As AI continues to reshape technology transactions, transactional lawyers have been forced to revisit long-standing risk allocation, revisit standard models, and develop new contracting mechanisms to address new uncertainties. Although the fundamental objectives of technology deals remain the same – to facilitate business outcomes and protect the enterprise – AI introduces distinctive pressure points around intellectual property, data, regulatory exposure and liability frameworks.
- Addressing ownership and licensing rights: In AI, questions that once focused on software and content now extend to models and results, including raw model results, post-processed or human-refined results, and deliverables incorporating the results. Prompt libraries, templates, and assessment tools can also provide significant value. Agreements should address ownership or licensing rights for each category, treatment of derivative works, assignment mechanisms and usage rights.
- Rights to training data remain essential: Rights to training data are increasingly critical in AI contracts. Customers providing training data should consider required consents, usage limitations, deletion and return rights, and data segregation. Vendors, on the other hand, could consider licensing this data to improve services, subject to regulatory and privacy limitations. Both parties will want to address issues regarding the lawful collection, use and commercialization of data, as well as restrictions on sensitive or regulated data.
- Keep regulatory compliance in mind: When negotiating agreements for the implementation and use of AI tools, regulatory compliance should be at the forefront during due diligence and negotiations. In addition to AI-specific regulations, these transactions require a review of regulations relating to privacy in general, import/export and fraudulent practices. Assigning oversight and compliance responsibilities are critical, and compliance programs, data privacy procedures, data governance controls, and security practices and certifications are at the heart of due diligence and solution assessments.
- Alleviate liability friction between parties: Responsibility frameworks are tailored to AI-specific risks and uncertainties. The concept of capping damages is generally preserved, but exclusions are highly negotiated. There is friction between the customer’s desire to hold the supplier accountable for product accuracy and quality and the supplier’s desire to align accountability with revenue generation. This led the parties to get creative in proposing possible solutions, including rework obligations and service level credits.
- From the outset, keep termination in mind: What happens at the time of termination is often as important as resolving transition issues. Arrangements may include continued access to the tool during the cessation period, measures to extract the tool where applicable, access and return of content and data, and deletion of data. These provisions make it possible to manage the transition smoothly and protect the interests of both parties when entering into the contract.
- Separate and General AI Disclaimers: With AI now integrated into almost every technology deal, lawyers should resist the temptation to rely on generic AI disclaimers. Instead, AI risk should be integrated into provisions throughout the contract, including intellectual property, compliance, data governance and protection, and liability provisions, reflecting how the technology is actually used and the business impact it can have.
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Morgan Lewis technology transactions, outsourcing and commercial contracts lawyers regularly advise clients on complex technology transactions, global sourcing strategies and evolving regulatory risks. If you have any questions about the topics covered above or would like to learn more, please contact any member of our team.