Why Training Data Has Become One of the Most Important Assets in AI Acquisitions

Artificial Intelligence companies are often valued based on more than their software, algorithms, or products.

In many cases, one of the most important assets is the data used to develop, train, test, and improve AI systems.

As acquisitions involving AI companies continue to increase, buyers are asking a new set of due diligence questions:

• What data was used to train the models?

• Where did the data originate?

• Does the company have rights to use the data?

• Are there contractual restrictions?

• Are third-party datasets involved?

• Are customer datasets included?

• Are there ongoing licensing obligations?

These questions are becoming increasingly important because training data can play a significant role in how AI systems perform and evolve.

For lawyers involved in technology transactions, training data reviews are becoming a regular part of AI-focused due diligence.

Source: https://www.cooley.com/news/insight

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Why Training Data Matters in AI M&A Transactions

Many AI systems depend on large amounts of data.

That data may be used for:

• Model development

• Model training

• Model testing

• Quality improvement

• Performance evaluation

• Product enhancement

As a result, buyers often view training data as an important part of the target company's technology ecosystem.

However, unlike traditional assets such as patents or physical property, questions regarding data rights can be more complex.

Different datasets may come from different sources and may be subject to different contractual, legal, or operational requirements.

Source: https://www.wsgr.com/en/insights

What Lawyers Mean by "Training Data"

Training data generally refers to information used to help AI systems learn patterns and generate outputs.

Depending on the company, training data may include:

• Publicly available information

• Licensed datasets

• Proprietary datasets

• Customer-provided information

• User-generated content

• Internal business records

• Third-party content

The specific composition of a training dataset can vary significantly from one company to another.

For this reason, lawyers frequently begin diligence by understanding exactly what types of data were used.

Why Ownership Questions Can Be Difficult

One reason training data attracts attention during acquisitions is that data rights may involve multiple layers.

For example:

• A company may collect data itself.

• A company may license data from third parties.

• A company may receive data through commercial agreements.

• A company may use publicly available information subject to terms of use.

• A company may rely on vendor-provided datasets.

As a result, lawyers often focus on understanding the rights associated with each category of data rather than assuming a single ownership model applies to all datasets.

Many legal commentators note that questions involving data rights can be highly fact-specific.

Source: https://www.goodwinlaw.com/en/insights

Why Buyers Are Expanding AI Due Diligence

Traditional technology due diligence often focuses on:

• Intellectual property

• Software ownership

• Commercial contracts

• Regulatory compliance

• Cybersecurity

Today, AI acquisitions frequently involve additional diligence focused specifically on training data.

Buyers increasingly review:

• Data sources

• Licensing agreements

• Vendor contracts

• Data governance policies

• Data retention practices

• Internal documentation

• Usage permissions

The goal is generally to understand how data supports the company's AI systems and whether material risks may exist.

Common Questions Asked During Training Data Reviews

Many AI transaction teams now maintain dedicated diligence checklists.

These reviews often include questions such as:

Where Did the Data Come From?

Understanding data origin is frequently a starting point for diligence.

Buyers often seek visibility regarding:

• Internal datasets

• Commercial datasets

• Open-source datasets

• Customer datasets

• Publicly available information

Are There License Restrictions?

Not all data is used under the same terms.

Some datasets may be subject to:

• License agreements

• Vendor contracts

• Usage limitations

• Renewal obligations

• Access restrictions

Reviewing these agreements can help buyers better understand future operational considerations.

Are Third-Party Providers Involved?

Many AI companies rely on external providers for at least part of their data infrastructure.

Transaction teams may review:

• Vendor agreements

• Data supply contracts

• Commercial licensing arrangements

• Platform dependencies

Understanding these relationships can become an important part of transaction planning.

Why Documentation Is Becoming More Important

One recurring theme in AI due diligence is documentation.

Buyers increasingly request information regarding:

• Data inventories

• Data maps

• Governance policies

• Collection procedures

• Vendor relationships

• Internal controls

Organizations that maintain clear documentation may find it easier to respond to diligence requests during a transaction process.

Source: https://www.nist.gov/artificial-intelligence

The Growing Focus on Data Governance

Data governance has become a significant topic in AI transactions.

Many companies now maintain policies addressing:

• Data management

• Access controls

• Data quality

• Retention procedures

• Security measures

• Vendor oversight

While governance approaches vary between organizations, buyers frequently evaluate whether governance frameworks are documented and consistently implemented.

Source: https://www.nist.gov/artificial-intelligence/ai-risk-management-framework

Training Data and Intellectual Property Reviews

Intellectual property diligence remains an important component of technology acquisitions.

When AI systems are involved, transaction teams may also evaluate:

• Dataset rights

• Licensing arrangements

• Proprietary content

• Third-party materials

• Contractual permissions

The purpose of these reviews is generally to better understand how training data relates to the company's broader intellectual property portfolio.

Source: https://www.lw.com/en/insights

Why AI Acquirers Are Building Specialized Playbooks

As AI transactions become more common, many law firms, private equity firms, and corporate development teams are creating internal AI diligence frameworks.

These frameworks often include dedicated sections covering:

• Training data reviews

• Data governance

• AI compliance

• Vendor dependencies

• AI licensing

• Documentation practices

Many organizations are updating these playbooks regularly as technology and regulatory developments continue to evolve.

The Role of Disclosure Schedules

Disclosure schedules can play an important role during AI acquisitions.

They may help identify:

• Material datasets

• Significant vendor relationships

• Data-related contracts

• Licensing arrangements

• Governance practices

Many lawyers view these disclosures as an important tool for improving transparency during transaction negotiations.

Why Sellers Are Preparing Earlier

The growing importance of AI diligence is influencing seller preparation efforts.

Companies considering strategic investments or future acquisitions are increasingly documenting:

• Data sources

• Vendor relationships

• Licensing agreements

• Governance procedures

• Internal controls

• Data management practices

Early preparation may help streamline future diligence processes.

What This Means for AI M&A Transactions

Training data has become an increasingly important diligence topic in modern AI acquisitions.

For many buyers, understanding training data is now viewed as an important part of evaluating:

• AI products

• AI models

• Technology assets

• Vendor relationships

• Commercial operations

• Long-term scalability

As a result, training data reviews are becoming a standard component of many AI-focused transaction processes.

The specific issues examined will vary from transaction to transaction, but the broader trend appears clear:

Lawyers, buyers, and advisors are spending more time evaluating how training data is sourced, documented, governed, and used within AI businesses.

Key Takeaways

• Training data is becoming a major diligence topic in AI acquisitions.

• Buyers increasingly review data sources, licenses, and vendor relationships.

• Data governance and documentation are receiving greater attention.

• AI transaction teams are developing specialized diligence playbooks.

• Disclosure schedules often play an important role in AI transactions.

• Training data reviews are becoming increasingly common in technology M&A.

• Lawyers continue to evaluate training data issues alongside broader intellectual property, compliance, and operational reviews.

Disclaimer

This article is provided for general informational purposes only and does not constitute legal, tax, financial, regulatory, or professional advice. Readers should consult qualified advisors regarding specific transactions, agreements, or legal issues.

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