Data Ownership Negotiations in Life Sciences Transactions Amid AI Growth

The increasing significance of data ownership in life sciences transactions is reshaping how pharmaceutical and biotech companies negotiate deals. As these firms leverage artificial intelligence (AI) and machine learning to enhance drug discovery, streamline clinical trials, and personalize therapies, the value of data—ranging from proprietary research to patient information—has surged. This trend highlights the need for dealmakers to understand the legal and commercial implications surrounding data ownership, access rights, and usage restrictions, which are now central to negotiations.

The landscape of data creation and collection is evolving rapidly. By 2026, the speed at which valuable datasets are generated has accelerated, prompting companies to consider not only existing data but also future data ownership and jointly created datasets. Negotiations often include third-party data, which may be synthesized with proprietary datasets or generated through AI tools. As a result, life sciences transactions are increasingly characterized by detailed definitions of data categories, encompassing manufactured, synthesized, and generated data. The growing complexity of data ownership necessitates more thorough due diligence, including tracing data provenance and verifying ownership rights. Dealmakers are investing significant resources to ensure that acquired data assets are free from encumbrances and compliant with regulatory requirements, extending their diligence efforts even after the deal is signed.

Ownership structures in life sciences agreements vary widely, influenced by the nature of the transaction and the bargaining power of the parties involved. In many cases, one party retains sole ownership of the data generated during the collaboration, particularly when significant capital or proprietary datasets are contributed. Alternatively, joint ownership arrangements are becoming more common, allowing both parties rights to use and license the data. The negotiation of ownership rights over improvements and derivative works is increasingly critical, especially when AI systems are trained on licensed data. The value of AI-generated summaries or infographics can lead to separate negotiations regarding ownership rights.

Data use rights and restrictions are equally vital in these transactions. Even when a party gains access to valuable data, the permitted scope of use is often tightly controlled. Common restrictions include compliance with privacy laws such as HIPAA and GDPR, as well as prohibitions against reverse engineering or re-identifying anonymized data. Competitive use restrictions may also be included, preventing a party from using licensed data to develop competing products. Additionally, parties often delineate fields of use, ensuring that data providers retain the ability to monetize their data while granting licensees meaningful rights.

As AI technologies become more prevalent in life sciences, companies are adapting their data use policies. While some parties impose strict prohibitions on AI usage, others permit it under defined conditions. This shift reflects a broader acceptance of AI's role in research and analysis, with many companies now allowing AI tools under enterprise licenses that safeguard intellectual property rights.

The escalating value of data assets in life sciences transactions signals a critical shift in market dynamics. As companies navigate these complexities, the ability to negotiate favorable terms around data scope, ownership, and use will be paramount. Organizations that proactively address these issues will not only capture greater value but also mitigate risks associated with data management in an increasingly data-driven landscape. The focus on data ownership will likely intensify, compelling firms to adopt more sophisticated strategies that leverage data as a core asset in their business models.