The generative AI market was valued at USD 12.1 billion globally in 2016, and over the course of the projected year, a CAGR of 32.5% is anticipated. A wide range of industries, including gaming, healthcare, banking, chatbots and virtual assistants, design and innovation, and content creation, use generative AI. In the manufacturing sector, generative AI is becoming more and more necessary to improve processes and optimize product design.
The market is expanding due to the growing need for generative artificial intelligence in the banking sector and in autonomous vehicles. The market is expanding dramatically due to factors such rising industry adoption, more processing power, an abundance of data, and innovative model architecture.

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Impact of Covid-19
COVID-19 has had a mixed effect on the market for generative AI. On the one side, the epidemic brought attention to how important technology is, speeding up efforts for digital transformation and raising interest in artificial intelligence (AI) solutions. The demand for generative AI applications was also spurred by remote work and the necessity for automation. Nonetheless, financial strains and unpredictability in the economy impacted particular sectors, which might have slowed the AI sector's expansion.
Growth Drivers
Generative AI is being used by companies in a wide range of industries to automate procedures, boost productivity, and simplify operations. GANs and transformer models, two deep learning techniques that are still being developed, have enhanced the capabilities of generative AI, making it more potent and adaptable.
In creative industries like design, painting, and content creation, generative AI is becoming more and more popular as a tool for producing original and creative material. Growth in the field is being driven by the healthcare industry's growing desire to investigate generative AI for tasks like drug discovery, customized medicine, and medical picture analysis.
Generative AI is being used by companies in a wide range of industries to automate procedures, boost productivity, and simplify operations. Hardware advancements like GPUs and TPUs make it possible to train complicated generative models more quickly, which makes it easier to integrate them into a wider range of applications. The market is expanding at a quick pace due to the rapid expansion of major language models.
Segmentation
By Application
Application by Business function
· Marketing and Sales
· Human Resource
· Operations
· Finance
· Research and Development
· Others
Application by Data Modality
· Text
· Code
· Image
· Video
· Audio and Speech
By Vertical
Media and Entertainment
· Digital Marketing
· Sales Intelligence
· Data Visualization
· Customer Experience
· Application Development and AI integration
· Media Editing
· Synthetic Data training
BFSI
· Digital Marketing
· Sales Intelligence
· Data Visualization
· Customer Experience
· Media Editing
· Risk Management
Healthcare and Life Sciences
· Digital Marketing
· Sales Intelligence
· Data Visualization
· Customer Experience
· Media Editing
· Synthetic Data Training
Regional Outlook
· Asia Pacific
· North America
· Europe
· Latin America
· Middle East and Africa
Key Players
· Microsoft
· IBM
· Google
· Adobe
· Meta
· Giphy
· Dialpad
· Persado
· Codacy
· Midjourney
· Floworks
· Inventive
· Sonet
· Speechify
· Zetaalfa
· Deep Search labs
· Font joy
· Magic Studio
· Other Players
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