Structuring AI-Heavy Deals: Allocating IP, Liability, and Regulatory Risk in M&A

In 2025, global M&A activity exceeded US$5.1 trillion, with technology and software deals accounting for more than 40% of the total transaction value. Within this context, AI-centric transactions have sustained double-digit annual growth, now accounting for an estimated 15-20% of strategic and private equity deal flow. Yet dealmakers are grappling with a paradox: while generative AI and advanced machine learning capabilities are driving valuation premiums, latent legal and regulatory exposures tied to these assets are escalating faster than market frameworks can adapt. 

Recent regulatory enforcement actions globally have imposed fines in the tens of millions of dollars for algorithmic bias and privacy violations linked to AI systems. At the same time, arbitration panels and courts are beginning to address disputes over ownership of AI training data and model derivatives that were previously assumed to transfer cleanly in a sale. In several high-profile cases involving major tech acquirers and sophisticated AI targets, deficiencies in contractual definitions around model rights and data lineage have led to material purchase price adjustments post-closing. 

Today, acquirers face a new reality: AI cannot be treated as another software module or bolt-on capability. Its value drivers are inextricably linked to deep technical dependencies, complex data rights, and an evolving global regulatory landscape. As a result, experienced M&A teams are recalibrating traditional diligence intensity, liability allocation, and intellectual property structuring into bespoke frameworks that materially affect valuation multiples and risk transfer mechanisms. 

Redefining Due Diligence Scopes for AI Assets 

In AI-driven transactions, traditional diligence frameworks are no longer sufficient. Models, training data, and inference pipelines are now core deal assets whose quality and compliance directly influence valuation and post-closing exposure. Expert buyers are moving beyond legal checks to combine technical audits, data provenance analysis, and model performance benchmarking, effectively quantifying latent risks and hidden liabilities before signing. 

More substantial Asset Characterisation Drives Price Certainty 

Traditional software due diligence focuses on code provenance and licensing compliance. AI transforms that scope: models, training data, feature pipelines, and inference logic are now discrete assets with distinct legal and operational qualities. According to a 2025 Deloitte survey, 72% of buyers now require technical model audits, up from 40% in 2022. 

Technical audits serve purposes beyond standard legal reviews: 

• Identifying undisclosed third-party dependencies, including proprietary APIs or model libraries with restricted redistribution rights. 
  

• Quantifying training data exposures, particularly where datasets include personal data triggering global privacy regimes such as GDPR, India’s DPDP, or CCPA. 
  

• Benchmarking performance drift risk, which can materially erode valuation assumptions in deals where revenue projections rely on generative AI outputs. 

Buyers are increasingly requiring data lineage documentation to trace the sources of datasets and their associated licensing terms. Deals with incomplete lineage disclosures often see 10–20% holdbacks against potential IP or privacy liabilities. 

Legal Representations Reflect Complex Ownership 

In AI-heavy deals, blanket “all rights owned” representations are insufficient. Modern agreements now include: 

• Detailed schedules of model weights, training artefacts, and derivative datasets. 
  

• Representations about compliance with specific statutes, including cross-border requirements. 
  

• Express disclosures of open-source dependencies and potential obligations to contribute enhancements. 

Where ambiguity persists, buyers utilise tiered indemnification provisions tied to specific AI asset classes, rather than broad, undifferentiated caps. 

Intellectual Property Allocation: Nuance Over Absolutes 

AI IP is inherently multi-layered and context-dependent, making conventional IP transfer clauses insufficient. Intellectual property now encompasses data sets, trained models, inference logic, and derivative outputs, each governed by distinct legal and operational rights. Sophisticated deal structures reflect this reality by tiering rights and creating bespoke ownership frameworks that protect value while limiting residual exposure. 

Layered Rights Over Singular Transfers 

AI intellectual property is multi-layered, encompassing: 

• Raw data 

• Trained model artefacts 

• Inference engines 

• Derived outputs 

Leading practitioners now use tiered rights frameworks: 

• Exclusive rights to core model weights and proprietary feature engineering. 
  

• Non-exclusive or field-limited rights to auxiliary models derived from external sources. 
  

• Retained rights for sellers to use pre-existing AI tooling that does not materially impact buyer revenue. 

Analysis by an international law firm reveals that up to 35% of post-close disputes in AI deals stem from poorly defined derivative rights in model outputs, rather than source code. 

Open Source and Third-Party Risk Management 

Open-source code remains both an enabler and a strategic risk. Licenses such as the GPL or AGPL can require the disclosure of proprietary enhancements. Leading buyers now mandate SPDX-compliant software composition analysis, with findings incorporated into indemnity carve-outs or price adjustments. 

Remediation strategies include: 

• Component replacement before signing 

• Escrowed remediation commitments 

• Price reductions tied to anticipated rewrite costs 

These mechanisms reflect the reality that open-source exposure can escalate into commercial disadvantage post-close. 

Liability Architecture in AI Transactions 

AI introduces new liability vectors that transcend traditional software defect risks. Algorithmic bias, data infringement, and regulatory non-compliance can manifest years after the closing, impacting both financial and reputational outcomes. Modern liability frameworks, therefore, align indemnification, escrow, and performance-based price adjustments with the unique contours of AI risk. 

Specific Liability Triggers Replace Generic Warranties 

Traditional warranty and indemnity models are insufficient for AI risk. Modern agreements use risk-aligned liability triggers mapped to: 

• Training data infringement 

• Algorithmic discrimination or bias claims 

• Unauthorised use of personal data 

• Regulatory non-compliance under AI-specific regimes 

  
  

These triggers are paired with indemnity baskets calibrated to risk class. Liability tied to the misuse of personal data may command a higher basket and more prolonged survival than legacy software IP violations. 

Escrow arrangements are evolving. Where AI performance materially influences transaction value, escrow amounts can represent 5–15% of enterprise value, released against performance benchmarks or regulatory clearance. 

Performance-Linked Price Adjustments 

Buyers increasingly include post-closing price adjustments tied to model performance or regulatory outcomes: 

• Revenue-based earnouts 

• Regulatory milestone releases 

• Cost recovery credits for compliance remediation 

These constructs hedge future uncertainty and align seller incentives with long-term value creation. 

Regulatory Risk Allocation: Integrating Legal and Strategic Design 

Regulatory oversight for AI is intensifying globally, encompassing antitrust, national security, and emerging legislation specific to the field of AI. Unlike conventional mergers and acquisitions (M&A), regulatory risk in AI deals can directly impact transaction structure, timing, and valuation. Leading practitioners incorporate proactive regulatory strategy into deal design, embedding compliance conditions, approval contingencies, and mitigation levers into contracts to safeguard against enforcement action. 

Competition and National Security Scrutiny 

Competition authorities in the US, EU, and select Asian markets now examine AI deals not merely through market share but through innovation concentration and data dominance metrics. In 2025, several technology deals triggered second-request processes based on the consolidation of AI capabilities, even where market share was modest. 

Foreign investment review boards scrutinise data sovereignty and AI governance controls, particularly in defence, healthcare, and critical infrastructure sectors. Effective deal design embeds regulatory pre-clearance conditions, tailored termination rights, and reverse break fees. 

Emerging AI-Specific Regulation 

The EU Artificial Intelligence Act and other emerging regulatory frameworks impose substantive compliance requirements on specific AI systems. Purchasers now: 

• Classify target AI systems under applicable laws 

• Allocate remediation obligations for non-conforming systems 

• Price regulatory compliance costs into valuations as contingent adjustments 

  
  

Regulatory compliance is now a deal term as consequential as IP or liability. 

Integration Imperatives Post-Close 

Paper allocations of risk must translate into operational governance. Leading acquirers implement: 

• AI risk oversight committees with cross-functional leadership 

• Continuous compliance monitoring linked to regulatory reporting obligations 

• Structured model retirement and upgrade roadmaps to forestall obsolescence or drift risk   

Integration plans increasingly include KPI definitions for model performance, bias mitigation, and scheduled audits. 

Conclusion: The New Playbook for AI-Heavy M&A 

AI is reshaping deal structuring with precision. Leading firms recognise that: 

• Due diligence must probe technical and legal dimensions with equal rigour. 
  

• Intellectual property must be unbundled, and rights tiered to reflect real economic value. 
  

• Liability constructs should be specific, measurable, and aligned with downstream risk. 
  

• Regulatory risk is a core deal term, not a peripheral compliance exercise. 

  
  

In high-stakes AI transactions, the margin between success and costly post-closing disputes is determined in the negotiation room. Structuring with clarity, data, and foresight is not just best practice; it is a strategic competitive advantage.