AI innovation often depends on partnerships. Whether it is a cloud provider offering infrastructure, a niche developer supplying a specialized model, or a data vendor providing essential inputs, these relationships move products forward. They also carry risk. If a vendor’s system malfunctions, violates a regulation, or misuses data, the consequences land at your company’s door.

For in-house counsel, the vendor agreement is the tool to turn uncertainty into clear, enforceable expectations. It is not just about legal protection. It is about setting the tone for how the AI will be developed, maintained, and governed throughout the life of the relationship.

Defining Responsibility Clearly

Every AI contract should start with an unambiguous allocation of responsibility. If the system produces harmful results, fails accuracy tests, or violates applicable laws, the agreement should state who is accountable. This includes performance standards, quality controls, and obligations to fix problems promptly.

Regulatory compliance cannot be assumed. Vendors should commit to meeting relevant laws and notify you immediately if legal changes require updates to the system or its deployment.

Demanding Operational Transparency

To manage risk, you need visibility into the AI system. That means contractual rights to documentation that explains how it works, where its data originates, and how it reaches its conclusions.

This might take the form of technical summaries, training data disclosures, and change logs for updates. Without this information, you may be left unprepared when a regulator asks for details or when a customer challenges the product’s decisions.

Clarifying Ownership And Use Rights

In AI projects, intellectual property rights are rarely straightforward. The contract should specify who owns the model, who owns the outputs, and whether the vendor can use your data to improve its technology for other clients.

Clear terms prevent misunderstandings about licensing scope, exclusivity, and the limits on reusing your proprietary information or derived datasets. Without this clarity, disputes can arise long after the product is in market.

Setting Data Governance Standards

Data is the lifeblood of AI and the source of many legal risks. Contracts should set explicit rules for how the vendor will handle your data, from storage security to deletion protocols.

Decide in advance whether production data can be used for further training or testing and under what safeguards. Strong governance clauses help maintain compliance with privacy regulations and align with your company’s own data policies.

Managing Change Over Time

AI systems are not static. Vendors may update models, integrate new datasets, or alter processing methods. The contract should require notice of any significant changes and your right to approve them before deployment.

Termination rights are also critical. You should be able to exit the relationship if changes compromise compliance, safety, or business fit. These protections are far easier to secure at the start than in the middle of a problem.

Contracts As Strategic Tools

An AI vendor contract is more than a risk-allocation exercise. Done well, it ensures that the vendor’s operations support your regulatory obligations, ethical commitments, and business priorities. It gives you the insight and control needed to deploy AI responsibly, even when the core technology comes from outside your organization.

For in-house counsel, moving from standard boilerplate to tailored AI clauses means building agreements that safeguard trust and foster collaboration. A strong contract does not just protect the company from harm. It helps the partnership deliver AI that is reliable, compliant, and aligned with the goals of the business.

Olga V. Mack is the CEO of TermScout, an AI-powered contract certification platform that accelerates revenue and eliminates friction by certifying contracts as fair, balanced, and market-ready. A serial CEO and legal tech executive, she previously led a company through a successful acquisition by LexisNexis. Olga is also a Fellow at CodeX, The Stanford Center for Legal Informatics, and the Generative AI Editor at law.MIT. She is a visionary executive reshaping how we law—how legal systems are built, experienced, and trusted. Olga teaches at Berkeley Law, lectures widely, and advises companies of all sizes, as well as boards and institutions. An award-winning general counsel turned builder, she also leads early-stage ventures including Virtual Gabby (Better Parenting Plan), Product Law Hub, ESI Flow, and Notes to My (Legal) Self, each rethinking the practice and business of law through technology, data, and human-centered design. She has authored The Rise of Product Lawyers, Legal Operations in the Age of AI and Data, Blockchain Value, and Get on Board, with Visual IQ for Lawyers (ABA) forthcoming. Olga is a 6x TEDx speaker and has been recognized as a Silicon Valley Woman of Influence and an ABA Woman in Legal Tech. Her work reimagines people’s relationship with law—making it more accessible, inclusive, data-driven, and aligned with how the world actually works. She is also the host of the Notes to My (Legal) Self podcast (streaming on Spotify, Apple Podcasts, and YouTube), and her insights regularly appear in Forbes, Bloomberg Law, Newsweek, VentureBeat, ACC Docket, and Above the Law. She earned her B.A. and J.D. from UC Berkeley. Follow her on LinkedIn and X @olgavmack.

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