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Practical Tips for Negotiating AI Terms in SaaS Contracts

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AI is transforming legal workflows, but negotiating AI-related contract terms can be tricky. Vendors often resist broad AI definitions, unrealistic warranties, and restrictive data clauses that don’t reflect how AI actually works.

If your organization is evaluating AI-driven SaaS vendors, here’s how to make contract terms practical, enforceable, and aligned with risk realities.

1. Define AI Narrowly and Purposefully

One of the most common pitfalls in AI contract negotiations is the use of overly broad AI definitions. Some contracts define AI as encompassing all "algorithm-driven" software, which can inadvertently capture standard automation tools or statistical models.

Instead, AI should be defined in relation to regulatory frameworks (e.g., the EU AI Act, Colorado AI Act) or specific functionalities such as generative AI or facial recognition.

Example Definition:

A system or software that simulates human intelligence processes through algorithms, machine learning, and other computational techniques, enabling it to perform tasks such as data analysis, decision-making, pattern recognition, and natural language processing.

A precise AI definition helps limit compliance obligations to truly AI-driven functionalities, reducing unnecessary legal burdens.

2. Set Realistic Expectations About AI’s Capabilities

Be cautious about imposing unrealistic performance guarantees on AI-powered software. AI models, by their nature, are probabilistic rather than deterministic—meaning that variability and occasional errors are inherent to the technology.

Contracts should avoid demanding guarantees such as “error-free outputs” or “no bias.” Instead, they should include disclaimers that outline the shared responsibility between the vendor and the customer, particularly regarding how user inputs impact AI-generated outputs.

Example Disclaimer:

THE AI SERVICES ARE NOT ERROR-FREE AND MAY GENERATE INCORRECT INFORMATION. YOUR USE OF THE AI SERVICES IS AT YOUR OWN RISK.

By setting realistic expectations, organizations can reduce the risk of disputes related to AI performance.

3. Allocate Intellectual Property (IP) Liability Appropriately

Many SaaS vendors do not build their own AI models from scratch. Instead, they rely on third-party providers such as OpenAI, Google AI, or AWS. As a result, vendors may lack full control over the AI outputs their software generates.

To address this, carefully consider how liability is allocated in AI-related IP disputes. In many cases, shifting liability for misuse or modifications to the user—rather than the vendor—makes practical sense.

Example Clause:

You must use the AI Services and the generated Output only (i) in a lawful manner and in compliance with all applicable laws, (ii) in accordance with these Terms of Service, our AUP, or any applicable third-party AI provider terms, or (iii) in a manner that does not infringe or attempt to infringe, misappropriate or otherwise violate any of our rights or those of any other person or entity, including but not limited to any intellectual property rights including trademark, copyright, patent or name, image and likeness rights, or the method, purpose or means of causing or attempting to cause the AI Services to generate content.

Clear IP ownership and liability provisions prevent unexpected legal exposure when AI-generated content is used commercially.

4. Focus Transparency Requirements on Material Risks

Regulators and customers increasingly demand greater transparency into AI decision-making processes, but requiring vendors to provide full logs or detailed model data may be impractical—especially for third-party AI systems.

Instead, transparency obligations should focus on material risks, such as autonomous decision-making, significant model updates, or changes that impact compliance obligations.

Example Clause:

Vendor will provide reasonable notice of any material changes to the Services.

This approach strikes a balance between ensuring transparency and avoiding excessive compliance burdens that could slow down AI adoption.

5. Allow Anonymized Data Use for Product Improvement

Many AI vendors improve their models by learning from user interactions. However, contracts that prohibit all customer data use—even in anonymized form—may limit a vendor’s ability to refine and enhance AI performance.

A more balanced approach is allowing anonymized or aggregated data use while ensuring strong privacy safeguards.

Example Clause:

Usage Data. Vendor AI may collect and use Usage Data to develop, improve, support, and operate the Service. Vendor AI may not share Usage Data that includes your Confidential Information with a third party except (a) in accordance with the Confidentiality provisions of this Agreement, or (b) to the extent the Usage Data is aggregated and anonymized such that you cannot be identified.

This allows vendors to maintain AI quality without compromising customer confidentiality.

6. Customize AI Contract Terms Based on Risk Level

Not all AI-powered SaaS tools carry the same level of risk. An AI-driven contract review tool carries significantly lower regulatory and compliance risks than an AI system used in healthcare or finance.

A pre-contract questionnaire can help assess vendor AI use cases, data flows, and dependencies to ensure the right level of contractual protection.

Example Questions:

  • What’s your approach to model training, retraining, and maintenance?
  • Provide some accuracy metrics — F1 Score, recall/precision scores...
  • How do you handle and store data used for training your AI models?
  • How do you ensure that your models are transparent and explainable?
  • By scaling contract terms to match AI risk levels, legal teams can avoid overburdening low-risk AI applications with unnecessary restrictions.

7. Support AI Contract Terms With Internal Safeguards and Insurance

Even the best AI contract provisions cannot fully eliminate risks. Legal teams should ensure their organizations have internal safeguards in place, such as:

  • AI governance policies to monitor compliance and ethical use

  • Employee training on responsible AI implementation

  • Data protection measures to ensure AI-generated content does not expose confidential information

  • AI-specific insurance for potential liabilities, including IP disputes, regulatory violations, and reputational risks

Example Clause:

Both parties will implement and maintain policies for the ethical and responsible use of AI features. These policies must promote transparency, mitigate bias, and ensure fairness and accountability in all applications of AI features under this Agreement.

Proactively adopting internal AI risk management measures strengthens an organization’s overall legal position.

Conclusion

AI contract negotiations should balance legal protection with business practicality. Overly rigid terms can delay deals and slow AI adoption, limit vendors’ ability to improve AI accuracy, and create compliance burdens that don’t reflect actual AI risks.

Instead, legal teams should focus on:

  • Precise AI definitions that align with regulations

  • Realistic liability allocations that account for third-party models

  • Scalable transparency and risk management obligations

By negotiating AI terms with a risk-based, business-aligned approach, organizations can safeguard compliance while enabling responsible AI adoption.