Fashion AI: scan, deep dive, implement.
A researched series on what universities, investors, fashion houses, consultancies and tech companies are saying about fashion AI in 2026, and how a fashion or jewellery brand takes it to implementation. Read it in order: one map, five deep dives, then three implementation guides.
The map
What the five voices agree on, where they disagree and what it means for a brand.
The five voices
One article per voice: the strongest sourced claims, how good the evidence is, and what follows for a brand.
- 02
Fashion AI in the lab: what universities and research labs say
Try-on looks real but does not show fit; measured gains are single-digit; AI helps individuals and narrows the group.
9 min read · Read → - 03
Fashion AI and the investors: where the money and the theses point
Apparel is the first category for shopping agents; product data is the bottleneck; investors admit hard sales data is not in yet.
10 min read · Read → - 04
Fashion AI inside the houses: what retailers, jewellers and luxury groups are doing
Zalando publishes numbers, luxury publishes principles; content, fit and service are where results show up.
9 min read · Read → - 05
Fashion AI by the numbers: what McKinsey, BCG, Bain, Deloitte and the analysts claim
Trillions projected, 4% would let an agent buy alone; discovery first, fashion and luxury less exposed, measure everything.
10 min read · Read → - 06
Fashion AI from the platforms: what Google, OpenAI, Amazon, Shopify and the model labs are building
Discovery moved into AI faster than checkout; every protocol keeps the merchant of record; product data is the entry ticket.
9 min read · Read →
Taking it to the store
A 90-day pilot, the data and governance underneath it, and how to measure the return.
- 07
A 90-day agentic AI pilot for a fashion or jewellery brand
One shopper-facing and one behind-the-scenes use, control stores, people approving drafts, and a decision rule set on day one.
9 min read · Read → - 08
Data foundations for fashion AI: consent, privacy, approvals, IP and model choice
Product data agents can read, consent per purpose, people approving drafts, written image rights, and models chosen per job.
9 min read · Read → - 09
How to measure the ROI of in-store and design AI
Difference-in-differences against control stores, margin not revenue, full costs, and realistic single-digit to mid-teens effects.
9 min read · Read →