What Must Be Included in an Artificial Intelligence Agreement?

An AI agreement must specify the exact scope and intended use of the AI system, including its input data types, processing methods, output formats, and performance thresholds or accuracy metrics. Vague language about what the system will do or perform creates enforcement gaps. Your contract should detail the version or release of the AI model being deployed, any training data parameters, and how updates or retraining will be handled.

Performance standards should be measurable and tied to real operational benchmarks. Rather than stating the system will work reliably, specify precision rates, latency windows, uptime percentages, or other quantifiable metrics relevant to your use case. Include provisions for testing, validation, and acceptance criteria so both parties understand what constitutes successful performance and what triggers a breach or right to terminate.

Data ownership and licensing rights are often the most contentious element of an AI agreement because they determine who controls the training data, output data, and any derivative works or improvements. Your contract must clearly state whether data provided to the AI system remains your property, transfers to the vendor, or is subject to a restricted license.

If you are providing proprietary or sensitive data to train or operate the AI system, your agreement should include explicit restrictions on the vendor's use of that data for competing purposes, secondary licensing, or aggregation with other clients' data. If you are licensing a pre-trained model, confirm whether you have rights to fine-tune the model, export outputs, or use the system's improvements. Include provisions on data retention, deletion timelines, and audit rights so you can verify compliance with your data governance policies.

Liability allocation is critical in AI agreements because algorithmic errors, biased outputs, or unintended system behavior can cause financial loss, regulatory penalties, or reputational harm. Your contract must define who bears responsibility if the AI system produces discriminatory results, violates intellectual property rights, or fails to comply with applicable laws.

Indemnification clauses should specify which party covers third-party claims arising from the AI system's output or performance. If you are deploying the AI system in a regulated industry such as finance, healthcare, or employment, clarify whether the vendor will indemnify you for regulatory investigations or fines triggered by the system's conduct. Include carve-outs so that liability caps do not apply to breaches of confidentiality, intellectual property infringement, or violations of law.

Governance provisions establish how the AI system will be monitored, audited, and updated over time. Your agreement should require the vendor to disclose the AI model's limitations, known biases, and any constraints on its use. If your industry is subject to explainability or transparency rules, the contract must obligate the vendor to provide documentation of how the AI system reaches its conclusions so you can satisfy regulatory or audit requirements.

Compliance obligations may include requirements for bias testing, fairness assessments, and regular audits to detect drift or degradation in the system's performance. Your contract should specify who is responsible for ongoing monitoring, how frequently testing must occur, and what happens if the system fails to meet performance or fairness benchmarks. In regulated sectors, compliance with artificial intelligence law and industry-specific rules should be explicitly assigned to the appropriate party.

New York courts apply standard contract interpretation principles to AI agreements, examining whether the parties intended to be bound, whether material terms are sufficiently definite, and whether the agreement reflects a meeting of the minds on critical issues. Courts may scrutinize performance standards and acceptance criteria to determine whether they are objective and measurable or so vague that enforcement is impossible.

If a dispute arises over whether the AI system performed as promised, you will likely need expert testimony to explain how the system operates and whether it met the contract's specifications. Documentation of testing results, performance metrics, and any communications about system limitations or failures becomes critical evidence. Courts have shown willingness to imply duties of good faith and fair dealing in AI contracts, so a vendor's failure to disclose known issues or to act reasonably in monitoring the system may create liability beyond the contract's express terms.

Intellectual property ownership in AI agreements often involves multiple layers: ownership of the pre-trained model, ownership of customizations or fine-tuning, ownership of outputs generated by the system, and rights to any improvements or derivative works. Your contract must clarify whether you acquire ownership of any custom code, model weights, or training modifications the vendor creates for you, or whether you receive only a limited license to use them.

Output ownership is equally important. If the AI system generates content, recommendations, or analysis, your agreement should specify who owns that output and whether you can use it for commercial purposes or modify it. Include explicit language addressing patent rights, trade secret protections, and whether either party will pursue patent applications related to improvements to the AI system. If third-party intellectual property is embedded in the AI system or training data, require the vendor to warrant that it has obtained necessary licenses or that it will indemnify you against infringement claims.

Termination and transition clauses are vital for protecting your interests if the relationship fails or the AI system becomes obsolete. Your agreement should specify termination rights for convenience, termination for cause (such as material breach or regulatory violation), and what happens to your data and any customizations after termination. Many vendors attempt to retain copies of your data or to prevent you from transitioning to a competing system, so explicit data return and deletion obligations are essential.

Include a transition period during which the vendor must continue supporting the AI system while you migrate to an alternative solution. Specify the format in which the vendor must return your data, the timeline for return, and whether the vendor may retain backup copies. If the vendor is providing a custom AI model, clarify whether you will receive source code, model documentation, or other materials necessary to operate the system independently after termination.

One frequent pitfall is drafting AI agreements with boilerplate language borrowed from software licensing without adapting terms to the unique risks of AI systems. AI systems are not deterministic; they can produce unpredictable outputs, exhibit bias, or degrade over time in ways that traditional software does not. Copying standard indemnity or liability caps from a SaaS contract into an AI agreement may leave you exposed to losses the vendor should have borne.

Another pitfall is failing to address regulatory or compliance obligations. If you are using AI in hiring, lending, healthcare, or other regulated domains, your contract must clearly allocate responsibility for compliance with anti-discrimination law and transparency rules. Additionally, many organizations overlook data governance and security obligations. Your agreement must explicitly require encryption, access controls, incident notification timelines, and compliance with data protection regulations. Organizations deploying artificial intelligence and related fields should also consider cross-border data transfer restrictions.

Finally, avoid agreements that lack clear acceptance and testing provisions. If the contract does not specify how you will validate that the AI system meets performance standards or how long you have to test it before accepting it, disputes about whether the system was ever compliant become difficult to resolve. Build in explicit testing windows, performance benchmarks, and acceptance sign-off procedures so that both parties have a clear record of what the system was expected to do and whether it achieved those objectives.

Before executing an AI agreement, document your organization's requirements, use case, and risk tolerance in writing. Create a detailed specification of what the AI system must accomplish, what data you will provide, and what performance metrics matter most to your business. This documentation serves as context for interpreting ambiguous contract language and helps a court understand what the parties intended.

Preserve email communications, meeting notes, and any representations about the AI system's capabilities or limitations. If a vendor representative made verbal assurances about performance, bias testing, or regulatory compliance that differ from the written contract, document those communications promptly. Before deployment, ensure that your organization completes any required bias audits, testing, or compliance reviews and that the AI system's limitations are documented and understood by the teams that will use it. This record-keeping practice protects you by demonstrating that you acted reasonably and that any subsequent failures were not due to your negligence or misuse.