AI Disclosure Examples for Customer Communications

When you disclose AI use to customers, keep it plain and brief. The examples below cover the common cases: chatbots, AI-assisted content, and AI-influenced decisions. Use them as a starting point, and lean toward openness wherever AI meaningfully shapes what the customer receives. Clumsy disclosure can worry customers, while clear disclosure builds trust, so the wording matters as much as the decision to disclose. The goal is honesty that is easy to find and easy to understand, not a clause buried in terms no one reads. Not every use needs the same treatment: a chatbot interacting directly should identify itself and offer a route to a person, content that is largely AI-generated and presented as expert work deserves a short note, and a decision affecting the customer should come with the option of human review, while routine human-reviewed drafts generally do not need a label. This guide gives you ready-to-adapt wording for each case, shows where disclosure matters most, and explains why keeping a human easily reachable resolves most of the discomfort people feel about AI.

Why wording matters

Clumsy disclosure can worry customers; clear disclosure builds trust. The Pew Research work on AI shows people value transparency, especially when AI touches their data or decisions about them.

Chatbot disclosure examples

• "You are chatting with an AI assistant. Type 'agent' any time to reach a person."
• "This is an automated assistant. For complex issues, we will connect you with our team."

Content disclosure examples

• "Parts of this article were drafted with AI and reviewed by our team."
• "We use AI to help prepare drafts; a person checks everything before it is published."

Decision disclosure examples

• "We use AI to help review applications. A team member makes the final decision."
• "AI assists our assessment; you can ask for a human review at any time."

Where disclosure is most important

Prioritize disclosure when a chatbot interacts directly, when content is largely AI-generated and presented as expert work, and when AI affects a decision about the customer. Decide your rules once and apply them consistently; our governance checklist shows how disclosure fits with your other rules.

Keep it honest and simple

Disclosure works when it is plain and easy to find, not buried in fine print. For broader context on responsible adoption, see the IMF analysis on AI and the economy.

Not every use of AI needs the same treatment. A chatbot interacting directly with a customer should say so up front and offer an easy route to a person. Content that is largely AI-generated and presented as expert work deserves a short note. A decision that affects the customer should come with the option of human review. But routine internal uses, like AI helping draft a reply that a person reviews and sends, generally do not need a label, because the human is accountable for the final output. Matching disclosure to the situation keeps it meaningful rather than turning it into noise customers learn to ignore.

When you are unsure, lean toward openness. Broad public research such as the Pew Research work on AI shows people value transparency, especially where AI touches their data or decisions about them. A little extra honesty rarely costs you and often builds trust.

Decide your rules once and apply them consistently

The simplest way to handle disclosure well is to set your rules in advance and write them into your policy, so every customer gets the same treatment and no one has to improvise in the moment. Decide when a chatbot identifies itself, when content carries a note, and when a decision comes with a human-review option, then apply those rules across the business. Consistency is what makes disclosure feel like a standard rather than a one-off, and it fits naturally alongside your data and review rules in our governance checklist.