1. Shifting Landscape in M&A
Artificial intelligence (AI) is increasingly shaping mergers and acquisitions (M&A) due diligence, but contrary to some claims, AI compliance is not yet a legally mandated core workstream in every transaction. Instead, it is an evolving focus area that reflects how regulators, industries, and buyers are adapting to the rapid integration of AI into business operations.
2. Regulatory Drivers
Recent developments, such as the SEC’s 2024 disclosure requirements, demonstrate that regulators now expect companies to account for AI use in financial reporting. Organizations must show that their AI systems generate explainable and auditable results. This marks an important step toward integrating AI oversight into compliance, but it remains sector- and jurisdiction-specific rather than universal.
3. Legal Due Diligence Challenges
The growing complexity of AI regulation means that legal due diligence must now consider which frameworks apply to the target. Global firms note that the EU’s AI Act, alongside data protection laws like GDPR and HIPAA, are becoming central to assessing risks. Depending on the industry and geography, compliance obligations can vary widely, creating uneven pressure on M&A processes.
4. Industry-Specific Pressures
The degree of AI scrutiny in M&A depends largely on the industry. Buyers acquiring companies with heavy AI reliance must ensure those systems comply with both local and international standards. For instance, healthcare acquisitions raise HIPAA concerns, while financial services face SEC and EU AI Act implications. This sectoral approach reinforces why AI due diligence is highly relevant but not universally mandatory.
5. Market Expectations
Beyond regulation, investor expectations are also driving change. As AI becomes embedded in business operations, buyers increasingly want assurances about compliance, governance, and ethical use. This creates market pressure for companies to treat AI due diligence as a best practice, even in industries where regulators have not yet imposed strict requirements.
6. Reality Check
Despite this momentum, AI compliance should be seen as an emerging standard rather than an absolute legal requirement across all deals. While regulators and industry leaders stress its importance, the claim that it is “mandatory in all M&A transactions” overstates the current reality. It is critical in AI-intensive deals, but less central in transactions where AI plays a minimal role.
7. Bottom Line
The future is moving toward deeper integration of AI compliance in M&A due diligence. As regulations mature and best practices solidify, AI scrutiny could become as routine as financial or cybersecurity checks. For now, it remains a rapidly growing, but not universal, component of dealmaking.
Opinion:
The current environment suggests that AI compliance is on track to become a mainstream requirement in M&A due diligence within the next few years, but it is premature to call it universally mandatory today. Overstating its status risks creating confusion, yet underestimating its importance could expose buyers to significant legal and operational risks. The prudent path is to treat AI compliance as an essential best practice now, in anticipation of its likely evolution into a true regulatory mandate.
✅ AI in M&A Due Diligence – Checklist
1. Regulatory & Legal Compliance
- Identify applicable laws (EU AI Act, GDPR, HIPAA, SEC disclosure rules).
- Confirm AI system explainability and auditability.
- Review contracts for AI-related compliance obligations.
- Assess cross-border AI use and jurisdictional risks.
2. Governance & Risk Management
- Evaluate AI governance policies and accountability structures.
- Check for AI ethics frameworks (bias, transparency, fairness).
- Review internal AI risk assessments or audits.
- Verify incident response procedures for AI-related failures.
3. Data Management
- Ensure compliance with data privacy and security standards.
- Confirm data provenance and consent for training datasets.
- Assess data retention and deletion practices.
- Review cross-border data transfer mechanisms.
4. Technical Due Diligence
- Evaluate accuracy, reliability, and robustness of AI models.
- Test explainability tools and outputs.
- Identify use of third-party AI vendors or APIs.
- Confirm compliance with model monitoring and update practices.
5. Industry-Specific Requirements
- Healthcare: HIPAA + medical device AI rules.
- Finance: SEC disclosure + algorithmic trading oversight.
- Consumer/Tech: GDPR + digital services laws.
- Defense/Energy: Export controls + critical infrastructure standards.
6. Deal Impact Considerations
- Assess potential liabilities tied to AI systems.
- Evaluate reputational risks from AI misuse or bias.
- Review IP ownership of AI models and training data.
- Consider future regulatory costs in valuation models.
7. Post-Deal Integration
- Plan for harmonizing AI governance with acquirer’s framework.
- Align ongoing compliance monitoring processes.
- Train staff on responsible AI use.
- Schedule periodic AI audits post-acquisition
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