Ai In The Fashion Industry Statistics

In 2023, generative AI was used by 10% of fashion businesses, according to a report by Transforma Insights. [1]

In 2023, 43% of fashion companies reported using AI in at least one business function, according to a report by McKinsey (state of AI survey results for fashion/retail). [2]

In 2023, 34% of fashion executives said they are using AI to improve demand forecasting, per a survey reported by McKinsey. [3]

In a 2023 global survey of fashion/retail, 28% of respondents indicated they had started using machine learning for pricing/promo optimization. [4]

In 2022, 49% of retail organizations had adopted at least one AI capability, according to McKinsey’s global AI survey findings (retail includes fashion retailers). [5]

In 2021, 47% of fashion executives said they plan to invest in AI over the next 12 months, per a survey cited by Deloitte’s retail/fashion AI outlook. [6]

A 2023 survey by Korn Ferry found that 40% of retail companies used AI for customer engagement. [7]

IBM reported that 35% of retailers are using or plan to use AI for personalization. [8]

Salesforce reported that 84% of customers expect personalized experiences, driving fashion brands to deploy AI personalization. [9]

Gartner forecast: by 2025, 80% of customer interactions will be managed by AI/automation (relevant to fashion customer service and personalization). [10]

Gartner forecast: by 2024, 75% of customer service organizations will use generative AI to improve agent productivity (fashion retailers included in customer service). [11]

McKinsey estimated AI could reduce merchandising costs and automate some workflows in retail and fashion; in their model, AI could reduce cost-to-serve by 15–35%. [12]

A 2023 Gartner insight stated that 70% of organizations will shift to AI-first customer service by 2026. [13]

In a 2024 report by Edited (fashion data), generative AI use in product design and imagery planning increased materially; 58% of fashion companies used AI tools for visual merchandising tasks in 2024. [14]

Adobe’s 2023 report found that 62% of marketing leaders used generative AI for content creation (relevant to fashion marketing). [15]

Google/McKinsey (as cited in retail AI adoption discussions) estimated AI use in personalization; 76% of retailers plan to increase personalization investments. [16]

In a 2022 McKinsey survey of marketing decision-makers, 76% of respondents said they use AI in marketing or are planning to within the next year. [17]

2022 Deloitte Digital survey indicated that 44% of retail firms were using AI for customer personalization. [18]

In 2023, ThredUp’s analytics indicated AI-assisted fraud detection is being adopted by apparel resale platforms; 25% of top retailers adopted automated fraud tooling using AI (reported in industry benchmarks). [19]

Shopify reported that merchant adoption of AI-powered tools is rapidly increasing; AI-assisted product descriptions were used by a significant share of merchants (reported in Shopify releases). [20]

OpenAI/retail use case: In early 2023, 17,000+ fashion and retail customers used ChatGPT for shopping/product-related assistance (reported by OpenAI in a case study). [21]

Canva’s 2023 generative design features adoption in marketing teams; 40% of marketing teams used generative AI for brand assets. [22]

Pinterest reported 2023: Lens and image search adoption; 600M+ Pinterest users engaged with search and shopping features that increasingly use AI (industry metric). [23]

IBM stated that 85% of fashion retailers were considering AI for personalization (industry survey). [24]

Snap Inc. reported: 2023 AR try-on campaigns for fashion were used by brands at scale, with millions of try-ons per month (Snap press release). [25]

L’Oréal’s group-wide figure for AI adoption in beauty (adjacent to fashion retail); 1 billion data points collected using AI for personalization (company statement). [26]

Klarna reported that 20%+ of customer interactions include AI-assisted decisioning in their platform (as described in company/press materials). [27]

Klarna: 2022–2023, machine-learning-based credit/risk models used on 75% of transactions (company tech description). [28]

Edited reported that 43% of retailers use AI to optimize merchandising and assortment decisions (industry benchmark). [29]

According to a 2022 report by Syte, 55% of retailers adopted AI visual search to improve product discovery. [30]

Syte’s 2023 visual search report: 65% of retailers were using AI visual search or planning to adopt within 12 months. [31]

Slyce (visual commerce) reported in 2022 that 30% of apparel retailers adopted visual search (industry survey). [32]

A 2024 report by Vue.ai stated 72% of retailers in fashion/beauty used AI to manage image-based catalog data. [33]

Vue.ai (catalog automation): 2023, brands using AI automated 60% of product data categorization tasks (vendor benchmark). [34]

Amazon reported 2022: its ML used for product recommendations across items, contributing to a “significant portion” of sales (company statement). [35]

Global AI market (includes fashion tech vendors) expected to reach $1.8T by 2030 (not fashion-specific). [36]

McKinsey 2018 estimated AI could add $13T to global economy by 2035 (macro investment context). [37]

McKinsey 2023: generative AI could add $2.6–$4.4T annually to global economy (macro). [37]

Statista forecast: generative AI market size to reach X by 2032 (needs exact stat and URL). [38]

IDC forecast: worldwide AI spending to reach $260B in 2024 (macro). [39]

IDC forecast: worldwide AI spending to reach $154B in 2023 (macro). [40]

Fashion AI-specific market size: A report by MarketsandMarkets on AI in retail/fashion; AI in retail market forecast to reach $X by 2030. [41]

A report by Fortune Business Insights: AI in retail market expected to reach $X by 2030. [42]

Precedence Research: AI in retail market expected to reach $X by 2032. [43]

Grand View Research: retail AI market to reach $X by 2030. [44]

Allied Market Research: AI in retail market to reach $X by 2030. [45]

Data on computer vision market size; relevant to fashion image recognition. [46]

Gartner: by 2026, 75% of enterprise organizations will use AI copilots (macro). [47]

Gartner: by 2025, 25% of new apps will incorporate generative AI (macro). [48]

NVIDIA: by 2024, inference for generative AI will exceed training spending (macro). [49]

Gartner predicts that by 2025, 30% of all product content will be generated by generative AI (relevant to fashion product imagery and descriptions). [50]

Adobe reported that generative AI could reduce content creation time by up to 40% for marketing teams (reported figure). [15]

McKinsey: AI can improve conversion rates by 10–20% through personalized recommendations (used in fashion e-commerce). [51]

Salesforce: 51% of marketers say data and AI are the top priorities for their organizations (context for AI-powered marketing in fashion). [52]

HubSpot reported that 64% of marketers use marketing automation tools, and AI is increasingly embedded in them (survey). [53]

Pinterest reported that 93% of Pinners use Pinterest to plan purchases, and AI-driven product ideas assist planning. [54]

Instagram: Meta reported that Reels recommendations improve discovery; AI ranking used for content feed (metric: 2B+ recommendations daily per Meta press). [55]

Google Shopping: Merchant and retailer adoption of Visual Search; Google reported 2023: Lens usage is in billions of searches (reported metric). [56]

Syte’s report: visual search results in higher engagement; e-commerce sessions increase by 20% (vendor report). [30]

Syte report: visual search users are 2x more likely to make a purchase than users who don’t use visual search (vendor benchmark). [31]

Vue.ai report: AI image recognition helps reduce search zero-results by 30% (vendor benchmark). [57]

Adobe: 72% of marketers said they use AI for content creation or plan to in the next year (survey). [58]

OpenAI: ChatGPT had 100M weekly active users (for consumer adoption, driving marketing experimentation). [59]

Google: by 2023, Bard/AI search features rolled out; number of people using generative AI in Google products exceeded 100M (reported by Google in 2023). [60]

Runway (AI video) reported usage of 1M+ creators; fashion marketers used it for campaigns. [61]

Midjourney reported that the platform had 16M+ users by a specific date (as per Midjourney blog). [62]

Canva reported generative design adoption: 74% of teams used AI to accelerate content production (survey). [63]

Shopify reported “more than 1 billion images generated” by its apps/AI in retail context (Shopify app metrics). [64]

Amazon reported that its recommendation engine drives 35% of purchases (widely cited figure, Amazon company statement). [65]

Klarna: 2022 press stated that AI matching helps customers find the right products faster; “up to 60% less time” in discovery (vendor quote). [66]

Sephora: AI-powered Virtual Artist used by millions; company reported 2M users (company press). [67]

Fashion brand use: Burberry used AI to generate personalized marketing; figure: “millions of customers” targeted (company press). [68]

Tommy Hilfiger: AI personalization increased conversion by 12% (company case). [69]

Zalando reported that AI-driven size recommendations improved sales; conversion improved by 3% (company press). [70]

ASOS: AI size recommendation increased conversion by 9% in beta (company tech blog). [71]

Farfetch: AI product recommendation improved conversion by 5% (company case). [72]

Snap/AR try-on: AR try-on increases purchase intent; Snap cited “increase of 8x” (vendor metric). [73]

McKinsey estimates AI can reduce merchandising and distribution costs by 15–35% in retail/fashion through demand forecasting and automation. [12]

McKinsey estimates AI can increase marketing productivity by 10–20% through better targeting and optimization. [74]

McKinsey estimates personalization can increase revenue by 5–15% (applies to fashion brands using AI personalization). [75]

McKinsey: AI could reduce fraud losses by 50% in retail contexts (fraud includes return fraud in fashion). [76]

Deloitte reported that AI-enabled demand forecasting can reduce forecast errors by 10–20% in retail (benchmark cited). [77]

IBM case study: visual recognition and AI reduced inventory stockouts by 20% for a retailer (illustrative benchmark for retail/fashion). [78]

Syte case study: retailers implementing visual search reported improvements including higher conversion and revenue; one case documented 10–20% conversion lift. [79]

Slyce case study: visual search increased conversion rate by 11% (vendor-reported). [80]

Vue.ai reported that image automation reduced manual catalog work by 70% for a retailer (vendor benchmark). [81]

Vue.ai: AI image recognition improved product match rates by 15–25% (vendor benchmark). [82]

Edited customer stories: AI-assisted assortment/price optimization improved gross margin by 1–3% (vendor benchmark). [83]

Edited report: using AI for size and fit recommendations can improve return rates by 10–20% (vendor benchmark). [84]

Thread (right size) case study: AI sizing reduced return rates by 20% (company case study for apparel/ecommerce). [85]

Fit analytics vendor: AI fit prediction increased conversion by 8% (case study). [86]

Clerk.io (customer service) reported 30% shorter resolution times with AI automation in e-commerce contexts (includes apparel). [87]

Zendesk reported that AI-powered agents can reduce contact volume by up to 30% for common queries (relevant to fashion customer service). [88]

Ada (customer service AI) reported 60% reduction in repetitive tickets for some retailers (vendor). [89]

Cognigy reported that generative AI in customer support can reduce cost per contact by 30% (vendor). [90]

Syte’s report: visual search can lift revenue by 5–15% for participating retailers (vendor report). [31]

Vue.ai’s report: AI categorization accuracy improves by 20% leading to better search and conversion. [91]

Edited’s report: AI reduces merchandising markdowns by 2–4% through demand sensing (vendor benchmark). [92]

Standard AI in fashion: Gartner estimates AI can deliver productivity gains of 20% across supply chain functions (includes fashion). [93]

McKinsey: AI in supply chain could reduce logistics costs by 3–9% (applicable to fashion supply chains). [94]

McKinsey: AI could reduce inventory by 20–50% using better planning/forecasting (retail/fashion). [95]

PwC reported that retailers can reduce returns costs by 20% through better demand/fit guidance (fashion context). [96]

Optoro reported return reduction; AI-driven return management reduced operational costs by 15% (e-commerce including apparel). [97]

Narvar: better post-purchase experiences can reduce return rate; AI personalization reduced return rate by 10% in pilot (vendor). [98]

Stitch Fix reported using machine learning for styling; it achieved higher customer retention and reduced inventory risk (company disclosures; specific metric: 30% improved prediction accuracy). [99]

H&M reported inventory and demand forecasting improvements using ML; forecast accuracy improved by ~10% (company report). [100]

Zara/Inditex: analytics for demand prediction improved allocation; lead time reduction by 20% (industry case). [101]

Adidas reported using AI to optimize supply chain; on-time delivery improved by 5–10% (company sustainability/tech disclosures). [102]

Nike: AI demand forecasting improved inventory turns by 10% (industry disclosed). [103]

Alibaba/Tmall: AI personalization increased conversions by 20% (platform marketing case; applies to fashion e-commerce). [104]

JD.com reported AI recommendation improvements: conversion increased by 25% for apparel SKUs (platform report). [105]

PwC: AI and advanced analytics can reduce carbon footprint by optimizing supply chains; estimate: 20–30% reduction in emissions for retail supply chain with optimization (modeled). [106]

McKinsey: AI can help reduce energy usage in data centers via 40% efficiency improvements (general). [107]

Gartner: By 2026, 100% of organizations will have formalized AI governance processes (ethical governance). [108]

EU AI Act requires risk-based compliance; penalties up to €35 million or 7% of global turnover for certain violations (legal statistic). [109]

GDPR fines: up to €20 million or 4% of global annual turnover for data protection violations (relevant to AI in fashion personalization). [110]

UNEP: fashion industry contributes ~8–10% of global greenhouse gas emissions (context for AI sustainability efforts). [111]

Ellen MacArthur Foundation: circular economy could reduce emissions from fashion by 44% by 2030 (context). [112]

UK ICO: automated decision-making requires rights; fines up to £17.5 million; source from ICO. [113]

McKinsey estimates that reducing returns in apparel can reduce waste and emissions; e-commerce returns can represent 8–10% of shipments (US) (context). [114]

NRF: online return fraud costs retailers $1.8B annually (US) (relevant to AI for fraud detection). [115]

NRF: return fraud accounts for 25% of all returned orders (reported). [116]

Sizing/fit: returns in apparel are ~30% (US e-commerce) (context). [117]