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Generative AI Market size was valued at USD 12.1 billion in 2023 and is anticipated to grow at a CAGR of over 30.3% between 2024 and 2032. The rising demand for generative AI applications is expected to drive workflow modernization in various industries. The evolution of Artificial Intelligence (AI) in BFSI permit easy data access is driving market growth. The introduction of AI-powered gaming with higher-level visuals & graphics, interactive ambience, and a more realistic feel is expected to boost market growth in the coming years. The North America market will be driven by investments in the AI and ML sectors.
Generative AI refers to a branch of artificial intelligence that focuses on creating or generating new content, such as images, texts, music, or videos, which is original and realistic. It involves training machine learning models to understand and learn the patterns of the existing data to generate new & unique content. Generative AI techniques often utilize deep learning algorithms, such as Generative Adversarial Networks (GANs) or Variational Auto-Encoders (VAEs), to generate content that closely resembles the input data. These models learn the underlying patterns & structures of the training data, followed by the generation of new content based on knowledge extrapolation.
Report Attribute | Details |
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Base Year: | 2023 |
Generative AI Market Size in 2023: | USD 12.1 Billion |
Forecast Period: | 2024 to 2032 |
Forecast Period 2024 to 2032 CAGR: | 30.3% |
2032 Value Projection: | USD 119.7 Billion |
Historical Data for: | 2018 - 2023 |
No. of Pages: | 300 |
Tables, Charts & Figures: | 318 |
Segments covered: | Component, Deployment Model, Technology, End-user |
Growth Drivers: |
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Pitfalls & Challenges: |
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Generative AI models heavily rely on the quality and diversity of the training data. If the training data is biased or incomplete, it can lead to biased or inaccurate outputs. The production of representative & diverse training datasets is a challenge as it requires careful data curation & pre-processing. Furthermore, the training of generative AI models often requires significant computational resources and time. The complexity of the models and the large amount of data needed for training can pose challenges in terms of scalability & infrastructure requirements. This can limit the accessibility & practicality of generative AI for smaller organizations or individuals with limited resources.
The COVID-19 pandemic had positive impacts on the generative AI market. In response to the pandemic, many organizations implemented Artificial Intelligence (AI) and Machine Learning (ML). During the pandemic, many major market players including Microsoft, IBM, Google LLC, and Amazon Web Services, Inc. witnessed an increase in AI-based technology sales. Furthermore, the rapid advancements in digital platforms facilitated the adoption of generative AI applications.
According to IBM's Global AI Adoption Index 2022 report, over 53% of IT professionals confirmed the accelerated roll-out of Artificial Intelligence (AI) in the last 24 months to respond to the pandemic. During the pandemic, advanced diagnostic tools based on Artificial Intelligence (AI) and various other imaging systems were also developed to detect the COVID-19 virus.
Generative AI uses unsupervised learning algorithms for spam detection, image compression, and in the pre-processing data stages such as the removal of noise from visual data to improve picture quality. Image classification and medical imaging both use supervised learning algorithms.
Generative AI has uses in several sectors including BFSI, healthcare, automotive & transportation, IT & telecommunications, as well as media & entertainment. It is a potent tool that can be used to generate new concepts, find solutions to issues, and produce new goods. Generative AI can improve efficiency, save time & money, and improve the quality of content produced by organizations. A few well-known generative AI tools are ChatGPT, GPT-3.5, DALL-E, MidJourney, and Stable Diffusion.
Based on component, the solution segment dominated around USD 6.8 billion revenue in 2023. Growing fraudulent activities, the overestimation of capabilities, unexpected outcomes, and rising concerns about data privacy will propel the solution segment growth. Through robust natural language processing models, generative AI is expected to play a significant role in various industries and sectors including fashion, entertainment, and transportation.
Based on technology, the generative AI market size from Generative Adversarial Networks (GANs) segment held over USD 3 billion in 2023. GANs enable the generation of realistic & high-quality data samples and are particularly useful in domains where data scarcity or privacy concerns limit the availability of large training datasets. GANs can generate synthetic data that closely resembles real data, thereby allowing for more diverse & extensive training.
North America generative AI market accounted for 30% of the revenue share in 2023. A rise in fake medical care and pseudo-imagination as well as increasing banking frauds will enable North America to dominate the industry over the forecast period. The presence of major companies in the U.S., such as Meta, Microsoft, and Google LLC, as well as industry professionals is likely to fuel expansion in the regional market. The demand for AI-generated content in industries including media & entertainment and healthcare as well as the accessibility to vast amounts of data for training generative models is an additional factor driving the regional market growth.
Some of the major companies operating in the generative AI industry are:
These companies are focused on strategic partnerships, new service launches, and commercialization efforts for market expansion. They are heavily investing in research to introduce innovative services and garner the maximum market revenue.
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Market, By Component
Market, By Deployment Model
Market, By Technology
Market, By End-user
The above information has been provided for the following regions and countries: