The regulatory framework is an important starting point. Article 50 of the European AI Act introduces specific transparency obligations for certain types of content generated or manipulated using Artificial Intelligence systems. On 2 August 2026, the Regulation entered its general application phase.
Providers of AI systems capable of generating synthetic images, audio, video or text — including platforms such as ChatGPT and Google's AI tools — are required to ensure that their outputs are marked in a machine-readable format, allowing them to be identified as artificially generated or manipulated.
However, this technical requirement does not automatically translate into the same obligation for businesses that use and publish AI-generated content.
Article 50 establishes specific disclosure requirements, particularly when AI-generated or manipulated images, audio or video constitute deepfakes. For clearly artistic, creative, satirical or fictional works, the Regulation provides for disclosure methods that do not interfere with the audience's experience of the content.
For businesses using AI in international communication, the practical implication is therefore not that every AI-generated image must automatically carry a visible label. Instead, companies should establish clear internal guidelines defining when the synthetic origin of content should be disclosed, how its provenance should be documented, which versions should be archived and what checks should be carried out before publication.
This approach should not be viewed solely as a compliance issue. Transparency is increasingly becoming an important element of the relationship between brands and consumers, particularly when images directly influence product evaluations.
A completely AI-generated setting may be appropriate for displaying a table in a residential environment. By contrast, altering the actual colour of its surface, the texture of a fabric or the proportions of a garment can influence purchasing decisions and create significantly greater concerns.
The issue of trust is also connected to an increasingly evident phenomenon: as the technical quality of AI image generators improves, users are becoming more sensitive to images that immediately appear to have been generated by AI.
Overly smooth faces, flawless environments, cinematic lighting applied indiscriminately, objects arranged with almost geometric precision and surfaces showing no signs of wear have become recurring features of a synthetic aesthetic. These characteristics can reveal the technology behind an image, even when the final result is technically sophisticated.
The paradox is that pursuing visual perfection may actually undermine credibility. A seemingly ordinary photograph can communicate greater authenticity than a spectacular yet excessively polished image.
Research into consumer responses to AI-generated advertising also suggests that attitudes remain mixed. Although AI systems have rapidly improved their ability to create realistic content, emotional engagement and consumer trust have not necessarily developed at the same pace.
AI Image Prompts: From Artificial Perfection to Art Direction
For this reason, introducing a certain degree of what might be described as "visual imperfection" into prompts is becoming increasingly valuable.
The objective is not to generate lower-quality images, but rather to reproduce some of the characteristics of real photography and reduce the statistical perfection that often makes AI-generated content immediately recognisable.
Slightly asymmetrical compositions, subjects positioned away from the centre, natural wrinkles in fabrics, subtle signs of wear, irregular shadows, realistic skin textures, elements partially outside the frame, uneven ambient lighting, credible depth of field, subtle photographic grain and barely perceptible motion blur can all make an image appear more authentic and closer to the visual language of editorial photography.
Expressions such as "candid photography", "slightly imperfect composition", "natural skin texture", "subtle film grain", "uneven natural lighting", "minor fabric wrinkles", "unpolished editorial photography" and "off-center framing" can therefore become part of the vocabulary businesses use when developing their visual content.
These instructions can be combined with detailed descriptions of the scene, product, target audience and destination market.
Tools such as ChatGPT Images and Google's Nano Banana models have made these capabilities accessible even to users without specialised graphic design expertise. They allow businesses to generate images, work from existing photographs and introduce successive modifications using natural language.
However, this simplicity should not create the impression that asking an AI system to produce an attractive product image is sufficient. Achieving professional results increasingly requires something resembling a comprehensive photography brief.
A well-structured prompt should describe the subject and its context, but it can also specify composition, camera angle, lighting, lens, depth of field, textures, atmosphere, target audience, destination market and final output format.
English can be particularly useful in more sophisticated workflows, as much of the terminology used in photography and cinematography — and consequently in AI image generation — originates from and has become standardised in this language.
With Midjourney, this approach becomes even more apparent. Alongside a description of the scene, users can introduce parameters controlling aspect ratio, stylisation, variation in results, Raw Mode, the exclusion of specific elements and the influence of reference images. The prompt thus evolves from a simple description into a genuine art direction tool, enabling companies to develop and maintain a consistent visual identity.
This approach is particularly valuable for businesses communicating across multiple markets. The goal is not simply to generate an effective image, but to create variations that preserve brand identity while adapting to the aesthetic conventions, cultural contexts and expectations of different audiences.
Applications of AI in Fashion, Design, Furniture and International E-commerce
AI-powered image generation offers particularly interesting opportunities in sectors where visual presentation directly influences perceptions of products and brands.
A clothing manufacturer, for example, can start with a real photograph of a jacket and experiment with different settings, seasons and editorial styles before organising a photoshoot.
Similarly, a fashion brand can use AI to develop mood boards, campaign concepts, visual merchandising ideas, virtual sets and creative variations tailored to different international markets.
The advantage does not necessarily lie in replacing traditional photography. AI can play an equally important role during the preliminary creative stages, allowing businesses to visualise numerous alternatives before deciding which concepts deserve further investment.
A creative team might quickly explore ten different visual directions, select the two or three most promising options and use them as the basis for the final photoshoot, reducing both experimentation costs and production timelines.
The same principle applies to design and furniture. A properly photographed lamp, table, chair or sofa can be placed in a wide variety of digitally generated environments: a Scandinavian apartment, an American loft, a contemporary Japanese home or a Mediterranean residence.
This allows businesses to adapt the visual context to different markets while keeping the product itself unchanged.
As a result, companies can create content for catalogues, websites, advertising campaigns, marketplaces and sales presentations without having to physically recreate every setting.
Specialised tools such as Flair.ai are designed specifically for product photography and the creation of consistent visual environments.
Solutions such as Magnific.ai can be used at a later stage to increase image resolution, enhance details or reinterpret an image through settings that control the balance between fidelity to the original and creative transformation.
For companies selling internationally, it is therefore essential to distinguish clearly between what must remain authentic and what can be generated synthetically.
Product characteristics that influence purchasing decisions must be represented accurately. Colours, dimensions, proportions, materials and finishes should never be left entirely to the interpretative capabilities of an AI image generator without appropriate checks.
Conversely, settings, atmosphere, scenery and creative variations offer much greater scope for experimentation.
This distinction is precisely where visual AI can make one of its most valuable contributions to digital export strategies.
By starting with authentic product images, businesses can rapidly expand the range of possible visual representations, test different creative approaches before producing a campaign, facilitate content localisation and adapt their communication more efficiently to different e-commerce platforms, marketplaces and international markets.
The real opportunity, therefore, is not to produce a single perfect image or indiscriminately replace traditional photography, creativity and post-production.
Instead, it lies in developing a process in which authentic and AI-generated images fulfil different but complementary roles, reinforcing one another.
In a world where artificial perfection has become increasingly easy to achieve, the quality of visual communication will depend more and more on consistency, transparency and credibility.
Because in international markets, trust may ultimately prove to be a more valuable competitive advantage than visual perfection.