Blog

AI Vendor Contracts: Who’s Liable When the Model Gets It Wrong?

Artificial intelligence (AI) tools are now embedded into products, services, and workflows across nearly every industry.

As with any technological shift, whether the AI boom becomes a lasting business advantage or a source of major legal risk depends heavily on how companies use it. In the meantime, business leaders are increasingly relying on AI tools to perform major business functions, often without fully understanding what happens when the model gets it wrong.

That means the question is not whether AI failure can create risk for your business, but whether your AI vendor contract offers adequate protection when it does. For many businesses, the answer is no.

Three Ways AI Failure Creates Business Liability

There are three primary areas where AI failure can pose serious legal and financial risk to businesses.

Hallucinations

AI models can fabricate facts, citations, or representations and present them as accurate. Courts have increasingly imposed significant sanctions on businesses and attorneys who relied on hallucinated output.

Biased Outputs

An AI model may produce discriminatory decisions affecting employment, credit, housing, or advertising. Intent does not eliminate regulatory exposure when an automated tool produces unlawful or biased results.

IP Infringement

AI-generated content may be derived from unlicensed training data, exposing the business using the tool to third-party intellectual property claims.

In each scenario, even if the fault appears to lie with the AI model, the businesses deploying the model often absorb the blame, financial exposure, and reputational risk.

The AI Vendor Indemnification Gap: By the Numbers

Most businesses assume their AI vendor will cover them if something goes wrong, but the data and the fine print often say otherwise.

Only 33% of AI vendors provide indemnification for third-party IP claims, and only 17% commit to full regulatory compliance. Both percentages fall markedly below the norm for the broader SaaS market.

Vendors routinely deny responsibility for output generated in response to user prompts, use-case deviations, or customer modifications. Because AI-related liability remains difficult to quantify, vendors also aggressively negotiate caps that limit their financial exposure regardless of the harm caused.

There is also nested vendor risk to consider. Many AI products are built on another vendor’s model, such as OpenAI or another large language model provider, and the terms governing that underlying relationship may alter or limit the warranties and indemnification provisions your business thought it had secured.

Unfortunately, this is an issue many businesses do not discover until a claim arises. That is why reviewing vendor and client agreements before signing can be critical.

AI Liability and the Vendor, Deployer, and End User Framework

AI liability rarely sits cleanly with one party. Instead, risk is often distributed across three parties: the vendor, the deployer, and the end user.

The vendor is generally responsible for model design, development, and training. The deployer, meaning your business, is responsible for implementation, configuration, monitoring, and oversight. The end user’s prompts, inputs, and behavior may also shape the output.

Each layer can create independent exposure.

The Equal Employment Opportunity Commission (EEOC), for example, has made clear that companies using AI tools for employment decisions may remain liable under employment discrimination laws, even when the product is developed or administered by a third-party vendor.

State and local AI laws are also reinforcing the need for oversight. For example, companies using AI hiring tools may face specific compliance obligations tied to automated employment decision systems. The broader takeaway is clear: deployers cannot assume they can contract their way out of compliance obligations simply by pointing to their vendor agreement.

AI Contract Risk by Industry

Businesses across every industry should scrutinize their AI vendor agreements, but the specific risks vary by sector.

Technology Companies

Technology companies face liability for products built on third-party models, compounded by nested vendor exposure.

Companies building AI into software, apps, platforms, or customer-facing tools should carefully review provisions governing model performance, output ownership, data use, training rights, warranties, indemnification, and liability caps. These issues often overlap with broader commercial contracts, SaaS agreements, and technology vendor agreements.

Consumer Brands and Agencies

Consumer brands and agencies should be alert to reputational harm stemming from biased or inaccurate AI-generated content.

Customer-facing chatbots can generate real-time responses that the business cannot fully control or predict. AI-assisted ad campaigns can create risk when targeting, personalization, or automated content produces misleading, discriminatory, or non-compliant results.

Agencies and brands using AI in marketing should also review related ad-tech contracts, data-sharing terms, and campaign vendor agreements.

Large Companies and In-House Legal Teams

Large companies and in-house legal teams must be mindful of how AI is used to support legal work.

Hallucinated legal citations and fabricated authorities can increase organizational liability, exposing companies to third-party claims, regulatory violations, and failed transactions. No matter how sophisticated the software, human verification of AI output is still required.

Attorneys are increasingly expected to take ownership of their organization’s legal technology governance. For larger organizations, this may require support from outsourced legal services that understand contracts, privacy, IP, and AI-related risk.

Key Contract Provisions to Negotiate Before Signing an AI Vendor Agreement

Understanding the risk is the first step, but addressing it contractually is the second. The following provisions deserve careful attention in any AI vendor agreement:

• Avoid caps on IP infringement indemnity.
• Negotiate elevated liability thresholds for data breach, confidentiality, and regulatory penalty exposure.
• Require representations and warranties that address bias testing, output accuracy, and ongoing regulatory compliance.
• Where possible, carve consequential damages out of mutual indemnity obligations to avoid inadvertently assuming liability for losses the vendor should bear.
• Ensure the agreement explicitly addresses model training and ownership of data.
• Decline the vendor’s use of your inputs to train its model for other clients.
• Include an amendment provision in your AI vendor contracts so terms can be updated as AI-related law evolves.
• Review whether the vendor’s data practices create additional data privacy obligations for your business.

The guidance above is a strong starting point, but a defensible contractual posture depends heavily on how your business is using AI, what data it is sharing, and what is required by your specific risk tolerance.

A one-size-fits-all approach rarely works. An experienced attorney can help you evaluate your AI vendor agreements, negotiate stronger protections, and build a contract strategy that reflects how your business actually uses AI.