Artificial Intelligence (AI) is making a revolution in the fashion and beauty industries by offering new opportunities for efficiency, personalisation, and creativity. The two prominent parts in AI are Machine Learning (ML) and Generative AI, which are at the forefront of this phenomenal transformation. By understanding tier applications and roles, the brands and companies can aim to stay ahead of this competitive market.

Artificial intelligence (AI) is a branch of computer science that simulates human intelligence in machines, enabling them to perform tasks like problem-solving, making decisions, and understanding languages. Machine learning (ML) is a subset of AI that utilises statistical techniques to allow machines to improve at tasks with data experience without being explicitly programmed. Generative AI is a part of AI models that can create new contexts such as images, texts, videos, and more by learning from the existing data and mimicking the creativity of humans.

Comparative Analysis

Aspect	Machine Learning	Generative AI
Primary Function	Makes analytics and decision-making through data study and predictions	Basically, on content creation and design generation
Data Utilisation	Analyses existing data to identify and confirm patterns	Learns and analyses the data to create new, unique content
Applications	Personalisation, organisation, forecasting, segmentation, and management	Virtual try-ons, content generation, design prototyping, and personalised strategies
Industry Impact	Increases operational efficiency and customer targeting	Launches innovation in product development and marketing

What Is Machine Learning in Fashion & Beauty?

Machine Learning (ML) is an AI system that analyses and learns from historical data and identifies patterns to make automated decisions and predictions. It is focused on optimisation, analytics, and automation, especially in regions where large datasets are involved.

The common applications of machine learning include

• Trend forecasting

• Personalised product recommendations

• Customer segmentation

• Inventory and supply chain organisation and management

• Pricing strategising

The brands that have used this are

• Burberry used integrated machine learning for its e-commerce strategy to enhance customer engagement. By analysing customer behaviour and preferences, it provided personalised product recommendations.

• H&M has ML algorithms to forecast demand and optimise their supply chain operations, decreasing markdowns and overproduction.

• Sephora uses ML to offer personalised skincare and beauty recommendations by analysing customer data like skin concerns and purchase history. The offerings also include suggesting products that align with one’s needs.

• L'Oréal Paris employs machine learning to predict consumer trends to stay ahead of competitors. The “TrendSpotter” program analyses social data from more than 3.500 online sources, enabling it to detect emerging beauty trends and quickly adapt the product offerings.

What Is Generative AI in Fashion & Beauty?

Generative AI uses AI capabilities to create a more expressive and creative dimension. Instead of just analysing data, it created the entire content from product descriptions, designs, images to marketing strategies, based on learned patterns. These models are created and trained on massive datasets and are capable of human-like text (i.e., ChatGPT-4), realistic images (i.e., Midjourney), and digital product mockups to make them the ideal tools for creative industries like fashion and beauty.

The common applications of generative AI include

• Virtual try-ons and makeup simulations

• AI stylists and fashion advisors

• Personalised beauty consultation through chatbots

• AI-generated fashion designs and prototypes

• Content generation for marketing and e-commerce.

The brands that have utilised this are

• LVMH awarded the 2024 Innovation Award to FancyTech, a startup specialised in Generative AI for creating videos from 3D product models. This technology enabled brands such as Givenchy and Hublot to produce the visual content efficiently for e-commerce strategies.

• MAC Cosmetics provides a Virtual Try-On tool that enables customers to experiment with more than 800 makeup shades online. It uses Generative AI to simulate how different products would look on the user’s face.

• Mango adapted AI-generated models in its advertising campaigns. In July 2024, it leveraged AI-generated glam bots in its ads. It allowed faster content creation and cost efficiency.

• Elizabeth Arden opened a virtual store based on its Fifth Avenue space. The VR store comprises multiple immersive rooms, containing edited and enhanced archive images using DALL·E - featuring the story of its ‘Victory Red’ lipstick created during WWII.

Integrating ML and Generative AI

Some of the forward-thinking brands are combining ML and Generative AI to harness the strengths of both technologies. By integrating these two, the brands are creating a holistic digital strategy. This dual-style adoption creates a seamless balance between creative innovation and data-driven efficiency.

• Stitch Fix, which leveraged ML to analyse customer preferences and feedback, enabled the stylists to curate personalised clothing collections. Its “Style Shuffle” showcase collects data on the customers’ preferences and dislikes, refining recommendations over time. It also employs Generative AI to automate the creation of product descriptions and outfit combinations. The Outfit Creation Model (OCM) creates millions of personalised outfit suggestions each day.

• Farfetch uses machine learning to personalise the recommendations and analyse user behaviour for better targeting. At the same time, it has generative AI tools to create SEO-friendly product descriptions and designs digital styling assistants. This hybrid model improves the performance in both experiential and operational fronts.

• Zalando GmbH collaborated with OpenAI to build the Zalando Assistant, an AI-based tool offering personalised product recommendations. Powered by GPT-4o mini, the assistant has improved user engagement, which is expected to lead to an increase in product clicks and wishlist additions. The brand also employed ML to understand customer behaviour.

• Levi Strauss & Co. partnered with a Dutch tech company, Lalaland.ai, to generate AI-based models for its online platforms. It aims to display a diverse range of ages, sizes, and skin tones, enhancing personalisation and inclusivity. By leveraging ML algorithms, Levi's can better understand the preferences of consumers and tailor product recommendations.

• Chanel has implemented AI across various platforms of its operations. The brand utilised ML to enhance customer engagement via personalisation and to optimise marketing strategies. In product design, Chanel uses AI to analyse trends and consumer preferences, ensuring products align with market demands.

• In March 2025, Estée Lauder integrated Adobe Firefly Services into its Adobe Creative Cloud workflows to increase efficiency and accelerate campaign execution. It has also formed an AI innovation lab in partnership with Microsoft to explore AI-driven solutions across its brand portfolio more effectively.

The integration of Machine Learning and Generative AI is reshaping the fashion and beauty landscape. As a brand, you will be needing both - leveraging ML's predictive capabilities and Generative AI's creative potential - to deliver efficient, personalised, and innovative experiences to consumers.

Cover Image: Stitch Fix's “Style Shuffle,” courtesy The Impression.