Key Takeaways
- Contracts are your strongest protection. The license you sign matters more than any assurance a vendor offers verbally or in marketing copy. It's how issues might be addressed later, even if they can't be addressed now.
- A promise without an audit right is not protection. Many AI vendor contracts pledge not to train on your data but include no contractual mechanism to verify that pledge.
- Licensing deals are only as good as their accounting. If a publisher cannot track how, how often, and to what effect their content is used, licensing revenue is difficult to defend as a strategy.
- Governance structure is a public signal. How an AI company is organized reveals more about its trustworthiness than any pitch deck.
- Ask hard questions before adoption, not after. The right time to define your terms is before you click accept.
Publishing contracts and AI. It's a sticky subject no matter what direction you're coming at it: authors are encountering a scattershot of AI clauses popping up without a lot of explanation or consistency. Publishers have AI seeping into their teams without oversight; we all click "accept" without thinking or leave terms of service unread. Few ever sit down to negotiate the language that decides how their intellectual property is treated inside a model.
This is a failure of diligence, if not a new one. We could all read the 40 pages of terms of service. Most of us don't. And most of that paperwork was drafted with software companies in mind, not book publishers, their backlists, or the authors whose careers depend on those rights.
Dr. Alexandra Andhov, chair in law and technology at the University of Auckland and co-director of the Center for Advancing Law and Technology Responsibly, made this point directly at the Ethical AI in Publishing Summit NCAI hosted earlier this summer.
The best tool available right now for protecting your interests in AI relationships is the contract.
But the gap between how widely AI is being used and how carefully those agreements are examined is one of the biggest risks in trade publishing today.
The AI Contract Problem Most Publishers Haven't Examined
Because AI adoption in publishing is haphazard, and most publishers don't have a clear staff-facing AI policy, all sorts of important contractual agreements are being made (right now) by unwitting staff members who are curious about trying a new AI-powered feature on an existing app, or a new tool.
The scale of this is easy to underestimate. A 2025 survey by the Book Industry Study Group and BookNet Canada, the most comprehensive study of AI adoption across the North American publishing ecosystem to date, found that nearly half of publishing professionals reported using AI individually, and nearly half said their organizations were using it too. Fewer than 30 percent of those organizations had a formal written AI policy. The report's own conclusion shared that the industry is adopting AI faster than it is becoming comfortable with it. The tools spread through organizations faster than the guardrails do.
The most valuable thing a publisher owns is the right to say how its content may be used. AI adoption in publishing tests that ownership at exactly the moment when the contracts governing it are least understood.
What 'No Right to Audit' Actually Means for Publishers
The majority of AI company contracts promise that your data won't be used for training, but they contain no mechanism to verify that promise. A 2025 analysis published through Stanford Law School's CodeX center, drawing on contract data from the certification platform TermScout, found that 92 percent of AI vendor contracts claim data usage rights that exceed what is necessary for service delivery, while audit rights and transparency commitments are largely absent. The Authors Guild has identified the same gap from the rights-holder side. It is actively lobbying for laws requiring AI developers to disclose what works they used to train their models, a demand that only makes sense because no such verification currently exists.
For a publishing house, this should be a familiar problem. You wouldn't sign a distribution agreement that promised royalty accounting with no right to audit the numbers; royalty audit clauses exist precisely because trust and verification are different things. The principle is identical here: a contractual promise without an audit mechanism is a goodwill gesture, not a legal protection. If an AI vendor is valued at billions of dollars, the technical limitations supposedly preventing verification are a choice, not a constraint.
We've seen this play out with authors who ask a reasonable question: "Can you confirm my book wasn't part of the training set?" They receive a reassuring email, not a document, an inspection right, or a third-party attestation. An email is not enforceable. A right to audit is. That distinction is where real protection lives.
Why AI Licensing Deals May Not Be the Answer Publishers Think
There's a version of the AI conversation happening in trade publishing that treats licensing as the solution to the IP problem. Publishers license their content to AI companies, AI companies pay for the training data, and the ecosystem reaches a kind of equilibrium. At the summit, Andhov was skeptical of this framing, and her skepticism is worth taking seriously.
The core issue is accountability. Even in existing licensing arrangements, publishers who ask AI companies how their content is being used, how many times a book was accessed for training, what outputs it influenced, and how a query was answered using it cannot get a clear answer. A licensing revenue model built on metrics that can't be reliably tracked is not a sustainable position for publishers who care about their authors' rights.
There is an honest trade-off to acknowledge here. Licensing deals can bring real revenue, and for some houses, that revenue is meaningful. The concern is not that licensing is inherently wrong, but that a deal you cannot audit is a deal you cannot fully understand. If you can't count it, you can't verify you're being paid fairly for it, and neither can the author whose work generated it.
What Publishers Should Ask Before Signing Any AI Company Contract
A good framework for evaluating AI vendors comes down to a set of critical questions that most organizations aren't asking. Turn them into a checklist and run every prospective tool through it before you sign:
- Who owns the tool, and what do we actually know about them? Ownership shapes incentives. A tool owned by an advertising company treats your data differently than one owned by a company whose business model is the software itself.
- How was the model developed? Was the training data licensed and consented to, or acquired by scraping without permission? A company that built its foundation on unlicensed work is telling you how it views IP.
- What data protection is contractually guaranteed? Look for explicit language prohibiting the use of your inputs for training and make sure it appears in the contract, not just the FAQ.
- What mechanisms exist to verify those protections? This is the audit question. Can you request records, inspect logs, or receive independent verification? If not, the guarantee is aspirational.
- What is the governance model of the company? Is there independent oversight? Are commitments structural or merely stated in marketing language?
- What happens if the contract is breached? Are there defined remedies, or does the agreement leave you with a grievance and no pathway?
Governance structure is public, and it is worth examining before you sign. Companies that have made real commitments to responsible AI development, not just in marketing language but in organizational architecture, tend to be more transparent about how their models work and what your data does inside them. When a vendor is reluctant to answer question five in plain terms, that reluctance is itself an answer.
Reading the fine print isn't the exciting part of AI adoption in publishing. But it's the part that decides whether AI serves your organization or works against it.
The pattern holds up: contracts protect you when they include real accountability, audit rights, verified data protections, and remedies you can enforce. Contracts fail you when they run on promises alone.
Publishing has faced moments like this before. The houses and agents who came out ahead were the ones who read closely and asked hard questions early, not the ones who assumed good faith would be enough.
Before you adopt your next AI tool, run it through the checklist above. Ask who owns it, how it was built, and what happens if the deal breaks. The answers you get, or don't get, will tell you what you need to know.
Craft stays human. Make sure the contracts protecting it are just as solid.
FAQ: AI Company Contract Clauses for Publishers
What should publishers look for in an AI company contract?
Look for explicit data protection provisions that prohibit use of your inputs for model training; a right to audit those provisions; clear definitions of what constitutes proprietary content; and accountability mechanisms if the contract is breached. General promises without enforcement pathways offer limited protection. Enterprise or closed-model agreements typically provide stronger terms than consumer-facing tools.
What is 'contractual sovereignty' in the context of AI and publishing?
Contractual sovereignty refers to a publisher's ability to define and enforce the terms under which their content, workflows, and data interact with AI systems. It means actively negotiating or selecting AI tools whose contracts protect IP, prohibit unauthorized training, and provide audit rights, rather than passively accepting default terms written to benefit the platform.
Are AI licensing deals good for publishers?
The value of licensing deals depends on what's actually being measured and verified. Legal scholar Alexandra Andhov noted at the 2026 Human-Aligned & Ethical AI in Publishing summit hosted by Next Chapter AI that even publishers who have signed licensing agreements cannot reliably determine how their content is being used, how often, or what revenue it's generating. Without reliable accounting and audit rights, licensing revenue is difficult to assess as a sustainable strategy.
What does 'no right to audit' mean in an AI contract?
A 'no right to audit' clause means that even if an AI vendor promises not to use your content for training, you have no legal mechanism to verify that promise. You cannot request records, inspect logs, or receive third-party verification. This converts a contractual guarantee into an unenforceable pledge and shifts all the risk to the publisher.
How can publishers evaluate the trustworthiness of an AI company?
Examine governance structure, not just marketing language. Look at how the company is organized, especially whether it has independent oversight, transparent accountability mechanisms, and a track record of honoring stated commitments.
