Mergers, Acquisitions, and Investments in AI Companies: Legal Due Diligence Tips
As Artificial Intelligence (AI) becomes more integrated into business operations, the demand for acquiring innovative AI companies is rising. However, these transactions come with their own set of legal, regulatory, and operational challenges, making a thorough due diligence exercise essential. This article therefore highlights key considerations for conducting legal due diligence in the context of mergers, acquisitions and investments in AI companies.
Conducting Due Diligence on AI-Related IP, Data, and Liabilities
AI-Related Intellectual Property
In AI, any company’s value often depends on its intellectual property. Proprietary algorithms, datasets, and technologies are highly valuable, so it is crucial to ensure the company has clear ownership or the necessary licenses to use them during the due diligence process. Failing to do so could result in costly legal issues and render the AI technology unusable.
The due diligence process should involve reviewing patents, IP registrations, and contracts with developers or contractors who may have helped create the algorithms of models. It is important to ensure there are no issues, such as pending claims or infringement concerns, that could impact the value of the IP. Particular attention should be given to any open-source software in the AI product, as open-source licenses often impose obligations that may restrict commercialisation or result in the loss of proprietary rights if not properly managed.
Data Security and Privacy Concerns
AI technologies rely heavily on data, both for training and day-to-day operations, making data security and privacy crucial during the due diligence process. It is important to assess whether the company has robust measures in place to protect sensitive data and ensure compliance with data privacy laws. Regulatory frameworks such as the European Union’s General Data Protection Regulation (GDPR) impose strict requirements on data processing, storage, and usage. AI companies must comply with these laws to avoid costly fines and reputational damage. Due diligence should verify compliance with these regulations and assess the legality of the datasets used for training their models, including obtaining necessary consents for data collection and processing.
Liability Risks and Contractual Issues
Risks associated with AI technologies can arise from issues like bias in algorithms, errors, or unexpected outcomes, leading to significant legal exposure and reputational harm. During the due diligence process, it is important to assess whether the AI company has faced any claims regarding its technology’s use or performance, as past litigation or complaints can reveal potential risks. Additionally, reviewing indemnification clauses, warranties, and risk mitigation strategies in existing contracts with customers and suppliers is important to ensure these agreements effectively limit exposure to claims.
Identifying Regulatory Risks in Acquisitions Involving AI Companies
AI regulations are rapidly evolving and vary widely across jurisdictions, posing challenges for companies in M&A transactions. It is crucial for companies to ensure compliance with local laws to avoid sanctions. Due diligence should assess the target company’s understanding of and engagement with these regulations, as well as its efforts to stay informed about potential legal changes.
AI applications also face sector-specific regulatory risks. For example, AI technologies used in healthcare must comply with strict regulations, including Medicines and Healthcare Products Regulatory Agency (MHRA) approval for medical devices in the UK, while AI in the financial services industry is subject to heavy regulations around transparency and consumer protection. Legal experts must assess the company’s compliance with these industry-specific rules to identify and address potential regulatory hurdles.
In addition, some AI technologies may have national security implications and could be subject to government export controls or security reviews. AI applications involving military, defence or critical infrastructure are particularly sensitive, often falling under strict export restrictions. In cross-border M&A transactions, it is essential to verify whether the target company’s AI products are subject to national security reviews or export restrictions, especially in countries with strict security regulations, such as the United States and China.
Structuring Transactions to Address AI-Specific Concerns
Tailoring Representations and Warranties
When structuring M&A transactions involving AI companies, it is essential to include representations and warranties that specifically address the unique risks associated with AI-related IP and data. Transaction documents should clearly define the ownership and licensing rights associated with AI technologies, along with the company’s compliance with data privacy laws and regulations. Warranties should also address the accuracy and performance of AI models, as well as the company’s liability for any potential failures or biases in the technology. Provisions should also account for emerging legal risks due to the constantly changing regulatory landscape.
Earnouts and Contingent Considerations
AI companies often face uncertainties around revenue projections and product development timelines. As a result, earnouts, where part of the purchase price depends on the future performance of the business, can be a useful tool in AI M&A transactions. Earnouts help the buyer to address uncertainties about the long-term profitability of the AI company, while providing the seller with an incentive to continue driving the company’s success post-acquisition.
Earnouts can also be used to motivate key personnel, such as developers and data scientists, to stay with the company and continue to innovate post-transaction. This is especially important for AI companies, where talent retention is a key factor in maintaining competitive edge.
Managing Post-Acquisition Integration
Integrating an AI company after an acquisition presents unique challenges, particularly in maintaining regulatory compliance and managing the integration of IP and data. Due diligence should address how the integration will take place, ensuring the buyer has the resources and expertise needed to manage the post-acquisition risks tied to AI operations. Ongoing monitoring and regular audits are essential to track potential risks, ensure compliance, and assess AI technology performance.
Summary
Legal due diligence is key in AI M&A transactions. By carefully assessing AI-related intellectual property, data privacy concerns, regulatory risks, and structuring transactions to address these unique challenges, companies can reduce potential risks and maximize the value of their investments. As the AI landscape evolves, staying ahead of regulatory changes and maintaining strong compliance frameworks will be key to navigating the complexities and risks of AI M&A.
If you’re involved in an M&A transaction in the AI sector, working with experienced solicitors who understand the intricacies of AI law and its rapidly changing regulatory environment is important.
This article is intended for general information only, applies to the law at the time of publication, is not specific to the facts of your case and is not intended to be a replacement for legal advice. It is recommended that specific professional advice is sought before relying on any of the information given. © Jonathan Lea Limited.
