AI in fashion: 50 questions answered

AI in fashion is the use of software that learns from data to support tasks across the value chain, from trend analysis and design to demand forecasting, pricing, marketing, customer service and returns handling. It includes long-established machine learning, such as forecasting and recommendation models, and newer generative AI that creates text, images and code.

Traditional, or predictive, AI analyses existing data to classify, forecast or recommend, for example predicting how many units of a style will sell. Generative AI produces new content, such as product descriptions, campaign images or design variations, from a prompt. Most fashion companies use both, often in the same workflow.

Agentic AI refers to systems that can plan and carry out multi-step tasks with some autonomy, such as researching products, comparing options and completing a purchase. For fashion, it raises the prospect of AI agents shopping on behalf of consumers and handling routine work such as order processing for businesses. McKinsey estimated in 2025 that agentic commerce could orchestrate up to $1 trillion of US B2C retail revenue by 2030, a forecast across all retail categories.

Use is widespread but uneven. In the BoF-McKinsey State of Fashion 2026 report, more than 35% of executives said their companies already used generative AI in areas such as customer service, image creation and copywriting. Across all sectors, Eurostat found that 19.95% of EU enterprises with 10 or more employees used AI in 2025.

Common obstacles include fragmented or poor-quality product and customer data, legacy systems, unclear ownership of business cases and a shortage of in-house expertise. Among EU firms that had considered AI but not adopted it, lack of relevant expertise was the most cited barrier in 2025, at 70.89%. Scaling also requires process changes and staff training, not only technology.

No, although large companies adopt it faster: Eurostat found that 55.03% of large EU enterprises used AI in 2025, against 17% of small ones. Off-the-shelf tools for copywriting, image editing, translation and customer service have lowered the entry cost for smaller brands and retailers. The main constraints for smaller firms tend to be data quality, skills and time rather than licence costs.

Designers use generative AI to explore mood boards, colourways, prints and silhouette variations quickly, and to visualise ideas before sampling. AI is also used to analyse past sales and trend signals to inform ranges. Most companies treat these outputs as starting points that designers refine, rather than finished designs.

Current evidence points to AI changing design work rather than replacing designers. Generative tools speed up ideation and visualisation, but judgement on brand identity, fit, materials, cost and manufacturability still rests with people. The bigger shift is in the mix of skills required, with designers increasingly expected to direct and edit AI outputs.

It depends on the jurisdiction and on how much human creative input is involved. The US Copyright Office has stated that material generated purely by AI without sufficient human authorship is not protected, and EU copyright law generally requires a work to be the author's own intellectual creation. Brands should document human contributions and seek legal advice before relying on protection for AI-assisted designs.

Yes, 3D design and AI-generated visualisation can replace some physical samples, particularly in early development and for internal reviews or wholesale previews. This can cut lead times, shipping and material waste. Physical samples are still widely used to check fit, handle and quality before production.

AI tools analyse large volumes of signals, such as social media images, search queries, runway coverage and sales data, to identify emerging colours, styles and product attributes. They can detect shifts faster than manual research, but outputs depend on the data sources used and need interpretation by merchandisers and designers. Forecasts are probabilities, not certainties.

The main risks are unintentional similarity to existing protected designs, unclear ownership of outputs, homogenised aesthetics if many brands use similar tools, and disclosure of confidential material when using public tools. Companies typically address these with approved tools, clear usage policies, similarity checks and legal review of final designs.

Machine learning models combine historical sales, product attributes, pricing, promotions, weather, seasonality and other signals to predict demand at style, size and store level. Better forecasts can reduce both stockouts and excess inventory. Accuracy is limited for genuinely new products and depends heavily on clean, consistent product data.

Yes, AI models can estimate the size mix each store or channel needs and recommend how to allocate and replenish stock. This addresses a common source of markdowns and lost sales, since a style can sell out in core sizes while fringe sizes remain. Results depend on granular sales and return data by size.

AI-based pricing tools estimate how demand responds to price changes and recommend the timing and depth of markdowns to clear stock while protecting margin. Some retailers also use them to set initial prices and promotional offers. Human oversight remains important to protect brand positioning and avoid prices that customers perceive as unfair.

AI is more likely to automate parts of the analysis than to replace buyers and merchandisers, who also negotiate with suppliers, judge product and balance commercial and brand goals. Routine tasks such as reporting, open-to-buy calculations and replenishment proposals are the most exposed. The role is shifting towards interpreting model outputs and making final decisions.

They typically need consistent product master data, including attributes such as category, colour, fabric and fit, plus clean sales, stock, price and returns histories at a detailed level. Gaps and inconsistencies in this data are one of the most common reasons AI planning projects underdeliver. Many companies start by improving product data before deploying models.

Better forecasting, allocation and smaller, more frequent orders supported by AI can reduce the volume of unsold stock. The issue has regulatory weight in Europe, where the Ecodesign for Sustainable Products Regulation bans large companies from destroying unsold clothing and footwear from July 2026. AI alone does not solve overproduction if business models still reward high volumes.

In wholesale, AI is used to recommend assortments to retail buyers, predict reorder needs, automate order entry and product data exchange, and generate product content for retailer platforms. Brands also use it to analyse sell-through data shared by retail partners. Adoption tends to follow digitalisation of the wholesale process itself, such as digital showrooms and online ordering.

Yes, recommendation models can suggest products and quantities based on a retailer's past orders, sell-through, store profile and similar accounts. This can help smaller accounts in particular, who may lack planning resources. Buyers still need to judge local demand and brand fit, so recommendations work best as decision support.

Accurate, structured product data, including attributes, images, sizes, prices and sustainability information, is the foundation for AI in wholesale. It feeds recommendations, search, automated content and data exchange with retailers. Incomplete or inconsistent data limits what AI can do and can spread errors across partners' systems.

AI agents could take over routine B2B tasks such as reordering basics, checking stock and delivery dates, and reconciling orders and invoices. For this to work, brands need machine-readable product catalogues, clear APIs and rules on what an agent may do on a buyer's behalf. The technology is early, and many agentic AI projects across industries are still at the experimental stage.

Yes, generative AI is widely used to draft product descriptions, translations and attribute data, and to create or adapt product images for different channels. This can speed up onboarding of large assortments to retail partners. Outputs should be checked for accuracy, especially for materials, care instructions and sustainability claims, which carry legal risk if wrong.

Consumers increasingly use AI chat tools to research products, compare prices, get style advice and summarise reviews. In a Capgemini survey of 12,000 consumers in 12 countries in late 2024, 58% said they had replaced traditional search engines with generative AI tools for product recommendations. The same survey found that 71% wanted generative AI integrated into their shopping experiences.

Generative engine optimisation is the practice of making a brand's products and content easier for AI assistants and AI search engines to find, understand and cite. It relies on accurate structured product data, clear and factual content, and presence in sources that AI systems draw on. It complements, rather than replaces, traditional search engine optimisation.

Yes, several large retailers use AI to generate or adapt imagery. Zalando reported that more than 90% of its onsite marketing content was generated by AI as of December 2025. The practice raises questions about consent and pay for models, disclosure to consumers and representation, which brands address through contracts, labelling and internal guidelines.

AI shopping assistants combine a language model with the retailer's product catalogue, customer data and business rules, so shoppers can ask questions in natural language and receive product suggestions. Their usefulness depends on catalogue data quality and on how well they handle size, fit and availability.

Personalisation of recommendations, content and offers is one of the most established uses of AI in fashion e-commerce, and retailers widely report gains in engagement and conversion. Published results vary greatly by company, method and baseline, so headline uplift figures should be treated with caution. Poorly executed personalisation can also feel intrusive and must comply with data protection law.

Consumer surveys suggest growing openness to AI recommendations, and a quarter of consumers in Capgemini's 2024 survey said they had bought products recommended by AI influencers. Trust drops when recommendations are irrelevant, when products are unavailable or when consumers suspect undisclosed commercial bias.

Virtual try-on and AI size advice aim to reduce returns caused by poor fit, one of the main reasons customers send clothing back. Evidence on their effect is mixed: a 2025 peer-reviewed study of a high-end fashion retailer found that size finder users were slightly more likely to return items but had higher customer lifetime value. Retailers should test such tools against their own data.

AI supports supplier selection and risk monitoring, production planning, quality inspection with computer vision, logistics optimisation and inventory positioning. It can also help map multi-tier supply chains for due diligence. Many of these uses rely on data from suppliers, which is often incomplete.

AI can contribute by reducing overproduction, cutting samples, optimising transport and improving sorting for recycling and resale. These gains can be offset if AI mainly accelerates the volume of products and content, and AI itself consumes energy. The European Commission notes that only around 1% of material in clothing is recycled into new clothing, so systemic change matters more than any single tool.

Training and running AI models requires data centres that consume electricity and water. The International Energy Agency expects data centre electricity demand to grow strongly this decade, with AI the main driver. Fashion companies can limit their share by choosing efficient models, avoiding unnecessary generation and asking providers for energy data.

AI is used to predict which orders are likely to be returned, give size advice, route returned items for resale or refurbishment, and detect fraud. In an NRF survey of large US retailers in 2025, 85% said they used AI to detect or prevent return fraud. Returns remain costly: US retailers expected 15.8% of 2025 sales to be returned.

The Digital Product Passport, introduced under the EU Ecodesign for Sustainable Products Regulation, will require textile products to carry structured information on aspects such as materials and circularity once product-specific rules are adopted. AI can help extract, classify and check this data from supplier documents, but the obligation is fundamentally about reliable data collection and traceability. Companies should follow the delegated acts for textiles for exact requirements and dates.

Yes, sensor-based systems combined with machine learning can identify fibre composition and colour to sort post-consumer textiles faster and more accurately than manual sorting. Better sorting is a precondition for fibre-to-fibre recycling. Mixed-fibre garments and trims remain technically difficult to recycle regardless of sorting technology.

Yes, it applies to companies that develop or use AI systems in the EU, including fashion brands and retailers. Most typical fashion uses, such as recommendations or demand forecasting, fall into minimal-risk categories with no specific obligations, but transparency duties apply to chatbots and certain AI-generated content. Uses such as AI in recruitment are classed as high-risk and face stricter rules.

The AI Act entered into force on 1 August 2024. Prohibited practices and AI literacy obligations have applied since 2 February 2025, obligations for general-purpose AI models since 2 August 2025, and transparency rules from August 2026. Following the AI Omnibus, rules for high-risk systems in areas such as employment apply from 2 December 2027, and for AI integrated into regulated products from 2 August 2028.

Under the EU AI Act, providers of generative AI must make AI-generated content identifiable, and deepfakes must be clearly labelled, with these transparency rules applying from August 2026. Whether a given campaign image counts as a deepfake depends on whether it realistically depicts real people, places or events. Separate consumer protection rules also prohibit misleading advertising, so images that misrepresent a product are risky regardless of how they were made.

Yes, under the EU AI Act people must be made aware that they are interacting with an AI system, unless this is obvious from the context. These transparency obligations apply from August 2026. Clear disclosure also helps maintain customer trust.

Concerns include the loss of work for models, photographers and stylists, the use of real people's likenesses without fair consent or pay, and unrealistic or narrow representation of bodies. Some companies address this by creating licensed digital twins of real models with contractual consent and payment. Disclosure to consumers is another open question.

In the EU and the UK, the General Data Protection Regulation governs the use of personal data for personalisation, profiling and AI training. Companies need a lawful basis, transparency towards customers and safeguards for automated decisions with significant effects. Body measurements, images and inferred characteristics require particular care.

Yes, models trained on unrepresentative data can underrepresent certain body types, skin tones, ages or sizes in imagery, recommendations and size advice. Bias can also affect AI used in hiring. Companies mitigate this by testing outputs across customer groups, diversifying training data and keeping human review.

Estimates are large but uncertain. McKinsey estimated in 2023 that generative AI could add $150 billion to $275 billion to the operating profits of the apparel, fashion and luxury sectors within three to five years. Such figures are modelled potentials, and realised value depends on adoption, execution and competition.

Start with a defined business metric for each use case, such as forecast accuracy, full-price sell-through, content production time, conversion or return rate, and compare against a control group or baseline. Include total costs, including data preparation, integration, licences, change management and monitoring. Many projects show value in pilots but lose it at scale, so measurement should continue after roll-out.

Most start with use cases that have clean data and a clear owner, such as product content, customer service, demand forecasting or returns. Executives in the State of Fashion 2024 survey most often cited marketing and copywriting (34%) and design and product development (28%) as generative AI use cases. Fixing product data and setting usage policies early makes later projects easier.

Most buy or license tools for common tasks such as copywriting, translation, customer service and forecasting, and reserve in-house development for uses that differentiate the brand or depend on proprietary data. Buying is usually faster to deploy, while building offers more control over data and differentiation. A hybrid model is common.

AI is expected to change many roles and reduce some, particularly in content production, customer service and routine analysis, while creating demand for data, AI and digital skills. Across industries, the World Economic Forum projects 170 million jobs created and 92 million displaced by 2030, and 41% of employers surveyed plan workforce reductions as AI automates tasks. Outcomes for fashion will depend on how companies redeploy and retrain staff.

Useful skills include data literacy, the ability to brief and critically review AI outputs, understanding of AI risks and regulation, and domain expertise that AI lacks, such as product, fit and brand judgement. The EU AI Act already requires organisations deploying AI to take measures to ensure sufficient AI literacy among their staff. Employers in the WEF survey most often plan to respond to change through upskilling, cited by 77%.

Good practice includes an inventory of AI systems in use, a policy on approved tools and data, clear accountability for each system, risk assessment against the EU AI Act and data protection law, and human review of customer-facing outputs. Governance should be proportionate, so low-risk productivity tools are not slowed by the same controls as high-risk uses.