Strategies for Negotiating AI Vendor Contracts

As artificial intelligence continues making inroads into the entertainment industry, AI vendor contracts are introducing new legal complexities that go beyond traditional “Software as a Service” (SaaS) agreements, often shifting significant risk onto customers. Unlike standard software contracts, AI agreements frequently limit liability, restrict indemnification, expand vendor data rights and minimize compliance commitments. These contractual structures require entertainment counsel to be more vigilant, so that their organizations are not unfairly exposed to financial, regulatory or operational risks. At the same time, AI vendors navigate the tension between mitigating legal risk and maintaining a scalable business model.
This article draws on data from TermScout’s contract intelligence platform, which evaluates vendor agreements and compares them against broader SaaS contract norms. The data highlights key tendencies, including the prevalence of liability caps, limited indemnification for third-party IP infringement, broad vendor data rights and weak compliance commitments. By identifying patterns in liability limitations, indemnification structures, data rights, warranties and compliance obligations, this analysis offers strategic guidance for negotiation.
One of the most overlooked gaps in AI vendor agreements is the lack of performance warranties. According to TermScout data, only 17% of AI vendor contracts studied offer a warranty that their product complies with documentation, compared to 42% in broader SaaS contracts. Without this protection, entertainment businesses adopting AI could face increased risks if systems underperform, generate biased outputs or fail to meet operational needs.
For companies negotiating AI contracts, it may be beneficial to consider tying warranties to clear performance metrics and functional reliability. High-stakes AI applications — such as compliance, fraud detection and automated decision-making — might benefit from stronger warranties, with potential remedies like model retraining, service credits or contract termination for non-compliance.
However, AI vendors may argue that AI models are probabilistic and constantly evolving, making rigid warranties difficult to enforce. This tension between enterprise buyers seeking assurances and vendors limiting legal exposure makes warranties a critical negotiation point.
To reduce friction, AI vendors might consider offering tiered warranties based on AI complexity, providing insurance-backed protections or aligning with emerging AI governance frameworks. As AI regulations evolve, vendors addressing warranty concerns proactively may gain a competitive advantage. AI vendor contracts increasingly limit indemnification obligations, shifting intellectual property and regulatory risks onto their customers. According to TermScout data, only 33% of AI vendors provide indemnification for third-party IP infringement, compared to 58% in broader SaaS agreements. This reluctance stems from the way AI models are trained-often on third-party datasets, pre-trained models or web-scraped content-introducing legal uncertainties around data ownership and licensing compliance.
For entertainment counsel negotiating AI contracts, broad indemnification protections could be a priority, particularly where AI models process regulated data or generate legally sensitive outputs. It may be worth considering coverage for claims related to unauthorized training data use, AI-generated outputs and dataset licensing issues. And indemnification may extend beyond IP infringement to cover bias-related lawsuits, regulatory fines and unfair outcomes caused by AI decisions.
AI vendors, on the other hand, balance customer expectations with their ability to control AI-related risks. One approach could be tiered indemnification models, distinguishing between vendor-controlled risks, shared risks and customer-modified AI models. Vendors might consider limiting indemnification obligations to areas within their control, so they are not liable for customer misuse, unauthorized modifications or end-user-generated content.
AI vendor contracts frequently grant vendors broad rights over customer data, often exceeding what is necessary for service functionality. According to TermScout data, 92% of AI vendor contracts claim usage rights beyond what is needed for performance improvement, significantly surpassing the 63% market average. Many contracts permit AI vendors to use customer data for AI model training, resale or commercialization, creating intellectual property and competitive risks for businesses.
Limiting vendor data rights could be a priority to protect proprietary and sensitive business information. AI contracts might benefit from explicitly restricting data usage to necessary functions, so customer data is used solely for service performance and not for broader AI training or resale. Additionally, contracts could include data deletion and anonymization guarantees, requiring vendors to delete customer data within a set time frame after contract termination.
But AI vendors rely on their customers’ data to enhance AI performance. To balance customer concerns with product scalability, AI vendors might consider offering customizable data-sharing models, allowing enterprise customers to opt out of AI model training in exchange for higher service fees or tighter service level provisions. Clear differentiation between anonymized and customer-specific data can help mitigate privacy and competitive risks.
AI vendors are often reluctant to commit to strong compliance guarantees, placing greater regulatory risk on their customers. According to TermScout data, only 17% of AI vendors explicitly commit to complying with all applicable laws, compared to 36% in broader SaaS agreements. This hesitancy likely stems from rapidly evolving AI legal frameworks, which make it challenging for vendors to guarantee long-term compliance.
For counsel negotiating AI contracts, regulatory compliance guarantees could be crucial. It may be beneficial to consider having AI vendors commit to complying with both existing and future regulatory updates. Contracts might also include compliance audit rights, allowing vendors’ customers to conduct independent AI risk assessments and consider vendor adherence to evolving legal requirements.
AI vendors, on the other hand, balance compliance commitments with operational feasibility. One way to address their customers’ concerns without overextending legal obligations could be to provide regulatory compliance roadmaps, outlining how the vendor plans to adapt to evolving AI laws. Instead of committing to global compliance across all jurisdictions, vendors might consider limiting obligations to relevant markets where their customers operate.
For entertainment lawyers negotiating AI contracts, understanding AI’s role in business operations and its regulatory exposure is critical. AI tools that influence decision-making in finance, hiring or legal automation pose greater compliance risks and may require stronger contractual protections. Counsel might consider prioritizing liability caps that reflect AI’s real-world impact, seeking broad indemnification for IP and regulatory risks, and restricting vendor data usage to prevent unauthorized AI training or commercialization.
For AI vendors, contract flexibility is key to balancing enterprise customer demands with sustainable legal risk management. Segmenting customers by risk tolerance can streamline negotiations. Large enterprises may require customized liability and compliance terms, while mid-market buyers often accept standardized agreements. Vendors might also consider offering tiered liability and indemnification models, so that higher-risk AI applications come with appropriate legal protections.
As AI adoption accelerates and regulatory frameworks take shape, legal professionals can advocate for balanced, forward-looking risk management strategies that protect both AI providers and enterprise customers while fostering responsible AI innovation.