Write about an AI feature by naming the task it does, the input it works from, how often a person needs to check its output, and where it falls short, and hold evidence for every performance claim before the page publishes. "AI-powered" is a label, not a benefit, so it should never stand in for one. When the feature relies on a third-party model, say what your product adds and what customer data it uses, in wording your security and legal teams have approved.
Software companies are under pressure to show AI on every page, and the copy often runs ahead of the product. A buyer evaluating an AI feature will reasonably ask what it does, what it gets wrong, and what happens to the data, and a page that answers with adjectives sends those questions to the sales call.
This is a content-planning question for software owners and marketing leads, not legal advice. Your counsel decides how advertising law applies to your claims, and your security team owns statements about data handling. For the wider page set, see our software and SaaS content guide.
Name the task, not the technology
A lot of AI copy describes the method. Buyers are evaluating the result. Calling a model is not the same as solving the buyer's problem, and the page should be written about the problem: what the user does today, what the feature does instead, and what the user still does.
| Vague claim | Specific claim | Evidence to hold |
|---|---|---|
| AI-powered insights | Summarizes each support conversation into a three-line note for the account owner | Product documentation and sample outputs |
| Eliminates manual data entry | Reads invoice fields from PDFs and flags the ones it is unsure of for review | Internal test results on a described set of invoices |
| Smarter forecasting | Suggests a forecast range from 12 months of your history; managers approve it | Method notes and a description of the test |
| Works like a human analyst | (cut it) | No test supports a comparison this broad |
The last row is there on purpose. Some claims cannot be narrowed into anything a team could test, and the right edit for those is deletion, not softer adjectives.
Hold the evidence before the page goes live
An AI claim is a performance claim like any other. The FTC's substantiation policy asks for "a reasonable basis for advertising claims before they are disseminated," and where an ad states its own level of support, such as a named test, the agency expects "at least the advertised level of substantiation." The FTC's September 2024 announcement of Operation AI Comply described firms that "have seized on the hype surrounding AI" to lure people into schemes. The same release quotes the agency's chair at the time: "there is no AI exemption from the laws on the books." A B2B feature page is far from those cases, and your counsel can say how those laws reach it.
Before an accuracy figure goes on a page, someone should ask the team that built the feature, "What did we test, on whose data, and would the result hold for a customer whose records look different?" If the honest answer is that the test used clean sample data, the page should say what the figure describes, or the figure should come off. A number that only holds in the lab is not ready for a buyer.
State the limits and the human check
Every AI feature has failure cases, and the buyer will find them in the trial if the page does not mention them. Say what kinds of input the feature handles poorly: handwriting, scanned images, languages it does not support, unusually formatted records. Say where a person stays in the loop, such as approving a suggested forecast or reviewing flagged fields, and whether the feature can be switched off. Availability belongs in the claim as well. If the feature is in beta, limited to certain plans, or offered only in some regions, say so on the feature page rather than in a footnote elsewhere, because a buyer who plans a rollout around a beta feature and learns its status later has a fair complaint.
Data questions belong on the page too, because the security reviewer will ask them. Does customer data train a shared model? Is a third-party model provider involved, and under what terms? Where is the data processed? These answers should come from your security team, in their wording, and link to your security documentation. Our page on explaining what your product does not do covers how to place limits beside the claims they qualify.
A fictional example: an invoice extraction feature
Here is a hypothetical case for a fictional accounts payable app. It describes no real product or test.
The original page says: "Our AI reads every invoice instantly, with near-perfect accuracy." The rewrite says: the app reads vendor name, invoice number, dates, line items, and totals from PDF invoices, and fills them into a draft bill. Fields it is unsure of are highlighted for a clerk to confirm. It handles typed PDFs well; scanned paper invoices and handwritten notes need more review. In our internal testing on a described set of vendor invoices, results are reported by field type, with the test date and method in the linked documentation. Customer invoices are not used to train shared models; the security page has the detail.
Each sentence in that rewrite traces to a record. The marketing team should not be inventing an accuracy number or guessing at the failure cases, because the engineers who evaluated the feature already wrote both down, and the gap between their notes and the original headline is where the risk sat.
Keep AI claims current
AI features change more often than most. A model provider updates its model, a prompt changes, a new document type is added, and last quarter's test no longer describes the product. Date each performance claim, tie it to the test it came from, and retest when the underlying model or method changes. Our page on publishing product updates so they stay useful covers how to record those changes so the feature page follows them.
In Hudson South's process, the Authority Asset gathers the documentation, test notes, and approved security answers behind each AI claim. Hudson Twenty-One records how your company communicates across 21 dimensions, so an AI feature page sounds like your product team rather than a trend piece. Market Intelligence researches the questions buyers and security reviewers ask about AI features in your category. AEO Creation turns those inputs into answer-first pages that state the task, the evidence, and the limits.
Make your AI claims match your test results
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- FTC Policy Statement Regarding Advertising Substantiation. Federal Trade Commission. Read 2026-10-16.
- FTC Announces Crackdown on Deceptive AI Claims and Schemes. Federal Trade Commission. Read 2026-10-16.