, as it directly impacts a company's ability to demonstrate compliance with evolving AI laws. This trend will likely drive greater investment in AI ethics, transparency, and accountability within U.S. corporations to maintain market value and attract investors.
What's Next?
As AI regulations continue to develop, PE buyers will likely further integrate AI-specific representations and warranties into purchase agreements, making them a standard component of M&A deals. Companies will face increasing pressure to provide clear evidence of AI model performance, intended use cases, known limitations, and comprehensive documentation. The legal framework for AI is still evolving, but buyers are prioritizing actual governance discipline over generic statements of 'monitoring evolving law.' This will necessitate that target companies proactively establish and demonstrate strong AI governance, data provenance, and compliance measures. Furthermore, the role of tailored representation and warranty insurance in AI-heavy transactions is expected to grow, though insurers will likely scrutinize data provenance and model performance more closely, potentially carving out certain high-risk issues from coverage.
Beyond the Headlines
The shift towards comprehensive AI due diligence reflects a broader societal and legal reckoning with the ethical and practical implications of AI. Beyond financial and legal risks, this trend highlights the growing importance of trust and transparency in AI systems. The demand for documented AI governance policies, bias mitigation, and explainability underscores a move towards responsible AI development and deployment. This could lead to a competitive advantage for companies that prioritize ethical AI practices, as they will be perceived as more reliable and less risky by investors and regulators. The focus on data provenance also raises fundamental questions about data ownership, privacy, and the potential for legal challenges related to web scraping and licensed data, pushing for clearer legal precedents in these areas.
