Register for our webinar, AI in Your Organization: Are Your Contracts Keeping Up?, on May 19 at noon CT, presented by Kris Kappel and Maggie Mannebach.
Artificial intelligence is no longer an emerging technology—it is embedded in the mainstream enterprise software, platforms, and vendor services that organizations rely on every day. In many cases, organizations are already using AI without realizing it because vendors have quietly integrated it into existing products. That reality raises an urgent question: are your contracts keeping pace?
This challenge is particularly acute for healthcare and life sciences organizations. Organizations in this sector face significant regulatory and privacy exposure when AI is embedded in their operations—from patient recruitment algorithms to drug discovery platforms. But even the most sophisticated compliance framework can fail if the underlying vendor contracts don’t support it.
Understanding the Stakes of AI in Contracting
AI is technology that enables computers to perform tasks that traditionally require human reasoning, such as analyzing information, recognizing patterns, making decisions, and generating content. AI learns from data rather than fixed rules; it operates on probability, and it exists on a spectrum of complexity. Generative AI is a unique category of AI that produces new content like text, images, code, audio, and video in response to user prompts, and is increasingly embedded in enterprise software. Because AI is powered by patterns learned from vast training datasets, it raises its own distinct set of legal, contractual, and governance concerns.
For healthcare and life sciences organizations in particular, the stakes are especially high. These organizations manage highly sensitive data and operate under a layered and complex regulatory environment that includes HIPAA, FDA requirements, state health privacy laws, and emerging AI-specific regulations. The consequences of getting AI contracting wrong can be severe, affecting patient safety, business operations, and creating significant financial exposure.
Why Contract Provisions Matter, and Where Most Fall Short
Despite accelerating legal and regulatory developments, including the EU AI Act and a growing body of U.S. state laws, most vendor agreements remain entirely silent on AI, leaving no contractual framework to govern its use. At a minimum, organizations should ensure their agreements clearly define key AI-related terms, require vendor consent before AI is used for sensitive applications, and explicitly prohibit vendors from using customer data to train their AI models without prior written consent. This last point is particularly critical: broad data-use provisions buried in vendor agreements can operate as open-ended training rights, and vendors may argue that “de-identified” data falls outside any restrictions—even when their characterization of de-identification may not meet HIPAA’s demanding standard.
For life sciences organizations, this issue is especially critical: AI models trained on clinical trial data, genomic information, or patient health records may be subject to HIPAA, state health privacy laws, consumer protection, and FDA data integrity requirements. Using inadequately “de-identified” data for model training without proper consent could trigger fines, regulatory and/or state attorney general enforcement, or compromise clinical trial integrity. Organizations should also pay close attention to the third-party infrastructure underpinning any AI tool, including hosting providers and foundation models like GPT-4, Claude 3, or Gemini, since a change in either can materially affect security, privacy, and performance without the customer ever being notified.
On the warranty and liability side, general contract language was not written with AI in mind and often provides no meaningful remedy when AI causes harm. Organizations should negotiate for AI-specific warranties that cover compliance with applicable laws, avoidance of bias and discrimination, and protection of third-party intellectual property rights to ensure that indemnification provisions explicitly cover AI-related claims such as privacy violations, erroneous outputs, and copyright infringement. Equally important is scrutinizing limitation of liability clauses: standard caps and indirect damages exclusions can leave organizations without recourse for some of the most significant harms AI can cause, including regulatory fines, breach notification costs, and reputational damage.
For healthcare and life sciences organizations navigating these challenges—whether you are a research institution deploying AI in clinical studies, a health system purchasing AI-enabled vendor services, or a life sciences company integrating AI into drug development—getting these contract provisions is a strategic imperative that affects regulatory compliance, patient safety, intellectual property protection, and your organization’s ability to defend its innovations.