Generative AI Market Is Booming Globally Forecast 2032

The analysis consists of studying the market from a miniscule level wherein we implement statistical tools which helps us in examining the data with accuracy and precision. The vertical segment includes BFSI, media and entertainment, healthcare, IT and telecommunication, automotive and transportation, and others. The media and entertainment segment dominated the market with a revenue share of 31.19% in 2022. Media and entertainment use generative AI for the creation of advertisement campaigns and to attract clients on different social media platforms. Adopting technology in the media and entertainment is providing lucrative growth opportunities to the generative AI market. Rising adoption of data-centric production approach- Data-centric design has many benefits for industrial undertakings.

On the basis of region, it is analyzed across North America, Europe, Asia-Pacific, and LAMEA. The generative AI market share in India is expected to reach US$ 13.2 billion, expanding at a CAGR of 31.8% during the forecast period. The market in India is forecasted to witness growth due to the increasing adoption of machine learning and artificial intelligence Yakov Livshits applications in numerous industries. Generative AI solutions can be used to create new content, improve the efficiency of production, and personalize user experiences. The Generative AI market has been expanding globally, driven by advancements in machine learning techniques, increased availability of large datasets, and improvements in computing power.

Generative AI market growth fueled by cloud storage innovation for easy data access

Our research reports feature both; quantitative and qualitative aspects for any market. Qualitative information for any market research process are fundamental because they reveal the customer needs and wants, usage and consumption for any product/service related to a specific industry. This in turn aids the marketers/investors in knowing certain perceptions of the customers. Qualitative research can enlighten about the different product concepts and designs along with unique service offering that in turn, helps define marketing problems and generate opportunities. On the other hand, quantitative research engages with the data collection process through interviews, e-mail interactions, surveys and pilot studies. Quantitative aspects for the market research are useful to validate the hypotheses generated during qualitative research method, explore empirical patterns in the data with the help of statistical tools, and finally make the market estimations.

In terms of market share for Generative AIs, North America grabbed the greatest market share of 42.10% in 2022, and it is anticipated that this dominance will last the entire projected period. According to projections, North America will dominate the generative AI market throughout the forecast period. Adopting generative AI technology as an effective tool for marketing and customer engagement across various verticals will likely drive revenue growth in the region. According to a report by Research and Markets, managed services primarily focus on enhancing the client experience, which is a crucial aspect that companies cannot compromise on.

TARGETED MARKET VIEW

The growing use of generative AI to create better advertisement campaigns is expected to drive demand for this technological means in the media and entertainment industries. On the other hand, BFSI sub-segment achieved substantial growth rate during the forthcoming years from 2022 to 2030. As per FMI’s generative AI market opportunity map, China is anticipated to reach a market share of US$ 19.4 billion, moving at a CAGR of 30% during the forecast period. The generative AI industry in China is expected to grow prominently due to the availability of diverse and abundant datasets, which is crucial for effectively training generative AI models. As individuals and businesses in China explore opportunities in automated content generation, they require reliable and fast platforms to fulfill the industry requirements. From 2018 to 2022, the global generative AI industry witnessed steady growth due to the rising demand for content generation.

Yakov Livshits
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.

The major companies that have contributed to the development of generative AI are Apple, Alphabet (Google), Microsoft, Midjourney, Jasper, etc. They have already created numerous generative AI technologies and are working on more of them. The following table displays the approximate market share of different segments according to the end use. The following table displays the market share of generative AI recorded in various regions. A recent study by economist David Autor cited in the report found that 60% of today’s workers are employed in occupations that didn’t exist in 1940. This implies that more than 85% of employment growth over the last 80 years is explained by the technology-driven creation of new positions, our economists write.

The Global Generative AI Market is encountering challenges regarding output imprecisions and legal entitlement. Consequently, it is essential to have human-in-the-loop safeguards in place to guide, monitor, and validate the generated content. Inaccuracies in Generative AI are referred to as hallucinations, where the model produces output that is not precise or pertinent to the original input. These inaccuracies Yakov Livshits can occur due to multiple factors such as incomplete or ambiguous input, flawed training data, or inadequate model architecture. Moreover, legal ownership of both machine-generated content and the data needed to train these algorithms is also a major apprehension with Generative AI. MRFR team have supreme objective to provide the optimum quality market research and intelligence services to our clients.

The increasing volume of data and the need to extract meaningful insights from it have propelled the demand for AI-driven solutions. Generative AI algorithms have proven to be highly effective in analyzing complex datasets, identifying patterns, and generating valuable predictions. Moreover, the development of advanced generative models, such as Deep Convolutional GANs (DCGANs) and StyleGANs, led to remarkable progress in generating high-quality and realistic images and videos. This trend had significant implications for industries such as entertainment, gaming, and visual content creation.

Recent Developments

The Global Generative AI Market Size accounted for USD 7.9 Billion in 2021 and is projected to occupy a market size of USD 110.8 billion by 2030 growing at a CAGR of 34.3% from 2022 to 2030, as per a report by Acumen Research and Consulting. The rise of AI has led to growing concerns, including from many of those crucial to its development, that the technology could cause a threat to humanity. In an open letter this week, the CEOs of major AI firms like Deepmind and OpenAI said AI poses a “risk of extinction” to humanity if not properly regulated. Generative AI is being harnessed to enhance digital assistants and chatbots, resulting in more natural and empathetic conversations with AI-powered avatars. These “digital humans” interact with customers more effectively than traditional chatbots and can be employed in immersive contexts, providing an improved customer service experience.

By combining real and generated data, generative AI models can be trained on augmented datasets to improve generalization and adaptability. This approach helps address challenges like limited real-world training data and enables generative AI models to handle a wider range of scenarios. The generative AI market is segmented on the basis of component, technology, end user, and region. By technology, it is segmented into generative adversarial networks (GANs), transformer, variational autoencoder (VAE), diffusion networks, and retrieval augmented generation. On the basis of end user, it is classified into media & entertainment, BFSI, IT & telecom, healthcare, automotive & transportation, and others.