Generative AI Market Size Surge Valuation of $76.8 Billion, at a CAGR of 31.5% by 2030

Northbrook, IL 60062 -- (SBWire) -- 09/08/2023 --The global Generative AI Market size is anticipated to experience substantial expansion, ascending from a value of USD 11.3 billion in 2023 to a substantial worth of USD 76.8 billion by the year 2030, according to research report by MarketsandMarkets™

This growth trajectory reflects a robust compound annual growth rate (CAGR) of 31.5% over the forecast period. The surge in the generative AI sector is poised to be driven by a confluence of influential factors within the business landscape. Notably, the advent of cloud storage solutions facilitating seamless data accessibility, coupled with the continuous advancement of AI and deep learning technologies, stands out as pivotal contributors to this trend. Additionally, the proliferation of content creation endeavors and the growing demand for innovative creative applications are playing a pivotal role in propelling the upward trajectory of this market.

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By offering, Software to register for the largest market share during the forecast period

The scope of the software segment comprises of rule based models, statistical models, deep learning, generative adversial networks (GANs), autoencoders, convolutional neural networks (CNNs) and transformer models. This dominance can be attributed to the accessibility and ease of adoption of software solutions, which typically have a wider user base. Furthermore, the anticipated technological maturity of Generative AI software tools during this period will likely offer a diverse range of applications, catering to various industries and driving increased adoption rates.

By application, marketing & sales segment of application by business function is expected to hold the largest market share during the forecast period

Marketing & sales segment is anticipated to command the largest market share. This is driven by the increasing recognition of Generative AI's potential to revolutionize customer engagement and decision-making processes. By automating content generation, customer insights analysis, and personalized recommendations, Generative AI can significantly enhance marketing strategies and sales interactions. As companies seek to gain a competitive edge through data-driven approaches, the adoption of Generative AI in marketing & sales functions is expected to see substantial growth, contributing to its dominant market position during the forecast period.

Regional Analysis of Generative AI Market:

Generative AI technology and solutions such as ChatGPT, DALL-E, and GPT have been increasingly adopted in organizational environments. Organizations in Europe deploy generative AI solutions to improve knowledge management and product/service innovation. Europe is set to exceed USD 20 billion in Generative AI reveneue by 2030.

UK has a robust financial and fintech sector where AI-driven generative models are used for risk assessment, fraud detection, and algorithmic trading, driving significant demand.

Germany's Generative AI market is driven by its strong manufacturing and automotive sectors. The need for efficient production processes, predictive maintenance, and quality control is pushing companies to adopt AI-driven generative models.

In France, the Generative AI market is propelled by its thriving creative industries, including art, fashion, and design. Generative AI's ability to assist in content generation, design, and personalization is in high demand.

Spain's Generative AI market benefits from its expanding e-commerce and digital marketing landscape. Businesses are leveraging generative models to enhance customer experiences through personalized recommendations and content generation.

Italy's Generative AI market is experiencing growth due to its strong presence in the luxury and fashion industries. Generative AI is being used for creative design, virtual try-ons, and personalized shopping experiences.

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Some major players in the generative AI market include Microsoft (US), IBM (US), Google (US), AWS (US), META (US), Adobe (US), OpenAI (US), Insilico Medicine (Hong Kong) and Simplified (US). Some startups include Paige.AI (US), Riffusion (US), PlayHT (US), Speechify (US), Media.io (France), Midjourney (US), FireFlies (US), Brandmark.io (Netherlands), Morphis Technologies (Portugal).

Key Dynamic Factors For Generative AI Market:

Technological Developments: The development of generative AI is being fueled by ongoing developments in the fields of neural networks, deep learning, and natural language processing (NLP). More complex content generation is now possible thanks to new structures and algorithms like transformers and GANs (Generative Adversarial Networks).

AI Hardware Acceleration: The availability of specialised AI hardware, such as GPUs and TPUs (Tensor Processing Units), has greatly improved the powers of generative AI models. Generic AI is now more accessible and useful thanks to this hardware, which speeds up model training and inference.

Applications in particular industries: Generative AI is finding use in a variety of fields, including content creation, the arts, entertainment, medicine, finance, and more. Customised solutions for particular sectors are propelling market expansion.

NLG, a branch of generative AI, is becoming more and more popular in fields including business reporting, journalism, and content production. NLG systems are useful for automating content production because they can produce human-like text from structured data.

Creative and artistic expression: Through the use of generative AI, artists and creators are now able to explore new creative possibilities. AI-generated art, such as music, visual art, and literature, is becoming more and more popular and valuable commercially.

Virtual assistants and chatbots: Generative AI enables virtual assistants and chatbots that provide more human-like interactions. Customer service, online shopping, and automated services all depend on these apps.

AI in Healthcare: In the field of medicine, generative AI is employed for the creation of clinical notes, drugs, and medical images. It might completely alter the way that drugs are developed and how patients are cared for.

The ethical use of generative AI, notably in content production and deepfake technologies, is a key concern. To solve these difficulties, laws and moral principles are developing.

Data accessibility: Generative AI models perform significantly better when given high-quality and abundant training data. Training robust models requires access to huge and varied datasets.

Market rivalry: In the generative AI market, startups, well-known tech companies, and academic institutions all contend. The creation of new applications is fueled by competition.

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Competitive and Segmentation Analysis:

Due to the rising need for AI-driven content creation and innovation across numerous industries, the generative AI market is characterised by fierce rivalry. Tech behemoths like OpenAI, Google, Microsoft, and Nvidia, which have created cutting-edge generative AI models and platforms, are among the market leaders. Startups and niche businesses, like DALL-E and Runway ML, are also significantly contributing by providing cutting-edge generative AI solutions suited to particular sectors and use cases. Enhancing model performance, assuring ethical use, increasing user experience, and investigating new application fields are frequently the focal points of competitive strategies. To increase market reach and capabilities, companies frequently form alliances and collaborate with other businesses in related industries, as well as content producers and developers.

Application Area: Natural language generation (NLG), picture synthesis, music composition, visual art creation, video generation, and other fields all find use for generative artificial intelligence. Different application fields have different user bases and requirements.

Industry Vertical: The market is divided into segments based on industry, including marketing, e-commerce, gaming, healthcare, entertainment and media, and education. To address the unique requirements of each business, such as content production, personalisation, and automation, generative AI systems are frequently customised.

Model Type: There are various forms of generative AI models, including transformer-based models like GPT (Generative Pre-trained Transformer), Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs). Each model type excels at particular creative projects and content generating activities.

Generative AI solutions can be set up either locally or in the cloud. Scalability and accessibility are advantages of cloud-based deployments, but greater security and data control are advantages of on-premises solutions.

User Base: The market serves a wide range of users, from small businesses and content platforms to independent artists and creators. Product offerings are influenced by user-specific needs, such as simplicity, adaptability, and integration potential.

Ethical Considerations: The segmentation of solutions centred on ethical content generation, content verification, and responsible AI usage is driven by ethical concerns about generative AI, particularly deepfakes and false information.

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