AI Images or Real Photos?

First, the strongest finding against our own thesis

Peer-reviewed study

Hartmann, Exner, and Domdey's International Journal of Research in Marketing'His study, published in , was generated using seven different models 10,320 synthetic marketing visuals 2,400 people compared it to a computer-generated image, and in total 254,400 user reviews topladı. Güçlü modeller kalitede, gerçekçilikte ve estetikte insan üretimini yakaladı ya da geçti. Dahası, 173.022 gösterimlik saha testinde en iyi AI banner'ı profesyonel stok fotoğraftan %50'ye kadar daha yüksek tıklama oranı aldı.

Any analysis that ignores this result is incomplete. The argument that "AI produces poor visuals" ends here. However, the study measured general marketing content and banner performance. The color, stitching, texture, scale, or match between the product and its packaging of an SKU were not tested at any stage. We are looking at the results of a different competition: proof of appeal, not proof of product accuracy.

1. Brand hypocrisy: the brand’s value proposition and its synthetic visuals don’t align

The most robust evidence in this area comes from Quan Xie, Sidharth Muralidharan, and Joe Phua of the Temerlin Institute of Advertising at Southern Methodist University Journal of Advertising'a study published in . What prompted the research team to ask this question was a familiar development: Levi's announcement that it would increase diversity using artificial intelligence models, and the wave of backlash that followed.

In a three-phase series of experiments, the use of AI-generated human models in body-positive and plus-size clothing campaigns elicited a strong brand hypocrisy It created a negative perception. Attitudes toward the brand, purchase intent, and the likelihood of recommending it all declined significantly.

Name of the mechanism The Theory of Social Being. As Xie puts it, consumers do not find AI models human, warm, or relatable. When you try to convey a message based on empathy using a synthetic figure instead of a real person, the message itself falls apart. The ad says, "We love you just the way you are," but there’s no one in the visual.

Brüns and Meißner's Journal of Retailing and Consumer Services'Three studies published in [source] in 2024 all point in the same direction: when GenAI generates social media content, it undermines perceived brand authenticity and behavioral responses. But there’s one detail—AI’s human supported frame, it significantly reduced damage compared to the frame it replaced. The same technology, a two-sentence difference, a different result.

2. Signal theory: Visuals convey not only the product but also the seller

Research based on Signal Theory makes a simple yet powerful point: a marketing image doesn’t just show the product to the consumer—it also how much effort the seller put in ...whispers. British Food Journal'Three scenario experiments published in 2026 found that images labeled as 'artificial intelligence" had an impact on consumers' perceived seller effort shows that it weakens the inference.

When a customer senses that the seller has taken the easy way out in the visual, they also lower their expectations regarding the product’s quality. The question on their mind is: if they cut corners here, what else did they cut corners on in the product? The impact is significantly more damaging for products commanding a high price premium. In other words, for a brand positioned as premium, the cost of an AI-generated image is higher than for a brand positioned as budget. The same image, two different bills.

3. Product interest is changing the table

Israfilzade’s 2025 2×2 experimental study tested both the source of the advertisement (AI or human) and the presence of transparent labeling. The independent variable Level of interest in the product It’s out. For low-involvement products, such as snacks, AI-generated images are accepted almost without resistance. However, for products that carry high financial and technical risks, such as laptops or luxury goods, a clear “AI” label sharply reduces trust and purchase intent.

This finding forms the academic basis for the category matrix discussed later. Its practical implication is also clear: the same production budget yields low marginal benefit when spent in a low-interest category, but high marginal benefit when spent in a high-interest category. Spreading the budget evenly is the most costly mistake.

4. The rationale is more decisive than the technical aspects

This is the most useful finding from a business perspective. It reveals what motivates brands to use artificial intelligence to reach consumers how he explained it In a series of experiments examining its effect on trust (last experiment, n=212), reasoning "privacy protection" When presented with these images, consumers showed similar levels of trust as they do with human-created images. The exact same technology "cost-effectiveness" However, when the rationale was explained, there were statistically significant declines in trust and purchase intent.

5. Vinted preview: The result is reversed depending on the season

Not peer-reviewed · Preprint

Published in August 2026, the study compared AI-generated product images with original photos from matched listings on Vinted across two seasonal waves. The results were not one-sided:

• During the fall-winter season AI-generated images yielded more positive results in metrics related to attention.
• During the spring-summer season The original photos stood out, particularly in metrics related to conversion.

The study’s own interpretation is that performance depends on the balance between aesthetic refinement, visual originality, perceived risk, and the need to validate the product. One limitation is clearly stated: Since AI-generated images are more polished, it is not possible to completely distinguish between "AI source" and "presentation quality," and the platform’s algorithm cannot be controlled. In other words, we have a context-sensitive signal It exists, but it is not a universal law of sales. In fact, anyone who searches for a universal law in this literature comes away empty-handed.

6. Tolerance varies depending on the visual type

A study conducted by the Münster School of Business (FH Münster) in partnership with the fashion retailer Ernsting's family, a CECIRE member, makes a practical distinction: consumers in decorative and background images shows a certain degree of tolerance toward artificial intelligence, but in photos of personalized products and models highly critical. This distinction forms the basis of the "decision table based on visual type" that follows—and is likely the most easily applicable finding in this article.

7. Six findings that are often overlooked but are important

• "Even the word "AI" itself can cause sales to drop. A study by Cicek, Gursoy, and Lu of Washington State University shows that the inclusion of the term "Artificial Intelligence" in product and service descriptions negatively affects purchase intent. The mechanism operates through emotional trust, and the effect is much more pronounced in high-risk categories such as expensive electronics, financial services, and medical devices.
• Perceptions change once the source is revealed. In Zhang and Hur’s 2025 study, when the source of the image was not disclosed, the difference between AI-generated and human-generated images remained minimal. However, when it was revealed that the image was AI-generated, trust and purchase intent dropped significantly. Same pixel, different results.
• The risk is high in hedonistic categories. Belanche et al. International Journal of Information Management'His study, published in [...], shows that the use of AI-generated imagery is perceived more negatively in hedonic and high-engagement decision-making processes.
• When it comes to emotional content, moral revulsion comes into play. Journal of Business Research'According to a study published in [...], when consumers believe that emotional marketing communications were written by AI rather than by humans, they find them less authentic, feel moral disgust, and their purchase intentions weaken — even though the text is exactly the same.
• An ethical stance can help soften the reaction. The finding reported by Forbes, based on four experiments led by Prof. Sean Sands of Swinburne University of Technology, suggests that a credible framework of social benefit and ethical responsibility can reduce negative reactions. Since the citation for the fully peer-reviewed article could not be found, this should be considered supporting evidence.
• "There is no data to support the assumption that "they'll get used to it over time.". Conjointly'nin 2023–2025 dalgalarında tutum değişimleri karışıktı; çoğu düşüş hata payı içindeyken AI'ın pazarlama içeriğinde kullanımına onay %55'ten %36'ya anlamlı biçimde geriledi. Fractl'ın ayrı araştırmasındaki güven kaybı ise genel AI arama ve içerik bağlamından geliyor — ürün fotoğrafı testi değil.

In e-commerce, visuals serve as a substitute for the product’s physical presence when consumers cannot touch it. The texture of fabric, the sheen of metal, the transparency of glass, the surface of leather, the true shade of a cosmetic pigment, the way a ring fits on a finger, the quality of the packaging—all of these are conveyed through a single image. As the customer looks at that image, they are actually trying to reach out and touch it.

That’s why an AI-generated image that makes the product appear more flawless, shinier, smoother, larger, or a different color —no matter how aesthetically successful it may be—poses a commercial risk. A single question arises in the customer’s mind, and that question determines both the conversion and the return: "Will the product really look like this?"

There’s another layer to luxury, cosmetics, and fashion. The problem highlighted by the study "When AI Doesn’t Sell Prada" is not just about reality. In luxury consumption, value is inextricably linked to human labor, craftsmanship, attention to detail, materials, and the story behind production. When AI visuals dilute this perception of labor, consumers begin to question how the brand creates value. A customer who wonders who sewed a bag’s stitches will also wonder who created its visual.

Data from 2026 brings this finding to the commercial level. In a survey conducted by Clutch in June 2026 with 408 consumers, the participants %36'sı markanın arkasında gerçek insanlar görmenin sadakatlerinin en güçlü sürükleyicisi olduğunu söylüyor — fiyattan da, ürün kalitesinden de, kolaylıktan da güçlü. Aynı araştırmada %49'u en akılda kalıcı kampanya formatı olarak gerçek müşteri hikâyelerini işaret ediyor.

Same image, different label, different level of trust

A study by the NIM (Nuremberg Institute for Market Decisions) reveals something troubling: Labeling the same image as a "photo" or an "AI-generated image" changes consumer perception. An image labeled as “AI-generated” is perceived as less emotional, less credible, and less memorable. In other words, transparency doesn’t always build trust; sometimes it just makes skepticism more apparent.

The third experiment in the SMU study rounds out the picture, and these two should be read together: While a simple "Generated by AI" label may erode trust, a well-crafted transparency statement that explains how the process works enhances brand perception. For example, explaining that the AI model was trained on a dataset consisting of photographs of real people for whom consent was obtained and who were licensed helps alleviate consumer skepticism. The difference lies not in the presence of the label, but in its content.

The one-sentence rule

AI should never be presented as a substitute for a value commitment. When claims of diversity, body positivity, sustainability, local production, handcrafted products, or social responsibility are supported by synthetic images, in the consumer’s mind brand hypocrisy is triggered. Technology should clearly be positioned as a tool that supports operational speed and the creative process. Any other use of it costs more than it brings.

In categories such as beauty, baby care, food, luxury goods, ready-to-wear, and analog photography, the message "We don’t use AI" is becoming increasingly visible. The reason is clear: in these categories, purchasing decisions aren’t based solely on price and function. Physical reality, tactility, human representation, the physical feel of the product, and the brand’s perspective on labor are all part of the decision.

Dove’s commitment not to create or distort images of women using AI, Aerie’s policy of not using AI-generated bodies or people, and Coterie’s decision not to use AI-generated social media images , and Polaroid’s campaigns highlighting the physical experience all point to the same commercial reality: In the age of AI, physical reality is becoming a brand asset again.

In 2026, this positioning evolved from a campaign level to a brand commitment level. Aerie expanded its '100% Aerie Real" commitment with a campaign featuring Pamela Anderson, and the ad’s narrative tackles the issue head-on right at its heart: Anderson has an AI tool generate models, tries to tweak the results to make them "more natural, less lifeless," eventually gives up and moves to a real set, where she turns to the camera and "You can't prompt this" he says. In January 2026, Almond Breeze released a video with the Jonas Brothers titled 'No AI Needed." Brands like Le Creuset began explaining their production process through pinned comments on their Instagram posts.

That’s why, in 2026, production quality shouldn’t mean 'perfection." The true surface texture, the product’s scale, its physical response to light, the character of the materials, and a controlled yet believable balance of retouching must be preserved. An overly plastic aesthetic that makes a real photograph look like it was created by AI is squandering the brand’s trust capital.

This trend is also changing the aesthetics of commercial photography. In 2025–2026, there’s a distinct trend in fashion and lifestyle photography: less polished, more dynamic, and less flawless images. Direct flash, slight grain, unplanned details within the frame, and the clutter of real spaces. The reason isn’t nostalgia— The error is now an authentication signal.

Still, it would be a mistake to apply this trend to product photography as is. In e-commerce, the product’s color, texture, and scale must be accurately conveyed. The right balance is this: The product information should be flawless; the presentation should have flaws. The product itself should be depicted technically accurately; but the setting, lighting, and composition should make the viewer feel as if they are actually there in a physical space. This is precisely where AI struggles the most—because the models converge toward an average aesthetic, that is, toward perfection.