The Global Generative Artificial Intelligence (AI) market is anticipated to grow at a significant CAGR of 48.5% during the forecast period (2022-2030). The growth is driven by the ongoing advancements in AI and deep learning technologies the surge in content creation endeavors and the associated demand for associated applications based on Large Language Models (LLM). LLM is primarily fueled by the evolution of innovative computing technologies such as Single-Shot Multi-Box Detectors (SSDL) and Generative Adversarial Networks (GANS). In addition, the introduction of cloud storage solutions is also contributing towards market growth, by enabling convenient data accessibility and removing barriers to data utilization.
Browse the full report description of “Generative AI Market Size, Share & Trends Analysis Report Market by Offering (Software and Services), by Application (Text, Image, Video, and Others (Audio)), Business Function (Marketing and Sales, Finance, Human Resource, Research and Development, and Others (Customer service and Education)), Vertical (BFSI, IT and Telecommunication, Healthcare, Media and Entertainment, Retail and E-Commerce, Manufacturing, Construction and Real Estate, and Others)) Forecast Period (2022-2030)” at https://www.omrglobal.com/industry-reports/generative-ai-market
Opportunities and Restraints to the Generative AI Market
- The global generative AI market is segmented by offering (into software and services), by application (into text, image, video, and others), by business function (into marketing and sales, human resources, research and development, finance, customer service, education, and others) vertical (into BFSI, IT and telecommunication, healthcare, media and entertainment, retail and e-commerce, manufacturing, construction and real estate, and others) and region (into North America, Europe, Asia-Pacific, and the Rest of the World).
- The software segment held the major market share of the generative AI market in 2022. This was backed by the growing demand for Generative AI applications across industries such as healthcare, finance, and manufacturing. The cloud-based generative AI software solutions and the availability of open-source generative AI software libraries are contributing positively towards segmental growth. Also, by function, marketing, and sales would exhibit the highest adoption of Generative AI. Additionally, the high-tech industry would demonstrate the highest growth during the forecast period.
- North America holds the largest market share among the global economies owing to the high adoption of generative AI technologies in the region, particularly in the US. The adoption is highly supported by the well-developed technology infrastructure in the region, for the effective development and deployment of the new technologies.
- Apart from North America, Asia Pacific is also a promising region for market growth, as it is home to several technology leaders, including Alibaba, Baidu, Tencent, and more. These companies are investing heavily in R&D, contributing significantly to the industry growth. The growth is also backed by government initiatives towards the adoption of AI technologies. Partnerships, collaborations, and new product launches will also present lucrative opportunities for the region in the coming years.
- However, the limited access to high-quality input data would act as a major restraint to the market's growth, as the data directly affects the caliber of the algorithms. Further, the presence of deficient, irrelevant, and manipulated datasets poses a financial risk, and brand image. In addition, mitigating bias is another major concern for LLM, which can potentially restrain the market growth. This can be dealt with by ensuring fairness considerations.
The major players of the market include Amazon Web Services, Inc., Microsoft, Corp., Google LLC., and IBM Corp., among others. These are contributing considerably towards the market growth through diverse strategies. However, the quality of output generated by Generative AI models can be a potential challenge to the industry. The outputs with suboptimal quality, inaccuracy, and irrelevance should be checked upon to ensure the development of competent and reliable systems.
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