How fashion brands are utilising Artificial Intelligence

Artificial intelligence (AI) has become one of the industry’s biggest competitive advantages by transforming how clothes are designed, manufactured and also how consumers discover, try on, and buy products.

Once associated mainly with recommendation engines on e-commerce websites, AI is now embedded across the entire fashion value chain.

Global luxury houses, fast-fashion retailers, online marketplaces and emerging African fashion brands are already adopting AI to improve efficiency, reduce costs, personalise customer experiences and predict consumer demand.

The State of Fashion 2025 report by McKinsey and The Business of Fashion reveals that artificial intelligence has shifted from an experimental technology to a core operational priority for the industry.

Executives are prioritising AI to enhance product discovery, demand forecasting, and design, aiming to address slow market growth and reduce inventory inefficiencies.

Why AI matters to fashion

Fashion has always been driven by trends, creativity and consumer behaviour. However, the industry faces several longstanding challenges such as overproduction which leads to unsold inventory.

Also, difficulty in predicting fast-changing consumer tastes, counterfeit products, rising production costs and pressure to become more sustainable are challenges faced.

Artificial Intelligence helps businesses address these issues by analysing enormous amounts of consumer, sales and market data much faster than humans can.

Rather than replacing designers, AI acts as a decision-support tool that enables faster and more informed choices.

How AI transforms fashion

Designers can now generate hundreds of design concepts within minutes using text prompts such as ‘Create a Nigerian-inspired evening gown using Ankara and silk.’

AI tools can produce multiple variations which allows designers to explore colors, silhouettes, fabrics and patterns before producing physical samples.

Benefits include faster ideation, design costs reduction, fewer physical prototype and shorter product development cycles

Luxury brands and global retailers use AI to create mood boards, visual concepts and digital sketches before involving human designers.

However, as generative AI is reshaping creative workflows there are brows raised around originality, copyright, labour and aesthetics.

AI analyses Instagram posts, TikTok videos, Pinterest searches, Google search trends, Online shopping behaviour, Celebrity wardrobes and Street fashion images.

Machine learning identifies emerging colours, fabrics and styles months before they become mainstream.

AI has also changed online shopping because instead of browsing thousands of products, consumers receive personalised recommendations based on body measurements, browsing behavior, purchase history, budget and location.

Modern AI shopping assistants can answer conversational questions such as ‘ I need a dress for a Lagos wedding under N80,000.’

The system then recommends products matching the shopper’s preferences.

According to the State of Fashion 2025 report, 50 percent of fashion executives see product discovery as the most important generative AI use case.

Eighty two percent of consumers want AI to reduce the time spent researching what to buy, according to McKinsey & Company.

One of e-commerce’s biggest problems is uncertainty because customers often wonder if an outfit will fit, the color will suit them or even how it will look in pictures.

AI-powered virtual fitting rooms now allow shoppers to upload photos, try on outfits digitally, compare different colours and see clothing from multiple angles.

Fashion companies lose billions annually because they either produce too much inventory, produce too little or stock the wrong sizes.

AI analyses historical sales, seasonal demand, local preferences, weather and economic conditions to forecast demand more accurately.

McKinsey reports that many fashion leaders are prioritising AI for demand forecasting, inventory optimisation and cost control.

AI helps with supply chain optimisation by predicting factory delays, optimising shipping routes, monitoring supplier performance, reduce production waste and managing warehouse operations

The result is lower operational costs and faster delivery times.

Generative AI now produces product descriptions, fashion campaign concepts, digital models and product photography.

Hence, brands can launch marketing campaigns much faster while personalising content for different customer segments.

McKinsey found that 45 percent of fashion executives view AI-driven marketing as a major value driver.

AI-powered chatbots now provide size recommendations, styling advice, order tracking, return processing, product suggestions and customer support in multiple languages.

Unlike traditional chatbots, generative AI can understand natural conversations and provide more personalised assistance.

Major fashion brands using AI

Many global brands have integrated AI into their operations, including Zara (inventory planning and demand forecasting), H&M (trend analysis and stock optimisation), Nike (personalisation and product recommendations).

LVMH (luxury retail operations and customer insights), ASOS (AI styling and outfit recommendations) and Shein (trend prediction and production planning).

What fashion executives prioritise first

For most fashion executives, AI is about business value hence top priorities are increasing sales through personalised customer experiences, improving demand forecasting and inventory management, strengthening supply chain resilience, and improving speed to market.

At the same time, many are focusing on responsible AI adoption by addressing copyright, data privacy, and governance concerns while investing in workforce reskilling to help employees work effectively alongside AI.

AI and sustainability

Fashion overproduction causes massive environmental harm because millions of unsold garments are discarded annually. The key factors driving this issue are fast-fashion business models, poor demand forecasting, and lack of recycling infrastructure.

AI can help reduce this waste by forecasting demand more accurately, producing smaller, data-driven collections, optimising material use, reducing unnecessary sampling and improving logistics.

However, AI systems themselves also require significant computing power, electricity and water for data centres, creating environmental trade-offs.

Challenges of AI in fashion

Despite its advantages, AI also raises copyright and intellectual property concerns because many generative AI models are trained on existing creative works.

This can result in outputs that closely resemble copyrighted designs, artworks, or characters, leading to disputes over ownership and infringement.

A notable example is the 2025 lawsuit filed by Disney and Universal against AI company Midjourney, which alleged that the platform generated images of copyrighted characters such as Darth Vader and the Minions without authorisation, highlighting the growing legal battle over how AI systems are trained and the originality of their outputs.

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Automation powered by AI is expected to reduce demand for routine and repetitive roles across the fashion value chain, particularly in design support, merchandising, marketing, customer service, and administrative functions.

For example, AI can now automate tasks such as product tagging, inventory forecasting, visual merchandising recommendations, social media content generation, customer service through AI chatbots, trend analysis, and basic graphic design.

This may reduce the need for entry-level design assistants, merchandising coordinators, customer support agents, and content production roles. At the same time, AI is creating demand for new positions such as AI fashion designers, prompt engineers, AI stylists, data analysts, digital product managers, AI model trainers, and AI governance specialists.

The shift shows the importance of reskilling and upskilling, with workers needing competencies in AI tools, data literacy, digital marketing, prompt engineering, creative direction, and human-AI collaboration.

Rather than replacing all jobs, AI is expected to automate specific tasks, allowing professionals who can effectively leverage AI to become more productive and competitive in the evolving fashion industry.

Overreliance on AI risks producing generic designs that lack the distinct creative identity of human designers.

For Nigerian designers and fashion entrepreneurs, AI could lower barriers to competing internationally by making sophisticated design, merchandising and marketing tools more accessible.

Artificial Intelligence is unlikely to replace fashion designers but instead, it is becoming a powerful collaborator by handling data-intensive and repetitive tasks while allowing human creativity to remain at the core of design and storytelling.

Bio

A writing enthusiast with a strong interest in communications and storytelling. Folake Balogun, as a journalist, brings human depth to stories.

She crafts compelling narratives that inform, engage, and resonate with an audience. Through her work, Folake Balogun invites readers to step into stories that spark thoughts and linger long after reading her articles.