Over the past couple of years, we’ve worked with several fashion e-commerce clients to help them transition further into the digital space. One key area we’ve explored — and continue to explore — is how artificial intelligence can be deployed as a practical tool to streamline internal operations and transform the customer experience. Today, we already use AI for several straightforward content and SEO tasks: generating engaging titles, meta descriptions, compelling product descriptions, automated tagging, and testing different versions of the copy we write. Our marketing platforms also use AI for product recommendations, send-time optimisation, and forecasting. But this only scratches the surface of what AI has to offer.

Design, Manufacturing, Production & Supply Chain Management

AI is revolutionising the fashion industry by bringing greater efficiency and automation to design, manufacturing, production, and supply chain management. During the design phase, AI analyses trends and customer behaviour to create designs more likely to resonate with shoppers. Generative design tools allow designers to set material or performance constraints, and the AI then produces concepts that meet those requirements. This results in a broader range of design possibilities and reduced waste. In manufacturing and production, AI enhances quality control by accurately sorting materials and reducing inspection errors. It ensures a higher and more consistent standard than manual processes alone, leading to better-quality garments. AI-powered robotics also improve material handling in factories, shortening turnaround times and ensuring safe movement of materials and finished products. AI is equally transformative in the supply chain. It can predict disruptions, optimise delivery routes, and automate repetitive tasks while collecting and sharing key data on inventory levels, demand trends, labour needs, and raw material availability and pricing. The result: better forecasting, reduced waste, and lower operational costs.

Artificial Intelligence in Fashion & the Customer Experience

For consumers, AI significantly enhances the online shopping journey. Personalised recommendation engines suggest products based on browsing and purchase behaviour, improving engagement and increasing conversions. Visual search enables shoppers to upload a photo and instantly find similar items. Virtual try-on tools, using AR and AI, allow customers to see how products — such as leather jackets — might look on them before buying, helping reduce uncertainty and return rates. AI is quickly becoming a vital asset in the fashion industry, streamlining processes, boosting efficiency, and elevating the customer experience.

Federated AI vs In-House AI

Small fashion brands often face challenges when adopting AI due to limited data and resources. Their machine learning models can become undertrained, resulting in weak performance. To overcome this, many opt for federated learning: pooling anonymised data and model updates across multiple brands. Each brand trains its model locally on its own data, sharing only the model improvements rather than the data itself. This preserves privacy while drastically improving model performance and allowing small businesses to benefit from AI without heavy infrastructure investment. Larger e-commerce brands, on the other hand, generate vast amounts of data and can support fully in-house data science teams. They can build and fine-tune sophisticated models, create detailed customer profiles, enhance recommendation systems, and forecast demand more accurately. Their scale also allows them to invest in advanced hardware, proprietary AI tools, and specialist talent — enabling customised AI solutions that smaller brands simply cannot replicate. With larger datasets, privacy is easier to maintain as there’s no need to share data externally.

10 Examples of How AI Is Transforming Fashion E-Commerce

Whether used directly by customers or integrated behind the scenes, AI is reshaping fashion e-commerce in countless ways. Here are ten of the most impactful:

1. Personalised Recommendations

AI analyses customer behaviour to recommend items that suit their style, preferences, and previous purchases — enhancing the shopping journey and increasing conversions. Example: Wiser

2. Virtual Try-Ons

Advances in AR and AI allow customers to virtually try on garments, helping them see how items might look before purchasing, reducing uncertainty and returns. Example: Robosize

3. Chatbots & Virtual Assistants

AI-powered assistants can answer questions, guide product selection, give styling advice, and even help complete purchases — improving service and supporting sales. Example: Tidio

4. Demand Forecasting

AI predicts future trends by analysing sales history, social media activity, and wider fashion trends. This helps retailers plan inventory more efficiently and reduce waste. Example: Merchbyte

5. Price Optimisation

By assessing demand, competitor pricing, and historical sales, AI can recommend optimal prices that maximise profit while keeping customers happy. Example: Intelis

6. Automated Marketing

AI analyses customer behaviour to create highly targeted campaigns that increase engagement and conversions. Example: Klaviyo

7. Intelligent Search

Predictive search and advanced filtering powered by AI give users faster, more relevant results — improving UX and boosting sales. Example: Quick Search

8. Visual Search

Customers can upload images to find similar products instantly. This makes shopping more intuitive and convenient. Example: Comparision

9. Multilingual Translation

AI translation tools can automatically translate an entire website, opening up markets across different languages and locations. Example: LangShop

10. Fashion Manufacturing

AI improves material sorting, automates data collection, reduces inspection errors, and supports more sustainable and customised production. Example: Digital Coats

Final Thoughts

The use of AI in fashion e-commerce is already extensive — and rapidly expanding. These examples are just the beginning. As AI continues to evolve, we can expect even more innovative, efficient, and customer-focused applications to emerge.