Maya Lindqvist
Reports on artificial intelligence in buying, sales, planning and product content.
Articles by Maya Lindqvist
What is AI in fashion? A complete guide to AI across the fashion value chain
From design and planning to wholesale, supply chain and e-commerce: what artificial intelligence really does in fashion today, what data it needs and where its limits are.
How to measure the ROI of AI in fashion
Most companies struggle to prove that AI pays off. A practical framework for fashion: baselines, control groups, the right metrics per use case and the full cost picture.
AI in fashion product development: from brief to sample
AI is reshaping the steps between a collection brief and the first approved sample: research, concepts, digital prototypes and range decisions. What works, what data it needs and where people stay in charge.
AI trend forecasting in fashion: how it works, what it sees and its limits
AI tools now scan runway images, social media and sales data to spot emerging styles. Here is what the signals can and cannot tell a design or buying team, and why human forecasters still matter.
AI, 3D and virtual sampling: fewer samples, faster decisions
3D garment simulation has been replacing some physical samples for years. AI now speeds up the steps around it. Here is what virtual sampling can really replace, what data it needs and where physical samples still win.
How does AI improve fit and sizing? From size advice to better patterns
Size and fit problems drive a large share of fashion returns. AI now powers size recommendations, body measurement and virtual fitting rooms, and its data can flow back into patterns and size charts.
How is AI used for colour, prints and materials in fashion?
From AI palette tools to generated prints and AI-assisted fibre research, colour and materials work is changing. A sober look at what is in use, what is still experimental and what data it depends on.
How does AI allocation and replenishment work across stores and channels?
Allocation decides where stock goes first; replenishment decides what follows. AI makes both decisions at size and location level. Here is how it works, what it needs and where it struggles.
What is size curve optimisation and how does AI help buy the right sizes?
Buying the wrong mix of sizes leaves fringe sizes on the rail and core sizes sold out. Here is how AI estimates true size demand, why stockouts mislead the data and what to watch for.
How can AI support range and line planning in fashion?
Range planning decides how many styles, at which prices and in what depth a collection should have. AI can forecast new items and test scenarios, but the creative and strategic call stays human.
Why is sell-out data essential for AI in fashion wholesale?
Brands that sell through retailers see what they ship, not what consumers buy. Sell-out data closes that gap, and without it most AI forecasting for wholesale works half blind.
AI in B2B fashion wholesale: the complete guide
Where artificial intelligence creates real value between brands and retail partners, what data it needs, how buyers already use it and where the limits are.
What makes a wholesale business AI-ready?
AI readiness in fashion wholesale is less about algorithms than about structured product, order and partner data, clear ownership and processes that capture why buyers decide.
How do AI re-order prediction and automated replenishment work for retailers?
Re-orders cover styles that have already proven themselves. AI can suggest or trigger them before a retail partner runs out, if sell-out and stock data are shared.
What can conversational AI assistants do for B2B fashion buyers?
Assistants that answer questions on availability, order status and terms are arriving in B2B portals. What they can do, what they must be connected to and where caution is needed.
How does AI personalisation work in fashion e-commerce?
Recommendations, ranked listings, size advice and conversational assistants all rely on the same thing: clean customer and product data. A practical guide to what works, what it costs and where the limits are.
How are fashion brands using AI in marketing, from content to measurement?
Generative AI has cut the time and cost of campaign content for some retailers, but brand control, rights and disclosure rules decide whether it works. A guide to the main uses and their limits.
AI-generated models in fashion: what are the ethics and disclosure rules?
Digital twins and fully synthetic models promise faster, cheaper imagery. Consent, pay, disclosure and audience trust now shape how brands can use them, and new rules in New York and the EU raise the bar.
How is AI search changing the way shoppers find fashion?
Shoppers now describe what they want to chatbots and AI search engines instead of typing keywords. How conversational shopping works, what the early evidence shows and what it means for fashion brands and retailers.
How is AI used in fashion customer service, and where does it fail?
AI chatbots and agent assistants can answer order, return and sizing questions around the clock. Customers still want a human when it matters, and companies stay liable for what their bots say.
How is AI used in physical fashion stores?
From associate assistants and clienteling to computer vision for stock and checkout, AI is moving onto the shop floor. What works, what it requires and where privacy law draws hard lines.
How does AI help sort and recycle textiles?
Recycling clothes into new fibres needs precise sorting by material and colour. Sensors and machine learning make that possible at scale, but capacity and economics are still limiting.
How can AI help fashion brands prepare for the Digital Product Passport?
The EU Digital Product Passport turns product data into a regulated deliverable. AI can help collect, check and structure that data, but it cannot invent what suppliers never recorded.
How Inditex uses AI and data: RFID and a demand-driven supply chain
Inditex's edge rests on item-level RFID data, integrated stock and production close to home. Where AI now enters, what results are public and what remains undisclosed.
How Nike uses AI: direct-to-consumer data and generative product creation
From demand sensing for its own channels to AI-generated footwear concepts and an AI shopping beta, what Nike has built, what it has disclosed and what changed under new leadership.
How adidas uses AI: from design archives to generative content
adidas has documented AI in footwear concepting, software engineering, review analysis and personalised campaigns. What it built, what results it reports and what it has not disclosed about planning.
How Levi Strauss & Co. uses AI: the data programme and its lessons
Levi Strauss & Co. trained its own staff in machine learning, stumbled with AI models in 2023 and is now rolling out agents for employees and shoppers. What the programme shows.
How Mango uses AI: generative design, virtual models and Mango Stylist
Mango has built more than 15 machine learning platforms since 2018, from the internal Lisa tool to AI-generated campaign models and the Mango Stylist assistant. What is documented and what is not.
How Stitch Fix uses AI: algorithms plus human stylists
Stitch Fix pairs recommendation algorithms with about 1,700 part-time stylists, and has added generative AI tools such as Vision. How the hybrid model works and what the numbers show.
How Shein uses data and algorithms: the on-demand model and its criticism
Shein says it orders new designs in batches of 100 to 200 pieces and scales them on demand signals. How the data-driven model works, what is claimed, and why regulators and designers criticise it.
AI in fashion: the September 2026 review
Meta's Muse agent, Amazon's block, Shopify's agent checkout, AI replenishment at Macy's and Zalando's AI imagery: the verified AI news for fashion from September 2026.
AI for fashion buyers: what changes in buying and how to start
Where AI already helps fashion buyers with trend scanning, line reviews, quantities and re-orders, what data it needs, what stays a human judgement and a 30-day plan to start.
AI for wholesale sales teams and key account managers
How AI helps fashion wholesale reps and key account managers prepare appointments, build retailer assortments, read sell-out data and handle re-orders, plus what buyers still expect from people.
AI for fashion CFOs: where the money is and how to govern it
Where AI creates measurable value in a fashion business, what finance teams can use it for, why many AI projects fail to pay back and how CFOs can govern AI spending and risk.
AI for CIOs and IT leaders in fashion: architecture, data and governance
What fashion CIOs and IT leaders need to get right for AI: product and sales data foundations, integration architecture, build or buy, security, EU AI Act duties and governance frameworks.
AI for sourcing and supply chain managers in fashion
How fashion sourcing and supply chain managers use AI for demand and production planning, cost and tariff scenarios, supplier risk, compliance and quality, with data needs, limits and a 30-day plan.
AI in fast fashion: speed, demand signals and the overproduction problem
Fast fashion lives on reading demand early and reacting fast. AI sharpens both, but it can also accelerate volume, and new EU rules make unsold stock more costly.
AI in footwear: fit, size data and faster product creation
In footwear, a few millimetres decide whether a sale sticks. AI helps with foot scanning, size advice and design iteration, but only on top of accurate last and product data.
AI in denim: how finishing, laser technology and water use are changing
Denim's look is made in finishing, traditionally with water, chemicals and manual abrasion. Digital design and laser finishing, increasingly guided by AI, change that, but the evidence base is still thin.
AI in mid-market and wholesale-led fashion brands: where to start
Mid-market brands that sell mainly through retail partners have less data and smaller teams than the giants. AI still pays off, if it targets order-taking, product data and sell-through insight.
AI in childrenswear and basics: forecasting replenishment-driven ranges
Basics and childrenswear sell steadily, in many sizes, at thin margins. That makes them ideal for AI forecasting and replenishment, and sensitive when the customer is a child.
Where AI fits in fashion wholesale today
AI is already useful in wholesale, but mostly in narrow, data-rich tasks. A sober map of where it helps sales, buying and operations, and where human judgement still decides.
Using AI in assortment planning without losing the brand
AI can sharpen depth, breadth and size decisions in assortment planning. The risk is a range that drifts towards safe averages. How to use the tools while keeping the brand's point of view.
AI recommendations in B2B sales: from co-purchase lists to next best action
Recommendation engines are moving from simple co-purchase lists to account-specific suggestions for sales reps and buyers. How the main approaches work and what makes them trusted in fashion wholesale.
Generative AI in fashion design: useful tool or distraction?
Image generators can produce mood boards and print ideas in seconds. Whether they improve design depends on where they sit in the process, and on how brands handle originality and rights.
Visual search and image recognition in fashion commerce
Image recognition lets shoppers and buyers search by picture and helps brands tag products automatically. How the technology works, where it performs well and what it depends on.