An image generator can place a product in an attractive room in seconds. The harder task is deciding what a customer will interpret as a real product feature. If the image invents accessories, an outcome or a use condition, carefully written copy may not correct that impression.
What Google announced
On 9 July 2026, Google announced additional AI transparency features. A How this ad was made section in My Ad Center provides information about AI use. Google described automatic disclosure for its own generative advertising tools and an advertiser control for content made elsewhere. A label on the ad itself may depend on local requirements.
That announcement describes platform features. Advertisers still need to check current platform rules and requirements for their market. An AI disclosure does not establish that the promise made in an advertisement is accurate.
Define the facts an image must preserve
Prepare a short product record before generating variants. Include actual specifications, what comes in the package, supported uses and limitations. For a service, record the working format, geography and the conditions used to calculate a price.
Mark details that must remain unchanged. A product's colour may be a material characteristic. An optional background can often be treated more freely. If a reviewer cannot confidently separate the actual product from the generator's additions, the creative needs further work.
Keep the source material available to the reviewer. It is difficult to verify an altered image when nobody can find the original photograph or the approved product description. A few minutes spent organising those references can prevent repeated uncertainty during revisions.
Review the impression, then the wording
First look at the image without its caption. What does it appear to promise? Then read the headline and landing page. They should describe a consistent offer.
For an illustrative renovation studio, a designed interior may be a project visualisation rather than a photograph of completed work. The audience should be able to understand that distinction. Adding an invented customer story or testimonial would create a separate accuracy problem. This example explains a review method; it is not a client case.
Inspect small details such as package text, numbers, dimensions, faces and logos. A person familiar with the product may spot an error that a composition review misses. Make factual review a distinct step before publication.
Keep a usable revision history
Save the original, the final asset and a short record of what changed. Note which facts were checked and who approved them. This helps when the team needs another aspect ratio, a revised offer or an explanation for replacing an advertisement.
Keep an idea separate from an approved asset. A strong visual concept may still need corrections before it can support a public product claim. The same principle applies to text: a promising draft is the start of the review process.
Our recommendation for a small team
Test a few versions of one offer. Change an identifiable element, such as composition, the opening line or the way a benefit is explained. Assess the inquiries as well as the appearance of the creative. Use consistent lead qualification criteria and a defined customer acquisition cost calculation.
Our editorial position is that AI can help prepare alternatives while the team remains responsible for the offer. That responsibility also matters when writing expert content with clear sources.
Source
Source checked on 23 October 2026. Examples are illustrative and recommendations are KUZKO editorial analysis.