Adding AI to your product changes more than the feature set. It changes what you promise customers, what data you handle, and who may be responsible when the system gets something wrong.
That legal layer is easy to miss when the team is focused on building. But once customers use the feature, your company, not just the model provider, is part of the experience.
For a company moving from prototype to a repeatable product, these are the questions to answer before launch.
1. Who owns the output, and what can they do with it?
“Customers own the output” sounds simple. It may not settle the important questions.
Start by separating the pieces: customer inputs, your product and prompts, model-provider technology, generated outputs, and any edits or arrangements made by a person. Your terms should explain who owns or can use each piece, and whether you may use feedback, usage data, or de-identified information to improve the product.
Then compare your customer promises with the model provider’s terms. Do they allow you to use the model’s output in a commercial product and let your customers use it too? Are there restrictions on the output or the way the feature can be used?
A contract can allocate rights between parties. It can’t guarantee that every output is unique or that purely AI-generated material qualifies for copyright protection. The U.S. Copyright Office’s report on AI and copyrightability explains that copyright protection depends on human authorship and the human contribution involved.
2. Do you know where the data came from, and what happens to it?
If the feature uses training, fine-tuning, retrieval, or customer-provided content, document what data goes in and why you have the right to use it. Keep a record of the source, permissions, and any limits attached to that material.
Then be specific about customer data. Is it used only to deliver the feature? Is it retained for abuse monitoring or troubleshooting? Can it be used to evaluate or improve a model? Does the answer differ by provider, product setting, or data type?
Avoid broad promises like “we never train on your data” unless the applicable terms, settings, and actual data flows support them. Make sure your privacy notice and customer agreements match what the product really does, including retention, subprocessors, deletion, and any model-improvement use.
3. Does your indemnity cover what you promise customers?
An indemnity is a promise to take responsibility for certain losses or claims. It is not a magic shield, and the details matter.
Check whether the model provider offers protection for claims involving its service or generated output. Then read the exclusions. Coverage may not apply if a claim involves customer-provided material, a modified output, a combination with your product, or a use that violates the provider’s terms.
Now compare that coverage with your own customer contract. If you promise customers more protection than you receive upstream, your company may be left with the difference.
Be precise about what each party is responsible for, what uses are allowed, and how claims are handled. Review the limits on liability, including whether they account for intellectual property claims, confidentiality, and data security. Don’t promise a blanket guarantee that an output is free of third-party rights issues unless you can support it and are prepared to bear the risk.
4. Are you clear with customers about where AI is involved?
Customers should not have to guess whether AI is producing, ranking, summarizing, or recommending something they rely on.
Explain the feature in plain language. Tell users what it does, what it does not do, and whether a person reviews the result. Be careful with claims like “accurate,” “unbiased,” or “fully automated.” If the system can be wrong, say so, and make sure your marketing does not promise more than your testing supports.
A disclaimer helps set expectations, but it cannot fix a misleading product design or an overconfident sales claim. Match the language in your product, contracts, documentation, and marketing.
Rules can also depend on where the product is used and what it does. The EU AI Act includes transparency and oversight obligations for certain AI systems. If your product serves users in multiple regions, assess the feature’s actual use and locations before launch.
5. Who checks consequential outputs?
If an AI output could affect someone’s job, finances, access to a service, health, safety, or legal position, “the model usually gets it right” is not a review process.
Decide when a qualified person must check the result, what they need to see, and whether they can correct or reject it. Build a clear path for users to flag errors or ask for human review.
Keep the oversight practical. A reviewer needs enough context to spot a bad answer, not just a button that says “approve.” For high-impact uses, review the rules that apply to your product and market, and make sure responsibility for decisions is clear in your customer contracts.
6. Do your contracts and policies describe the product you are shipping?
A customer agreement written before the AI feature existed may make promises that no longer fit.
Review your customer contract, privacy policy, data-processing terms, and security representations before you launch. Check whether they accurately describe:
- What customer data the feature processes and why.
- Whether data is shared with a model provider or other subprocessors.
- How long inputs, outputs, and logs are kept.
- Who may use generated outputs and for what purposes.
- What customers should, and should not, rely on the feature to do.
- How model changes, incidents, and support requests are handled.
Pay special attention to statements about security and data use. If your security terms promise strict control over data, confirm that the AI feature and its providers actually meet that promise.
7. Make legal review part of the launch checklist
The legal work is not just a one-time contract edit. Model terms can change, features evolve, and customer expectations grow with them.
A practical launch checklist should identify the model and version in use, confirm the provider terms permit your product use, document data flows and retention, test performance against known failure cases, and assign an owner for monitoring issues. Include a clear process for escalating errors, pausing the feature, or switching models if something material changes.
This is where a fractional general counsel can help connect the product decision to the contract language. That can mean reviewing model-provider terms, redrafting customer agreements and privacy disclosures, and building a launch checklist your product and operations teams can actually use.
AI in your product is not only a technical decision. It is also a set of promises to customers. Make sure those promises match the system you are shipping.
Learn more about fractional general counsel services, or email contact@thechiefcounsel.com to discuss support.