Generative AI Market

Generative AI Market Size

The global generative AI market size was valued at USD 21.3 Billion in 2024 and is projected to grow at a CAGR of 24.3% between 2025 and 2034. The rising demand for automated content generation in various industry sectors, including media, marketing, and e-commerce, presents a significant opportunity in the market. The use of generative AI has enabled businesses to create personalized text, images, audio, and video on a large scale while reducing the time and cost to produce the same while keeping the quality. This outcry is especially high in fields heavily dependent on digital interaction and prompt content distribution.

For instance, in May 2025, Amazon announced the rollout of Enhance My Listing (EML), a generative AI-powered tool to help sellers automatically update and optimise their existing product listings. This is a major milestone towards Amazon’s major attempt at simplifying listing management, increasing customer engagement and driving better sales performance for its selling partners.

Advancements in deep learning algorithms, transformer architectures, and the availability of big cloud resource computing have expedited the advancement of generative AI. Such AI models as GPT, DALL-E, or Stable Diffusion are increasingly becoming more efficient and powerful, which is propelling, entrepreneurs to engage AI technology for creative and analytical work. The improved processing abilities allow real-time generation as well as improved integration over enterprise workflows.

Generative AI is adopted by organizations across industries as part of more general digital transformation trends. Generative AI increases effectiveness through the automated performance of customer service, code creation, report creation, and product design. Such applications enhance efficiency, facilitate innovation and cut down operational overhead thus, generative AI is a critical technology investment for aspirational organizations.

For instance, in May 2025, IBM announced new hybrid technologies for scaling enterprise AI aimed at allowing companies to develop AI agents with their enterprise data. IBM predicts that, by 2028, there will be over 1 billion apps, while putting pressure on businesses to scale in ever increasingly fragmented environments. Integration and data readiness are a must for this.

Generative AI Market Trends

• Generative AI is increasingly being embedded into mainstream enterprise platforms such as Microsoft Office, Salesforce, Adobe Creative Cloud, and Google Workspace. These integrations enable users to automate tasks like writing emails, generating reports, designing graphics, or creating presentations within tools they already use. This trend is driving faster adoption across business functions and expanding generative AI’s role from a standalone tool to a core productivity enhancer.
  
• For instance, in May 2025, GenAI research startup Bud Ecosystem launched Bud Runtime, a pioneering solution that enables generative AI deployment on CPU-based infrastructure, reducing costs and improving accessibility. Aimed at tackling the rising financial and environmental costs of generative AI, Bud Runtime allows organizations to deploy AI models using their existing hardware, bypassing expensive and often scarce GPUs.
  
• As generative AI matures, there's a growing shift from general-purpose models to domain-specific ones tailored for fields like healthcare, finance, law, and engineering. These specialized models are trained on industry-specific data to provide more accurate, compliant, and context-aware outputs. This trend addresses concerns over reliability and regulation while unlocking deeper use cases within professional and regulated environments.
  
• for instance, in May 2025, Zest AI, announced the launch of LuLu Strategy, the newest module of a first-of-its kind generative AI lending intelligence platform, which delivers lending performance insights with generative AI simulations and actionable analysis. Following the successful rollout of LuLu Pulse, the platform's first module, LuLu Strategy represents the next phase in Zest AI's comprehensive roadmap for generative AI-powered financial intelligence solutions. In its initial launch, the new offering will be available exclusively to MeridianLink, Inc.
  
• Generative AI uses unsupervised learning algorithms for spam detection, image compression, and in the pre-processing data stages such as the removal of noise from visual data to improve picture quality. Image classification and medical imaging both use supervised learning algorithms.
  
• Generative AI has uses in several sectors including BFSI, healthcare, automotive & transportation, IT & telecommunications, as well as media & entertainment. It is a potent tool that can be used to generate new concepts, find solutions to issues, and produce new goods. Generative AI can improve efficiency, save time & money, and improve the quality of content produced by organizations. A few well-known generative AI tools are ChatGPT, GPT-3.5, DALL-E, MidJourney, and Stable Diffusion.
  

Trump Administration Tariffs

• Generative AI development heavily depends on high-performance computing hardware, such as GPUs, semiconductors, and data center equipment. Tariffs imposed on Chinese-made electronics during the Trump administration increased costs for these components.
  
• As a result, U.S.-based AI firms faced higher infrastructure expenses, potentially slowing innovation and raising the cost of deploying generative AI solutions. Smaller startups and academic institutions were especially impacted, as they operate on tighter budgets compared to tech giants with broader supply chain options.
  
• Tariff tensions and broader U.S.–China trade restrictions under the Trump administration created friction in global tech collaboration, including in AI research and development. Visa restrictions and growing mistrust between the two countries hindered the free flow of AI talent, research exchange, and joint ventures. Generative AI, being a rapidly evolving field, thrives on global collaboration so such geopolitical divisions slowed the pace of innovation and limited the diversity of datasets and perspectives that feed into large AI models.
  
• The tariffs pushed U.S. tech companies to reconsider their reliance on Chinese manufacturing and supply chains for AI infrastructure components. In response, many began diversifying suppliers or reshoring production, investing in domestic chip fabrication, and sourcing from alternative countries.
  
• While this shift aimed to reduce long-term geopolitical risk, it also temporarily disrupted supply availability and increased short-term costs. The market, reliant on timely access to advanced hardware, faced slower deployment timelines during this transition.
  

Generative AI Market Analysis

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Based on components, the market is divided into solutions and services. In 2024, the solution segment dominated the market accounting for around 66% share and is expected to grow at a CAGR of over 24% during the forecast period.

• The largest proportion of the market share is enjoyed by the solution segment in the area of the market, because of direct participation in delivering tangible, application-driven AI platforms and tools across various industries. The solutions include AI software in content creation, image creation, virtual assistants, code generation and data enhancement.
  
• Enterprises need end-to-end solutions that are pre-trained and scalable, easy to integrate, and require minimal in-house AI expertise. Also, with the arrival of industry-related generative AI applications in areas such as marketing, healthcare, finance, and design, there is a huge demand for customizable, off-the-shelf platforms.
  
• For instance, in May 2025, Finch AI announced the launch of its solutions at AWS Marketplace, making it simple to discover, test, purchase, and deploy software. Finch AI currently offers three innovative, AI-driven products. They include Finch for Text, which is an entity intelligence solution that leverages our proprietary approach to semantic understanding and a retrieval augmented generation pipeline to provide a non-negotiable base layer for getting data ready for use in AI applications.
  
• Finch Analyst, which is an AI-powered discovery and exploration surface where users can interact with their data and the inherent relationships within it from a single pane of glass; and Finch Insight Reports, which are AI-enabled entity intelligence reports that can be generated in seconds and shared instantly and easily with key stakeholders.
  

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Based on deployment mode, the generative AI market is segmented into cloud and on-premises. In 2024, the cloud segment dominates the market with 57% of market share in 2024.

• The cloud segment has the largest market share within the market due to its scalability, affordability, and ease of deployment. Generative AI models are extremely computationally intensive, especially during training and inference, which cloud platforms offer economically with high-end GPUs and TPUs. Cloud deployment also offers real-time processing, live updates, and connectivity with other AI and analytics products.
  
• It enables organizations to gain access to strong generative models, including large language models and image generators, without having to invest in costly infrastructure. Additionally, cloud-based platforms enable collaboration, accelerated innovation cycles, and access to pre-trained models through APIs.
  
• As companies are more and more embracing AI-as-a-service offerings, cloud infrastructure has emerged as the foundation for the deployment of generative AI in industries such as marketing, design, customer service, and healthcare and is the most convenient and available deployment model in the market.
  
• for instance, in February 2025, Fujitsu released the Fujitsu Cloud Service Generative AI Platform. The new offering, which bridges data confidentiality and ease of use with the cloud, will become available in Japan during fiscal year 2025, with rollout globally to be pursued in the future.
  

Based on technology, the generative AI market is segmented into generative adversarial networks (GANS), transformer models, variational auto-encoders, diffusion models and others, with the transformer model’s category expected to dominate due to their extensive use of AI technologies across complex operations, which increases the need for structured oversight.

• Transformers-based models take the top of the market because of superior performance about natural language processing, text generation tasks, and multimodal tasks. Unlike other architectures, transformer architecture employs self-attention mechanisms that allow processing massive amounts of sequential data quickly, and enables GPT, BERT, and T5 models to rule AI applications.
  
• Being scalable, domain adaptable, and successful in powering chatbots, code generators, content creators, and virtual assistants has made them the pillar of commercial generative AI solutions.
  
• For instance, in January 2024, Google Cloud and Hugging Face revealed a new strategic partnership whereby Google Cloud can offer its infrastructure to be used for all Hugging Face services and trains and serves Hugging Face models on Google Cloud. The partnership takes the mission of Hugging Face to democratize AI to the next level and goes a long way to further support the open-source AI ecosystem development from Google Cloud. With this partnership, Google Cloud is now a strategic cloud partner to Hugging Face and a preferred home for Hugging Face training and inference workloads.
  

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In 2024, the U.S. in North America dominated the generative AI market with around 70% market share in North America and generated around USD 4.7 billion in revenue.

• The U.S. dominates the market because of its leadership in AI innovation, location of major tech players (e.g., OpenAI, Google, Meta), and robust venture capital investment. Strong enterprise adoption in industries such as healthcare, finance, and media is fueling fast growth.
  
• Favorable government policies and collaborations with cloud providers also give the market a boost. The U.S. also leads global trends in ethical AI development, regulatory environments, and research on foundational models, thus becoming the hub of technological as well as commercial innovation in generative AI.
  
• As an example, FDA Commissioner Martin A. Makary, M.D., M.P.H., today announced an aggressive timeline to scale use of artificial intelligence (AI) internally across all FDA centers by June 30, 2025, following the completion of a new generative AI pilot for scientific reviewers.
  

The generative AI market in the UK is expected to experience significant and promising growth from 2025 to 2034.

• The UK is a leading hub for generative AI in Europe, driven by a strong research ecosystem, active startup scene, and government support. London is home to major AI labs, including Google DeepMind, and numerous AI-focused startups.
  
• Generative AI is strong in Europe with the UK as a leading hub supported with tight research ecosystem, vibrant startup scene and government support. Big AI labs such as the Google DeepMind are in London, and there are many AI-oriented start-ups.
  
• The Government of the UK has implemented initiatives in order to position the country as a global hegemon in AI with money to invest in innovation and ethical governance in AI. The rapid adoption of generative AI is seen in finance, media, healthcare and legal services. Paying attention to responsible AI and strong data protection (GDPR) the UK attracts trust and commercial adoption.
  
• For example, in March 2025, the Google Cloud has recently launched new AI products, and recruitment, initiative and skills training for the UK at the exclusive ‘Gemini for the United Kingdom’ event, hosted at the Google DeepMind headquarters in London. The event confirmed Google Cloud’s long-term commitment to the UK, which will now benefit from a $1 bn investment in a new data centre opening this year, and continued efforts to drive local AI evolution.
  

The generative AI market in the China is expected to experience significant and promising growth from 2025 to 2034.

• China leads in the deployment scope, with e-commerce, manufacturing, and social media as its primary stronghold, facilitated by sufficient datasets and funded programs by the government.
  
• There is a focus on Japan and South Korea on robotics and its use thereof, and India develops enterprise AI and IT services. Cloud infrastructure buildup and the penetration of smartphones are fueling the increased usage of generative AI. Indeed, both local tech titans and new entrants are also significantly pouring money into bespoke AI models.
  
• For instance, in January 2025, a Chinese startup, DeepSeek has introduced a new AI model. It has attracted great attention from the tech community because it appears to be a direct competitor to ChatGPT from OpenAI, which made generative AI a household name. The emergence of this new company has also pointed out a new frontier for Chinese AI companies to find ways to circumvent similar challenges, such as the US chip export sanctions.
  

Generative AI Market Share

• The top 7 companies of the market are NVIDIA, Google LLC (Alphabet Inc.), Microsoft, Amazon Web Services (AWS), Meta, Adobe, and IBM holding around 31% of the market in 2024.
  
• NVIDIA focuses on dominating the generative AI infrastructure layer by advancing GPU hardware (like H100) and software ecosystems such as CUDA and TensorRT. It invests heavily in partnerships with AI startups, provides optimized frameworks (like NeMo), and supports AI model training at scale via its DGX systems and cloud services. Its strategy strengthens its position as the go-to provider for AI compute power.
  
• Google leverages its proprietary models like Gemini and integrates generative AI into its ecosystem, including Search, Workspace, and Android. Through DeepMind, it advances foundational models and ethical AI development. Google Cloud offers tools like Vertex AI to attract enterprise clients. Its strategy blends AI research leadership with mass product integration to solidify its competitive edge across consumer and enterprise segments.
  
• Microsoft integrates generative AI broadly across its products via Copilot in Office 365, Azure OpenAI Service, and GitHub Copilot. By partnering closely with OpenAI, it leverages cutting-edge models like GPT-4 while enhancing Azure's infrastructure for scalable AI deployment. Its strategy centers on embedding generative AI into enterprise workflows, cloud platforms, and developer tools to drive productivity and AI adoption globally.
  

Generative AI Market Companies

Major players operating in the generative AI industry are:

• NVIDIA
• Adobe
• Amazon Web Services (AWS)
• Autodesk
• Baidu
• Google LLC
• IBM
• Lighttricks
• Meta
• Microsoft
  

OpenAI has a mission to broaden the horizons of large language models such as GPT, where commercially it is driven towards ChatGPT and API access through its platform and partnerships, including, notably, with Microsoft. It seeks a balance between strong AI capabilities, alignment and safety research. OpenAI find a continuous means of monetization in premium subscription, enterprise licenses, and developer tools while investing in reinforcement learning, multimodal models and developing long-term AGI (Artificial General Intelligence).

Google is the pioneer of foundational AI models such as Gemini that are driven by DeepMind and Google Research. Its strategy is to embed generative AI across such core products as Search, Workspace (Docs, Gmail), and Android. Google Cloud brings in Vertex AI for enterprise customers. The company is working on multimodal AI, responsible AI principles and expanding its ecosystem using open models and partnering up with academic and industry players.

Generative AI Industry News

• In May 2025, IBM worked with Oracle to bring the power of watsonx, IBM’s flagship portfolio of AI products, to Oracle Cloud Infrastructure (OCI). Leveraging OCI’s native AI services, the latest milestone in IBM’s technology partnership with Oracle is designed to fuel a new era of multi-agentic, AI-driven productivity and efficiency across the enterprise.
  
• In January 2025, NTT DATA, a global digital business and IT services leader, announced the international launch of its next-generation Smart AI AgentTM. This advanced AI tool is a cornerstone of the company's strategy to accelerate the adoption of Generative AI, with an estimated USD 2 billion in revenue that we aim to achieve from Smart AI AgentTM-related business by 2027.
  
• In November 2024, ServiceNow introduced new generative AI and governance innovations on its Now Platform, enhancing workflow automation and productivity across industries. This tool connects ServiceNow instances to OpenAI APIs and Azure OpenAI models, enabling seamless integration of generative AI capabilities. It supports tasks like answering questions, content creation, and automating workflows through low-code tools. These tools help organizations adopt generative AI faster by providing expert support, demos, and training to align AI investments with business goals.
  
• In October 2024, the BharatGen initiative was launched BharatGen, an initiative to make generative AI available to citizens in different Indian languages, with Science and Technology Minister Jitendra Singh asserting that it was the world's first State-funded project of its kind.
  

The generative AI market research report includes in-depth coverage of the industry with estimates & forecasts in terms of revenue ($ Mn/Bn) from 2021 to 2034, for the following segments:

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Market, By Component

• Solution
• Service

Market, By Deployment Mode

• Cloud
• On-premises

Market, By Technology

• Generative adversarial networks (GANs)
• Transformers model
• Variational auto-encoders
• Diffusion models
• Others

Market, By End Use

• Healthcare
• Retail and e-commerce
• Manufacturing
• BFSI
• Media and entertainment
• Others

The above information is provided for the following regions and countries:

• North America
  • U.S.
  • Canada
• Europe
  • Germany
  • UK
  • France
  • Italy
  • Spain
  • Russia
  • Nordics
• Asia Pacific
  • China
  • Japan
  • India
  • South Korea
  • ANZ
  • Southeast Asia
• Latin America
  • Brazil
  • Mexico
  • Argentina
• MEA
  • UAE
  • Saudi Arabia
  • South Africa