The 7 Secrets to Boost Your Sales with Generative AI

By Charlotte Journo-Baur, Founder of WISHIBAM

Introduction: Understanding Generative AI and Its Impact on Retail

A few months ago, a sales director from a French textile brand confided in me, somewhat embarrassed, that he had spent three weeks waiting for product visuals to be retouched by his photo studio. Three weeks. For images that, ultimately, didn’t convert as hoped. I’ve heard this story on repeat since generative AI entered our retail conversations. And frankly, it perfectly illustrates the gap that still exists between those who’ve understood what this technology really changes — and those who continue operating as if we were still in 2018.

So, what is generative AI? Let’s define things clearly, because the term is often misused. Generative AI refers to artificial intelligence systems capable of creating original content — text, images, sound, video, data — from models trained on massive datasets. Unlike so-called “discriminative” AI that classifies or predicts, generative AI produces. It generates. Examples like GPT-4, Midjourney, DALL-E, Stable Diffusion, or Adobe Firefly are its best-known manifestations. In retail, this generation capability takes on a particular dimension: it directly affects the value chain, from product creation to customer experience, including visual management and large-scale personalization.

According to a McKinsey study published in 2023, generative AI could generate between $240 and $390 billion in annual value for the retail and consumer goods sector globally. This isn’t fanciful projection. It’s a reality that some players have already begun materializing in their quarterly results.

Generative AI Applications to Boost Your Sales

Generative AI in Retail: A Revolution for E-commerce

Retail has always been an experimental sector. But generative AI isn’t just another experiment — it’s a paradigm shift. And the distinction matters.

For years, e-commerce teams have juggled structural constraints: gigantic product catalogs to update, incompressible content production deadlines, photo budgets that explode with each new collection. Generative AI in retail directly tackles these bottlenecks. It doesn’t replace creative teams — that misconception deserves to be buried — but it allows them to produce ten times faster, with visual and editorial consistency that was impossible to achieve manually.

Take a concrete example. A ready-to-wear brand with 2,000 references per season had to, until recently, organize photo shoots for each piece, in multiple colors, on multiple types of models. Average cost: between €50 and €150 per visual. With generative AI, this same catalog can be produced in a few days, with virtual models adapted to different body types, customized backgrounds according to target markets, and guaranteed style consistency across the entire catalog. Zalando, Amazon, and ASOS have already integrated these workflows. Independent brands are starting to follow.

Beyond visuals, generative AI in retail also transforms product description writing, SEO-optimized description generation, and even targeted advertising campaign creation. According to Gartner, by 2025, 30% of outbound marketing messages from large organizations will be AI-generated. We’re not far from that anymore.

What’s striking is the adoption speed. In 18 months, generative AI has gone from technological curiosity to operational tool in retailers’ marketing departments. Those who delay aren’t just falling behind — they’re leaving margin to their competitors.

What does generative AI concretely bring to e-commerce?
It enables the production of visuals, text, and personalized content at scale, drastically reducing production timelines and costs. For e-commerce, this translates into richer catalogs, better-optimized product pages, and a more consistent customer experience.

AI Product Personalization: A Conversion Lever

Personalization isn’t a new concept in retail. What’s new is the scale at which generative AI enables its deployment. And that’s where it gets really interesting.

Until now, AI product personalization was often limited to algorithmic recommendations based on purchase history — the famous “customers who bought this item also bought…”. Useful, but limited. Generative AI goes much further: it creates truly individualized product experiences, in real-time, from behavioral, contextual, and even emotional data.

Imagine a customer browsing a product page for a sofa. Thanks to generative AI, the site can automatically offer them a visualization of that sofa in an interior that resembles theirs — by analyzing photos they’ve shared in their customer account, or simply based on their stated preferences. This is no longer science fiction. Solutions like those developed by WISHIBAM enable precisely this type of experience, connecting retailers’ product data to advanced personalization engines.

The numbers speak for themselves. According to an Epsilon study, 80% of consumers are more likely to purchase from a brand that offers personalized experiences. And McKinsey estimates that personalization can generate between 10 and 15% additional revenue for retailers who truly master it.

AI product personalization isn’t limited to visuals. It also affects dynamic pricing, targeted promotional offers, descriptions adapted to the buyer’s profile. The same product can be presented differently depending on whether it’s addressing a first-time buyer or a loyal customer, an urbanite or someone in a rural area. This granularity was inaccessible just three years ago.

• Product visual adaptation based on user profile
• Personalized description generation by customer segment
• Real-time contextual recommendations
• Dynamic pricing based on behavioral signals
• Personalized virtual try-on experiences

What distinguishes retailers that convert from those that struggle is often this ability to speak to each customer as if they were the only one. Generative AI makes this possible, at scale.

Enhancing Images with AI: The Importance of Visual Quality

In e-commerce, the image is the product. This statement may seem excessive, but it reflects a reality every retail professional knows: in the absence of physical contact with merchandise, it’s the visual that makes — or breaks — the purchase decision.

An MDG Advertising study reveals that 67% of consumers consider product image quality “very important” in their purchase decision. More tellingly: poor-quality images are cited as one of the top reasons for cart abandonment. In other words, investing in visual quality isn’t an aesthetic option — it’s a business decision.

This is where enhancing images with AI makes complete sense. Current tools can accomplish in seconds what took hours for an experienced retoucher: background removal, color correction, resolution enhancement, light harmonization, contextual background generation. And the quality obtained is, in the vast majority of cases, indistinguishable from professional manual retouching.

But beyond retouching, generative AI enables something more ambitious: creating visuals that don’t yet exist. A product in development can be visualized in different colors, different usage contexts, different stagings — before the first prototype is even manufactured. For merchandising teams, this is a revolution in how collections are presented to buyers and commercial partners.

Why is product image quality so critical in e-commerce?
Because online, the visual replaces the physical product experience. A blurry, poorly lit, or unrepresentative image generates distrust and inhibits purchase. Conversely, a polished visual creates trust and reduces product returns.

Tools and Techniques to Optimize Your Visuals with AI

AI Photo Retouching Tools: How to Choose the Right Software

The market for AI photo retouching tools has literally exploded in two years. Between solutions integrated into existing creative suites, dedicated SaaS platforms, and APIs to integrate into your own workflows, the choice can quickly become paralyzing. Here’s how to navigate it.

The first question to ask isn’t “what’s the best tool?” but “what’s my primary use case?”. A retailer managing 500 references per year doesn’t have the same needs as a marketplace processing 50,000 new products each month. Selection criteria vary accordingly.

Here’s a comparative table of the main criteria to evaluate:

Among the most-used solutions in the European retail ecosystem are Adobe Firefly (integrated into Photoshop and Express), Canva Magic Studio, Remove.bg for background removal, Topaz Labs for resolution enhancement, and more specialized solutions like Pebblely or Booth.ai for product staging generation.

One piece of advice I systematically give retailers I support: don’t choose a tool based on a sales demo. Test it on your own images, with your own constraints. Performance gaps between what a tool promises and what it delivers in real cases can be significant.

AI Photo Retouching: Transform Your Images in a Few Clicks

AI photo retouching has democratized something that was, just five years ago, the preserve of professional photo studios and experienced graphic designers. Today, a product manager without technical training can obtain commercial-quality visuals in minutes. This is a profound transformation of professions, and it deserves attention.

Concretely, how do you use AI for product photos? The standard process generally unfolds in several steps:

• Import raw image (often a photo taken in a light studio or even with a smartphone)
• Automatic cutout and background removal
• Automatic correction of exposure, white balance, and contrast
• Application of an AI-generated background (contextual, neutral, or customized according to brand guidelines)
• Upscaling to reach required resolution
• Export in formats adapted to each channel (web, mobile, print, social media)

This workflow, which once took several hours per image, can now be executed in under two minutes. And with tools like Adobe Firefly or Midjourney, you can go even further: modify an image’s context, change a garment’s color without retaking the photo, add decor elements consistent with the brand universe.

AI photo retouching raises legitimate ethical questions — particularly regarding body representation and transparency toward consumers. Several European countries are beginning to legislate on the obligation to flag AI-retouched images. Retailers must integrate this dimension into their visual strategy, not just for legal reasons, but because consumer trust is a precious asset.

That said, used responsibly, AI photo retouching is a considerable competitive accelerator. Retailers who’ve integrated it into their workflow report time savings of 60 to 80% on their visual catalog production.

AI Photo Modification: Tips for Impactful Visuals

Modifying a photo with AI goes far beyond simple retouching. This is where creativity meets technology, and possibilities become truly dizzying for retail marketing teams.

Here are seven concrete tips for getting the most from AI tools when modifying your product visuals:

• Inpainting: Use AI to correct details in a photo without altering the entire image. Remove a wrinkle or fix a reflection instantly.
• Contextual background generation: Adapt your visuals to different markets without a new photo shoot.
• Channel-ready formatting: Create versions tailored for each channel (Instagram, Stories, banners) via automated resizing.
• Style transfer: Harmonize visual rendering between studio and lifestyle shots for brand consistency.
• Color variants: Generate new product colorways digitally, avoiding extra photoshoots.
• Lifestyle compositing: Integrate products in realistic scenes from simple packshots.
• Automated branding: Batch apply watermarks, logos, and banners across your catalog via AI workflows.

These techniques, intelligently combined, enable the production of visual catalogs with a richness and consistency that were, until now, reserved for major brands with substantial photo budgets.

Conclusion: Integrating Generative AI into Your Online Sales Strategy

Where to Find AI for Photos: Resources and Recommendations

The question “where to find AI for photos” comes up often in conversations I have with retailers beginning their digital transformation. The good news: the offering is abundant. The less good news: it’s also sometimes confusing, with marketing promises that don’t always match performance reality.

Here’s a map of the main resources, classified by use:

• For background removal: Remove.bg, Clipping Magic, PhotoRoom
• For lifestyle visual generation: Pebblely, Booth.ai, Flair.ai
• For resolution enhancement: Topaz Gigapixel AI, Let’s Enhance
• For complete retouching and generation: Adobe Firefly, Midjourney, DALL-E 3
• For automated workflows: Zapier + AI integrations, Make (formerly Integromat), custom solutions via API
• For retailers seeking a solution integrated into their e-commerce ecosystem: platforms like WISHIBAM offer connectors that enable these capabilities to be integrated directly into catalog management flows

Generative AI is not magic. It is a tool, which, when well-integrated, unleashes extraordinary power for all those who know how to exploit it. The future of retail is already here — it remains to be seized.