AI helps retailers not only communicate with customers, but also control what ends up on the shelves. When it comes to fresh produce or fast-moving goods, automated quality control is critical — it directly affects reputation and returns.
When the product range is updated daily and new SKUs require descriptions, photos, and banners, generative AI becomes the basis for content creation. Marketplaces and chains use LLM to create product cards: descriptions, titles, attributes, and even images are generated automatically. This speeds up the release of the product range and maintains a consistent tone of communication.
One notable case is Amazon. At the end of 2023, the company launched an AI tool that creates realistic lifestyle images of products for advertising banners. All the seller has to do is upload a regular photo on a white background and write a text prompt — the AI independently places the product in context.
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For example, a sofa from a studio shoot becomes part of a virtual living room with decor and lighting, so the buyer immediately sees the product in a real setting. After the introduction of generative AI, the clickability of advertisements increased by +40%.
Roadmap: where to start and how to scale AI in retail
For AI to cease being a one-off experiment and start delivering sustainable growth, retailers need to develop a conscious approach to choosing scenarios, solution architecture and organisational structure.
Step 1. Start with business objectives, not technology
Choose specific problems: reducing write-offs, personalising promotions, optimising product range, automating support. Focusing on 2-3 promising scenarios will allow you to achieve results faster and justify scaling.
In this case, not only the metrics of the AI model (accuracy, speed) are important, but also business indicators: LTV growth, reduction of out-of-stock, increase in the margin of the first order, speed of reaching the result.
Step 2. Define the boundary between in-house development and off-the-shelf solutions
If the AI model is related to unique tasks or requires deep integration with internal processes (e.g., pricing or logistics), it can be built by your own team.
However, content generation, review analytics, and chatbots can be implemented through external platforms and APIs, especially at the start. The key selection criterion is the ability to control the quality of the model’s performance and retrain it using your own data.
Step 3. Build scalability and risk management from day one
It is important to design processes for managing the quality, risks, and transparency of AI solutions in advance. This includes data architecture, model monitoring, input and output parameter auditing, and ethical verification.
Organisations that integrate AI into their operating model, management system, and staff training programmes from the outset gain sustainable competitive advantages.
The era of autonomous AI agents in e-commerce and retail
While some companies are mastering basic scenarios — automating reviews and generating content — analysts are already talking about the next wave of e-commerce transformation. A recent McKinsey report describes a new shopping model: autonomous AI agents that make purchases on behalf of consumers. They anticipate needs, compare products on platforms, and conduct transactions with virtually no human involvement.
McKinsey predicts that by 2030, AI agents could handle up to $3-5 trillion of global B2C commerce. The boundaries between platforms and services will blur: shopping will become a single personalised process driven by customer intent rather than retailers’ advertising funnels.
The challenge for businesses is to adapt to the era of agents — companies will have to change their monetisation models and ways of interacting with customers and master new technologies in order not to lose access to consumers, who will increasingly be represented by their personal digital agents.
