{"id":34418,"date":"2026-05-06T10:22:00","date_gmt":"2026-05-06T08:22:00","guid":{"rendered":"https:\/\/askme.it\/insights\/synthetic-data-is-changing-how-ai-gets-trained\/"},"modified":"2026-03-26T12:22:42","modified_gmt":"2026-03-26T11:22:42","slug":"synthetic-data-is-changing-how-ai-gets-trained","status":"publish","type":"insights","link":"https:\/\/askme.it\/en\/insights\/synthetic-data-is-changing-how-ai-gets-trained\/","title":{"rendered":"Synthetic data is changing how AI gets trained"},"content":{"rendered":"
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Training an AI model requires large volumes of data. Collecting, labeling, and making them usable is an expensive, slow, and often problematic process from a privacy standpoint. Synthetic data offers an alternative: artificially generated data that replicates the statistical properties of real data without containing personally identifiable information. According to Gartner, this technology is already in the early mainstream phase, with penetration between 5% and 20% of the target market, and adoption is growing across all sectors.<\/p>\n<\/section>\n

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What synthetic data is<\/h2>\n

Synthetic data is a class of data generated artificially rather than obtained through direct observation of the real world. It is used as a proxy for real data across a wide variety of use cases: data anonymization, AI and machine learning model development, cross-organization data sharing, and data monetization.<\/p>\n

The critical point is that it can be generated quickly, cost-effectively, and without containing personally identifiable information or protected health data. This makes it a valuable technology for privacy preservation, an increasingly strict requirement in regulations across many sectors.<\/p>\n<\/section>\n

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Why it’s needed<\/h2>\n

Collecting and labeling real data for AI model development is a task that requires significant time and resources. For some use cases, such as training autonomous vehicle models, collecting real data that covers 100% of edge cases is practically impossible. Synthetic data solves this problem by enabling the generation of rare or dangerous scenarios without the cost and risk of reproducing them in reality.<\/p>\n

Gartner identifies six main areas of impact: avoiding the use of personal data in model training through synthetic variants; reducing costs and timelines in machine learning development; improving model performance with data better suited to the specific purpose; enabling new use cases for which little real data is available; addressing bias and toxicity issues in datasets; and enabling software testing on realistic but private data without regulatory risks.<\/p>\n<\/section>\n

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The sectors where it’s growing fastest<\/h2>\n

In regulated sectors like healthcare and finance, buyer interest is growing rapidly. Synthetic tabular data enables privacy preservation in AI training datasets while complying with data protection regulations. To meet the growing demand for synthetic data for natural language automation training — particularly for chatbots and voice applications — vendors are bringing new solutions to market that expand the supplier landscape and accelerate adoption.<\/p>\n

Synthetic data applications have expanded beyond their original use cases in automotive and computer vision to include data monetization, support for analytics shared with external partners, platform evaluation, and test data development.<\/p>\n<\/section>\n

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The connection to foundation models<\/h2>\n

Large foundation models, including GenAI models, already use synthetic data for their own training. Transformer and diffusion architectures, which form the technological foundations of GenAI, are enabling the generation of increasingly high-quality synthetic data. The emergence of frontier models has highlighted synthetic data as a cost-effective method for building scalable models.<\/p>\n<\/section>\n

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What to keep in mind when adopting<\/h2>\n

Synthetic data has some limitations worth knowing about. Training multimodal models on synthetic data is more complex because multimodal data has varying degrees of quality and formats compared to unimodal data, amplifying challenges related to cost, training time, and output accuracy. Data availability can be limited in some modalities, such as large-scale audio datasets or healthcare images, constraining training quality. Regulations and standards in this area are still evolving and often lag behind technological capabilities.<\/p>\n<\/section>\n

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The takeaway<\/h2>\n

Synthetic data is not a workaround for those who lack real data. It is a tool that solves concrete problems around privacy, cost, availability, and quality of training data. Organizations that integrate it into their AI stack gain more flexibility in model development, lower regulatory risk, and the ability to work on use cases that would be impractical with exclusively real data.<\/p>\n<\/section>\n","protected":false},"excerpt":{"rendered":"

Synthetic data is artificially generated data used to train AI models, preserve privacy, and test systems. How it works, where it’s used, and why adoption is growing in regulated sectors like healthcare and finance.<\/p>\n","protected":false},"featured_media":34420,"menu_order":0,"template":"","insights_category":[579],"insights_tags":[623,729,771,811,855],"class_list":["post-34418","insights","type-insights","status-publish","has-post-thumbnail","hentry","insights_category-technology-and-ai","insights_tags-ai-training","insights_tags-generative-ai","insights_tags-machine-learning-en","insights_tags-privacy-en","insights_tags-synthetic-data"],"acf":[],"_links":{"self":[{"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/insights\/34418","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/insights"}],"about":[{"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/types\/insights"}],"version-history":[{"count":1,"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/insights\/34418\/revisions"}],"predecessor-version":[{"id":34419,"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/insights\/34418\/revisions\/34419"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/media\/34420"}],"wp:attachment":[{"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/media?parent=34418"}],"wp:term":[{"taxonomy":"insights_category","embeddable":true,"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/insights_category?post=34418"},{"taxonomy":"insights_tags","embeddable":true,"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/insights_tags?post=34418"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}