74% Would Buy Again After the AI Ad Scenario, Consumer Poll Shows

A survey of 446 U.S. adults found that about 74% would be at least somewhat likely to buy again after an AI advertising scenario. The result changed sharply depending on when consumers learned about the AI use.

The figure comes from two separate survey groups that imagined seeing an online ad for a product they had not planned to buy. In both scenarios, the ad worked and the buyer was satisfied with the product. One group learned before buying that AI had created the ad’s words and images. The other learned afterward, when the ad’s lack of disclosure was also revealed.

About 74% of all respondents said they would be likely to buy from the advertiser again. That combined figure hides the main finding: 79.8% of respondents who saw the disclosure upfront expressed repeat purchase intent, compared with 67.3% of those who learned about the AI later.

The poll points to a straightforward business risk. A brand can make a sale with AI-generated creative and still weaken future trust if consumers discover the AI involvement after the decision has been made.

Repeat purchase intent was not the only measure that moved. Trust in the advertiser also declined among people who learned about the AI later.

Among respondents told about the AI before the purchase, 76.7% said they would trust the advertiser a great deal or a fair amount. Among those who learned afterward, the share dropped to 65.0%.

That 11.7-point difference remained statistically significant after the analysis adjusted for the survey’s eight comparisons. The corresponding 95% confidence interval ranged from 3.3 to 20.0 points in the direction of lower trust for the later-discovery group.

Feeling misled moved in the other direction. Seventy percent of respondents who learned about the AI afterward said they felt misled a lot or some. The figure was 57.0% among those who knew upfront, a 13-point difference.

Those findings create a mismatch that advertisers should take seriously:

A good product experience gives the brand a strong starting point. It does not guarantee that consumers will view the advertising process favorably.

A ContentEngine survey of 446 U.S. adults suggests that the timing of AI advertising disclosure may influence consumer trust and future buying intent. In a hypothetical scenario involving a successful unplanned purchase, 79.8% of respondents who saw the AI disclosure before buying said they would be likely to buy from the advertiser again, compared with 67.3% of those who learned about the AI afterward. Later discovery was also associated with lower trust and more feelings of being misled. The results do not prove that timing alone caused the gap, but they point to a practical rule for brands: explain AI involvement before consumers act, match the disclosure to the actual workflow and keep a person accountable for the final ad.

ContentEngine surveyed two samples of 223 U.S. adults, for a combined total of 446 respondents. Each sample answered the same questions after reading a slightly different version of the purchase scenario.

The upfront-disclosure group read that the advertisement clearly stated that its words and images had been created entirely using AI. The later-discovery group read that the ad persuaded the respondent to buy, that the product worked well, and that the AI involvement was disclosed only afterward.

The results for repeat purchase intent were:

The combined result is useful for understanding the headline, but the gap between the groups carries more practical weight. Respondents who knew about the AI before purchasing were 12.6 percentage points more likely to say they would buy from the advertiser again.

The wording also matters. The later-discovery group was told that the advertisement had failed to disclose its AI use. That detail may have influenced feelings of deception directly. The poll therefore shows an association between disclosure timing and consumer reactions. It does not isolate timing as the only possible cause.

The two groups were also separate samples rather than people randomly assigned to one scenario or the other. Differences in attitudes between the groups could have contributed to the results, even though the measured demographic characteristics were similar.

An upfront disclosure gives consumers information while they are still deciding whether to click, buy or continue considering an offer. A later notice gives information after the commercial decision has already taken place.

That difference changes the role of the disclosure. Before the purchase, it can help a consumer decide whether the brand’s use of AI fits their expectations. After the purchase, it mainly explains something the consumer believes should have been visible earlier.

For a disclosure to serve that first purpose, it needs to be reasonably easy to notice and understand. A buried explanation in a terms page may technically mention AI while doing little to inform the person viewing the ad.

A practical pre-purchase disclosure should answer basic questions in plain language:

The appropriate level of detail will vary by format. A short label may work for a social post, while a landing page or video campaign may need a linked explanation. The core point remains consistent: the information should arrive before the consumer acts and should be understandable in the setting where the ad appears.

The strongest lesson is not that every AI-generated ad needs the same label or that disclosure automatically creates trust. It is that hidden production choices can become part of the consumer experience once people discover them. Brands should treat disclosure as an informed-choice and governance practice, not a compliance footnote: explain what AI did, disclose it in time to matter and ensure a qualified person can challenge the claims, imagery and targeting before publication. The survey’s limitations call for caution, but they do not erase the operational risk of letting consumers feel that important information was withheld until after the sale.

The survey does not support a simple claim that disclosure solves every concern. Even among respondents who knew about the AI before buying, 57.0% said they felt misled at least somewhat. About 62.3% said they felt manipulated at least somewhat.

Several features of the scenario may explain that discomfort. Respondents imagined an advertisement for an unplanned purchase that successfully persuaded them to act. The scenario also described an ad created entirely with AI, which is a stronger condition than using AI to resize an image, suggest headlines or produce several copy variations.

The survey did not ask respondents why they felt misled or manipulated. Their reactions could have reflected several parts of the scenario:

That uncertainty matters for campaign planning. A disclosure can give consumers information about how an ad was made, while separate questions remain about the ad’s claims, targeting and persuasive techniques.

An AI label should therefore sit inside a broader review process. It should not become a substitute for checking whether the message is accurate, appropriate and likely to be received as intended.

Disclosure and human review attracted broad support across both survey groups. Overall, 90.1% of respondents said it was very or somewhat important for brands to tell consumers when an advertisement had been created entirely using AI.

A nearly identical 88.3% said it was very or somewhat important to know that a person had reviewed and approved an AI-generated advertisement before publication. Support for human review was 87.9% in the upfront-disclosure group and 88.8% in the later-discovery group.

That consistency suggests consumers view two issues separately:

Human approval should involve more than checking for obvious spelling errors. A meaningful review can cover factual claims, visual accuracy, implied promises, sensitive targeting, brand fit and the way the ad represents people or products.

The reviewer also needs enough authority to reject or revise the material. A nominal approval step adds little if the production schedule treats the AI output as final or if the reviewer cannot question the campaign’s premise.

Industry guidance is moving in this direction. The Interactive Advertising Bureau’s AI Transparency and Disclosure Framework V2 uses a risk-based and materiality-driven approach to deciding when and how AI involvement should be disclosed in consumer-facing advertising. European Union transparency rules under Article 50 of the AI Act also reflect a broader policy focus on informing people when AI systems are used in certain contexts. Specific obligations vary by jurisdiction, system and use case, so brands need legal review for campaigns operating across markets.

The survey’s results point to several concrete process changes for marketing teams.

A disclosure should appear in the ad or in an immediately connected location before the click, sign-up or purchase. A post-purchase notice cannot give consumers the same choice.

A label that says “AI assisted” can mislead if AI generated the entire image, script and voice. A label that says “AI generated” can also be too broad if a person supplied the concept and wrote most of the copy. The wording should match the actual production process.

If a person reviewed the ad, the brand should be able to explain what that review covered. A simple statement such as “Reviewed and approved by the brand” can help, provided the review actually happened and the brand can stand behind the message.

A review record can identify:

Those records can help resolve consumer complaints, support internal accountability and show whether the disclosure language matched the real workflow.

A campaign can perform well before consumers know how it was made and receive a different reaction after the label appears. Testing should measure more than click-through rate. Trust, perceived honesty, understanding of the disclosure and willingness to recommend the brand can reveal problems that immediate conversion data misses.

The findings provide a useful warning, but they do not represent a precise estimate of how all U.S. adults would behave. The respondents came from online nonprobability panels and were not randomly selected from the population. The samples were not weighted, and the survey did not report a conventional margin of sampling error.

The scenarios were hypothetical. Respondents did not see a real advertisement, spend money or use a real product. Stated intent can differ from behavior in a live marketplace, especially when price, brand familiarity and product category enter the decision.

The survey also tested an all-AI advertisement. Results could differ when AI performs a smaller task, such as editing a background, creating image variations or helping a human copywriter brainstorm. Consumers may respond differently to an ad that combines visible human authorship with limited AI assistance.

The later-discovery scenario included an explicit statement that the ad had not disclosed its AI use. That wording may have increased the 70% share who felt misled. The survey also did not measure education, race or ethnicity, and it did not ask respondents to explain their answers.

Even with those limits, the main pattern remained clear across the poll’s strongest measures. Later discovery was associated with lower trust, lower repeat purchase intent and more reported feelings of being misled. The differences remained statistically significant after adjustment for multiple comparisons.

AI advertising programs need to move disclosure and accountability earlier in the consumer journey.

The poll fits a wider shift in how consumers encounter advertising. Gallup reported that 79% of Americans had seen advertisements in the previous 30 days that appeared to be made using AI, according to a 2026 Bentley University-Gallup survey. A Canva study reported that 70% of consumers believed it would eventually become impossible to tell whether an ad was AI-generated without disclosure.

As visual and written content becomes harder to identify by inspection, the disclosure becomes more important as a source of context. Consumers cannot be expected to infer a brand’s production process from an image that looks polished or a voice that sounds natural.

That context can influence the meaning people assign to the ad. Some consumers may care about originality, labor, privacy or the use of personal data. Others may mainly want reassurance that a person checked the claims. Clear disclosure gives each consumer more information before the commercial exchange is complete.

The 74% repeat-purchase result should therefore be read alongside the group differences. A majority in both scenarios remained open to buying again, but the later-discovery group was less willing to return. For brands that depend on repeat purchases, subscriptions or recommendations, a double-digit gap can matter more than the first sale.

Brands using AI in advertising should disclose that use before consumers make decisions, explain the role AI played and identify real human accountability for the final material.

The ContentEngine survey does not prove that every hidden AI disclosure will damage a brand by the same amount. It does show how quickly a satisfactory purchase can be followed by weaker trust when consumers learn that the advertising process was concealed.

The safest approach is simple: give consumers the relevant information early, make the label match the workflow and require a person to review the claims and presentation before publication. A campaign should be judged by the relationship it leaves behind, not only by whether it gets someone to buy once.