AI-Assisted Due Diligence: Governance for M&A and Corporate Transactions

# AI-Assisted Due Diligence: Governance for M&A and Corporate Transactions **Category: Legal & Professional Services AI** *Published by the AI Accountability Standards Council (AASC)* --- ## Executive Summary The integration of artificial intelligence into mergers, acquisitions, and broader corporate transaction workflows offers significant efficiency gains, but introduces governance challenges that legal and compliance professionals must carefully navigate. AI systems used in due diligence contexts may process sensitive financial data, surface legal risks, and inform high-stakes recommendations—activities that demand rigorous human oversight and documented accountability frameworks. Organizations pursuing AI-assisted due diligence should consider establishing clear governance structures before deployment, not after. --- ## Key Considerations ### 1. The Stakes Are Materially Higher Than Routine AI Use Due diligence is not a low-risk AI application domain. The outputs of AI systems used in M&A contexts may directly influence transaction valuations, representations and warranties, regulatory filings, and post-close integration planning. A miscategorized contract clause, an overlooked environmental liability, or a misread financial covenant can translate into significant financial exposure for acquiring entities and their advisors. This context places AI-assisted due diligence squarely within what the **EU AI Act** would characterize as high-stakes application territory. While the Act does not explicitly enumerate M&A due diligence as a regulated high-risk use case, organizations should be aware that AI systems processing sensitive commercial data in consequential decision-making contexts will likely face increasing regulatory scrutiny as frameworks mature globally. ### 2. Model Transparency and Explainability Are Not Optional Legal professionals relying on AI-generated summaries, risk flags, or document classifications need to understand how those outputs were produced. The concept of explainability—a core pillar of the **NIST AI Risk Management Framework (AI RMF)**—is particularly critical here. Attorneys, investment bankers, and compliance officers bear professional obligations to exercise independent judgment; they cannot fulfill those obligations if AI reasoning is opaque. Organizations should seek to understand, at minimum, what training data informed the model, how confidence scores are calculated, and what the system's documented limitations are for the specific document types being analyzed. Vendor claims of accuracy should be treated skeptically unless accompanied by verifiable validation data relevant to the transaction type and jurisdiction in question. ### 3. Data Governance and Confidentiality Exposure Deserve Dedicated Attention Due diligence processes typically involve highly sensitive third-party data—target company financials, employee records, customer contracts, and proprietary operational information. Feeding this material into AI systems, particularly those hosted by third-party vendors, raises legitimate questions about data residency, model training practices, and information barrier integrity. **ISO 42001**, the international standard for AI management systems, provides a useful framework for organizations establishing data governance policies specific to AI workloads. Key questions include whether vendor agreements contain explicit prohibitions on using client data for model training, where data is processed and stored, and whether confidentiality obligations owed to transaction counterparties are adequately preserved throughout the AI processing pipeline. ### 4. Human Accountability Cannot Be Delegated to the Algorithm Regardless of how sophisticated an AI due diligence tool may be, professional responsibility for transaction advice remains with the human practitioners involved. This principle should be embedded structurally, not assumed informally. Organizations should establish clear protocols defining which AI outputs require independent verification, at what thresholds human review is mandatory, and who holds documented accountability for each category of AI-assisted analysis. --- ## Implementation Framework Organizations deploying AI in due diligence workflows may benefit from a phased governance approach: **Phase 1 – Pre-Deployment Assessment** Before introducing any AI tool into live transaction work, conduct a structured risk assessment addressing the tool's intended scope, data handling practices, model validation history, and vendor accountability mechanisms. Document the assessment and ensure senior legal or compliance leadership has reviewed and signed off. **Phase 2 – Pilot and Validation** Run AI tools in parallel with existing manual processes on a representative sample of transaction types before full deployment. Compare outputs, document discrepancies, and establish baseline performance benchmarks. This phase may also surface edge cases or document types where the tool performs poorly. **Phase 3 – Operational Controls** Establish written protocols covering: which staff are authorized to use AI tools, what output requires mandatory human verification, how AI-generated findings are labeled in work product, and how errors or anomalies are escalated and recorded. **Phase 4 – Ongoing Review** AI tools and their underlying models change over time. Build periodic review cycles into your governance calendar—at minimum annually, or whenever significant model updates occur—to reassess whether existing controls remain adequate. --- ## Documentation Requirements Accountable AI use in M&A and corporate transactions typically requires maintaining records across several dimensions: - **Tool inventory**: A log of AI systems used in transaction workflows, including vendor name, version, deployment date, and stated capabilities - **Risk assessments**: Dated, signed records of pre-deployment and periodic risk evaluations - **Data processing records**: Documentation of what data was processed by AI systems, under what contractual terms, and whether any confidentiality restrictions applied - **Validation records**: Evidence of testing performed and results achieved, with clear notation of any known limitations - **Human review logs**: Records demonstrating that AI outputs were reviewed and verified by identified qualified professionals before being incorporated into transaction advice or deliverables - **Incident records**: Documentation of any instances where AI outputs were found to be materially inaccurate or misleading, including remediation steps taken These records may prove valuable not only for internal governance purposes but in the event of post-transaction disputes, regulatory inquiries, or professional liability proceedings. --- ## Risk Assessment Checklist Organizations should assess the following risks before and during AI-assisted due diligence engagements: - **Accuracy risk**: Has the AI tool been validated on document types, languages, and jurisdictions relevant to this transaction? - **Confidentiality risk**: Do vendor agreements adequately protect sensitive target company data from unauthorized use or disclosure? - **Explainability risk**: Can practitioners understand and articulate how AI-generated findings were produced? - **Overreliance risk**: Are staff appropriately skeptical of AI outputs, or are there cultural pressures to accept AI findings uncritically? - **Coverage risk**: Are there known categories of documents or issues the AI tool may miss or mischaracterize? - **Vendor dependency risk**: What happens to transaction continuity if the vendor experiences an outage, changes pricing, or discontinues the tool? - **Regulatory evolution risk**: Are current practices likely to remain appropriate as AI governance regulations mature in relevant jurisdictions? - **Integration risk**: How are AI-generated outputs integrated into broader work product, and are there adequate checkpoints to catch propagation of errors? --- ## Best Practices Industry experience and emerging governance guidance suggest several practical approaches for legal and professional services firms: - **Separate the AI tool from the AI opinion.** AI output should function as a first-pass triage mechanism, not a final analytical conclusion. Work product should clearly distinguish AI-assisted findings from practitioner-verified assessments. - **Invest in staff training before deployment.** Practitioners need to understand both the capabilities and the limitations of the tools they are using. Training should include deliberate exposure to cases where the tool produces inaccurate or incomplete results. - **Establish matter-specific governance plans.** Large, complex transactions may warrant bespoke AI governance documentation beyond standard firm-wide policies, particularly where novel document types or cross-border data flows are involved. - **Build vendor accountability into procurement.** AI vendor agreements should include provisions addressing model change notification, audit rights, data handling obligations, and representations regarding system performance—not just standard SaaS terms. - **Align internal AI governance with evolving external frameworks.** Organizations that structure their practices to be broadly aligned with the NIST AI RMF, ISO 42001, and the principles underlying the EU AI Act will be better positioned to adapt as formal requirements crystallize. --- ## Next Steps Establishing accountable AI governance in M&A and corporate transaction contexts is an ongoing process, and most organizations are at different stages of that journey. Whether your firm is evaluating AI tools for the first time or seeking to strengthen existing oversight mechanisms, building from a structured framework is generally more effective than addressing issues reactively. The **AI Accountability Standards Council** offers a range of free resources designed to help legal and compliance professionals develop practical, proportionate AI governance practices. These include self-assessment tools, documentation templates, and guidance documents covering high-stakes AI applications across professional services contexts. Practitioners may find these resources particularly useful as a starting point for internal policy development or as a reference for evaluating vendor claims against recognized governance principles. Exploring the AASC resource library requires no commitment—the goal is simply to make thoughtful AI governance more accessible to the organizations that need it most. --- *This article is intended for educational purposes only and does not constitute legal advice. Organizations should consult qualified legal counsel regarding their specific obligations and circumstances.* *© AI Accountability Standards Council. Reproduction for non-commercial purposes is permitted with attribution.*