Do AI-generated product lifestyle images need to be labeled as AI-made?

It depends on whether the image could lead people to believe it shows a real product or a real result. An obvious illustration or concept visual carries a lower risk of being mistaken for reality. But if it looks like a real product photo, a before-and-after comparison, or a person using the product, consumers may judge what the product is actually like from it, so you should avoid misleading them and, where needed, label it as illustrative or AI-generated. Each platform also has its own rules for labeling AI-generated content, so check the current rules before publishing, and for advertising in regulated industries, consult a professional.

Can we have AI polish genuine customer reviews before putting them on our website?

Rewriting the content of reviews is not recommended. A customer review is valuable because it is the customer's own words. Once it has been rewritten, even if the meaning is close, it is no longer what the customer said, and using it in promotion may be misleading. The safer approach is to keep the original, correct only obvious typos with the customer's consent if needed, and, if a review is too long, excerpt it rather than rewrite it and mark it as an excerpt. When you want to pull out the main points from a large number of reviews, AI can help sort and summarize them, but when you present the result publicly, make clear it is the brand's summary, not the customers' words.

If our team writes articles with AI, will search engines rank them lower?

The public position of search engines is that they care about whether content helps readers, not who or what tool produced it. The real risk is mass-producing articles that lack original insight, contain errors, or closely resemble other sites; content like that struggles to perform whether or not AI wrote it. When AI helps, the key is to add your own experience, examples, and judgment, and to fact-check every sentence. How a brand gets mentioned in AI search and chat tools is a separate topic, covered in our article on AI search visibility.

AI-Generated Content in Marketing: The Limits

“Can AI write this copy?” “Can we put this AI image straight into an ad?” More and more marketing teams run into these questions in their daily work. AI-generated content has made production much faster, but it also brings new risks: factual errors, inconsistent tone, images that look real but are not, and the most serious of all, being used to manufacture fake reviews and fake testimonials.

The question was never whether you can use AI. It is which work goes to AI and which work a person must own, and whether readers should be told once you have used it.

This guide works through, in order, the work that suits AI and the work that does not, the red lines you must never cross, a fact-checking process, how to keep your brand voice consistent, how to judge when to disclose, and a checklist to run before publishing.

Scope: using AI to make content, not being found by AI

First, a note on scope. This article is about how brands use AI tools to produce marketing content, and the boundaries and disclosure that go with it. How a brand gets mentioned and cited in the answers of AI search and chat tools such as ChatGPT and Gemini is a separate topic, covered in full in our AI Search Brand Visibility Guide. The two are related, but one is about production and the other about being discovered, and they call for different approaches.

Work that suits AI

AI is good at work that has a clear framework and can be checked quickly by a person. Handing these kinds of tasks to AI usually saves time with manageable risk:

Drafts and structure: Article outlines, section order, and headline options as a starting point for human writing
Rewriting and versioning: Condensing a long article into a social post or newsletter summary, or adapting length for different platforms
Sorting and summarizing: Grouping large volumes of customer feedback or interview transcripts to find recurring themes
Formatting and proofreading: Catching typos, standardizing terminology, converting formats
Brainstorming: Event names, topic directions, lists of questions

What these tasks have in common: a person makes the final call on whether and how to use the output. AI provides options and first drafts; people decide and take responsibility.

Imagine a small shop that wants a weekly social post. Asking AI for five headline ideas and a rough outline is a sensible use: the owner picks one, adds what actually happened in the shop that week, and rewrites it in the brand’s voice. Asking AI to write the finished post, product details and all, and publishing it unread is a different thing. The first keeps a person in charge of every claim; the second hands that responsibility to a tool that cannot carry it.

Situations that do not suit AI

Some work should not be led by AI, even if AI can produce it:

Content involving facts and data: Product specifications, prices, explanations of regulations, statistics. AI can produce information that sounds plausible but is wrong
Content that needs real experience: Product experiences, case stories, founder viewpoints. Their value lies in being true
High-sensitivity situations: Complaint replies, crisis statements, apologies. Readers can tell whether the other side means it
Content in regulated industries: Claims in healthcare, food, finance, and health supplements need professional review
Content that speaks for other people: Customer reviews, expert endorsements, partners’ statements

The test is simple: if this content is wrong, who is responsible, and who gets hurt? If the answer involves readers’ health, money, or trust, it should not be left to AI to decide.

Red lines: fake reviews, fake testimonials, and fake accounts

This is the boundary AI-generated content most needs to draw clearly. AI makes it very easy to produce large volumes of realistic-looking text, but the following are off-limits whether or not AI is involved:

Using AI to produce fake customer reviews and posting them on Google, e-commerce platforms, or social media
Using AI to invent customer testimonials or use cases, including made-up names, photos, and stories
Using AI-generated faces to pose as real customers or experts recommending a product
Using AI to mass-produce comments from fake accounts to manufacture buzz
Rewriting genuine customer reviews to make them more positive than they were

These practices mislead consumers, break platform rules, and may also fall foul of fair trade and consumer protection law. For specifics on your situation, consult the relevant regulator or a legal professional.

On the other side, AI makes spotting fake reviews more important too, and both consumers and platforms are becoming more alert. For how to identify fake reviews, see the Fake Review Detection Guide; for the overall legal principles on recommendations and disclosure, see the Word-of-Mouth Marketing Compliance Guide.

Fact-checking: every sentence needs a source

AI writes fluently, and that makes it easy to relax your guard. Make checking a fixed process rather than relying on how something feels in the moment:

The checking process

Mark every factual statement: Numbers, dates, names of people and organizations, names of regulations, product specifications, causal claims
Find a source for each: Internal data, official documents, original reports. Delete any sentence you cannot source, or rewrite it as opinion
Check that citations exist: AI can invent studies or article titles that look real, so always go back to the original source to confirm
Check timeliness: Make sure regulations, prices, and platform rules are the current versions
Have a second person review: At least one person who did not help write it should read the factual passages

A practical threshold: any sentence that attributes a conclusion to an unnamed authority such as “a study,” “experts,” or “most consumers” without a named source does not go out.

The threshold is crude, but it needs no special expertise to apply, and it catches the kind of sentence AI is most likely to produce with confidence and least likely to get right. It also changes habits on the team. Once writers know such sentences will be stopped, they start asking for the source while drafting rather than at the end, and the checking step gets faster each time.

Consistent brand voice: give AI a specification

Content produced with AI by different people at different times often varies in tone, and sometimes uses exaggerated words the brand never uses. The fix is a reusable voice specification:

How the brand addresses readers: Formal or informal, singular or plural
Preferred and banned words: For example, banning hype such as “revolutionary,” “the best,” or “guaranteed”
Sentence style: Short or long sentences, whether to use emoji
Example passages: Three to five passages widely agreed to represent the brand best
Promises you cannot make: For example, no promised results and no disparaging other brands

Give AI this specification every time you use it, and after it produces something, have someone who knows the brand revise it against the specification. Over time, update the specification itself based on how it is actually used.

For how brand content ties into search, see the Brand Blog Content and SEO Guide.

When to disclose AI involvement

There is no one-size-fits-all standard for disclosure, but you can judge it by asking whether readers would be misled by not knowing:

Situation	Suggested approach
AI helped proofread or outline; a person wrote the content and is responsible for it	Usually no specific label needed
An image looks like a real photo but was generated by AI	Label it as illustrative or AI-generated
A person’s likeness is AI-generated and could be mistaken for a real person	Disclose clearly, and never pose as a real customer or expert
A customer service bot or chatbot replies to customers conversationally	Let customers know they are talking to an automated system
A collaboration with a creator or influencer includes AI-generated elements	The partnership itself must still be disclosed; label the AI elements according to platform rules

Social and advertising platforms keep updating their rules for labeling AI-generated content, so check the current rules before publishing.

When a case does not fit neatly into the table, go back to the underlying question: would a reasonable reader feel misled if they later learned how this was made? If the honest answer is yes, disclose. A short label costs very little, while being found out afterward costs the trust you were trying to build.

Pre-publication checklist

For every piece of content AI was involved in, go through this list before publishing:

Every factual statement has a source, or has been rewritten as opinion
There are no invented studies, statistics, cases, or quotes
No customer’s or other person’s original words have been rewritten
The tone matches the brand specification, with no exaggerated or guarantee-style language
Realistic-looking images or people are labeled as the situation requires
Any partnership or paid content discloses the relationship
Content in regulated industries has been checked by a professional
At least one person who did not help write it has read it

AI can help you produce drafts faster, but it cannot take responsibility for the consequences of the content. Treat AI as an assistant rather than an author, and make fact-checking and disclosure a fixed process. That way you gain efficiency while protecting the thing a brand finds hardest to rebuild: readers’ trust.

If you want to give your team a set of rules for dividing AI work, a voice specification, and a checking process, plan your content workflow with NETVANA, or first learn about our content and word-of-mouth services.

Further reading: For how brands get mentioned by AI search and chat tools, see the AI Search Brand Visibility Guide. For how to identify fake reviews, see the Fake Review Detection Guide. For the legal principles on recommendations and disclosure, see the Word-of-Mouth Marketing Compliance Guide. For brand content and search, see the Brand Blog Content and SEO Guide. And for producing social content in-house or outsourcing it, see Social Media In-House or Agency?.