Generative AI Market worth $51.8 billion by 2028, growing at a CAGR of 35.6%: Report by MarketsandMarkets™

Chicago, Aug. 21, 2023 (GLOBE NEWSWIRE) -- The global Generative AI Market size is anticipated to grow at a CAGR of  35.6% during the forecast period, from USD 11.3 billion in 2023 to USD 51.8 billion by 2028, according to a new report by MarketsandMarkets™.  The Generative AI Market is expected to grow at a significant rate during the forecast period, owing to various business drivers. The innovation of cloud storage enabling easy access to data, evolution of AI and deep learning and rise in the era of content creation and creative applications are some factors guiding the growth of the Generative AI Market. 

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Scope of the Report

Generative technology, a new form of human-machine partnership that leverages deep learning engines to generate content and ideas like humans, is witnessing rapid investments. Jasper, a generative tech company, reportedly achieved revenue of nearly USD 100 million and a valuation of USD 1.5 billion within 18 months of launching. OpenAI, the company behind GPT-3 and other AI models, is said to be raising capital at a valuation of several billion dollars. Additionally, Anthropic, a prominent AI model builder, has raised over USD 700 million. These developments indicate a growing interest in and potential for generative technology in the market. 

Based on offering, the Generative AI Market is segmented into software and services. The software segment is expected to garner a higher revenue during the forecast period, attributed to growing importance of generative AI software among creative professionals to develop models for image and video generation, style transfer, and text-to-image synthesis to enhance customer experience is expected to drive the demand for generative AI software. The services segment is projected to witness a higher growth rate during the forecast period.
  
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Based on application, the segment has been bifurcated into natural language generation (MLG), ML-based predictive modeling, computer vision, robotics and automation, speech recognition, music and art generation, education and training, finance and accounting, legal, customer service and support, augmented reality (AR) and virtual reality (VR), synthetic data generation, game design and character production, 3D modeling and reconstruction, and video editing/generation. The computer vision segment is expected to garner a higher revenue during the forecast period. The growth of computer vision in generative AI is driven by the increasing demand for intelligent automation and decision-making capabilities in various industries, such as healthcare, automotive, and retail. 

The Generative AI Market has been segmented into five regions: North America, Europe, the Middle East and Africa, the Asia Pacific, and Latin America. North America is estimated to account for the largest market share during the forecast period, while the Asia Pacific region is expected to grow at the highest growth rate. Generative AI software and services in North America are highly effective across most organizations and verticals. Asia Pacific is gradually advancing toward incorporating generative AI within its region. Europe, the Middle East, and Africa, followed by Latin America, are showing a substantial rise in generative AI software and services. 

Top Key Companies in Generative AI Market:

Some major players in the Generative AI Market include Microsoft (US), IBM (US), Google (US), AWS (US), META (US), Adobe (US), OpenAI (US). Some startups include Paige.AI (US), Riffusion (US), Play.ht (India), Speechify (US), Media.io (France), Midjourney (US), FireFlies (US), Brandmark.io (Netherlands), Morphis Technologies (Portugal).

Top Trends in Global Generative AI Market:

• In a variety of industries, including healthcare and finance as well as art and design, generative AI is being used more and more. Innovation and the investigation of novel possibilities were being driven by generative models' capacity to produce original ideas for concepts, designs, and solutions.
• Generative AI has been applied in the arts and humanities to produce poetry, music, and visual art. Through the use of this technology, producers and artists might work with AI to create original and cutting-edge material.
• In order to create numerous design iterations and prototypes, generative AI was used in product design. This sped up the design iteration procedure and enabled designers to investigate a wide range of options prior to completing a product.
• Generative AI was used in the healthcare industry for tasks like medication discovery, imaging analysis, and personalised treatment planning. AI algorithms were able to forecast patient outcomes, improve medical imaging, and create molecular structures.
• NLP was advancing thanks to generative AI, making it possible to produce text that is more coherent and contextually relevant. Generic model-driven chatbots and virtual assistants were getting better at having interactions that sounded more human.
• In order to allow artists and designers to develop distinctive visual styles, generative AI was utilised to transfer styles from one piece of material to another. It was also used to improve films and photos of low resolution.
• An active field of study and development was generative adversarial networks (GANs). For GANs to produce realistic and high-quality content, such as photographs, the generator and discriminator neural networks had to be trained.
• In order to aid writers, designers, and artists in their creative processes, generative AI-powered technologies are being incorporated into creative software packages.

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Key Industry Development:

• Significant traction was achieved in the use of generative AI to produce original works of art, musical compositions, and other creative stuff. AI algorithms and creatives worked together to develop original and cutting-edge works in a variety of media.
• Healthcare has benefited significantly from generative AI thanks to its assistance in disease detection, drug research, and medical picture analysis. To create molecular structures, improve medical imaging, and forecast patient outcomes, AI models were used.
• By using style transfer techniques, designers were able to change the visual aesthetic of one image or piece of artwork into another. This invention encouraged the development of distinctive and individual visual styles.
• Improved conversational AI experiences were made possible by advances in natural language processing (NLP). To engage in more natural and contextually relevant interactions, chatbots and virtual assistants have included generative AI.
• More complex and realistic material is now being produced because to the continuing development of Generative Adversarial Networks (GANs). GANs were used in a variety of fields, including as voice synthesis, video production, and image synthesis.
• The development of intricate designs, layouts, and prototypes was made possible with the help of generative AI, which was crucial in the field of architecture and product design. Faster design iterations and improved solutions were made possible by this.
• As generative AI developed, worries about its ethical application and the usage of deepfake technology arose. Discussions on regulation and responsible use were sparked by the potential for misuse of AI-generated content and its implications for privacy and false information.
• Collaborations involving AI academics, subject matter experts, and business professionals have been observed in the field of generative AI. These collaborations resulted in the creation of useful applications catered to particular industries.
• The incorporation of generative AI elements into creative software tools allowed producers to include AI support into their creative processes. For writers, designers, and artists, this democratisation of AI-driven creativity opened up new opportunities.

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