Fashion AI Report 2025

As machine learning reshapes the fashion industry, The Interline’s Fashion AI Report 2025 offers a look at how these changes are unfolding throughout the supply chain and what they might mean for those of us in the parallel field of entertainment costume design and production. While the report focuses on the fashion and beauty industries, costuming coexists in the margins of both; its insights are relevant.

You can access the entire 140-page report at this link if you’d like to read any or all of it — I’ve read the whole thing and I’m sharing this summary in case you are interested but don’t have the time or mental real estate to check it out right now. 

The report marks a shift from speculative AI to practical tools. When the 2024 report came out, “AI” was still a buzzword. In 2025, it’s a force to consider. For costume designers, this might mean:

• Faster design development: AI can generate visual concepts based on scripts, moods, or historical references—although the kleptomaniacal ethics of generative AI models’ training datasets may be insurmountable for theatre artists, nevermind the issue of script rights 

• Enhanced research & procurement: Tools now assist with fabric sourcing, garment shopping, and even predicting wearability under stage conditions particularly for long-running & touring shows (this feels like the more likely area of applicability in our field).

Google/Shopify partnership’s AI shopping assistant

The report is structured around four sections:

1. Introductions & Context — why this report exists & who compiled it.
2. Essays & Interviews— written by journalists & scholars.
3. Technology Vendor Profiles — these highlight companies providing AI tools to global fashion brands. They paid for the compilation of this report so the profiles should be viewed as advertising content. That said, they include interviews with executives with interesting perspectives.
4. Market Analysis — largely irrelevant given the scale most theatrical productions operate at, barring perhaps Cirque du Soleil/Disney.

Despite the focus on the rise of AI, the report emphasizes that people remain at the heart of fashion—and by extension, costume. AI can assist but it can’t replace the intuition, empathy, and narrative understanding that costume designers bring to the stage.

The Interline hints at deeper automation in the years ahead. For theatres, this could mean:

• AI-assisted costume stock archiving and reuse planning
• Digital wardrobe previews for directors and actors.
• Sustainability insights for eco-conscious companies, departments, or designers

If you want to read some of the report, but not the whole 140 pages, I have a couple of sections to recommend, which you can find via the table of contents on pp. 9-10:

• The essay on “Upstream AI” has some fascinating strategies for sustainability, including AI-driven genetic engineering of the cotton plant
• The essay “Trust Me, I’m Not Real” is a dystopian look into AI Influencers and “post-human authenticity”
• The interview with Isaac Korn at Perry Ellis “AI in Practice” has some good comments about their AI Governance Policy [1] and how it helps with ethics and security

As I was writing and compiling this post, I reached out to former students of mine working in the fashion industry and corporate costume production as patternmakers and technical designers (and an imagineer) to inquire whether they knew about AI capabilities their companies had incorporated. Most had no idea and said that if so, it was in areas outside of their field of responsibility.  

I got the idea from the report that machine learning adoptions in the field were not widespread, and that the report was perhaps created to engender broader uptake.

[1] I recommend all theaters consider writing one of these policies, particularly those embedded in academic departments..

Tl;dr — It sounds like there are some applications that will come into play for big companies like Cirque/Disney, but for much of the costume industry, once the Midjourney/ChatGPT hype dies down, we might find value in machine learning tools primarily aimed at consumers like AI shoppers.