McLeuker Research · Topic
AI in the fashion industry — what's changing and why.
9 articles on McLeuker Research

Half the AI tools the fashion industry tried in 2024 didn't survive. Here's what's left, what's working, and what's coming next — from the team building agentic AI for fashion full-time.

Fashion has always run on instinct. AI didn't replace instinct — it gave instinct evidence. Here's what's changing across trend forecasting, sourcing, and brand strategy.

Most fashion AI is a chatbot with a fashion logo. Agentic AI is different — it plans, acts, and ships deliverables. Here's what that actually looks like in production.

Behind the dashboards: what fashion trend analysis AI actually looks like under the hood, why most of it is unreliable, and what separates a real forecast from a pretty visualisation.

AI-driven brand forecasting isn't a crystal ball. It's a structured way to see what's already moving in your competitive landscape — before your strategy meeting catches up.

One model can't do everything. The fashion teams getting the most out of AI are the ones running multiple specialised models behind a single interface.

Our fashion trend analysis AI processed 4,200+ looks across NYFW, LFW, MFW, and PFW for SS26. Here are the silhouette, color, and material signals — with honest notes on confidence.

Generic AI sees the web. Fashion-domain AI sees fashion. The difference is everywhere — data sources, prompting, evaluation, output format. Why specialisation is winning.

If you're a creative director, brand strategist, or fashion executive evaluating AI tools, here's the practical checklist. What to ask, what to ignore, what to test.
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