H&M, the world's largest clothing brand, says it will start using AI to customize inventory in each of its 4,200 stores to entice customers as sales slump
Saabira Chaudhuri / Wall Street Journal :
Context & Ripple Effects
H&M's announcement lands in an industry where algorithmic rivals were already setting the pace: analysts had predicted Amazon would become the largest US apparel seller while it explored selling custom-fit clothing, and online players like Stitch Fix had shown algorithms displacing skilled merchandising work. For the world's largest clothing brand, per-store AI inventory customization is a defensive answer to that pressure amid a sales slump.
The move also prefigures a pattern the later coverage confirms: LVMH later called AI central to navigating a luxury-demand slowdown, and by 2026 Zalando, Zara, and ASOS were betting on AI virtual fitting rooms to lift conversion and cut returns. H&M's 2018 decision reads as an early instance of apparel incumbents reaching for AI specifically when demand weakens.
First-order effects
- Inventory allocation across H&M's 4,200 stores shifts from uniform or planner-driven stocking to per-store algorithmic customization, changing what shoppers find on the rack in each location right away.
- Merchandising and planning staff at H&M see their assortment decisions increasingly mediated or overridden by the AI system as it becomes the tool for enticing customers during the slump.
Second-order effects
- Rivals face pressure to match the capability: Zara, Zalando, and ASOS were already investing in AI shopping tools like virtual fitting rooms, and LVMH's use of AI in supply chains and pricing shows large apparel groups treating AI as table stakes in a downturn rather than an experiment.
- Vendors of retail-AI tools gain a marquee reference customer, strengthening the pitch startups like Catches make to apparel brands that AI can directly improve sell-through and reduce costly mismatches between stock and local demand.
Third-order effects
- If per-store algorithmic merchandising proves out, the merchandiser role consolidates around overseeing models rather than picking assortments — extending the pattern of algorithms absorbing skilled white-collar retail work seen at firms like Stitch Fix.
- Apparel competition structurally shifts toward whoever holds the best demand-sensing data, making AI capability a durable differentiator between large incumbents and pushing laggards toward either building or buying the same stack.
The trend: Apparel retail is adopting AI as its primary lever against demand slumps and algorithm-native competitors, moving from online personalization into physical-store operations.