Demand in particular has grown as more high-quality datasets train large language models such as GPT-3 which reduce biases from AI output that become essential components in industries that rely heavily on AI decision-making capabilities or automation needs.
The market for generative AI data labeling solutions has seen significant expansion, driven by rising demand for high-quality datasets to support
machine learning models. AI-powered tools are providing greater efficiency and accuracy in labeling processes while decreasing human effort and time requirements, making this type of solution especially popular across industries such as
healthcare, automotive and finance.
Recent advances in generative AI are revolutionizing data labeling services. Advanced algorithms now generate labeled data automatically, drastically increasing speed and scalability - revolutionizing sectors that depend heavily on accurate data for training AI models as well as speeding deployment of machine learning applications.
Due to AI's proliferation, businesses are adopting more generative AI solutions for data labeling. This enables more efficient handling of large volumes of data while helping organizations address growing complexity of their data sets. As AI becomes increasingly adopted, demand for automated labeling services will likely continue to expand as well.
Market opportunities in data labeling with artificial intelligence (AI) are substantial. Companies providing AI-powered labeling solutions are taking advantage of industries' need for accurate training data to create tailored industry-specific labeling tools which streamline operations while improving machine learning results.
As per salesforce Salesforce's State of IT report highlights that 86% of IT leaders expect generative AI to play an increasing role in their organizations in the coming months, yet only 67% have prioritized it for implementation within 18 months due to concerns around security, skillset and integration challenges.
Generative AI remains popular among marketers despite mixed feelings on its use. A Salesforce survey of over 1,000 marketers indicates 51% are already using it and 22% intending to do so soon; with 71% expecting it to eliminate busy work and save five hours weekly. As a result, this technology allows more time for strategic tasks than previously.
However, significant reservations remain. Marketers cited accuracy and quality as primary concerns of using AI for marketing. 39% admitted not knowing how to safely utilize it while 43% are uncertain how best to utilize it to maximize value; yet 7/10 marketers feel their employers don't provide adequate training.
The US Generative AI in Data Labelling Solution and Services Market
The US Generative AI in Data Labelling Solution and Services market is expected to reach USD 5.0 billion by the end of 2024 and is projected to grow significantly to an estimated USD 28.7 billion by 2033, with a CAGR of 21.6%.
As organizations generate greater amounts of data, organizations require high-quality labeled datasets to train machine learning models effectively and reliably, driving an increased demand for AI solutions and services that facilitate data labeling efficiently and accurately. Efficient labeling plays an integral part in guaranteeing AI model reliability; efficient labeling services thus play a vital role in maintaining AI models' effectiveness over time.
Semi-supervised and self-supervised learning approaches in generative AI for data labeling have become a rapidly increasing trend in the United States, due to their ability to reduce dependence on extensive labeled datasets by drawing upon both labeled and unlabeled information to increase efficiency while decreasing costs.
Key Takeaways
- Market Growth: It is projected that the global Generative AI in Data Labelling Solution and Services market will experience growth of 81.0 billion at a CAGR of 23.0 % from 2025-2033.
- Market Definition: Generative AI in data labeling solutions and services uses advanced algorithms to automate and enhance the process of annotating and categorizing data.
- Sourcing Type Analysis: In-house sourcing is expected to dominate the Generative AI in Data Labeling Solution and Services Market with a revenue share of 55.3% in 2024.
- Type Analysis: Image/ video-based labeling is expected to dominate the Generative AI in Data Labeling Solution and Services Market, securing over a 40.0% share in 2024
- Labelling Type Analysis: Semi-supervised labeling is dominating the generative AI in the data labeling solution and services market with a revenue share of 39.6% in 2024.
- Vertical Analysis: IT data is expected to dominate the generative AI in data labeling solution and services market with a market share of 29.1% in 2024.
- Regional Analysis: North America is predicted to lead the Generative AI in Data Labelling Solution and Services market globally with a 39.1% market share by 2024.
Use Cases
- Automated Image Annotation: Generative AI's automated annotation technology makes an impactful contribution in industries such as autonomous driving where large volumes of visual data need to be annotated for use with machine learning models. This capability makes Generative AI the go-to solution when annotating large visual datasets like autonomous driving photos for proper training of machine learning models.
- Synthetic Data Generation: Generative AI can produce synthetic datasets that reflect real-world information in situations when real data may be scarce or hard to come by, such as medical imaging. This application of Generative AI provides solutions that mimic reality where available real-world information may not exist or would take too much work and time to gather - for instance in medical imaging scenarios.
- Video Frame Labeling: Generative AI's Video Frame Labeling feature can identify and track objects or people across video frames for easy labeling of individual frames for video surveillance, analytics, and sports analysis applications, where accurate labeling across frames is critical for model training purposes. This functionality also comes in handy during surveillance operations involving several surveillance cameras or when labeling individual videos manually isn't possible due to time constraint considerations.
- Multi-Modal Data Labeling: Generative AI can be employed to label multi-modal datasets combining text, images, and audio that require AI models that need to comprehend information from multiple sources simultaneously.
Market Dynamic
Drivers
Rising Demand for High-Quality Labeled DataThe growing need for high-quality labeled data is a significant driver in the generative AI in data labeling solution and services market. The performance & accuracy of AI and machine learning models are directly influenced by the quality of the input data used during training. As industries like healthcare, finance, and retail increasingly integrate AI into their operations, the demand for precise and reliable labeled data has surged. This need for refined data labeling services is fueling market expansion, as organizations seek dependable solutions to meet their data requirements.
Automation and Cost Efficiency
The automation of data labeling processes through generative AI significantly reduces the time and cost associated with manual labeling. As organizations seek to optimize their AI development processes, the cost efficiency and scalability offered by generative AI become major drivers, fueling the adoption of these solutions across various industries.
Restraints
Data Accuracy and Quality Concerns
Inaccurate labels can lead to poorly trained AI models, resulting in unreliable outcomes, especially in precision-critical industries like healthcare and finance. These concerns may slow the adoption of AI-driven labeling solutions, hindering market growth.
High Costs
The high costs associated with developing and maintaining AI-powered data labeling solutions present a barrier to the growth of the market. Small and medium-sized enterprises (SMEs) may struggle with the substantial financial investment required, limiting their ability to adopt these advanced technologies and slowing overall market expansion.
Opportunities
Integration with Advanced AI Models
The rise of large-scale AI models, such as GPT and BERT, presents a significant opportunity for generative AI in data labeling solutions and services. These advanced models require vast amounts of labeled data for training and fine-tuning, creating demand for efficient and scalable data labeling services. Companies that can integrate generative AI with these models will likely capture a significant share of this growing market.
Expansion into Emerging Markets
Emerging markets are increasingly adopting AI technologies across various industries, creating new opportunities for data labeling solutions. As more companies in these regions embrace AI, there will be a growing need for localized and culturally relevant labeled data. Generative AI can cater to this demand by providing tailored data labeling services, enabling expansion into these markets.
Trends
Shift Towards Semi-Automated and Automated Labeling
The trend towards leveraging semi-automated and fully automated data labeling techniques is gaining traction. By using generative AI models, companies can significantly reduce the time and cost associated with manual data labeling, while improving the scalability and efficiency of the process.
Integration of AI with Human-assisted Systems
Another trend is the growing integration of AI-driven data labeling with assisted systems. This hybrid approach ensures higher accuracy by combining the speed of AI with the nuanced understanding of human annotators, particularly in complex or ambiguous labeling tasks.
Research Scope and Analysis
By Sourcing Type
In-house sourcing is expected to dominate the Generative AI in Data Labeling Solution and Services Market with a revenue share of 55.3% in 2024. The dominance of in-house data labeling is driven by the increasing need for control over data quality and adherence to industry-specific regulations. In sectors like healthcare and finance, companies prefer to keep their data labeling processes in-house to maintain control over their data and mitigate the risks associated with outsourcing, such as data breaches or unauthorized access.
They allow companies to use internal expertise, ensuring that the labeled data is closely aligned with their AI model goals. In-house teams, being closely integrated with the company's operations and goals, possess a deeper understanding of the business context and specific requirements of the AI models being developed. Thus, a demand for high-quality and domain-specific labeled data is a major driving factor for the growth of the market.
It also enables greater flexibility and customization in the labeling process, allowing organizations to adapt quickly to changing project needs and iterate on their AI models with greater agility. Meanwhile, the outsourced type is mainly preferred by companies needing to process large volumes of data quickly and affordably, especially when internal resources are limited.
By Type
Image/ video-based labeling is projected to lead the Generative AI Data Labeling Solution and Services Market in 2024 with over 40.0% share, due to an increasing need for high-quality visual content across various AI applications. Image/video data labeling is crucial in developing and training artificial intelligence models used for autonomous vehicles, healthcare, entertainment, and retail industries, where images/video data play an integral part.
Modern self-driving cars rely heavily on precisely labeled image and video data to interpret their environment and take appropriate actions. Due to the nature and scale of image/video data and annotation requirements, this segment represents one of the largest and resource-heaviest segments within the market.
Text-based data labeling has long been established as more mature than image/ video labeling, requiring less computational power and resources for processing, making them ideal for natural language processing (NLP) applications such as chatbots, sentiment analysis, and machine translation. Text data has demonstrated substantial growth due to its easy nature as well as large pre-labeled datasets which help foster market expansion.
By Labeling Type
Semi-supervised labeling is dominating the generative AI in the data labeling solution and services market with a revenue share of 39.6% in 2024, due to its ability to balance the efficiency of automation with the accuracy of human intervention. This method uses a small amount of labeled data alongside a larger set of unlabeled data which become increasingly popular due to its efficiency and cost-effectiveness.
It allows the training of AI models with minimal manual input, reducing the time & resources needed for data labeling while maintaining high accuracy. As AI models grow more complex & data volumes expand, many organizations are turning to semi-supervised labeling as a preferred solution.
The initial manual labeling of semi-supervised type helps the AI system learn the intricacies of the data, improving its ability to label the rest of the dataset accurately. This labeling is highly adaptable to many applications and industries, making it a versatile choice for organizations.
It can be used across different types of data, including text, images, & video, and is particularly effective in scenarios where fully labeled datasets are scarce or expensive to produce. On the other hand, the manual and automatic labeling segments are predicted to show notable growth in the upcoming year.
Manual Labelling is known for its accuracy & control, making it ideal for specialized or critical applications, but it is limited by scalability & cost. Meanwhile, automatic labeling, powered by AI and machine learning algorithms, is gaining traction but faces challenges related to the need for high-quality training data and potential inaccuracies.
By Vertical
IT data is expected to dominate the generative AI in data labeling solution and services market with a market share of 29.1% in 2024, due to the large volume & complexity of data generated within the IT industry. Organizations across different sectors are increasingly adopting digital technologies that generate massive amounts of unstructured & structured data.
This data needs to be accurately labeled to train generative AI models, which are essential for tasks like data mining, predictive analytics, cybersecurity, and infrastructure management. The dominance of IT Data is also fueled by the rapid growth of cloud computing, big data analytics, & artificial intelligence-driven applications within the industry. These technologies rely heavily on well-labeled datasets to function effectively. Meanwhile, the government sector presents a significant vertical in this market, particularly in areas such as surveillance, national security, and public service optimization.
They require labeled data to develop AI models for monitoring and analyzing vast amounts of information, such as video footage, social media content, and public records. Moreover, financial services depend on data labeling for applications like fraud detection, customer sentiment analysis, & algorithmic trading.
Global Generative AI in Data Labelling Solution and Services Market Report is segmented based on the following
By Sourcing Type
By Type
- Image/Video-Based
- Audio-Based
- Text-Based
By Labeling Type
- Automatic
- Manual
- Semi-Supervised
By Vertical
- IT Data
- Government
- Financial Services
- Healthcare
- Automotive
- Retail
- Others
Regional Analysis
North America is expected to dominate the generative AI in data labeling solutions and services market with a
revenue share of 39.1% in 2024. This dominance is due to this region’s advanced technological infrastructure, high AI adoption rates, and key industry players. Substantial investments in AI research and development, particularly within the United States, where major tech companies are at the forefront of deploying generative AI for data labeling solutions, contribute to the growth of the market.
This region offers a dynamic ecosystem comprising startups, established companies, and research institutions that are consistently driving innovation in AI. The rising demand for high-quality data labeling services is evident as organizations across sectors such as automotive, healthcare, and finance increasingly depend on AI-driven models, which require extensive amounts of accurately labeled data.
North America's regulatory framework is supportive of AI innovation, with policies that promote the development and deployment of AI technologies while ensuring data privacy and security. North America's regulatory framework is supportive of AI innovation, with policies that promote the development and deployment of AI technologies while ensuring data privacy and security.
By Region
North America
Europe
- Germany
- The U.K.
- France
- Italy
- Russia
- Spain
- Benelux
- Nordic
- Rest of Europe
Asia-Pacific
- China
- Japan
- South Korea
- India
- ANZ
- ASEAN
- Rest of Asia-Pacific
Latin America
- Brazil
- Mexico
- Argentina
- Colombia
- Rest of Latin America
Middle East & Africa
- Saudi Arabia
- UAE
- South Africa
- Israel
- Egypt
- Rest of MEA
Competitive Landscape
Global Generative AI in data labeling solution and services market is characterized by intense competition, with numerous large and small players offering software and services to domestic and international markets. The market is currently moderately fragmented and is moving toward a more fragmented state.
Some key players in this market include IBM Corporation, Open AI and DataRobot Major players operating in the market are adopting strategies such as innovating their products and services and engaging in mergers and acquisitions to expand the functionality of their product portfolios and maintain competitiveness.
In 2024, the Generative AI in Data Labeling Solution and Services Market is experiencing notable progress, driven by leading players who are advancing to address the rising need for high-quality, scalable AI training data. Scale AI and DataRobot are at the forefront, offering sophisticated data labeling platforms that are becoming integral to AI development pipelines across different industries.
Some of the prominent players in the Global Generative AI In Data Labelling Solution And Services market are
- Scale AI
- DataRobot
- Amazon Web Services
- OpenAI
- Cognilytica
- Snorkel AI
- Google (DeepMind)
- iMerit
- IBM
- Salesforce
- Alegion
- Microsoft
- Others
Recent Development
- In August 2023, DataRobot, the leader in Value-Driven AI, announced a new generative AI offering, including platform capabilities and applied AI services, to accelerate the path from concept to value with generative AI. This offering uniquely brings both generative and predictive AI capabilities together in the DataRobot AI Platform, delivering an open and end-to-end solution for you to experiment, build, deploy, monitor, and moderate enterprise-grade AI applications and assistants, and drive impact for your business.
- In October 2023, MicroStrategy Incorporated, the largest independent publicly traded analytics and business intelligence company, unveiled MicroStrategy AI. This groundbreaking product allows organizations to incorporate generative AI into their data applications seamlessly which transforms how they analyze data and interact with data insights, making the entire process simpler, faster, and more accessible for everyone.
- In March 2023, Salesforce, the global leader in CRM, launched Einstein GPT, the world’s first generative AI CRM technology, which delivers AI-created content across every sales, service, marketing, commerce, and IT interaction, at hyperscale. With Einstein GPT, Salesforce will transform every customer experience with generative AI.
- In October 2022, Accenture and Google Cloud today announced an expansion of their global partnership through a renewed commitment to growing their respective talent, increasing their joint capabilities, developing new solutions using data and AI, and providing enhanced support to help clients build a strong digital core and reinvent their enterprises on the cloud.
- In October 2022, Appen collaborated with Novatics and offered shared synergies in the Latin American region to expand client offerings. This collaboration is another step in Appen's strategy to provide inclusive data for the AI lifecycle. As part of the collaboration, Novatics will be connecting Appen with key strategic clients in the Latin America region.
Report Details
| Report Characteristics |
| Market Size (2024) |
USD 15.5 Bn |
| Forecast Value (2033) |
USD 99.7 Bn |
| CAGR (2024-2033) |
23.0% |
| Historical Data |
2018 – 2023 |
| The US Market Size (2024) |
USD 5.0 Bn |
| Forecast Data |
2025 – 2033 |
| Base Year |
2023 |
| Estimate Year |
2024 |
| Report Coverage |
Market Revenue Estimation, Market Dynamics, Competitive Landscape, Growth Factors and etc. |
| Segments Covered |
By Sourcing Type (In-House, and Outsourced), By Type (Image/Video-Based, Audio-Based, Text-Based), By Labeling Type (Automatic, Manual, Semi-Supervised), By Vertical (IT Data, Government, Financial Services, Healthcare, Automotive, Retail, and Others) |
| Regional Coverage |
North America – The US and Canada; Europe – Germany, The UK, France, Russia, Spain, Italy, Benelux, Nordic, & Rest of Europe; Asia- Pacific– China, Japan, South Korea, India, ANZ, ASEAN, Rest of APAC; Latin America – Brazil, Mexico, Argentina, Colombia, Rest of Latin America; Middle East & Africa – Saudi Arabia, UAE, South Africa, Turkey, Egypt, Israel, & Rest of MEA
|
| Prominent Players |
Scale AI, DataRobot, Amazon Web Services (AWS), OpenAI, Cognilytica, Snorkel AI, Google (DeepMind), iMerit, IBM, Salesforce, Alegion, Microsoft, and Other Key Players |
| Purchase Options |
We have three licenses to opt for: Single User License (Limited to 1 user), Multi-User License (Up to 5 Users) and Corporate Use License (Unlimited User) along with free report customization equivalent to 0 analyst working days, 3 analysts working days and 5 analysts working days respectively. |