AI uptake across fashion businesses is likely to spike in 2025 as fashion executives look to lower consumers' ‘choice paralysis’.

AI is proving an essential tool for fashion businesses as brands are facing a surge of abandoned carts due to customers being overwhelmed by too many options.

According to McKinsey’s State of Fashion 2025 report, 74% of shoppers report walking away from purchases due to volume of choice and 80% say they are annoyed by the online search function which poses a barrier to purchase.

Some brands have reacted by cutting the number of brands they showcase – ASOS for example – has reduced stock intake by 30% year on year in the first quarter of 2024, and is planning a further 16% reduction in stock by the end of 2024 as it looks to offer “more relevant brands”.

With regards to search function dissatisfaction, more brands are turning to generative AI which is resulting in higher customer engagement.

There is a growing demand for AI-powered shopping experiences according to McKinsey & Co. as 79% of customers surveyed by Google said they would find it helpful for AI to understand their specific needs and recommend products. The majority (82%) said they want AI to reduce their time spent researching what to buy. To address this, 84% of organisations regard hyper-personalised experiences across customer touchpoints as a priority for the next 12 months.

And the options for AI are improving in terms of quality and cost as competition between providers grows. Google DeepMind’s latest AI model, Gemini, will offer AI overviews featuring refined recommendations, multi-step reasoning, planning and multi-modal capabilities. In October 2024, the company introduced a new shopping experience centred on AI features such as a personalised shopping feed and guides that summarise relevant product information. Meta has also upgraded its open-source AI model, Llama 3.1, with monthly users increasing 10x from January to July 2024.

The competition has also driven accuracy improvements. For example, OpenAI’s GPT-4o is 15% to 20% more accurate than previous models, generating fewer hallucinations across a range of tasks.

How seriously are fashion brands taking AI?

• 50% of fashion executives see product discovery as the key use case for generative AI in 2025

• 82% of customers want AI to assist in reducing the time they spend researching what to buy

• The latest AI model of GPT-4o from OpenAI is 15% to 20% more accurate than its predecessors, exhibiting fewer hallucinations.

Is it worth it?

According to McKinsey, some brands that have partnered with Constructor’s AI product search have seen a 20x increase in return on investment. Some of its partners include Under Armour and Birkenstock.

Meanwhile, players like Alibaba which set up a “digital tech” firm under its ecommerce unit TTG in August 2024 has seen a 30% improvement in click-through rate with personalised content on Wenwen, a large language model chatbot that provides personalised recommendations to consumer queries using multi-modal outputs such as text, image, video and audio, that is the first fully integrated AI ecommerce user application in China.

German online retailer Zalando on the other hand which is investing in Gen AI to become a “one-stop” destination for customers, spanning both product discovery and inspiration as well as seamless search has been using an AI assistant that leverages ChatGPT technology. It has been used by over 500,000 customers since its launch in 2023. It leverages data from ongoing interactions with users to refine and improve output and accuracy over time. As part of its shift to enhanced content curation, it acquired Highsnobiety and has seen 7m new users since.

Priorities for fashion brands looking to harness AI

Build AI foundations: Embed AI literacy in the hiring criteria for adjacent roles, such as in marketing functions, in relation to customer experience and brand perception. Upskill the existing workforce on the appropriate use of AI. Establish a technology backbone (including tech stack and infrastructure) that provides flexibility to adopt and scale search and discovery use cases. Identify relevant tech partners for cost-effective generative AI deployment or build in-house capabilities through acquisition. Ensure product data is optimised for AI search, identifying relevant product features and attributes, for both organic search and content-led discovery.

Prioritise value and accuracy, then scale: Apply a prioritisation framework to identify the discovery and search use cases with the highest value based on customer insights. Employ a test-and-learn approach, starting with use cases that perform specific tasks with consistently accurate results before scaling more broadly across a larger customer base or set of activities. Assess on an ongoing basis the trade-offs retailers may need to make between showing customers the most relevant products to improve conversion and monetising search results by allowing brands to sponsor listings.

Manage risks and ethics: Implement AI best practice frameworks to guide teams through the appropriate use and communication of AI in content and search to gain customer trust. Consistently monitor how AI models are developed and trained, incorporating broader data sets that consider all customers. Monitor search accuracy and model output through human validation and A/B testing to ensure resonance with customers. Balance changes with brand tone of voice, prioritising authenticity and avoiding rigid algorithm-driven outputs.

 By Just Style