When companies pursue mergers and acquisitions involving technology firms, the focus often shifts to the underlying intellectual property. Performing effective AI asset due diligence in M&A is a critical step to help ensure that the technology you are acquiring is legally sound and commercially viable.
Understanding the Scope of AI Due Diligence
AI assets often rely on a combination of proprietary code, training data, and third-party components. Unlike traditional software, the value of an AI system is frequently tied to the quality and origin of the data used to train it. A failure to verify these elements can lead to potential post-closing liabilities.
Data Rights and Ownership
A primary question in any transaction is whether the target company has the necessary rights to the data used to train its models. If data was obtained without authorization or licensed under restrictive terms, the AI model could be subject to legal challenges. Buyers should review data licensing agreements to determine if there are prohibitions against using the data for commercial AI development.
Open Source and Licensing Risks
Many AI developers integrate open-source libraries into their workflows. While this can accelerate development, it may introduce obligations that affect the proprietary nature of the software. Reviewing the compliance of these libraries is a standard part of protecting trade secrets and proprietary software during the transition.
Evaluating Intellectual Property Portfolios
Beyond data, the core algorithms and models require evaluation. Are there existing patent strategies in place? It is essential to confirm that employment and consulting agreements contain intellectual property assignment clauses to establish ownership of the work produced.
Regulatory Considerations
AI is subject to evolving government oversight. Buyers should assess whether the target’s AI tools align with applicable laws regarding data privacy and transparency. A lack of compliance may result in regulatory scrutiny after the merger is finalized.
Practical Guidance for Buyers
- Audit training datasets for origin and usage rights.
- Review the use of open-source components to identify potential license compliance risks.
- Verify that intellectual property created by staff and contractors is assigned to the company.
- Assess internal documentation regarding model training and testing.
- Consider the need for specific indemnification clauses in the purchase agreement to address potential AI-related legal risks.
Conclusion
Conducting thorough AI asset due diligence in M&A is important for mitigating risk and evaluating the long-term value of an acquisition. By focusing on data provenance, IP ownership, and regulatory compliance, buyers can make informed decisions. Consult with professional legal counsel to conduct a detailed audit to identify potential liabilities before the deal closes.
This article is provided for general informational purposes only and does not constitute legal advice. Laws and procedures may change, and the application of law depends on the specific facts and jurisdiction. Consult a qualified attorney regarding your situation.