Enhancing images with AI while staying true to brand authenticity
AI is becoming part of retail’s creative team, with brands moving from cautiously experimenting with AI-generated images to building entire campaigns around it.
What once took weeks can now happen in hours.
For digital commerce teams under pressure to create more content across more and more channels, AI can press down on the accelerator.
A retailer launching across multiple regions can create localized campaign images in hours instead of weeks; a fashion brand can generate seasonal lifestyle scenes without organizing expensive shoots; a business can automate product recommendations, summaries, and merchandising decisions at scale.
And consumers are not rejecting AI outright.
Research by Trustpilot found that AI-powered shopping experiences such as product recommendations and summaries are generally well received, while more advanced tools like shopping assistants and fraud detection scored satisfaction rates above 90%.
The problem is not AI itself, but trust.
The same technology helping brands move faster is also starting to erode one of the most valuable assets in retail: trust.
Negative AI experiences have placed £8.6 billion of UK ecommerce sales at risk over the past year, including reputational damage caused by poor customer experiences.
Why? Because customers know how to spot images that feel artificial or inconsistent. And when it starts to feel synthetic, brand authenticity starts to weaken alongside it.
That tension is one of the defining aspects in the next phase of AI adoption in retail.
The rush toward AI-generated imagery
The first wave of AI adoption focused on efficiency: faster production, lower costs, more content, more personalization.
Creative teams have struggled for years with the growing demand for content across all digital commerce touchpoints. And AI appears to solve this bottleneck.
However, speaking with brands and clients, a few patterns keep emerging:
1. The trust gap
Some brands are learning the hard way after using AI-generated models and backgrounds in a campaign without clearly disclosing it. Customers questioned why a premium brand charging premium prices would cut corners on the very thing luxury retail depends on: aspiration.
For many shoppers, the backlash was not really about AI. It was about expectation. If customers are investing in a luxury experience, they expect brands to invest in authentic, human-led creative.
2. Inaccurate backgrounds
Many brands are using AI to generate new environments around existing products, allowing them to scale seasonal campaigns and lifestyle imagery faster. But this is where human creative oversight still matters.
AI can build visually striking scenes, yet it often struggles with product accuracy, introducing subtle inconsistencies in shape, texture, lighting, or proportion that can weaken trust in the final image.
3. The challenge of precision
Generating visually appealing imagery is relatively easy, but generating precise imagery is much harder. AI models need to identify products down to the smallest detail, whether that’s the finish on a chair, the ports on a laptop, the stitching on a jacket.
Achieving that level of accuracy still requires extensive manual input, tagging, and validation behind the scenes. Without it, the gap between the product shown and the product delivered starts to widen.
4. Hallucinations
AI-generated content still carries the risk of visual hallucinations: distorted body parts, nonsense text, missing details, unrealistic objects.
High-profile examples have shown the need for human input, as even small visual errors can make customers question the credibility of the entire brand experience.
Colgate received backlash after promoting flavored toothpaste variants in an ad with warped labels and unreadable text. The criticism was not just about the aesthetics, but the level of care expected from a global brand.
Mango faced scrutiny after using AI-generated fashion models in campaigns, with customers questioning whether they could trust the clothing if the people wearing the garments were not real.
What customers think
Research from SCAYLE reinforces these concerns.
Its survey found that only 16% of shoppers feel comfortable with how retailers are using AI today, while 44% are uncomfortable with AI-generated product images and models.
Customers use images to assess quality, dimensions, material, fit, etc. Small inaccuracies can create large problems, ending in abandoned carts, complaints, returns, ultimately distrust.
To achieve precision, AI models often require manual tagging, training, validation, and human oversight.
That is why the most effective retail AI strategies are not fully autonomous; rather, they are human-guided.
How to enhance imagery in the age of AI
The strongest AI strategies enhance the world around the product while keeping the product itself grounded in reality.
Tryzens has been working with clients on this.
We’ve been exploring a multistep background replacement approach designed to preserve product authenticity while allowing brands to scale visual storytelling more efficiently.
Here is how it works:
Background replacement
The process starts by isolating the original product image, removing the background cleanly and precisely so the product itself remains completely untouched.
From there, the AI agent generates new photorealistic environments around it. Focused prompts – such as “product on a shelf in a store” – guide the output without overcomplicating the generation process.
Rather than relying on fully generative imagery, brands retain a single source of truth for the item. AI enhances the context, not the product itself.
A lightweight validation step also scans all imagery before assets move into production, guarding against hallucinations, where generated scenes occasionally introduce nonsense text or unrealistic labels that can undermine credibility.
Image crop
Producing banner variants manually – resized and recomposed for every viewport, platform, and advertising channel – is one of the most labor-intensive tasks in digital production.
Our image crop agent automates this entirely from a single base image, intelligently identifying the main product, detecting faces and key objects, and understanding what must be preserved as the focal point of any crop.
From there, it resizes and reframes the image to meet each target resolution, whether a widescreen display banner, a square social asset, or a narrow mobile format, extending the background where needed to maintain composition.
The result is a full set of channel-ready variants produced in a fraction of the time, with no manual intervention required.
Promotion assets
Creating marketing material at scale has traditionally required significant resources. Our promotion asset agent changes that by accepting a structured input file containing marketing messages and a design template reference.
Then it automatically locates the relevant products in the ecommerce platform and retrieves the images. An LLM analyzes each product to determine its key attributes, ensuring the creative output is contextually accurate and on-brand.
From there, the agent produces finished marketing images for individual products, as well as videos that bring multiple products together into a single asset: ready for paid social, email, or on-site placements, without manual design work at every step.
The broader point is that AI works best when brands use it to support authenticity instead of a way to shortcut it.
It should be used to scale storytelling while preserving the trust customers place in the product itself.
Your next step
The pressure on brands to produce more content, across more channels, at greater speed is only increasing. The presence of AI is accelerating that.
The opportunity for retailers is to get the balance right between using AI to scale creative while protecting the authenticity of the product.
Tryzens is working with brands to explore how AI can scale digital commerce storytelling. If you’re exploring how to integrate AI into your digital commerce experiences, talk to us.