Source: JD Supra

Artificial intelligence is increasingly embedded as a layer across existing technology services rather than sold as a standalone product, appearing inside SaaS platforms, operating systems, security tools, customer support software, and analytics environments. Disputes tend to focus less on the abstract technology and more on how AI was deployed, the contractual promises governing its use, and how responsibility for AI-driven outcomes is allocated between providers and customers. AI commonly magnifies preexisting contractual risks where responsibilities are unclear, data use is poorly defined, or liability is broadly disclaimed. Key litigation flashpoints include ownership and permitted use of customer inputs and prompts, the accuracy and exploitability of AI outputs, and whether providers may retain or use customer data to train general models.

Providers frequently try to allocate risk through broad disclaimers, “as-is” language, human-verification requirements, and reserved rights to update acceptable use policies or use customer inputs for model training; courts and arbitrators will assess enforceability of those provisions in light of marketing, deployment, and reasonableness of reliance. Distinctions between general/open models (which often reserve expansive training rights) and closed/enterprise systems (which may promise tighter controls) drive many disputes when customer expectations about isolation or non-training collide with contractual language. Additional issues arise from layering AI terms over existing SaaS agreements without clear precedence, unilateral midstream changes to features or policies, and allocation of liability among resellers, platforms, and downstream providers. Practical recommendations include mapping where AI is deployed, evaluating AI terms within the broader contract ecosystem, aligning internal data-use and reliance policies with external agreements, conducting periodic gap analyses, and treating AI add-ons as legal risk events requiring contract adaptation.