What’s New and What’s Next: Navigating AI in Technology Transactions
As AI continues to reshape technology transactions, deal lawyers are compelled to revisit longstanding allocations of risk, boilerplate provisions, and develop new contracting mechanics to address novel uncertainties. While the core goals of technology deals remain the same—facilitating commercial outcomes and protecting the business—AI introduces distinctive pressure points across intellectual property, data, regulatory exposure, and liability frameworks.
Addressing Ownership and License Rights
In the realm of AI, questions that once focused on software and content now extend to models and outputs, including:
- Raw model outputs
- Post-processed or human-refined outputs
- Deliverables incorporating outputs
Prompt libraries, templates, and evaluator tools can also embody significant value. Agreements should address ownership or license rights for each category, including derivative works treatment, assignment mechanics, and usage rights.
Training Data Rights Remain Critical
Rights in training data are increasingly critical in AI contracts. Customers providing training data should consider:
- Required consents
- Use limitations
- Deletion and return rights
- Data segregation
Vendors may wish to consider licenses to such data to improve services, subject to regulatory and confidentiality limitations. Both parties need to address lawful data collection, use, commercialization, and restrictions related to sensitive or regulated data.
Keep Regulatory Compliance Top of Mind
When negotiating agreements for the implementation and use of AI tools, regulatory compliance should be prioritized during diligence and negotiations. In addition to AI-specific regulations, these transactions require a review of:
- Privacy regulations
- Import/export regulations
- Fraudulent practices
Allocation of monitoring and compliance responsibility is critical, with compliance programs, data privacy procedures, data governance controls, and security practices central to diligence and solution evaluation assessments.
Mitigating Liability Friction Between Parties
Liability frameworks are being tailored to address AI-specific risks and uncertainties. While the concept of damages caps is generally preserved, carveouts are highly negotiated. There is friction between the customer’s desire to hold the vendor responsible for the accuracy and quality of output and the vendor’s aim to align liability with revenue generation. This has led to creative remedies, including re-work obligations and service-level credits.
From the Outset, Keep Termination in Mind
Understanding what happens at termination is often as important as addressing transition-in issues. Provisions may include:
- Ongoing access to the tool during wind down
- Measures for tool extraction, if applicable
- Content and data access and return
- Data deletion
These provisions help manage the transition smoothly and protect both parties’ interests as the contract concludes.
Distinct vs. General AI Disclaimers
As AI becomes embedded in nearly every technology deal, lawyers should resist the urge to rely on generic AI disclaimers. Instead, AI risk should be integrated into contract provisions throughout, including intellectual property, compliance, data governance and protection, and liability provisions—reflecting how the technology is actually used and its business impact.
In conclusion, as AI evolves, so too must the frameworks governing its use in technology transactions. Legal practitioners must adapt to these changes to ensure that contracts remain relevant and effective in navigating the complexities introduced by AI.
