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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.