Acquiring an AI Business – Not a typical technology transaction



The race for leadership in artificial intelligence (AI) technology picked up speed last year. Multinational and local actors have relentlessly sought breakthrough AI-enabled solutions and accompanying talent, including leading data scientists and seasoned machine learning engineers. While the development and advancement of AI technologies continues to be an important growth area for these issuers, growth through acquisitions in 2021 was certainly a noticeable trend that we expect to continue to intensify in the years to come.

The past year has also made it clear that acquiring an AI company carries unique risks for buyers that are in many ways different from doing business with traditional technology and software companies. These risks require rethinking legal due diligence and risk diversification in sales contracts. With the novelty of this type of transaction, AI acquisitions expertise is still evolving and buyers should ensure they have professional advisors with the best experience and knowledge available to protect themselves.

Understanding the different value drivers for AI companies is crucial in the context of M&A transactions as these drivers change the buyer’s focus. Likewise, advisers who want to protect their buyer customers need to appreciate this distinction in order to provide the right advice.

Why AI company acquisitions require new thinking

The starting point for effective legal advice in an M&A transaction is understanding the client’s business reasons for the acquisition. Without insight into the client’s assessment of the target company’s assets, it is a challenge to ensure that the client’s interests are protected by both the sales contract and the legal due diligence based on it. In all technology transactions, it is important to understand how the target company’s technology resources will be used by the buyer.

For a traditional technology company, the most important strategic asset is usually its software. Understanding software-driven companies and the risks that need to be investigated in these companies in connection with an acquisition transaction is a well-trodden path. When performing software due diligence, it is typical that the buyer and their professional advisers delve deep into the target company’s software code and software development practices, with an emphasis on intellectual property and data security. This often includes evaluating the use of open source software by the target and the presence of software bugs and security vulnerabilities. These considerations are also reflected in several elements of the transaction purchase agreement, including software-related representations and warranties, indemnities, and closing conditions.

Unlike a software company, the core value of an AI company often resides in the company’s rights to data sets and the proprietary models used to ingest and analyze the data. It is the combination of data and these models that enables computers to mimic human intelligence and learn over time as they train themselves to perform increasingly complex tasks. Although an AI company may have developed proprietary software, e.g. For example, a user interface to display the analyzes performed by the company’s models, the code for the software usually performs a function that is only of minor value to the company’s core business.

Understanding the different value drivers for AI companies is crucial in the context of M&A transactions as these drivers change the buyer’s focus. Likewise, advisers who want to protect their buyer customers need to appreciate this distinction in order to provide the right advice.

When evaluating an AI target from a due diligence perspective, the buyer and their advisors must adopt an approach that reflects the value of the target’s data set and proprietary models. Rather than stressing the consideration of software development and data security issues, buyers need to broaden their focus to include the target’s rights to own and use data, the target’s ownership of proprietary models and enhancements, the “outputs” of the models, and the practices of the Involve the company for training, improve, test, maintain and explain such models. Examining complex data sets and models from a legal diligence perspective requires a thorough understanding of the construction and use of these assets, which is very different from traditional technology acquisitions. With the rapidly growing use of AI, understanding privacy issues is also critical.

After a buyer and his advisors have adequately assessed the underlying assets and risks and carefully assessed the target, these findings must be adequately reflected in the transaction purchase agreement. It is important that a sales contract for an AI business is tailored to AI and its unique characteristics and risks. While each transaction must be considered individually, there are a number of key considerations that should be considered.

In particular, definitions require careful elaboration to ensure that the agreement adequately captures relevant characteristics of artificial intelligence. For example, definitions that focus on “AI technology” should be broad enough to encompass both of the techniques that enable computers to mimic human intelligence, including deep learning, machine learning, and algorithms that use neural networks, statistical learning algorithms, or Use reinforcement learning as well as software and hardware to train, test and deploy the AI ​​solution.

In making representations and warranties regarding the deal, the buyer should seek full disclosure and agent protection and warranties, which are included among other considerations that can be determined through care

  • Ownership of and rights to use AI models and data sets, including those that are both owned and licensed
  • the quality of the company’s records, including the degree of completeness, consistency and accuracy
  • the company’s practices in relation to testing, improving, and developing AI models
  • Responsible use and ethical design of AI, including testing for bias or other harmful effects
  • Using facial recognition or other high risk use cases that use AI
  • the assignment of AI-related liability in agreements with suppliers and customers
  • Compliance with laws and industry standards and practices that apply to AI

Special attention to these representations and warranties is required to ensure that all aspects of the AI ​​business are subject to full disclosure. When setting up exemption clauses in the sales contract, due care must be taken in order to adequately balance liability. In particular, it should be checked whether the amount of the “hold-back” should be increased or the deadline for paying out the hold-back should be extended.

Look to the future

Over the past year, seasoned buyers have demonstrated their willingness to invest time and resources in following AI acquisition best practices to increase their ownership of these assets. This includes having their professional advisors examine the nuances of AI as part of the diligence process and adapt sales contracts to their findings. We anticipate this trend will continue given the significant growth in AI-focused companies, significant growth in AI M&A, and continued demand and competition for assets and talent. We also expect that AI company buyers will increasingly seek to hire experienced legal advisors who have a thorough understanding of AI and can protect their clients’ interests.



Comments are closed.