AI Diligence in Dutch M&A and VC Deals: From Market Hype to Drafting Consequences

Why AI diligence now needs legal translation

AI is attracting extraordinary amounts of capital. But in Dutch M&A and VC deals, AI diligence should not stop at market potential, product demos or revenue narratives. The real question is how AI diligence findings should be translated into warranties, disclosure, governance controls, IP provisions, data obligations and post-closing risk allocation.

For international investors, growth funds, tech buyers and counsel, this is especially relevant when investing in or acquiring Dutch AI, SaaS or data-driven companies. Many Dutch targets will use AI in product features, internal development, customer delivery, data analysis, automation or decision-support tools. Some will be true AI-native companies. Others will use AI as a commercial label.

The legal work is to separate hype from implementation risk.

This article explains how AI diligence in Dutch M&A and VC deals should affect drafting and transaction structure.

This insight is part of the ViottaLaw series on US VC Terms and Dutch BV Structures, Dutch Implementation of US-Style Investor Rights, Delaware Flip Structures Involving Dutch BV Companies and Venture Capital Insights.

Start with what the target actually means by AI

The first diligence issue is definitional. What does the Dutch target mean when it says it uses AI?

AI may refer to a proprietary model, fine-tuning, use of third-party foundation models, workflow automation, analytics, machine learning tools, customer-facing AI features or internal productivity tools.

A buyer or investor should not rely on the pitch deck. The diligence process should identify which AI systems are used, who owns or licenses them, what data they process, whether they are embedded in the product, and whether customers rely on the AI output.

That classification drives the legal drafting. A company that merely uses third-party AI internally creates a different risk profile from a company selling AI-driven output to regulated customers.

IP ownership and model dependence

AI diligence should test whether the target owns what it claims to own.

For Dutch software and AI companies, IP risk may arise from founder-created code, contractor work, open-source components, university or research institute links, customer-funded development, third-party model licences and data licensing restrictions.

If a target depends on third-party AI models or APIs, the buyer should understand the commercial and legal dependency. Can the provider change terms? Are outputs licensed? Can customer data be used for training? Is the target allowed to commercialise the output? What happens if the API is withdrawn or pricing changes?

These findings should be reflected in IP warranties, disclosure schedules, third-party dependency disclosure and, where relevant, specific indemnities.

Data rights and training data

Data is often the most sensitive part of AI diligence.

The buyer or investor should understand which data is used for training, fine-tuning, testing, inference and product improvement. Is the data personal data? Customer data? Public data? Scraped data? Licensed data? Synthetic data?

In Dutch and EU practice, AI data questions often overlap with GDPR, contractual confidentiality, database rights, customer contracts and sector-specific regulation.

If a Dutch target cannot explain the legal basis for using its data, the AI story becomes a drafting issue. The SPA or investment agreement should include warranties on data rights, lawful processing, customer restrictions, use of third-party datasets and compliance with data protection obligations.

Customer contracts and AI use

Many AI risks sit in customer contracts.

A Dutch B2B SaaS company may use AI to process customer data, generate output, automate decisions or support customer workflows. Customer agreements may restrict subcontracting, data transfer, automated processing, model training or use of customer data for product improvement.

If those restrictions exist, AI functionality may create breach risk.

Buyers should review customer contracts, DPAs, SLAs, security schedules and product terms. The drafting consequence may be targeted disclosure, conditions to obtain consents, updated customer terms or a post-closing remediation covenant.

AI warranties should not be generic

Generic technology warranties are often not enough for AI businesses.

Depending on the target, the SPA or investment agreement may need warranties covering model ownership or licensing, training data rights, open-source compliance, third-party model terms, data processing, security, output claims, customer restrictions, compliance with applicable AI regulation and absence of material disputes.

For VC investments, the warranties may be lighter, but the diligence findings should still influence disclosure, information rights, board reporting and investor consent rights.

For M&A deals, buyers may require more specific protection if AI functionality is central to valuation.

Disclosure should explain the risk, not hide it

AI risks are often nuanced. A disclosure schedule should not simply list tools or licences. It should explain the relevant dependency or limitation.

For example, if the target uses third-party foundation models, disclose the provider, use case, customer relevance, data flow, contractual restrictions and replacement risk. If open-source AI components are used, disclose the licence and compliance position. If customer data is used to improve models, disclose the contractual basis.

Good disclosure helps avoid post-closing disputes. Poor disclosure creates ambiguity and claim risk.

Governance controls in VC and growth rounds

In VC and growth equity deals, AI diligence should also influence governance.

Investors may require information rights on model development, data practices, regulatory compliance, cyber incidents, major customer restrictions, changes in AI provider relationships and material IP claims.

Reserved matters may be needed for transfer of core IP, changes to data-use policy, entry into high-risk AI applications, material model licensing, regulated-sector deployments or relocation of development activities.

The goal is not to control day-to-day product decisions. The goal is to ensure that investors have visibility over risks that can affect valuation, financing or exit.

Dutch BV implementation of AI investor rights

International investors may expect US-style information rights, board reporting and protective provisions. In a Dutch BV, those rights must be implemented through the investment agreement, shareholders’ agreement, board rules and sometimes the articles of association.

AI-specific governance rights should not remain high-level term sheet language. They should be connected to concrete reporting obligations and consent matters.

If a future Delaware flip or US-led financing is expected, the Dutch documentation should also be capable of supporting due diligence by US investors later.

Exit and post-closing risk

AI diligence matters most when the deal exits the hype phase.

A buyer may discover after closing that the target does not own key IP, customer data cannot be used as expected, third-party model terms restrict the product, open-source issues create compliance concerns or AI claims were overstated.

That can lead to warranty claims, earn-out disputes, purchase price adjustment pressure, customer renegotiations and integration problems.

The best way to reduce that risk is to translate AI diligence findings into specific drafting before signing.

Conclusion

AI diligence in Dutch M&A and VC deals should move from market narrative to legal consequences. Buyers and investors should ask not only whether the company uses AI, but how AI affects IP, data, customer contracts, regulatory compliance, governance and exit.

For Dutch targets, clear documentation can improve credibility. For foreign investors, precise drafting can turn AI diligence findings into enforceable rights and risk allocation.

In Dutch AI deals, the legal issue is not whether AI is exciting. The issue is whether the AI business can be diligenced, documented and governed.

FAQ

What is AI diligence in a Dutch M&A or VC deal?

AI diligence is the review of how a Dutch target uses AI, including IP, data rights, third-party models, customer contracts, regulatory compliance and product claims.

Why are generic IP warranties not enough?

AI businesses may involve training data, model dependencies, open-source tools, third-party AI providers and customer data restrictions. These often require more specific drafting.

Can AI diligence affect investor rights?

Yes. Investors may require information rights, reporting obligations and reserved matters relating to AI development, data use, IP, compliance and material customer restrictions.

What is the main AI risk in a Dutch acquisition?

A common risk is that the target’s AI value depends on data, models or IP that it does not fully own or cannot use as expected.

Should AI risks be disclosed separately?

Yes. Disclosure should explain the relevant AI dependencies, limitations and contractual restrictions, not merely list tools.

About Dirk de Waard

Dirk de Waard is a Dutch corporate, M&A and venture capital lawyer and partner at Venture Lawyers in Amsterdam. He advises foreign investors, founders, scale-ups and buyers on Dutch AI transactions, VC rounds, M&A diligence, warranties, disclosure, investor rights and Dutch BV implementation.

Translate AI diligence into Dutch transaction documents?

AI diligence only creates value if the findings are translated into warranties, disclosure, information rights, governance controls and closing conditions where needed.

Dirk de Waard advises foreign investors, buyers and founders on AI diligence and Dutch transaction documentation. Contact Dirk at dirk.dewaard@viottalaw.com to translate AI diligence findings into Dutch warranties, disclosure and governance controls.