Generative AI Market Size, Landscape, Industry Analysis, Business Outlook, Current and Future Growth By 2030
It has the capacity to break down the boundaries of human imagination and produce new concepts that were previously unimaginable. The Global Generative AI Market size is expected to reach $54 billion by 2028, rising at a market growth of 32.2% CAGR during the forecast period. The generative AI market is expected to see significant growth in the coming years, with North America, Europe, and the Asia-Pacific region being some of the key regions driving this growth. Parent market analysis, Market growth inducers and obstacles, Fast-growing and slow-growing segment analysis, COVID-19 impact and recovery analysis and future consumer dynamics, and Market condition analysis for the forecast period. I agree the report was timely delivered, meeting the key objectives of the engagement. At S&P Global Market Intelligence, we understand the importance of accurate, deep and insightful information.
This project aims to unite makers and enthusiasts to build a global network of prosthetics models that can be quickly 3D printed. Along with this, the market is also being driven forward by the rising popularity of generative AI, which helps chatbots create effective conversations and increase customer satisfaction. A generative chatbot is an open-domain program that generates original language combinations rather than selecting from pre-defined responses. AI developers frequently use generative AI to create game environments and new virtual worlds. It enables virtual reality (VR) developers to create a boundless library of exclusive and immersive game environments.
Top Key Players of Generative AI Market
The Natural Language Processing segment occupied the highest market share in the year 2022. The growth can be attributed to the diverse applications of Generative AI in NLP, which is a branch of AI focused on computer-human language interaction. NLP employs ML algorithms to analyze and comprehend human language, as well as generate text that closely mirrors human-generated content in both style and substance. A prevalent usage of Generative AI in NLP entails the automated generation of news articles or social media posts. These systems are trained on extensive datasets of human-generated text and utilize that knowledge to generate fresh, authentic text that aligns with the training data about style and content. Furthermore, Generative AI can be leveraged to generate responses to customer inquiries or craft individualized marketing messages.
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. This transition led to a shift toward virtual and augmented reality, as well as other forms of digital content. With many people working and learning from home, there was an increase in demand for digital experiences such as virtual tours, online classes, and digital events.
Global Generative AI Market Research Report: Forecast (2023-
A detailed breakup and analysis of the market based on application has also been provided in the report. This includes healthcare, generative intelligence, media and entertainment, and others. According to the report, media and entertainment Yakov Livshits accounted for the largest market share. A detailed breakup and analysis of the market based on technology has also been provided in the report. According to the report, generative adversarial networks accounted for the largest market share.
- Generative AI can help to accelerate those efforts by enabling mass personalization and adapting marketing messages to resonate more successfully with diverse client demographics, resulting in higher conversion rates.
- Hence, this resulted in the adoption of cloud-based AI generative services by various companies.
- Next, primary
interviews are conducted
with industry experts and key stakeholders to gather their insights and perspectives on the market.
- Generative AI is a powerful tool that can be used to create new ideas, solve problems, and create new products.
- Applications of LLM-driven generative AI are being applied across several skill sets and industries, and in a few instances, they are already maturing.
- Currently, Generative AI software is utilized in diverse fields like natural language processing, computer vision, image creation and enhancement, and generative design.
In 2021, North America generated the largest revenue in the overall generative AI market. The rising number of fraudulent activities, increased adoption of digitally advanced healthcare devices, and presence of technologically advanced players such as Google, Meta, Microsoft, and IBM among others. In terms of growth, the Asia-Pacific region will obtain the highest growth rate from 2022 to 2030. The rising digitization across sectors, rising investments in AI platforms, and growing AI startups in countries such as China, India, Japan, and South Korea are some of the factors that are favoring the Asia-Pacific generative AI market. Based on the end-user category, the media & entertainment segment gathered the utmost shares and will continue to do so in the coming years.
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.
For instance, on 13 April 2023, Deloitte announced a new practice aimed at assisting clients in harnessing the potential of foundation models and generative AI to greatly increase productivity and quicken the speed of company innovation. Generative AI is notably well-liked especially in terms of image generation, from converting simple verbal instructions into images and videos to creating poetic graphics and even 3D animation. Generative AI models for image and art generation can quickly produce realistic images of high quality, which is challenging or impossible to achieve with conventional techniques. Generative AI models are being utilized in industries such as art and design to produce magnificent new works of art and designs that push the limits of creativity. In medicine, synthetic medical images are produced using generative AI models for image synthesis for training and diagnostic reasons.
Technology advancement increases its capability and usability, which promotes the use of generative AI. In this competitive scenario, businesses need information across all industry verticals; the information about customer wants, market demand, competition, industry trends, distribution channels Yakov Livshits etc. This information needs to be updated regularly because businesses operate in a dynamic environment. Our organization, The Brainy Insights incorporates scientific and systematic research procedures in order to get proper market insights and industry analysis for overall business success.
Text Generative AI platforms like ChatGPT have gained immense popularity owing to their ability to generate an extensive array of content such as articles, blog posts, dialogues, text summaries, translations, and website text. These platforms undergo training on extensive datasets to ensure the creation of authentic and up-to-date content. Leveraging Natural Language Processing (NLP) and Natural Language Understanding (NLU) techniques, text-generation AI comprehends text prompts, discerns context, and produces intelligent responses. Beyond content generation, these AI tools excel in tasks like question answering, text completion, text classification, content improvement, and engaging in human-like discussions. On the basis of channel, the global market is segregated into search engine marketing, email marketing, social media marketing, mobile marketing, and others.
Transparency and interpretability can help identify and mitigate bias; additionally involving diverse perspectives and ethical frameworks during development can contribute to meeting ethical concerns more directly. Generative AI systems can be vulnerable to adversarial attacks by malicious actors attempting to manipulate or deceive models with specific inputs or perturbations. Therefore, it is crucial for generative AI systems to ensure robustness and security measures are put in place in order to mitigate threats against misuse and safeguard against potential vulnerabilities. Artists, designers, and content creators can use generative AI tools to explore novel ideas and produce original creations quickly and efficiently – giving creative professionals more freedom to push the limits and offer audiences engaging experiences. In comparison to the world average, the Asia Pacific area is anticipated to develop rapidly.
Market Share & Key Players Analysis
Comparatively, Europe owns a market share of 26%, and Latin America holds a market share of 8% as of 2022. These facts and figures will help you to understand the latest trends in the field that will boost your business, and simplify your workflow. Plus, most industries and businesses look forward to adapting the generative AI technology in their workflows.
The use of synthetic data has the potential to solve the problems the banking sector is now experiencing, particularly with regard to data protection. In place of client data that cannot be shared owing to privacy issues, shareable data can be created using synthetic data. Additionally, artificial consumer data are perfect for training machine learning (ML) models that help banks assess whether and how much they can offer a client in the way of credit or a mortgage loan. The term generative AI refers to a new branch of machine learning that builds new things using neural networks, which are models based on the organization of animal brains. Traditional machine learning algorithms can only interpret the data that was provided to them by their human designers; they are not capable of producing new data on their own.