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Generative Artificial Intelligence (AI) Market Size 2025-2029

The generative artificial intelligence (AI) market size is forecast to increase by USD 185.82 billion at a CAGR of 59.4% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for AI-generated content. This trend is being driven by the accelerated deployment of large language models (LLMs), which are capable of generating human-like text, music, and visual content. However, the market faces a notable challenge: the lack of quality data. Despite the promising advancements in AI technology, the availability and quality of data remain a significant obstacle. To effectively train and improve AI models, high-quality, diverse, and representative data are essential. The scarcity and biases in existing data sets can limit the performance and generalizability of AI systems, posing challenges for businesses seeking to capitalize on the market opportunities presented by generative AI.
Companies must prioritize investing in data collection, curation, and ethics to address this challenge and ensure their AI solutions deliver accurate, unbiased, and valuable results. By focusing on data quality, businesses can navigate this challenge and unlock the full potential of generative AI in various industries, including content creation, customer service, and research and development.

What will be the Size of the Generative Artificial Intelligence (AI) Market during the forecast period?

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The market continues to evolve, driven by advancements in foundation models and large language models. These models undergo constant refinement through prompt engineering and model safety measures, ensuring they deliver personalized experiences for various applications. Research and development in open-source models, language modeling, knowledge graph, product design, and audio generation propel innovation. Neural networks, machine learning, and deep learning techniques fuel data analysis, while model fine-tuning and predictive analytics optimize business intelligence. Ethical considerations, responsible AI, and model explainability are integral parts of the ongoing conversation.
Model bias, data privacy, and data security remain critical concerns. Transformer models and conversational AI are transforming customer service, while code generation, image generation, text generation, video generation, and topic modeling expand content creation possibilities. Ongoing research in natural language processing, sentiment analysis, and predictive analytics continues to shape the market landscape.

How is this Generative Artificial Intelligence (AI) Industry segmented?

The generative artificial intelligence (AI) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Software
  Services


Technology

  Transformers
  Generative adversarial networks (GANs)
  Variational autoencoder (VAE)
  Diffusion networks


Application

  Computer Vision
  NLP
  Robotics & Automation
  Content Generation
  Chatbots & Intelligent Virtual Assistants
  Predictive Analytics
  Others


End-Use

  Media & Entertainment
  BFSI
  IT & Telecommunication
  Healthcare
  Automotive & Transportation
  Gaming
  Others


Model

  Large Language Models
  Image & Video Generative Models
  Multi-modal Generative Models
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    Spain
    The Netherlands
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.

Generative Artificial Intelligence (AI) is revolutionizing the tech landscape with its ability to create unique and personalized content. Foundation models, such as GPT-4, employ deep learning techniques to generate human-like text, while large language models fine-tune these models for specific applications. Prompt engineering and model safety are crucial in ensuring accurate and responsible AI usage. Businesses leverage these technologies for various purposes, including content creation, customer service, and product design. Research and development in generative AI is ongoing, with open-source models and transformer models leading the way. Neural networks and deep learning power these models, enabling advanced capabilities like audio generation, data analysis, and predictive analytics.

Natural language processing, sentiment analysis, and conversational AI are essential applications, enhancing business intelligence and customer experiences. Ethica

Japan Diverse Images Dataset

  Overview

This dataset is a comprehensive collection of high-quality images (and some videos) capturing the diverse aspects of Japan, including urban landscapes, natural scenery, historical sites, contemporary art, everyday life, and culinary experiences. It is designed to provide a rich and varied representation of Japan for AI training purposes. Note that the photos were taken by myself in the 2020s, mainly from 2024 to 2025, with some… See the full description on the dataset page: https://huggingface.co/datasets/ThePioneer/japanese-photos-2-with-vids.

Granular, transactional level real purchase data available on an almost real-time basis from our own proprietary consumer panel.

Measurable AI sources its e-receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients focusing primarily in the emerging markets.

Our clients leverage on our datasets to produce actionable consumer insights such as market share analysis, user behavioural traits (e.g. retention rates), average order values, and promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Most of our clients are the fast-growing tech companies, financial institutions, buyside firms, market research agencies, consultancies and acadamia.

This NPPS Data Set is comprised of JGOFS datasets obtained during the JGOFS NPPS as well as the JGOFS related datasets in the North Pacific. All of these data were used in the JGOFS Synthesis Study following the NPPS field programs. The NPTT/NPSG has discussed about the production of the dataset resulting from the NPPS, mainly to establish a basis for the future biogeochemical studies in the North Pacific as a legacy of the JGOFS NPPS. The NPSG asked the Japan Oceanographic Data Center, hosting the JGOFS Japan Data Management Office, to work on it, and the JODC kindly agreed to publish the NPPS Data Set for the worldwide distribution for free. It is our sincere hope, by publishing this NPPS Data Set, to make the oceanographic data and information in the North Pacific, especially in the western North Pacific, more visible and available to the international research community. I would like to thank all of the people who contributed to provide valuable data compiled in this NPPS Data Set.

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Text-To-Speech Market Size 2025-2029

The text-to-speech market size is forecast to increase by USD 3.99 billion, at a CAGR of 14.1% between 2024 and 2029.

The Text-To-Speech (TTS) market is experiencing significant growth, driven primarily by the increasing demand for voice-enabled devices. This trend is expected to continue as technology advances and voice interfaces become more integrated into daily life. Another key driver is the development of AI-based TTS models, which offer improved accuracy and natural-sounding voices. However, regulatory compliance poses a significant challenge for market players. Technology advancements, such as artificial intelligence and machine learning, are revolutionizing the delivery. As governments and regulatory bodies impose stricter guidelines on data privacy and security, TTS providers must ensure their solutions meet these requirements to maintain customer trust and avoid potential legal issues.
The proliferation of high-speed internet, smartphones, and tablets has further fueled market expansion. Companies seeking to capitalize on market opportunities in the TTS space should focus on developing advanced, AI-driven TTS models while prioritizing regulatory compliance to navigate this complex landscape.

What will be the Size of the Text-To-Speech Market during the forecast period?

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The text-to-speech (TTS) market is experiencing significant advancements in speech recognition technology and voice search optimization. Metrics such as speech recognition dataset, voice modulation, and voice cloning play a crucial role in evaluating TTS systems' performance. Speech synthesis evaluation and voice cloning evaluation are essential for ensuring high-quality audiobook narration and call center automation. Voice modulation technology and voice cloning technology are revolutionizing industries like interactive voice response and speech interface design. VPNs and secure platforms are essential to ensure data security. Convolutional neural networks and transformer networks are driving improvements in speech recognition quality and speech synthesis quality. Voice commerce and human-computer interaction are benefiting from these advancements, with voice modulation metrics and speech-to-text metrics playing a key role in voice commerce evaluation.
Audiobook narration and speech-to-text quality are essential for digital signage applications. Vocal training and speech therapy are also utilizing speech-to-text datasets and deep neural networks for data augmentation, enhancing the overall effectiveness of these applications. Voice banking and voice interface design are further expanding the use cases for TTS technology. In summary, the TTS market is witnessing continuous innovation, with advancements in speech recognition, voice modulation, and voice cloning metrics driving improvements in various industries, including call centers, e-commerce, and digital signage.

How is this Text-To-Speech Industry segmented?

The text-to-speech industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Language

  English
  Chinese
  Spanish
  Others


Technology

  Neural TTS
  Concatenative TTS
  Formant-based TTS


Type

  Natural voices
  Synthetic voices


End-user

  Automotive and transportation
  Healthcare
  Consumer Electronics
  Finance
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Language Insights

The english segment is estimated to witness significant growth during the forecast period. The Text-to-Speech (TTS) market is witnessing significant growth, driven by the increasing adoption of English language systems in various sectors. English, as the most widely used language, holds a dominant position in this market due to its extensive application in business, education, media, and technology. TTS solutions for English are developed with a diverse range of voice options, including regional accents such as American, British, and Australian, and multiple speaking styles, from formal and instructional to conversational and expressive. Virtual assistants, customer service platforms, e-learning modules, and accessibility tools are among the major applications of English TTS systems.

The integration of these solutions in these domains reflects both the global reach of the English language and the technological advancements supporting it. Advanced functionalities such as speech recognition, speaker identification, and conversational AI are becoming increasingly common in TTS systems, enhancing their capabilities and usability. Moreover, the integration of TTS t

Table shows estimated geographic structure of RABVs using the Bayesian Tip-Significance testing (BaTS) software tool for Asian dataset 1 based on the estimated tree shown in figure 1a. Countries are assigned to the following states according to their geographic regions. A: Kazakhstan, Mongolia, Russia; B: South Korea; C: China; D: Japan; E: Afghanistan, India, Nepal, Pakistan, Sri Lanka; F: Cambodia, Laos, Myanmar, Thailand, Viet Nam; G: Philippines. Strength of geographical association for these locations across the entire tree is estimated by calculating the association index (AI) and the parsimony score (PS). Low AI and PS values correspond to strong phylogeny-trait associations. The correlation for each specific location is estimated by calculating the associated maximum monophyletic clade size (MC); larger MC values indicate stronger phylogeny-trait associations. The low AI and PS statistics indicate the isolates are mostly clustered according to their geographic origin. The large MC values (compared to the null value) indicate all the defined geographic regions exhibit population subdivision with the exception of region D (Japan) which indicates gene flow from other regions. See Materials and Methods for details.