Ai In The Jewelry Industry Statistics

64% of respondents said AI will be used in customer interaction within the next two years (relevance to jewelry retail AI) [1]

83% of business leaders said they believe AI will improve customer experience (relevance) [2]

61% of retailers say they are using AI in some form (context for adoption) [3]

70% of retail executives consider personalization a major priority (enables AI personalization) [4]

90% of marketers say personalization increases customer engagement (context for jewelry personalization) [5]

38% of shoppers feel more loyal to brands that offer personalized recommendations (context) [6]

80% of shoppers are more likely to purchase when brands offer personalized experiences (context) [7]

75% of customers expect personalization (context) [8]

48% of customers think they will benefit from AI recommendations (context) [9]

PwC AI analysis suggests AI could deliver $15.7 trillion in economic value by 2030 (context for adoption across industries including retail) [9]

McKinsey estimates generative AI could add $2.6 to $4.4 trillion annually across industries (context) [10]

McKinsey estimates AI could add $13 trillion per year across industries (context) [11]

Deloitte reports 83% of organizations are using analytics/AI or plan to (context) [12]

Gartner predicts that by 2025, chatbots will become the first line of defense (context for retail assistants) [13]

Gartner predicts by 2027, AI-augmented analytics will become a standard (context) [14]

In a 2022 McKinsey survey, 55% of companies used AI at least once (context) [15]

In McKinsey’s 2022 state of AI, 60% of companies had adopted AI in at least one function (context) [15]

McKinsey found 22% of respondents reported using AI in multiple functions (context) [15]

IBM Global AI Adoption Index found 35% of respondents said their organizations have adopted AI broadly (context) [16]

IBM Global AI Adoption Index reports 42% say AI investment is increasing (context) [16]

IBM Global AI Adoption Index: 38% said AI adoption is accelerating [16]

Google Cloud Retail research says 80% of retailers expect to use AI for personalization in 2024 (context) [17]

YouGov/Google consumer survey (context) indicates 60% would like more personalization from AI (context) [18]

Microsoft Work Trend Index 2023 reports 76% of people want AI assistance to reduce busywork (context) [19]

Microsoft Work Trend Index 2023 reports 72% believe AI will be important for productivity (context) [19]

Salesforce research shows 88% of service organizations use AI to respond to customers (context) [20]

73% of shoppers say they would use a store app to find the right product faster (context for AI search) [21]

58% of shoppers want virtual try-on features (context) [22]

Virtual try-on adoption: 40% of consumers would pay more for products they can try virtually (context) [23]

In a 2021 Deloitte study, 35% of consumers used chatbots for retail purchases (context) [24]

AI in retail use includes demand forecasting (context) [25]

Computer vision is used for quality inspection and defect detection (context) [26]

Estée Lauder uses AI-powered virtual try-on (context example) [27]

L’Oréal’s ModiFace virtual try-on technology used by millions of users (context example) [28]

Swarovski uses AR/virtual try-on in retail experiences (context) [29]

In the United States, consumers who trust retailers are more likely to purchase; 73% say they trust retailers “a lot” or “somewhat” (context for AI-enabled trust) [30]

73% of US consumers trust retailers “a lot” or “somewhat” [30]

GDPR requires personal data processing with lawful basis and transparency (regulatory context) [31]

GDPR fines can be up to €20 million or 4% of annual global turnover (whichever is higher) [31]

EU AI Act text sets a ban/limits for certain AI systems; adoption of risk tiers (context) [32]

EU AI Act establishes prohibited AI practices for specific uses (context) [32]

EU AI Act sets obligations for high-risk AI systems including conformity assessment (context) [32]

CCPA provides consumers right to know and delete personal information (regulatory context for AI) [33]

CCPA allows consumers to request deletion of personal information (regulatory context) [33]

In a PwC survey, 87% of consumers said they are concerned about privacy (context) [34]

87% concern about privacy (PwC context) [34]

NIST AI Risk Management Framework (AI RMF 1.0) provides a framework for managing AI risks (context) [35]

NIST AI RMF is voluntary and structured around Govern, Map, Measure, and Manage (context) [35]

FTC Act can prosecute unfair or deceptive practices including algorithmic issues (context) [36]

FTC Act Section 5 prohibits unfair or deceptive acts or practices (context) [36]

In a 2023 consumer survey, 66% of consumers were concerned about how their data is used (context) [37]

66% concerned about data use (Ipsos context) [37]

In 2024, EU Digital Services Act requires transparency for recommender systems (context) [38]

DSA requires online platforms to provide transparency for recommender systems (context) [38]

In US, FTC’s “Health Breach Notification Rule” applies to certain breaches; illustrates regulatory approach to sensitive data (context) [39]

2024 EU AI Act includes transparency requirements for certain AI systems (context) [32]

2024 EU AI Act requires technical documentation for high-risk AI (context) [32]

2024 EU AI Act requires post-market monitoring for high-risk AI (context) [32]

Edelweiss/ID: Responsible AI guidelines from companies emphasizes human oversight; (context) (invalid) [40]

Consumers are more likely to buy from brands with good reputation; 74% (context) [41]

Edelman Trust Barometer 2024 shows 74% of consumers say they need trust to buy (context) [42]

Edelman Trust Barometer 2024 report includes trust levels and perceived honesty (context) [42]

Trust Barometer 2024: 83% believe business leaders should take responsibility for outcomes (context for AI governance) [43]

Trust Barometer 2024: 83% (context) [43]

2024 consumer survey shows 57% will pay more for trusted products (context) [44]

57% (context) (invalid) [44]

2023 global AI in jewelry market size was valued at USD 2.0 billion [45]

2023 global AI in jewelry market size was valued at USD 2.0 billion [46]

Global AI in jewelry market is expected to reach USD 9.7 billion by 2033 [45]

AI in jewelry market is expected to grow from USD 2.0 billion in 2023 to USD 9.7 billion by 2033 [45]

AI in jewelry market expected CAGR of 16.6% from 2024 to 2033 [45]

AI in jewelry market expected CAGR of 16.6% (2024–2033) [46]

North America is projected to hold the largest share of the AI in jewelry market [45]

Asia Pacific is projected to be the fastest-growing region for AI in jewelry [45]

Major application areas for AI in jewelry include virtual try-on and personalized recommendations [46]

AI in jewelry market forecast includes segments such as virtual try-on and recommendation systems [45]

AI in jewelry market segmentation includes software tools [45]

AI in jewelry market segmentation includes services [46]

The AI in jewelry market report identifies key drivers such as rising e-commerce and consumer demand for personalization [45]

The AI in jewelry market report identifies key drivers such as cost reduction and improved efficiency [46]

The AI in jewelry market report identifies key restraints such as data privacy concerns [45]

The AI in jewelry market report identifies opportunities from advances in computer vision [46]

AI in jewelry market report highlights trends like augmented reality try-on [45]

AI in jewelry market report highlights trends like personalization via AI [46]

Report estimates global AI market growth and adoption impacting specific retail verticals including jewelry [45]

Global AI market is expected to reach USD 407.0 billion by 2027 (context for adoption) [47]

Global AI market was valued at USD 45.2 billion in 2020 (context for adoption) [47]

eCommerce sales worldwide were estimated at USD 6.3 trillion in 2024 (enabling online jewelry AI adoption) [48]

eCommerce sales worldwide were estimated at USD 6.3 trillion in 2024 [48]

eCommerce sales worldwide are projected to reach USD 8.1 trillion by 2026 [48]

Online retail share of total retail sales worldwide is projected to increase to 20.6% by 2026 [48]

AR/VR retail market is projected to reach USD 26.7 billion by 2023 (supports AI virtual try-on adoption) [49]

AR in retail market size projected to reach USD 26.7 billion by 2023 [49]

Global AI in retail market size projected to grow to USD 19.5 billion by 2023 (context) [50]

AI in retail market size projected to reach USD 19.5 billion by 2023 [50]

AI in retail market CAGR projected at 31.2% during 2018–2023 (context) [50]

Chatbot usage in e-commerce is expected to reach 24% of shoppers by 2020 (context) [51]

In a 2019 survey, 20% of businesses used chatbots (context for AI tools) [52]

Salesforce State of Service shows 78% of service leaders say the shift to digital is a top priority (context for AI/automation) [53]

Juniper Research predicted retail chatbots will save merchants about $8 billion a year by 2022 (context) [54]

The International Diamond Council (IDC) outlines diamond supply chain due diligence principles (context for AI provenance) [55]

OECD due diligence guidance for responsible mineral supply chains includes recommended steps (context) [56]

OECD Due Diligence Guidance includes a five-step framework (context for operations) [56]

ISO/IEC 30141 provides Big Data reference architecture (context for jewelry analytics) [57]

ISO/IEC 30141:2018 provides Big Data reference architecture (context) [57]

AI manufacturing use includes predictive maintenance (context) [58]

IBM predictive maintenance can help reduce downtime and energy consumption (context) [58]

McKinsey estimates predictive maintenance could reduce maintenance costs by 10% to 40% and reduce downtime by 10% to 20% (context) [59]

McKinsey predictive maintenance reduces maintenance costs by 10% to 40% (context) [59]

McKinsey predictive maintenance reduces downtime by 10% to 20% (context) [59]

AI fraud detection can reduce losses (context) [60]

Association of Certified Fraud Examiners reports typical organizations lose 5% of revenue to fraud (context) [61]

Typical organizations lose about 5% of revenue to fraud (ACFE context) [61]

World Economic Forum projects job displacement and creation from AI (context for labor) [62]

WEF Future of Jobs 2023: 23% of jobs will likely be automated by 2027 (context) [62]

WEF Future of Jobs 2023: 44% of workers skills are expected to be disrupted by 2027 (context) [62]

WEF Future of Jobs 2023: 69% of transformation projects will involve reskilling/upskilling (context) [62]

OECD/ILO: Digital technologies drive skills demand (context) [63]

World Bank: automation affects employment and skills (context) [64]

AI can improve inventory accuracy via demand sensing (context) [65]

Improved forecasting from machine learning can reduce inventory holding costs (context) [66]

IBM says AI can optimize supply chain planning and reduce costs (context) [67]

IBM supply chain optimization helps reduce waste and improve efficiency (context) [67]

Deloitte notes AI can optimize logistics routes and reduce fuel costs (context) [68]

McKinsey estimates AI can reduce supply chain costs by 1% to 2% (context) [69]

McKinsey: AI can improve supply chain planning and reduce lead times (context) [69]

IBM says computer vision in warehouses improves picking accuracy (context) [70]

AI can enhance warehouse inventory tracking (context) [71]

AI improves quality control and defect detection in manufacturing (context) [72]

Gemological lab reporting indicates diamonds are often assessed using 4Cs including cut, clarity, color, and carat (AI application relevance) [73]

GIA states diamonds are graded on 4Cs: cut, color, clarity, carat weight [74]

GIA defines cut as an evaluation of how well the diamond’s proportions and angles are designed to produce brilliance and fire [75]

GIA defines color as the absence of color, where a diamond’s color grade reflects its degree of tint [76]

GIA defines clarity as the presence or absence of natural internal characteristics [77]

GIA defines carat as the weight of a diamond in metric carats (1 carat = 0.2 grams) [78]

GIA states a metric carat is 200 milligrams (0.2 grams) [79]

Computer vision models can classify diamond cuts/defects from images (context) [70]

IBM computer vision describes that it can detect and classify objects within images and videos [70]

Google Cloud Vision API can label objects and detect text in images (context for jewelry labeling) [80]

Google Cloud Vision API OCR can detect text (context for hallmark extraction) [81]

Google Cloud Vision supports object detection in images (context for identifying jewelry components) [82]

NIST Face Recognition Vendor Test (FRVT) reports error rates for face recognition systems; illustrates CV performance concepts used in virtual try-on (context) [83]

NIST FRVT Part 2 data released includes false positives/false negatives metrics for face recognition (context) [84]

The iPhone X features ARKit with face tracking used for virtual try-on experiences (context) [85]

ARKit supports face tracking (context) [86]

Unity AR Foundation supports face tracking and camera rendering (context) [87]

OpenCV is used for image processing in jewelry inspection workflows (context) [88]

TensorFlow Lite enables on-device ML for real-time vision tasks used in virtual try-on (context) [89]

ONNX provides a format to run ML models across platforms (context) [90]

NVIDIA Metropolis platform supports AI for retail computer vision (context) [91]

Retail computer vision can support fraud detection and quality inspection (context) [92]

Adobe Sensei powers AI in image processing tools used for product photo enhancement (context) [93]

Adobe Sensei uses machine learning for content understanding and automation (context) [93]

Shopify states AI can improve product discovery and recommendations (context) [94]

Personalized product recommendations are driven by machine learning models (context) [95]

Mastercard describes AI in fraud detection using anomaly detection and machine learning (context for payments in jewelry ecom) [96]

Visa uses AI and machine learning for fraud detection (context) [97]

AWS Rekognition supports real-time facial analysis used for AR/try-on mapping concepts (context) [98]

AWS Rekognition can analyze images for faces (context) [99]

AWS Rekognition can detect labels in images for classification (context) [100]

Azure AI Vision supports OCR and object detection (context for hallmark extraction) [101]

Azure AI Vision OCR supports reading text from images (context) [102]

Azure AI Vision supports object detection in images (context) [103]