AI in the retail sector is projected to generate US$9.7 billion in revenue by 2030. This investment is directly impacting garment workflows, where AI now handles tasks from fabric inspection to waste reduction. The following statistics quantify the adoption driven by consumer sustainability demands and operational cost pressures.
Key Takeaways
- 39% of consumers expect brands to act in a sustainable manner (driving AI use-cases in traceability, recommendation, and waste reduction).
- 12,000+ retail stores use RFID for inventory tracking (context for AI integrating sensor data for apparel inventory accuracy).
- 26% of companies have deployed or are piloting AI for ESG/sustainability reporting (applies to apparel traceability and compliance data).
- 18% of retailers use AI for visual search in production or retail experiences (includes fashion product search use-cases).
- 60% of EU consumers believe eco-labels help them to identify sustainable products (driving AI-enabled label and product information extraction).
- 25% of apparel shoppers expect virtual try-on to be available (demand indicator for AI-driven AR/virtual try-on tools).
- 10% CAGR projected for the global AI in retail market through 2030 (supports AI adoption in apparel retail merchandising, search, and personalization).
- US$4.8 billion is projected as the 2024 market size for AI in retail (used as a macro indicator for AI spend affecting apparel).
- US$1.9 billion is the 2023 market size for computer vision in manufacturing (relevant to AI-enabled garment defect detection and quality inspection).
- 22% of apparel consumers purchased online in 2019 (baseline for digitalization enabling AI personalization and virtual try-on).
- 93% accuracy is achieved in fabric texture classification tasks in a published study using deep learning (demonstrates feasibility for material recognition in garment workflows).
- 86.7% mean IoU is reported for semantic segmentation in a garment-related vision dataset study (useful for pattern/region labeling for manufacturing).
- 2.5x faster sorting is reported for AI-assisted automated textile sorting systems compared with manual classification (supports sustainability sorting efficiency).
- 4.0% of garments are lost or damaged due to handling errors in some reported warehouse operations (drives computer vision/AI quality and process controls).
- €1.0 billion in annual costs is estimated from poor inventory accuracy in retail (AI-driven inventory reconciliation helps apparel retailers).
AI adoption is accelerating in apparel with strong consumer demand, fast-growing retail markets, and proven vision and segmentation performance.
Related reading
01 · Category
Industry Trends5 stats
01
39% of consumers expect brands to act in a sustainable manner (driving AI use-cases in traceability, recommendation, and waste reduction).
02
12,000+ retail stores use RFID for inventory tracking (context for AI integrating sensor data for apparel inventory accuracy).
03
26% of companies have deployed or are piloting AI for ESG/sustainability reporting (applies to apparel traceability and compliance data).
04
78% of executives expect AI to increase their operational efficiency within 12–24 months (applies to garment operations optimization).
05
In 2023, global e-commerce sales were US$5.3 trillion (context for large online apparel markets where AI personalization is valuable)
Interpretation
Industry Trends Interpretation
With 78% of executives expecting AI to boost operational efficiency within 12 to 24 months, industry trends in garment are clearly moving toward practical AI-driven improvements like traceability, inventory accuracy, and sustainability reporting.
02 · Category
User Adoption5 stats
01
18% of retailers use AI for visual search in production or retail experiences (includes fashion product search use-cases).
02
60% of EU consumers believe eco-labels help them to identify sustainable products (driving AI-enabled label and product information extraction).
03
25% of apparel shoppers expect virtual try-on to be available (demand indicator for AI-driven AR/virtual try-on tools).
04
71% of organizations report using AI in at least one business function (supports cross-functional AI rollouts across apparel value chains)
05
44% of respondents say their organizations have already implemented AI in one or more areas (adoption benchmark for fashion firms deploying AI)
Interpretation
User Adoption Interpretation
For the user adoption angle, the clearest trend is momentum toward mainstream use, with 71% of organizations reporting AI adoption in at least one business function and another 44% already implementing it in one or more areas, while retail-facing tools are gaining traction as 25% of shoppers expect virtual try-on and 18% of retailers use AI for visual search.
03 · Category
Market Size10 stats
01
10% CAGR projected for the global AI in retail market through 2030 (supports AI adoption in apparel retail merchandising, search, and personalization).
02
US$4.8 billion is projected as the 2024 market size for AI in retail (used as a macro indicator for AI spend affecting apparel).
03
US$1.9 billion is the 2023 market size for computer vision in manufacturing (relevant to AI-enabled garment defect detection and quality inspection).
04
US$3.3 billion is the 2023 market size for AI in manufacturing (useful proxy for automation/inspection/optimization tools in garment factories).
05
US$29.7 million was invested in AI companies in fashion and retail in 2023 (venture funding indicator for AI in apparel ecosystem).
06
US$1.4 billion was invested in computer vision startups globally in 2022 (supports tech availability for garment QC and analytics).
07
US$1.5 billion market size for virtual try-on is projected by 2030 (supports AI/AR adoption in apparel).
08
US$70.3 million is the value of the global AI fashion retail segment in 2023 (macro market indicator).
09
US$9.7 billion revenue is forecast for AI in retail by 2030 (macro indicator for apparel retailers adopting AI).
10
US$14.4 billion global computer vision market in 2022, forecast to reach US$84.9 billion by 2030 (supports demand for CV used in garment QC and visual merchandising)
Interpretation
Market Size Interpretation
The market size signals strong momentum for AI in the garment industry, with global AI in retail projected to grow at a 10% CAGR through 2030 and the broader AI in retail market reaching an estimated US$4.8 billion in 2024, alongside manufacturing-focused AI spending of US$3.3 billion in 2023.
04 · Category
Labor & Productivity1 stats
01
22% of apparel consumers purchased online in 2019 (baseline for digitalization enabling AI personalization and virtual try-on).
Interpretation
Labor & Productivity Interpretation
With 22% of apparel consumers buying online in 2019, the labor and productivity impact is clear because digital channels create demand for AI-driven personalization and virtual try-on workflows that can streamline how garment teams serve customers.
More related reading
05 · Category
Performance Metrics6 stats
01
93% accuracy is achieved in fabric texture classification tasks in a published study using deep learning (demonstrates feasibility for material recognition in garment workflows).
02
86.7% mean IoU is reported for semantic segmentation in a garment-related vision dataset study (useful for pattern/region labeling for manufacturing).
03
2.5x faster sorting is reported for AI-assisted automated textile sorting systems compared with manual classification (supports sustainability sorting efficiency).
04
95% classification accuracy is reported for a textile sorting deep-learning approach in a peer-reviewed study (supports AI for garment material identification).
05
RFID systems can reduce inventory out-of-stocks by about 16% in retail operations (supports AI-driven planning using more accurate inventory signals)
06
Deep learning segmentation models can reach mean Intersection over Union (mIoU) above 0.85 on benchmark datasets when properly trained (supports expectations for high-quality garment region segmentation)
Interpretation
Performance Metrics Interpretation
Across performance metrics, recent garment-focused AI studies and systems are showing strong measurable gains, with accuracy reaching 93% for fabric texture classification and 95% for textile sorting while segmentation achieves mIoU around 0.85 to 86.7% and sorting speed improves by 2.5x over manual methods.
06 · Category
Cost Analysis5 stats
01
4.0% of garments are lost or damaged due to handling errors in some reported warehouse operations (drives computer vision/AI quality and process controls).
02
€1.0 billion in annual costs is estimated from poor inventory accuracy in retail (AI-driven inventory reconciliation helps apparel retailers).
03
US$1.6 billion: global investment in computer vision (CV) startups in 2023 (supports availability and growth of CV tech for automated inspection and labeling)
04
US$2.7 billion: total VC investment into AI startups in 2023 globally (macro indicator for funding of AI solutions used in retail/manufacturing)
05
Fraud and chargebacks can cost US merchants around 1%–2% of sales annually (AI-driven risk scoring is relevant to online apparel returns and payments)
Interpretation
Cost Analysis Interpretation
With losses from handling errors at 4.0% of garments and poor inventory accuracy costing retailers about €1.0 billion annually, AI is increasingly justified in cost analysis through targeted solutions like computer vision and risk scoring, further supported by major funding such as US$1.6 billion in computer vision investments and total US$2.7 billion in global AI startup VC in 2023.
07 · Category
Sustainability1 stats
01
30–60% of fashion returns are attributed to fit issues according to industry estimates (drives AI sizing/fit recommendation).
Interpretation
Sustainability Interpretation
With 30 to 60 percent of fashion returns driven by fit issues, AI sizing and fit recommendations are a key sustainability lever because they can reduce wasted shipments and returns tied to misfitting garments.
report visual · Key figures
AI adoption momentum in garment & retail
A majority of executives and organizations expect or are already using AI, while consumer demand for AI shopping experiences is growing.
78%
78% of executives expect AI to increase their operational efficiency within 12–24 months (applies to garment operations
71%
71% of organizations report using AI in at least one business function (supports cross-functional AI rollouts across app
44%
44% of respondents say their organizations have already implemented AI in one or more areas (adoption benchmark for fash
25%
25% of apparel shoppers expect virtual try-on to be available (demand indicator for AI-driven AR/virtual try-on tools).
18%
18% of retailers use AI for visual search in production or retail experiences (includes fashion product search use-cases
source-verifiedgartner.com · ibm.com · techopedia.com
Reference
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
APA
Aisha Okonkwo. (2026, February 13). AI In The Garment Industry Statistics. Gitnux. https://gitnux.org/ai-in-the-garment-industry-statistics
MLA
Aisha Okonkwo. "AI In The Garment Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-in-the-garment-industry-statistics.
Chicago
Aisha Okonkwo. 2026. "AI In The Garment Industry Statistics." Gitnux. https://gitnux.org/ai-in-the-garment-industry-statistics.
Sources & references
33 datasets cited across this report · attribution is report-level
+11 additional datasets cited (not shown individually)