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AI Training Dataset Market worth $9.58 billion by 2029 – Exclusive Report by MarketsandMarkets™

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DELRAY BEACH, Fla., Oct. 24, 2024 /PRNewswire/ — The AI Training Dataset Market is slated to expand from USD 2.82 billion in 2024 to USD 9.58 billion by the year 2029 at a robust CAGR of 27.7% over the forecast period, according to a new report by MarketsandMarkets™.

Browse in-depth TOC on “AI Training Dataset Market”

487 – Tables
66 – Figures
446 – Pages

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Scope of the Report

Report Metrics

Details

Market size available for years

2019–2029

Base year considered

2023

Forecast period

2024–2029

Forecast units

USD (Billion)

Segments covered

Offering, Dataset Creation, Dataset Selling, Type, Data Modality, Annotation Type, End User, and Region

Geographies covered

North America, Europe, Asia Pacific, Middle East & Africa, and Latin America

Companies covered

Google (US), IBM (US), AWS (US), Microsoft (US), NVIDIA (US), Snorkel (US), Gretel (US), Shaip (US), Clickworker (US), Appen (Australia), Nexdata (US), Bitext (US), AIMLEAP (US), Deep Vision Data (US), Cogito Tech (US), Sama (US), Scale AI (US), Lionbridge Technologies (US), Alegion (US), TELUS International (Canada), iMerit (US), Labelbox (US), V7Labs (UK), Defined.ai (US), SuperAnnotate (US), LXT (Canada), Toloka AI (Netherlands), Innodata (US), Kili (France), HumanSignal (US), Superb AI (US), Hugging Face (US), CloudFactory (UK), FileMarket (Hong Kong), TagX (UAE), Roboflow (US), Supervise.ly (Estonia), Encord (UK), TransPerfect (US), Keylabs (Israel), and data.world (US).

The market for AI training datasets has gained substantial traction, with the major catalyst being the need for fair and unbiased datasets. Enterprises are gradually realizing the implications of bias within the dataset. Such bias was highlighted in the case of the Apple Card, where women were given lower credit limits than men due to biased training data embedded in the credit disbursal algorithms. Large language models have also been criticized for making negative stereotypes, such as when OpenAI’s GPT-3 unintentionally linked objectionable words to certain ethnic groups. These cases stress the need for curating well-balanced training datasets that adequately capture real life scenarios; and are inclusive as well. Other factors helping the market growth include the rise of synthetic data to address privacy concerns and scarcity issues, allowing industries like healthcare and autonomous vehicles to simulate rare scenarios. Other pivotal market trends include the progressively increasing use of multimodal datasets, to power virtual assistants and smart gadgets that require the simultaneous processing of text, images and audio.

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By offering, dataset creation segment will account for largest market share in 2024 owing to high demand for accurately labelled datasets.

The market for data labeling & annotation software is expected to hold major market share in 2024, spurred along by the rising need for accurate and precisely labelled data. One of the main factors for growth is the rising demand for context-specific annotations that go beyond basic labeling. Companies like Tempus Labs are using intricately labeled genomic and clinical data to develop precision medicine AI tools, requiring highly detailed and specialized annotations from medical experts. Furthermore, with the introduction of AI-powered annotation automation tools such as SuperAnnotate, the AI annotation is combined with human annotators, creating a human-in-the-loop (HITL) system that enhances workflow efficiency. This has become a popular trend as organizations want to reduce the amount of manual work while maintaining good standards. For example, Aptiv is leveraging such HITL datasets for training advanced driver-assistance systems (ADAS). Another major factor is the progressive increase in the adoption of multimodal data, which require highly accurate and robustly annotated dataset across various modalities.

Rising consumption of high-quality datasets to develop domain-specific AI models will push software & technology providers as the fastest growing end user segment during the forecast period

The software and technology providers segment is experiencing the fastest growth in the AI Training Dataset Market, driven by increasing demand for scalable and high-quality dataset creation solutions. These providers, especially cloud hyperscalers like AWS and Google Cloud, are leveraging massive datasets to enhance AI offerings like voice recognition, computer vision, and natural language processing. Microsoft Azure, for instance, has launched several services like Azure Machine Learning that take advantage of large amounts of data to train advanced AI models. Foundation models providers, such as Cohere and Anthropic, are also investing a lot of resources into the procurement of datasets in order to train and custom design LLMs. Furthermore, IT services companies are developing end-to-end data pipelines for their customers, allowing them to scale AI applications with ethically sourced and unbiased training datasets. The segment’s robust expansion is also aided by the growing use of industry specific datasets for niche applications like AI in cyber security and supply chain analytics.

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North America is set to hold the largest market share in 2024, fueled by a strong regulatory environment and increasing investments in responsible AI deployment

North America has emerged as the largest regional market for AI training dataset, owing to hefty R&D investments being poured into AI. As reported in the 2022 US budget, the federal AI spending of the US government was greater than USD 3.3 billion dollars, which created a demand for quality training datasets. The region’s strong focus on advancing large-scale AI models like GPT-4 by OpenAI and DeepMind’s AlphaFold also showcases the requirement for multimodal and high-quality training datasets to develop such models. Also, the existence of cloud hyperscalers like AWS, Microsoft Azure, and Google Cloud has sped up the provision of scalable AI solutions, including data annotation and management, as part of their cloud services. In Canada, companies like Element AI (acquired by ServiceNow) are creating sophisticated AI models for sectors like finance and logistics, driving the need for reliable datasets to ensure precision and effectiveness.

This trend is also assisted by the North American regulatory landscape, which favors responsible artificial intelligence practices, increasing the market demand for data sets that are both transparent and free from bias. A similar trend is reflected in California’s Automated Decision Systems Accountability Act (AB-13) which seeks to ensure that AI systems are fair and accountable.

Top Key Companies in AI Training Dataset Market:

The major players in the AI Training Dataset Market include Scale AI (US), Appen (Australia), Lionbridge Technologies (US), AWS (US), and Sama (US), along with SMEs and startups such as Snorkel AI (US), V7 Labs (UK), Alegion (US), Toloka AI (US), and iMerit (US).

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MarketsandMarkets™ is a blue ocean alternative in growth consulting and program management, leveraging a man-machine offering to drive supernormal growth for progressive organizations in the B2B space. We have the widest lens on emerging technologies, making us proficient in co-creating supernormal growth for clients.

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Safetyfirst Systems, LLC Provides Notice of Data Security Event

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PARSIPPANY, N.J., July 23, 2026 /PRNewswire/ — Safetyfirst Systems, LLC (“SFS”) is providing notice of a data security event that may involve information relating to certain individuals. While SFS is not aware of any misuse of information associated with this event, it is providing notice to potentially affected individuals out of an abundance of caution.

On January 19, 2026, SFS identified suspicious activity involving a limited portion of its server environment. Upon discovering the activity, SFS quickly took steps to secure its systems, notified federal law enforcement, engaged leading third-party forensic specialists, and performed a detailed investigation into the nature, scope, and impact of the activity. The investigation determined that an unauthorized actor accessed and/or acquired certain files from limited SFS systems between January 16, 2026, and January 19, 2026. SFS then conducted a comprehensive review of the affected files to determine what information may have been involved and identify the individuals to whom the information relates. The review has recently concluded, and SFS is providing this notification to potentially impacted individuals out of an abundance of caution. Although the types of information vary by individual, the affected information may include names, Social Security numbers, and driver’s license numbers.

Protecting the privacy and security of the information entrusted to SFS is a responsibility the company takes very seriously. In response to this event, SFS promptly strengthened security measures, continues to enhance its technical safeguards and monitoring capabilities, and is reviewing existing policies and procedures to further protect against similar incidents in the future. SFS is also providing notice to potentially affected individuals and, where required, appropriate regulatory authorities.

Although SFS is unaware of any misuse of personal information impacted by this event, individuals are encouraged to remain vigilant against events of identity theft by reviewing account statements, explanation of benefits, and monitoring free credit reports for suspicious activity and to detect errors. Any suspicious activity should be reported to the appropriate insurance company, health care provider, or financial institution.

Individuals seeking additional information regarding this event can contact SFS’s dedicated assistance line at 1-833-289-5523 between the hours of 7:00 a.m. to 7:00 p.m. Eastern time, Monday through Friday, excluding holidays. Individuals may also write to SFS at PO Box 101, 3299 US Highway 46, Parsippany, NJ 07054-9998.

 

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SOURCE Safetyfirst Systems, LLC

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Sunrate and Mastercard Release White Paper on Agentic AI and the Future of B2B Global Payments

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SHANGHAI, July 24, 2026 /PRNewswire/ — Sunrate, the global payment and treasury management platform, and Mastercard, a global technology company in the payments industry, unveiled a joint white paper, Beyond Automation: Defining Agentic Global Payments, at the 2026 World Artificial Intelligence Conference (WAIC).

Among the first reports in the payments industry to examine the impact of Agentic AI on B2B cross-border payments, the white paper provides a comprehensive framework for understanding how AI agents are reshaping enterprise payment operations. It proposes that cross-border payments are evolving beyond digitisation and automation into a new stage: Autonomy—where AI agents with reasoning, planning, and execution capabilities can independently orchestrate and optimise end-to-end payment and treasury workflows within defined governance frameworks.

As businesses expand across borders, B2B cross-border payments continue to be constrained by fragmented workflows, disconnected systems, foreign exchange inefficiencies, rising compliance requirements, and complex reconciliation processes. While traditional automation improves individual tasks, the white paper demonstrates that Agentic AI represents a fundamental shift by enabling intelligent agents to coordinate entire payment journeys across systems, counterparties, and approval workflows.

Drawing on Sunrate’s global payment infrastructure and AI-native product capabilities, together with Mastercard’s expertise in secure payment networks and data intelligence, the white paper defines Agentic Global Payments — a new category of AI-native global payment infrastructure built to automate and manage complex enterprise workflows.

The report identifies 16 major pain points across the B2B payment lifecycle and outlines 13 high-value AI use cases spanning supplier onboarding, accounts payable and receivable, virtual commercial cards, payment routing, foreign exchange management, compliance screening, fraud detection, reconciliation, and conversational operational support. It also demonstrates how AI agents can automate complex workflows—from extracting information across multiple document formats and conducting compliance checks to initiating payments, optimising FX execution, and completing reconciliation—while operating within enterprise governance and control frameworks.

The white paper further highlights that trusted adoption of agentic payments depends on more than technological capability. It identifies governance, transparency, security, and ecosystem collaboration as essential foundations for enterprise deployment, supported by frameworks such as Know Your Agent (KYA), payment tokenisation, auditability, and cross-industry interoperability.

Sunrate.AI portfolio currently includes the Payment Agent, FX Agent, Compliance Agent, Onboarding Agent, and Chat Agent, designed to help enterprises automate and optimise critical payment and treasury processes while maintaining compliance and operational control.

Mastercard has also been actively building the foundations for trusted agentic commerce – combining AI capabilities with verifiable authorisation, clear accountability and proven payments security. Its work in this area, including Agent Pay (alongside Agent Pay for Machines) and Verifiable Intent, are proof points in how Mastercard is enabling AI to participate in commerce safely and transparently. 

“Our mission is to make global payments seamless, compliant, and intelligent,” said Paul Meng, Co-founder and CEO of Sunrate. “As businesses continue expanding internationally, AI agents will fundamentally reshape how enterprises manage global payments—enabling smoother capital flows, reducing operational friction, and embedding real-time intelligence into every payment decision. This white paper represents an important step in helping the industry understand how Agentic AI can be deployed responsibly at enterprise scale.”

“Agentic commerce is changing how businesses make and execute payment decisions, but speed without accountability creates new categories of risk,” said Anouska Ladds, Executive Vice President, Commercial & New Payment Flows, Asia Pacific, Mastercard. “As AI starts to act on behalf of businesses, autonomous payment decisions need a clear, auditable chain of identity, intent and action. That’s what allows organisations to delegate with genuine confidence — and what will determine whether agentic commerce scales past pilots.”

Released under WAIC 2026’s theme, “Intelligent Partners, Co-creating the Future,” the white paper provides business leaders with practical guidance on adopting AI-driven payment capabilities, covering implementation approaches, governance considerations, and real-world enterprise applications.

By combining Sunrate’s expertise in global payments and treasury management with Mastercard’s trusted payment infrastructure and network capabilities, the collaboration reflects a shared commitment to accelerating the next generation of intelligent, secure, and autonomous B2B global payments.

Click here to check the white paper.

About Sunrate

Sunrate is a leading global payment and treasury management platform for businesses worldwide. Founded in 2016, Sunrate has enabled companies to operate and scale both locally and globally in 190+ countries and regions with its cutting-edge infrastructure, global network, and unified solutions.

Sunrate operates through offices across key markets, including Singapore, Kuala Lumpur, Jakarta, Hong Kong, Shanghai, and London. The company partners with the top global financial institutions, such as Citibank, Standard Chartered, Barclays, J.P. Morgan. Sunrate is also the principal member of Mastercard and Visa. To learn more about Sunrate, visit https://www.sunrate.com/.

About Mastercard

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re building a resilient economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. 

www.mastercard.com

SOURCE Sunrate

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JAMS Launches AI for Enterprise Job Scheduling: JAX and JAMS MCP

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A new AI agent and an open-standard connector let IT teams query, diagnose, and manage automation in plain language, on the model they choose, with operational data able to stay onshore inside their own network

SYDNEY, July 24, 2026 /PRNewswire/ — JAMS Software, an orchestration solution for scheduled and event-driven automation, today announced the general availability of two AI capabilities for enterprise job scheduling: JAX, an AI agent built into the JAMS Web Client, and JAMS MCP, a connector built on the open Model Context Protocol standard that brings JAMS into external AI coding tools. Both capabilities ship at no additional cost as part of JAMS Web.

Automation environments grow faster than the teams that run them. Jobs multiply across SQL Server, Azure Data Factory, Airflow, SAP, JDE, and Banner, and when one fails, finding the root cause often means searching several consoles at once, frequently outside business hours. At the same time, IT leaders carry pressure to adopt AI while staying accountable for where operational data goes. JAX and JAMS MCP close both gaps together.

Full details on how JAX and JAMS MCP work, including the control model behind every action, are available at jamsscheduler.com/product/ai.

JAX is an AI agent that runs inside the JAMS Web Client. It finds jobs, troubleshoots failures, and answers how-to questions in plain language, with each response grounded in the JAMS user guide and checked against a built-in glossary. JAX acts only when a user asks it to. Reads flow freely, and every write action pauses for the user’s explicit approval before it runs. JAX does not learn between sessions, and conversations are not retained on the server.

JAMS MCP is a connector, built on the open Model Context Protocol standard, that brings JAMS into the AI tools engineering teams already use, including Cursor, VS Code with Copilot, Claude Code, Claude Desktop, and Codex. Users query jobs, investigate failures, and manage runs in plain language without leaving their tool.

Both capabilities run inside the customer’s own network and act as the signed-in user, with that user’s exact JAMS permissions. There is no elevated AI account: whatever a user cannot do in the JAMS interface, JAX and JAMS MCP cannot do on that user’s behalf. Every JAX and MCP operation is recorded in its own dedicated log, and changes made through the JAMS API land in the JAMS audit trail like any other change. Customers choose their own AI model, whether a commercial provider such as OpenAI or Anthropic or a model running entirely on their own hardware, and JAMS never trains on customer data. In the current release, neither feature edits or deletes a job, folder, schedule, or agent definition. For teams that need operational data to stay onshore, JAX runs on a local model entirely inside the customer’s own network, so nothing leaves at all.

“Adopting AI usually means giving something up, most often visibility into where your data goes,” said Pete Hegland, Chief Executive Officer of JAMS Software. “We built JAX and JAMS MCP so that trade does not have to happen. Every action runs as the signed-in user, every change waits for approval, and the model can run on the customer’s own hardware, keeping operational data onshore.”

“For teams across Australia, New Zealand, and Singapore, two things matter: keeping data onshore, and getting answers when a job fails after hours,” said Shayne Cooper, Account Executive for APAC at JAMS Software. “JAX and JAMS MCP address both. The model can run on the customer’s own hardware, and the answer arrives in plain language at the moment it is needed.”

JAX and JAMS MCP are available now to all JAMS Web customers across Australia, New Zealand, and Singapore, with no separate licence, SKU, or additional cost. AI-assisted creation of new jobs and workflows from a plain-language description is on the roadmap for a future release, gated by the same approvals and permissions as every other action.

Learn how JAX and JAMS MCP work at https://jamsscheduler.com/product/ai.

Fast facts

JAX is an AI agent built into the JAMS Web Client for job scheduling and workflow automation.JAMS MCP is a connector built on the open Model Context Protocol standard, for Cursor, VS Code with Copilot, Claude Code, Claude Desktop, and Codex.Both act as the signed-in user, with that user’s exact JAMS permissions, and there is no elevated AI account.Customers choose the AI model, including a local model that runs entirely inside their own network.JAMS never trains on customer data.Both are available now at no additional cost as part of JAMS Web.

About JAMS Software
Founded in 1987, JAMS Software is an orchestration solution that helps IT teams centralize, automate, and manage scheduled and event-driven jobs across complex, hybrid environments. Over 850 customers rely on JAMS to run their automated workloads. JAMS Software, LLC is headquartered at 108 Patriot Drive, Suite A, Middletown, DE 19709.

Media Contact
Bobby Schmidt, Vice President of Marketing
press@jamssoftware.com
800.261.4267

 

 

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