Gitnux/Report 2026

Data Labeling Industry Statistics

With the global data labeling market projected to reach $10.7 billion by 2032 and synthetic data at $12.2 billion by 2032, the real story is what it takes to make labels trustworthy enough for training. The page pairs market momentum with lab-tested gains like a 20% lower error rate from consensus labeling and up to 40% of enterprise AI budgets tied to data preparation, so you can see where cost and quality actually move.
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November 11, 2026Updated
Data Labeling Industry Statistics
Min-ji ParkWritten by Min-ji Park·Edited by Margot Villeneuve·Fact-checked by Rajesh Patel
Published ·Updated
How this report is researched
01Editorial Scope

Before any source is gathered, an editor locks the question, what qualifies, and how the piece will be judged.

02Source Research

Search agents collect the source set and pull each data point out with its provenance attached.

Read our full methodology →

Search agents attach provenance to every extracted figure.

Next review: within 31 days

01 Industry overview

The data labeling industry at a glance

Data labeling is expanding rapidly as generative AI, autonomous systems and regulated machine learning increase demand for human-reviewed training data; the global market is projected to reach $27.6 billion in 2026.

$27.6B[17]
2026 global market size
20.3%[17]
2025–2030 forecast CAGR
33.9%[17]
North America share, 2024
$214.2M[21]
TaskUs AI Services revenue, 2025
Market size
Grand View Research estimates the global data labeling solution and services market at $18.6 billion in 2024 and $27.6 billion in 2026. A narrower AI data labeling and annotation definition from Meticulous Research estimates the 2025 market at $5.12 billion, illustrating the effect of differing industry scopes.[17][55]
Forecast & CAGR
The broad data labeling solution and services market is forecast to reach $57.6 billion by 2030, representing a 20.3% CAGR from 2025 to 2030. The narrower AI data labeling and annotation market is projected to reach $34.5 billion by 2036, growing at an 18.6% CAGR from 2026 to 2036.[17][55]
Market share & regions
North America held 33.9% of global data labeling solution and services revenue in 2024, according to Grand View Research. In TaskUs’s 2025 service-delivery mix, the Philippines represented 54% of revenue, India 13%, the United States 11% and the rest of the world 22%, providing evidence of the industry’s substantial offshore delivery base.[17][56]
Segmentation
The market is commonly segmented by sourcing model, data type, labeling approach, end-use vertical and region. Grand View Research identifies in-house and outsourced sourcing, with text, image/video and audio as major data-type categories; TaskUs describes services spanning large-language-model support, annotation, context relevance, transcription and autonomous-vehicle and robotics data.[17][56]
Key players & concentration
Key providers include Scale AI, Appen, TELUS Digital, TaskUs, Innodata, Sama, Labelbox and AWS Ground Truth, alongside many specialist and regional vendors. Public-company disclosures show meaningful scale among leading providers: TaskUs generated $214.2 million in AI Services revenue in 2025, while Innodata reported $251.7 million of total 2025 revenue, although its figure includes its broader integrated AI-services business.[21][57][16]
Pricing
Published provider benchmarks show wide price dispersion: image classification is listed at about $0.02–$0.10 per image, bounding-box work at roughly $0.03–$1.00 per box, and managed annotation at approximately $3–$60 per hour. AWS-related benchmarks cited in a copyright study list $0.012 per basic image-classification object, $0.036 per boundary-box label and $0.84 per semantic-segmentation label.[58][59]
Trends, drivers & challenges
Growth is being driven by generative AI, foundation-model development, autonomous vehicles, robotics and demand for expert evaluation and reinforcement data. TaskUs’s AI Services revenue rose 58.6% in 2025 to $214.2 million, while industry challenges include quality assurance, worker availability, bias, privacy, copyright provenance and the shift toward automated or synthetic labeling.[21][60]
Consumer behavior & competition
Enterprise buyers increasingly compete on accuracy, turnaround time, security, domain expertise, multilingual coverage and the ability to combine human review with automation. TaskUs ended 2025 with approximately 200 clients, including 21 generating at least $10 million of annual revenue, while its AI Services revenue grew 58.6% year over year, indicating strong demand but also competition for large accounts.[21]
M&A
The sector’s most significant recent strategic transaction was Meta’s $13.8 billion minority investment in Scale AI, recorded by Meta as a 2025 non-marketable equity investment. The transaction valued Scale AI at approximately $29 billion and demonstrated the strategic importance of data-labeling infrastructure to large AI platforms.[61][62]
Regulation
Article 10 of the EU Artificial Intelligence Act requires high-risk AI systems using trained models to rely on governed training, validation and testing datasets. The requirements specifically cover data origin, preparation operations including annotation and labelling, representativeness, error rates, completeness and bias detection or mitigation.[60]
Latest news
In 2026, TaskUs reported $203.0 million of AI Services revenue for the first nine months, up 29.5% from the comparable 2025 period. Innodata reported $72.4 million of third-quarter 2026 revenue and highlighted new enterprise-AI relationships, while the sector continues shifting from basic annotation toward model evaluation, expert response writing and agentic-AI data services.[56][57]

02 Ranking

Top 10 Companies in the Data Labeling Industry

The leading companies in the data labeling industry, ranked on market impact, performance, momentum, innovation and brand popularity.

Updated November 11, 2026 · Next review December 11, 2026

Verdict

Appen and TELUS Digital are tied at the top of Gitnux's 2026 data labeling ranking with an Overall score of 9.1 each.

Ranked by Overall score · How we score ↓

Editor's top 3 picks

Where to start, by need.

  1. #1 Overall · Leader in multilingual AI data annotation

    Appen

    AU

    Ranked #1 · 9.1 · Multilingual AI data annotation

    Why we picked it: It supplies large-scale multilingual labeling and data collection capacity for AI training and evaluation programs.

    See in ranking ↓Visit appen.com ↗

  2. Leader in computer-vision and physical AI labeling

    TELUS Digital

    CA

    Ranked #2 · 9.1 · Computer-vision and physical AI labeling

    Why we picked it: It delivers global AI data, annotation, and evaluation services with an international workforce base.

    See in ranking ↓Visit telusdigital.com ↗

  3. Leader in training data and RLHF services

    Scale AI

    US

    Ranked #3 · 9.1 · Training data and RLHF services

    Why we picked it: It targets training-data, annotation, and RLHF needs used by frontier-model development pipelines.

    See in ranking ↓Visit scale.com ↗

Pick 1 is the #1 company overall. Picks 2 and 3 are the highest-ranked companies in the top 5 that add a new segment.

Full ranking

Sort by
Top 10 companies in the data labeling industry, 2026 – Gitnux ranking
#CompanyOverall ↓Company snapshot
1

Appen

Multilingual AI data annotation · AU

9.1
2

TELUS Digital

Computer-vision and physical AI labeling · CA

9.1
3

Scale AI

Training data and RLHF services · US

9.1
4

Toloka

Expert evaluation and human review · NL

8.6
5

Centific

Human-in-the-loop labeling workflows · US

8.6
6

Sama

Computer-vision and generative-AI annotation · US

8.4
7

iMerit

Multimodal, medical and geospatial annotation · US

8.4
8

Labelbox

Data-labeling workflows and AI data operations · US

8.2
9

LXT

Multilingual data collection and labeling · CA

8.1
10

Defined.ai

AI data collection, annotation and evaluation · US

7.8

How we score

  • Impact (30%): scale and weight in the market (revenue, market share, employees, market position).

Popularity (brand search demand, 15%) counts toward Overall but is not published as a column. Overall leaves out what is not known.

Scale Scores are shown on a 7–10 scale with one decimal; bars start at 7.0.

Cadence Recalculated when new figures are published; next review December 11, 2026.

Independence. Rankings are editorially independent. Scores, ranks, picks and the verdict follow the method above; no company can pay to change them.

Cite this ranking

APA

Min-ji Park. (2026, February 13). Top 10 companies in the data labeling industry. In Data Labeling Industry Statistics. Gitnux. https://gitnux.org/data-labeling-industry-statistics/#top-companies

report visual · Breakdown

Share of the global data annotation and related services market by geography, low estimate

Non-US holds the largest share at 67.1%.

32.9%
US
67.1%
Non-US
source-verifiedstatic.scale.com

04 Statistics

Technology & Innovation

Contributor views tie labeling work to transferable AI skills and longer-run career progression. Visual- and physical-AI teams also expect annotation spending to stay stable or increase in 2026 (78%).

AI in the data labeling industry

  • 69% of surveyed contributors believed their AI skills were transferable across industries in 2024.[1]
  • 200,000 U.S. earning opportunities were directly supported by the data-annotation industry in 2024.[2]
  • $10.3 billion–$19.0 billion is the projected U.S. data-annotation market size for 2030.[2]
  • 9,000 additional full-time U.S. jobs were directly supported by the data-annotation industry in 2024.[2]
  • $5.7 billion was the estimated U.S. GDP contribution of data annotation in 2024.[2]
  • 64% of surveyed contributors believed their AI skills from annotator work offered good future career-progression opportunities in 2024.[1]

Automation in the data labeling industry

  • 78% of surveyed visual- and physical-AI teams expected annotation spending to remain stable or increase over the following year in 2026.[3]
  • 44% of surveyed visual- and physical-AI teams expected annotation spending to increase over the following year in 2026.[3]
  • AI-assisted automation was the third-ranked reason teams expected annotation costs to rise in 2026.[3]
  • 34% of surveyed visual- and physical-AI teams expected annotation spending to remain steady over the following year in 2026.[3]
  • 7% of surveyed visual- and physical-AI teams expected annotation spending to decrease significantly over the following year in 2026.[3]
  • 22% of surveyed visual- and physical-AI teams expected annotation spending to decrease over the following year in 2026.[3]

Digital Transformation in the software and IT services sector (sector benchmark)

  • 54.8% of surveyed organizations reported having a chief digital officer or another executive responsible for digital transformation.[4]
  • 75% of digital leaders globally planned to increase AI-related business expenditure in 2024.[5]
  • 45% of North American technology and business decision-makers planned to adopt AI and machine learning to improve business within 12 to 24 months.[5]
  • 34% of surveyed organizations globally identified replacing or upgrading legacy technology as a top digital-transformation goal in 2024.[5]
  • 72% of digital leaders globally expected to increase technology spending in 2024, compared with 50% of digital laggards, according to a survey of 855 technology and business decision-makers.[5]

Technology Adoption in the software and IT services sector (sector benchmark)

  • More than 50% of surveyed global IT decision-makers were exploring AI use cases or piloting AI technologies in 2024.[5]
  • 57.7% of surveyed organizations reported having a chief information security officer or equivalent in 2024.[4]
  • 51.8% of surveyed organizations reported that AI solutions were somewhat or extremely rare in 2024.[4]
  • 67.6% of IT services were delivered through cloud infrastructure among surveyed organizations in 2024.[4]

Cloud Computing in the software and IT services sector (sector benchmark)

  • 65.5% of EU enterprises using paid cloud services used them for security software applications in 2025.[6]
  • 71.7% of EU enterprises using paid cloud services used them for office software in 2025.[6]
  • 52.7% of EU enterprises with at least 10 employees used paid cloud-computing services in 2025.[6]
  • 85.2% of EU enterprises using paid cloud services used them for email in 2025.[6]
  • 26.1% of EU enterprises using paid cloud services used computing platforms for application development, testing or deployment in 2025.[6]
  • 71.5% of EU enterprises using paid cloud services used them for file storage in 2025.[6]

Big Data in the software and IT services sector (sector benchmark)

  • 14% of enterprises with at least 10 employees across the OECD used big-data analytics on average, based on 2022 data or the latest available year.[7]
  • 3% was the lowest reported enterprise adoption rate for big-data analytics across OECD countries in the latest available data through 2023.[7]
  • 40% was the highest reported enterprise adoption rate for big-data analytics across OECD countries in the latest available data through 2023.[7]

05 Statistics

Data & Analytics

Platform-scale production indicators show the operational link between human hours and annotation throughput. Labelbox reports 200,000+ human hours supporting more than 50 million annotations in one month (production data factory, year not stated).

Data analytics in the data labeling industry

  • 200,000+ human hours supported more than 50 million annotations in one month in Labelbox’s production data factory; year not stated, company platform operations.[8]
  • 5,000+ annual annotation hours were saved by an enterprise multi-agent annotation framework deployed at JPMorgan Chase; 2026, United States financial-services customer.[9]
  • 1 million utterances formed the annotation backlog addressed by JPMorgan Chase’s production multi-agent annotation framework; 2026, United States customer deployment.[9]
  • 50 million+ annotations were created in one month by Labelbox’s data factory, using more than 200,000 human hours; year not stated, company platform operations.[8]

Data Quality in the software and IT services sector (sector benchmark)

  • In 2025, 35% of organizations globally rated their data excellent for consistency and standardization, across surveyed technology and business organizations.[10]
  • In 2025, 46% of organizations globally said they were fully confident in their data quality, across surveyed technology and business organizations.[10]
  • In 2025, 46% of organizations globally rated their data excellent for accuracy and integrity, across surveyed technology and business organizations.[10]
  • In 2024, 95% of enterprise data leaders and practitioners reported a data-quality issue with a direct impact on business outcomes, across companies with at least $1 billion in revenue.[11]
  • In 2025, 43% of organizations globally rated their data excellent for quality and completeness, across surveyed technology and business organizations.[10]
  • In 2025, 34% of organizations globally rated their data excellent for relevance and suitability, across surveyed technology and business organizations.[10]

Data Governance in the software and IT services sector (sector benchmark)

  • In 2024, 61% of supply-management organizations reported having a dedicated data-governance function, based on an Institute for Supply Management survey.[12]
  • In 2024, 32% of global technology leaders prioritized data governance among their data-management areas for the next 12 months.[13]
  • In 2024, 81.7% of organizations reported having no data-governance or catalog solution as part of their governance program, according to a global governance study.[14]
  • In 2024, 18.3% of organizations reported having a data-governance or catalog solution as part of their governance program, according to a global governance study.[14]
  • In 2024, 35% of U.S. technology executives prioritized data governance among their data-management areas for the next 12 months.[13]
  • In 2026, 76% of global data leaders said their company’s AI governance did not completely keep pace with employee use of AI technology.[14]

KPIs in the data labeling industry

  • 6,000 cumulative annotation hours and more than 200,000 processed text strings were reported for Traveloka’s search-data project; year not stated, Indonesia-focused program.[15]
  • 50 million+ monthly annotations and 200,000+ human hours are Labelbox data-factory production indicators used to monitor annotation scale and labor efficiency; year not stated, global platform operations.[8]
  • 180,000 hours was the minimum annual production commitment in Appen’s Nearmap annotation program, an operational capacity KPI for high-volume computer-vision work; year not stated.[16]
  • 50,000+ data units were delivered in Appen’s physical-AI project, a production-volume KPI for egocentric-video annotation and robot-performance evaluation; year not stated, global project.[16]

06 Statistics

Strategy & Growth

Forecast framing and example delivery programs emphasize both market expansion and execution capacity. The broad market is forecast to reach $57.6 billion by 2030 with 20.3% CAGR from 2025 to 2030.

Growth in the data labeling industry

  • $57.6 billion is Grand View Research’s forecast for the global data-labeling solution and services market in 2030.[17]
  • $18.6 billion was the global data-labeling solution and services market size in 2024.[17]
  • 22.48% is the projected compound annual growth rate for hybrid data-labeling sourcing through 2031.[18]
  • 20.3% is the projected compound annual growth rate for the global data-labeling solution and services market from 2025 to 2030.[17]
  • 22.16% is the projected compound annual growth rate for self-supervised and programmatic labeling techniques through 2031.[18]

Differentiation in the data labeling industry

  • 459,968 files underwent quality-assurance review in TELUS Digital’s Indonesian Bersama annotation program.[19]
  • 351,949 images were labeled in TELUS Digital’s Indonesian Bersama annotation program.[19]
  • 1 million-plus registered users and 115,000 active members across about 104 countries support TELUS Digital’s AI-training and annotation offering.[19]
  • 50,000 advanced-degree holders in STEM, healthcare, law and finance were included in TELUS Digital’s AI-training community in 2026.[19]

Mergers and acquisitions in the data labeling industry

  • 2021 was the year TELUS International acquired Playment, adding computer-vision tools and 2D, 3D-image, video and LiDAR services.[19]
  • 2020 was the year TELUS International acquired Lionbridge AI, adding managed training-data and annotation services.[19]
  • $200 million was the announced value of LiveRamp’s 2024 acquisition agreement for Habu, a data-collaboration software provider adjacent to AI-data workflows.[20]
  • $1.225 billion was the purchase price for TELUS International’s acquisition of WillowTree, completed in January 2023.[19]

Strategic partnerships in the data labeling industry

  • 48,200 employees worked across 28 locations in 12 countries at TaskUs when it announced its V7 data-labeling partnership.[21]
  • 670,000-plus annotators were included in the TaskUs–V7 partnership’s combined enterprise data-labeling workforce in 2024.[21]
  • 50–55 clients were served by TELUS International’s data-annotation and labeling practice in the first half of 2023, according to Everest Group.[19]
  • 25,000-plus medical-expert annotators were included in the TaskUs–V7 partnership’s enterprise annotation offering in 2024.[21]
  • 1 million-plus global workers were available to TELUS International through its annotation community for scaling project demand in the 2023 vendor assessment.[19]

07 Statistics

People & Workforce

Workforce metrics show education mix, remote and hybrid prevalence, and training durations tied to annotation roles. Sama’s AI 101 program lasts 10 days for data-training agents.

Hiring in the data labeling industry

  • 1% of surveyed U.S. data annotators reported vocational training as their highest education level in 2024.[22]
  • 84% of surveyed U.S. data annotators held at least a bachelor’s degree in 2024.[2]
  • 94% of surveyed U.S. data annotators reported that supplemental earnings attracted them to annotation opportunities in 2024.[2]
  • 44% of surveyed U.S. data annotators held a bachelor’s degree in 2024.[22]
  • 7% of surveyed U.S. data annotators had completed high school as their highest level of education in 2024.[22]

Remote Work in the software and IT services sector (sector benchmark)

  • 34.8% of global software and IT services respondents worked remotely in 2026, according to the Stack Overflow Developer Survey.[23]
  • 10% of technology-services respondents worked under fully flexible arrangements in 2025, allowing employees substantial control over remote work.[24]
  • 48% of U.S. software and IT professionals surveyed worked remotely in 2026, compared with 21% in France.[23]
  • 6% of global respondents worked fully remotely in 2025, defined as spending less than one day per week in the office.[24]
  • 84% of technology-services employers agreed in 2025 that the ability to work remotely is important for employee retention.[24]

Hybrid Work in the software and IT services sector (sector benchmark)

  • 56% of IT employees worked in hybrid arrangements in 2025, while 31% were fully remote and 13% attended the office daily.[25]
  • 47% of global respondents worked in hybrid arrangements in 2025, down from 63% in Cisco’s 2022 study.[24]
  • 61% of U.S. employees with remote-capable jobs preferred hybrid work in November 2025, compared with 33% preferring fully remote work.[26]
  • 52% of U.S. employees with remote-capable jobs worked in hybrid arrangements in May 2026, according to Gallup.[26]
  • 55% of technology-services respondents worked in a hybrid arrangement in 2025, the highest share among industries measured by Cisco.[24]

Upskilling in the data labeling industry

  • 39% of surveyed data annotators rated communication or collaboration skills as important in 2024.[22]
  • 74% of surveyed data annotators rated problem-solving or critical thinking as important in 2024.[22]
  • 41% of surveyed data annotators rated comfort using technology and advanced software as important in 2024.[22]
  • 10 days is the duration of Sama’s AI 101 workforce-development program for data-training agents.[27]
  • 56% of surveyed data annotators rated time management and meeting deadlines as important in 2024.[22]

Employee Engagement in the software and IT services sector (sector benchmark)

  • 54% of exclusively remote U.S. employees were thriving in May 2026, compared with 52% of hybrid employees and 49% of on-site employees with remote-capable jobs.[26]
  • 22.2% of global software and IT respondents were happy with their current professional role in 2026, while 32.6% were unhappy.[23]
  • 74% of respondents approved of fully flexible hybrid policies in 2025, the highest positive sentiment among the policy types Cisco measured.[24]
  • 37% of exclusively remote U.S. employees were engaged in May 2026, compared with 34% of hybrid employees and 29% of on-site employees with remote-capable jobs.[26]
  • 92% of employees ranked collaboration and community as very important aspects of office culture in Cisco’s 2025 global study.[24]

DEI in the data labeling industry

  • 214 active annotators worked with Humans in the Loop across all locations in 2023.[28]
  • 220 people graduated from Humans in the Loop instructor-led courses in 2023.[28]
  • 60% higher average earnings were reported for women trained and included in Sama’s hiring pool than for women in the control group in the MIT evaluation.[27]
  • 106 annotation projects were completed by Humans in the Loop in 2023.[28]
  • 44 training sessions were delivered by Humans in the Loop in 2023.[28]

08 Statistics

Customer & Sales

Reported customer outcomes highlight measurable effects from annotated data in multilingual settings. One example cites 95% transcription accuracy with 10,000+ audio hours completed globally.

Customer experience in the data labeling industry

  • NPS moved above 30 across all three markets for the first time in two years after a bank used annotated customer-support conversations to improve sentiment detection.[29]
  • 90% categorization accuracy was achieved when an LLM categorized 6.5 million customer reviews monthly in more than 30 languages using human validation.[30]
  • 18% average model-recall uplift was reported after an online bank integrated 1,000 newly annotated samples in each of three languages.[29]
  • 4.55 out of 5 was the average quality score for nearly 400,000 evaluations of 59 new features across 11 products in 50-plus languages.[30]
  • 95% transcription accuracy was maintained for a global IT company’s multilingual virtual-assistant data-labeling project, with 10,000-plus audio hours completed globally.[19]

Customer retention in the data labeling industry

  • 98.5% customer retention was achieved across more than 100,000 accounts in a machine-learning churn-prediction case study.[31]
  • 16% churn reduction and $1.8 million in incremental revenue followed deployment of a data-driven customer-retention framework covering approximately 15,000 customers.[32]
  • Almost 20% reduction in company-wide churn was attributed to improved customer-service targeting after an online bank used annotated support conversations.[29]

Customer acquisition in the data labeling industry

  • 650 clients in approximately 50 countries were supported by TELUS Digital in 2024 across its AI, customer-experience and digital-services operations.[19]
  • 438 clients were served by TELUS Digital’s Americas customer-experience-management business in 2023.[19]
  • More than 2,000 participants were engaged within a few hours for a Lionbridge AI customer’s data-collection project.[30]
  • 420 clients were served by TELUS Digital’s Americas customer-experience-management business in 2022.[19]

Lead generation in the data labeling industry

  • 50% of surveyed organizations used specialized data-labeling service providers when scaling AI training-data preparation.[33]
  • 5% of organizations cited managing data-labeling vendors as a challenge in Scale AI’s survey of AI organizations preparing training data.[33]

09 Statistics

Marketing & Brand

Brand-adjacent signals here reflect how training data and annotation capacity gets packaged inside broader digital efforts. It also includes usage figures such as 69% relying on unstructured text, image, audio, or video data to train labeled AI.

Marketing in the data labeling industry

  • $1.243 billion was TELUS Digital’s Americas customer-experience-management revenue in 2022.[19]
  • $2.7 billion was TELUS Digital’s annual revenue in 2023 across its AI, customer-experience and digital-services businesses.[19]
  • $1.364 billion was TELUS Digital’s Americas customer-experience-management revenue in 2023.[19]
  • $1.309 billion was TELUS Digital’s revenue in the first half of 2024 across its digital-services operations.[19]

Digital marketing in the data labeling industry

  • 50% of surveyed AI organizations used data-labeling service providers to prepare training data, indicating managed annotation’s role in digital-AI development workflows in 2024.[33]
  • 69% of surveyed organizations relied on unstructured text, image, audio or video data to train AI models requiring labeling or annotation.[33]

Branding in the data labeling industry

  • 5,000 hours of annotated video were delivered in one month for a major social-media company training an emotion-aware language model.[30]
  • 28,000 AI-training data points were delivered within one week for a customer after an initially requested 20,000-point batch was completed in four days.[30]

Social media in the data labeling industry

  • 3,500 U.S.-based participants were recruited for a social-media company’s emotion-focused AI training dataset.[30]
  • 5,000 hours of video were annotated for a major social-media company to train an LLM to understand emotions, relationships and personality.[30]

Influencer Marketing in the software and IT services sector (sector benchmark)

  • Across B2B companies, 43% of marketers reported outstanding results from their influencer programs in the 2025 B2B Influencer Marketing Report.[34]
  • Across B2B companies, another 29% of marketing organizations planned to adopt influencer marketing within the next year in 2025, based on an Ipsos survey of 1,500 marketing leaders.[35]
  • Across B2B companies, 58% of marketing teams reported using an always-on influencer marketing approach in the 2025 B2B Influencer Marketing Report.[34]
  • Across technology companies, 27% of marketers reported dedicated but flat budgets for creator or influencer marketing in 2026, according to Nielsen’s global marketing survey.[36]
  • Across technology companies, 64% of marketers reported dedicated and growing budgets for creator or influencer marketing in 2026, according to Nielsen’s global marketing survey.[36]

10 Statistics

Operations & Execution

Operational execution data connects labeling quality and turnaround timing to managed workflows. Scale AI notes that 99% or higher labeling quality on large datasets may require more than 1,000 data labelers.

Process optimization in the data labeling industry

  • 50% of surveyed organizations used data-labeling service providers for AI training data in 2024.[33]
  • 40% of managed-labeling users received high-quality labeled data within one week to one month in 2024.[33]
  • 29% of surveyed organizations used crowdsourced labelers for AI training data in 2024.[33]
  • 27% of surveyed organizations used business-process-outsourcing labelers for AI training data in 2024.[33]
  • 1 million-plus contributors across more than 180 countries formed Appen’s global data-labeling crowd in 2021.[16]

Operational efficiency in the data labeling industry

  • 5% of surveyed AI organizations identified managing data-labeling vendors as a top training-data challenge in 2024.[33]
  • 18% of surveyed AI organizations identified curating data for labeling as a top training-data challenge in 2024.[33]
  • 74,000 employees worked across about 68 delivery centers in 32 countries at TELUS Digital in the first half of 2024.[19]
  • 18% of surveyed AI organizations identified managing data labelers as a top training-data challenge in 2024.[33]
  • 14% of surveyed AI organizations identified compliance, including data privacy, as a top training-data challenge in 2024.[33]

Quality management in the data labeling industry

  • 43% of chief operations officers identified data-quality issues as their most significant data priority in IBM’s 2025 research on AI data quality.[37]
  • 69% of surveyed organizations used unstructured data types whose labeling quality affects AI model training in 2024.[33]
  • 35% of surveyed organizations ranked data quality as the largest obstacle to acquiring AI training data in 2024.[33]
  • 24% of surveyed AI organizations ranked labeling quality as their top challenge in preparing training data in 2024.[33]
  • 99% or higher labeling quality on large datasets may require more than 1,000 data labelers on a project, according to Scale AI’s guidance.[1]

Supply chain in the data labeling industry

  • BPO suppliers can subcontract AI annotation work to companies and regions with lower labor costs, according to a 2025 study of China and U.S. data-labeling industries.[38]
  • More than 1 million Appen contributors supplied data-collection and labeling capacity across over 180 countries in 2021.[16]
  • Welo Data accounted for 26.6% of open data-annotation and labeling roles on October 17, 2026.[39]
  • Open data-annotation and labeling roles attracted 2.54 times the application clicks of the average AI-training role during September–October 2026.[39]
  • TELUS Digital supported approximately 650 clients across about 50 languages in the first half of 2024.[19]

Outsourcing / Offshoring in the software and IT services sector (sector benchmark)

  • Across EU enterprises sourcing business functions internationally during 2021–2023, 28.7% moved information and communication technology services abroad.[6]
  • Across German digital-experience service providers, 13.2% of services for customer projects were delivered nearshore in 2024.[40]
  • Among German IT-service generalists, 19.5% of digital-experience services for customer projects were delivered nearshore in 2024.[40]
  • Among German IT-service generalists, 10.2% of digital-experience services for customer projects were delivered offshore in 2024.[40]
  • Across German digital-experience service providers, 7.6% of services for customer projects were delivered offshore in 2024.[40]

11 Statistics

Finance & Pricing

Pricing dispersion and role rates show unit economics vary widely by task type and coverage. Published examples include about $0.02–$0.10 per image and roughly $3–$60 per hour for managed annotation.

Pricing strategy in the data labeling industry

  • $4.40 per record was the listed market rate for medical and clinical data in November 2026.[41]
  • $0.208 per image was the listed market rate for image data in November 2026.[41]
  • $30 per hour was the median advertised rate for data-annotation and labeling roles on October 17, 2026.[39]
  • Data-annotation and labeling roles paid 54% less than the $65-per-hour median advertised rate for all AI-training work in September–October 2026.[39]
  • $150 per hour was reported as the rate for expert finance-modeling and prompt work supplied to OpenAI in October 2025.[42]

Cost reduction in the data labeling industry

  • $2.7 billion to $5.0 billion was the estimated U.S. data-annotation market size in 2024, indicating the scale of spending addressable by efficiency measures.[2]
  • 5% to 10% of AI software cost was estimated to represent data annotation and related human-data work in 2023.[43]
  • 40% of managed-labeling users received high-quality labeled data within one week to one month, reducing long-cycle delivery exposure in 2024.[33]
  • $10.3 billion to $19.0 billion was Oxford Economics’ projected U.S. data-annotation market size for 2030.[2]
  • $8.2 billion to $15.1 billion was the estimated global data-annotation and related-services market size in 2024.[22]

Profitability in the data labeling industry

  • 30% to 40% was the estimated retained share of a $500 million gross run rate for micro1 in August 2026.[42]
  • 39.3% was Appen’s gross margin in fiscal 2024, compared with 40.3% in fiscal 2025.[42]
  • 40.3% was Appen’s gross margin in fiscal 2025, compared with 39.3% in fiscal 2024.[42]
  • 59.7% of Appen revenue represented crowd expenses in fiscal 2025.[42]
  • 27% was the reported gross margin for Mercor in 2025.[42]

Financial planning in the data labeling industry

  • $10.3 billion to $19.0 billion was the projected U.S. data-annotation market revenue range for 2030.[22]
  • 9,000 additional full-time employee jobs were directly supported by the U.S. data-annotation industry in 2024.[2]
  • $5.7 billion was the estimated contribution of the U.S. data-annotation industry to GDP in 2024.[2]
  • Nearly 200,000 flexible earning opportunities were directly supported by the U.S. data-annotation industry in 2024.[2]
  • $8.2 billion to $15.1 billion was the estimated global data-annotation and related-services market revenue range in 2024.[22]

12 Statistics

Product & Service

Productization trends show annotation workflows expanding into physical-AI prototyping and model-foundry-style task reduction. Labelbox Model Foundry is reported to achieve up to 88% reduction in human labeling for complex tasks.

Product development in the data labeling industry

  • 100,000+ production hours had been completed in Scale AI’s physical-AI prototyping laboratory for robotics data development; year not stated, United States and global contributors.[1]
  • 88% is the maximum reduction in human labeling reported in early Fortune 500 customer tests of Labelbox Model Foundry for complex data-labeling tasks; year not stated, global customers.[8]
  • 76 product combinations became searchable with one click after Traveloka’s 200,000-text-string annotation project conducted by Lionbridge AI; year not stated, Indonesia and regional-language data.[15]
  • 50,000+ physical-AI data units were delivered by Appen for a frontier robotics laboratory, supporting robot-performance evaluation and model development; year not stated, global project.[16]

Product lifecycle in the data labeling industry

  • 6,000 cumulative annotation hours were used to parse more than 200,000 text strings in Traveloka’s search-improvement project; year not stated, Indonesia-focused language data.[15]
  • 7 lifecycle stages are described in Appen’s data-collection workflow, from analyzing requirements through designing, engaging, collecting, uploading, annotating and packaging data; year not stated, global operations.[44]
  • 30+ years of AI-training-data experience are reported by Appen, covering speech data, search relevance, image annotation, LLM training and frontier-model work; current company figure, global.[16]
  • 2020–2022 marks Appen’s reported expansion into LLMs and reinforcement learning from human feedback, followed by frontier and agentic-AI data work from 2024 onward; global company history.[16]

After-sales in the data labeling industry

  • 1 million+ AI-community contributors support TELUS Digital customers with collecting, enhancing, training, translating and localizing content; current company figure, global.[45]
  • 320+ customers were served worldwide by Appen in 2021, supporting thousands of AI projects and ongoing training-data programs; global company figure.[16]
  • 180,000 hours was Nearmap’s minimum annual commitment for Appen computer-vision annotation services, with staffing flexed during peak demand; year not stated, global customer program.[16]
  • 500+ languages and dialects are handled by TELUS Digital AI Data Solutions for customers improving machine-learning systems; year not stated, global operations.[19]

Value proposition in the data labeling industry

  • 1 million+ vetted contributors, 170+ countries and 235+ languages are reported by Appen as its global training-data supply base; current company figures.[16]
  • 80% of the world’s leading LLM builders are reported as Appen customers, reflecting the company’s value proposition for frontier-model training data; current global figure.[16]
  • 28 countries were covered by TELUS International operations when its Ireland AI-data site opened, supporting regional and global customer delivery; 2022, global company operations.[19]
  • 1 million+ crowd contractors across 170+ countries and 235 languages were cited by Appen as supporting scalable, diverse annotation and data collection; year not stated.[46]

13 Statistics

Sustainability & Ethics

Sustainability reporting includes detailed emissions inventories tied to annotation operations. Appen reports 14,588 tCO2e combined Scope 1, 2 and 3 emissions in 2025.

Sustainability in the data labeling industry

  • 50% of Appen directors were women in 2025.[47]
  • 54% of Appen’s senior leadership team were women in 2025.[47]
  • 58% of Appen employees were women in 2025.[47]
  • More than 1 million contractors across over 200 countries supported Appen’s data-labeling operations in 2025.[48]
  • 4 out of 10 was Appen’s Fairwork AI Supply Chain score for its Philippine BPO operations.[49]

ESG in the data labeling industry

  • 99.9% was Appen’s reported system availability in each year from 2022 through 2025.[47]
  • 2,647 tCO2e were Appen’s 2025 location-based Scope 2 emissions from electricity.[50]
  • 11,929 tCO2e were Appen’s Scope 3 emissions in 2025.[50]
  • 2,622 tCO2e were Appen’s 2025 market-based Scope 2 emissions.[50]
  • 14,588 tCO2e were Appen’s combined Scope 1, 2 and 3 emissions in 2025.[50]

Ethics in the data labeling industry

  • 14 in-depth worker interviews informed Fairwork’s 2025 assessment of Appen’s Philippine BPO center.[49]
  • 1 Engagement Forum was established by Appen for worker participation in policy and governance discussions.[49]
  • 1 Content Moderator Wellness Programme was launched by Appen to address task-specific worker risks.[49]
  • 1 independent worker survey and 14 interviews were used in Fairwork’s 2025 Appen assessment.[49]
  • 15 changes were introduced by Appen after the Fairwork assessment to improve worker standards.[49]

CSR in the data labeling industry

  • 200+ countries supplied contributors to Appen’s data-labeling and AI-training activities in 2025.[48]
  • 1 million+ skilled contractors formed Appen’s global crowd supporting AI data services in 2025.[48]
  • 2 priority Sustainable Development Goals were identified by Appen in 2025: Decent Work and Economic Growth, and Reduced Inequalities.[48]
  • 58% of Appen’s employees were women in 2025, reflecting its reported workforce-diversity program.[47]
  • 500+ languages were covered by Appen’s crowd-based data services in 2025.[48]

Carbon footprint in the data labeling industry

  • 502.9 tCO2e came from Appen’s business travel in its 2025 Scope 3 inventory.[47]
  • 12,509 tCO2e were Appen’s Scope 3 emissions in 2024.[50]
  • 2,647 tCO2e were Appen’s 2025 location-based Scope 2 emissions.[50]
  • 1,945 tCO2e were Appen’s 2024 location-based Scope 2 emissions.[50]
  • 14,588 tCO2e was Appen’s total Scope 1, 2 and 3 carbon footprint in 2025.[50]

15 Statistics

Globalization & Expansion

Global delivery models remain central, supported by large international workforces and multi-language coverage. TaskUs service delivery mix lists the Philippines at 54% of revenue in 2025, with India at 13% and the United States at 11%.

Globalization in the data labeling industry

  • 1 million-plus skilled contractors in more than 200 countries support Appen’s global AI-data operations, according to its 2025 annual report.[47]
  • 35.0% of global data-labeling solution and services revenue came from North America in 2024, the largest regional share.[17]
  • 500-plus AI-data languages and dialects are supported by TELUS Digital across its global data-services network in 2026.[19]
  • 82,000-plus team members across more than 35 countries support TELUS Digital’s AI-data and digital-delivery operations in 2026.[19]

Cultural adaptation in the data labeling industry

  • 4 languages—English, Persian, Indonesian and Arabic—were used for speech-to-text annotation in TELUS Digital’s Indonesian refugee-workforce program.[19]
  • 7 nationalities were represented in TELUS Digital’s Indonesian Bersama annotation workforce, which delivered more than 2,700 work hours weekly.[19]
  • 500-plus languages and dialects are covered by TELUS Digital’s AI-data services, supporting localization and cultural variation in model training.[19]

Supplier management in the data labeling industry

  • 60–90 workers formed TELUS Digital’s Indonesian Bersama annotation roster, supplying more than 2,700 hours of work per week across over 50 projects.[19]
  • 25,000 additional full-time jobs were supported through suppliers and local spending connected to the U.S. data-annotation industry in 2024.[22]
  • 2.7 was the U.S. data-annotation industry’s GDP multiplier in 2024, including indirect economic effects through suppliers.[22]
  • 80% was the retention rate reported for the workforce in TELUS Digital’s Indonesian Bersama annotation program.[19]

Stakeholder management in the data labeling industry

  • 91% of Scale contributors identified flexibility as a core reason for doing data-annotation work in the U.S. study covering 2024 activity.[1]
  • 87% of Scale contributors used supplemental data-annotation income to offset lifestyle costs in the U.S. study covering 2024 activity.[1]
  • 20% of contributors in Scale’s U.S. study reported living with a disability, highlighting an important workforce stakeholder group.[1]
  • 94% of Scale contributors said additional income attracted them to data-annotation work in the U.S. study covering 2024 activity.[1]

Distribution channels in the data labeling industry

  • 54.19% of global data-labeling market revenue came from computer-vision applications in 2025, according to Mordor Intelligence.[18]
  • 36.26% of global data-labeling market revenue came from image data in 2025, according to Mordor Intelligence.[18]
  • 63.43% of the global data-labeling market used outsourced sourcing in 2025, according to Mordor Intelligence.[18]
  • 42.31% of the global data-labeling market used manual labeling in 2025, according to Mordor Intelligence.[18]

Key questions about the Data Labeling Industry

What technology shift is changing how buyers use labeling in 2026?
Buyers increasingly demand AI evaluation rather than basic annotation, alongside foundation-model development and expert evaluation needs. Demand also shifts toward multimodal data and physical AI use cases.
How is the market typically structured around sourcing and data types?
The market is commonly segmented by sourcing model, data type, labeling approach, end-use vertical, and region. Grand View Research identifies in-house and outsourced sourcing, with text, image/video, and audio as major data-type categories.
Which companies lead the data labeling industry?
Appen and TELUS Digital are tied at the top of Gitnux's 2026 data labeling ranking with an Overall score of 9.1 each.
What compliance driver affects dataset governance and labeling practices?
Article 10 of the EU AI Act requires governed training, validation, and testing datasets for high-risk AI systems using trained models. It covers data origin, preparation operations including annotation and labelling, representativeness, and bias detection or mitigation.
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
Min-ji Park. (2026, February 13). Data Labeling Industry Statistics. Gitnux. https://gitnux.org/data-labeling-industry-statistics
MLA
Min-ji Park. "Data Labeling Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/data-labeling-industry-statistics.
Chicago
Min-ji Park. 2026. "Data Labeling Industry Statistics." Gitnux. https://gitnux.org/data-labeling-industry-statistics.

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