Generative AI in the Enterprise Market By Technology (Deep Learning, Generative Adversarial Networks, Cloud Computing, and Others), By Deployment Mode (Cloud-Based and On-Premises), By Application (Content Generation, Design & Creativity), By Region and Companies - Industry Segment Outlook, Market Assessment, Competition Scenario, Trends, and Forecast 2023-2032

Report Overview

Generative AI in the Enterprise Market size is expected to be worth around USD 37,656 Mn by 2032 from USD 1,476 Mn in 2022, growing at a CAGR of 39.4% during the forecast period from 2023 to 2032.

Generative AI has revolutionized the enterprise market by opening up unprecedented possibilities for content production, design, personalization, and decision-making. Deep learning technology such as GANs and VAEs enables organizations to produce realistic and creative content, automate customer support through chatbots, and develop personalized recommendations for customers.

Generative AI models can assist with data augmentation, simulate scenarios for planning and forecasting purposes, as well as increase natural language processing capabilities. Key market players like OpenAI, NVIDIA, IBM, Microsoft, Google, and AWS provide businesses with powerful tools, platforms, and APIs to harness the potential of generative AI. As this field continues to advance, generative AI stands to unlock greater efficiency, innovation, and competitive advantages across industries.

Driving factors

Generative AI offers significant time and cost savings by automating content production, data augmentation, and customer support processes. This enables businesses to allocate their resources more strategically while focusing on higher-value tasks. Generative AI also empowers businesses to enhance customer experiences with tailored recommendations and content creation tailored specifically to individual user preferences and behaviors. Businesses can utilize this data to deliver offerings with targeted, specific messaging that results in increased engagement and customer satisfaction.

Generative AI also fosters innovation by offering creative solutions and sparking fresh concepts. It can assist companies with design processes, ideation sessions, and scenario planning to keep ahead of the competition and drive growth. Recent advances in deep learning techniques, cloud computing infrastructure, and pre-trained models have all made generative AI more accessible and scalable for enterprises - contributing significantly to its increasing adoption in enterprise markets.

Restraining Factors

Ethical concerns and potential biases pose significant obstacles for AI models that rely on large datasets for training purposes, and if those datasets contain inaccurate or biased information, their output could produce biased or inappropriate content. Ensuring fairness, transparency, and responsible use of generative AI is vital to prevent negative impacts and foster trust between its users and AI systems.

Furthermore, data privacy and security also present significant concerns. Generational AI models often need access to large amounts of data, raising privacy issues and heightening the risks of data breaches if adequate security measures aren't in place. Organizations should implement stringent data protection measures and adhere to regulations to secure sensitive data.

Moreover, the complex and resource-intensive nature of generative AI may create barriers. Developing and deploying generative AI models requires experts with expertise in deep learning, computational power, and substantial amounts of training data. Organizations may face difficulty acquiring the required skillsets, infrastructure, and data resources for effective use.

Furthermore, legal and regulatory frameworks need to evolve further to accommodate its unique set of challenges, including intellectual property rights protection and accountability for content generated from AI algorithms.

Covid-19 Impact on Generative AI in the Enterprise Market

The COVID-19 pandemic has substantially affected enterprise markets worldwide, driving the adoption and application of generative AI. Though disrupting businesses across industries, its presence accelerated digital transformation efforts and automation initiatives.

Generative AI proved an indispensable technology during this period, enabling remote work, content production, and customer engagement. Due to restrictions in face-to-face interactions, businesses increasingly turned to generative AI as an automated means of customer support through chatbots, content for marketing campaigns, and personalized recommendations for users online.

This pandemic demonstrated the value of data-driven decision-making, with generative AI playing an instrumental role in simulating various scenarios to support business planning and forecasting. Unfortunately, this pandemic also presented challenges, including a lack of relevant training data availability, robust infrastructure requirements to support increased AI workloads, as well as ethical concerns associated with using AI-generated content during an emergency.

By Technology Analysis

The Generative Adversarial Networks Segment is Dominant in Global Generative AI in the Enterprise Market

Based on technology, the generative AI in the enterprise market is segmented into Deep Learning, Generative Adversarial Networks, Natural Language Processing, Cloud Computing, and Other Technologies. Among these technologies, the generative adversarial networks segment is the most lucrative in the global generative AI in the enterprise market.

The total revenue share of the generative adversarial networks segment was 30% in 2022.  GANs have gained significant interest and acceptance across a range of industries due to their ability to generate realistic and high-quality synthetic data. Enterprise customers use GANs for image and video synthesis, text generation, data augmentation, and creative design tasks.

By Deployment Mode Analysis

The Cloud Mode is Dominant in Global Generative AI in the Enterprise Market

Based on deployment mode, the generative AI in the enterprise market is segmented into cloud and on-premise. Among these modes, the cloud mode is estimated to be the most lucrative segment in the global generative AI in the enterprise market, with a market share of 58%, in 2022.

Cloud computing has become increasingly popular due to its scalability, flexibility, and cost-efficiency - qualities that make it ideal for deploying AI models. Enterprises can leverage cloud managed service providers' computational power and resources, thereby eliminating on-premise infrastructure requirements and saving upfront costs and maintenance overheads.

By Application Analysis

Data Augmentation is Dominant in Global Generative AI in the Enterprise Market

By application, the generative AI in the enterprise market is further divided into content generation, design & creativity, virtual assistants & chatbots, data augmentations, and other applications. The data augmentation segment is estimated to be the most lucrative segment in the global generative AI in the enterprise market, with a market share of 35%, in 2022.

Generative AI models are used to produce synthetic data that can supplement limited or unbalanced datasets, aiding machine learning model training by increasing performance & generalization. Synthetic data also serves to address data scarcity issues while decreasing the requirement for large labeled datasets.

Key Market Segments

Based on Technology

• Deep Learning
• Generative Adversarial Networks
• Natural Language Processing
• Cloud Computing
• Other Technologies

Based on the Deployment Mode

• Cloud
• On-premises

Based on Application

• Content Generation
• Design & Creativity
• Virtual Assistants & Chatbots
• Data Augmentations
• Other Applications

Growth Opportunity

Generative AI presents significant growth opportunities for enterprises. Generative AI can assist organizations in creating striking designs, artwork, and content that attract customer interest and distinguish their brand from competitors' products and services. This opens doors for product development, marketing campaigns, and customer engagement strategies that drive growth and competitiveness in business environments. Generative AI also facilitates customized customer experiences. Businesses that leverage customer data and generative models can offer tailored recommendations, customized products, and personalized interactions to increase customer satisfaction and loyalty while building lasting customer relationships.

Furthermore, AI technology can unlock operational efficiency. Organizations can leverage automation for content production, data analysis, and customer support processes to streamline operations, overcome costs, and free up human resources to tackle higher-value tasks. Generative AI has the power to accelerate research and development across various fields by aiding scientists, engineers, and analysts with creating simulations, prototypes, insights, or anything else they require for analysis. As it continues to develop and mature, enterprises that leverage its capabilities may gain an edge and drive growth within the market.

Latest Trends

Generative AI in the enterprise market is witnessing multiple recent trends shaping its adoption and applications. First of these is an increasing emphasis on ethical AI practices. When using generative AI systems, businesses increasingly emphasize ethical considerations, fairness, and transparency. Aims include mitigating biases, guaranteeing accountability, and creating guidelines for the responsible use of the technology.

Second, multimodal generative AI has garnered increasing interest. This involves blending different modes, such as text, images, and audio, into interactive and immersive user experiences. Multimodal generative AI applications range from virtual reality, augmented reality, and content generation that caters to an array of user preferences to natural language processing applications.

With GPT-3 language models and their advancement, there is a rising focus on text generation, translation, chatbots, dialogue systems, and automated customer support systems. Organizations have used generative AI to automate customer support services, improve communication channels and generate human-like text content for customer support purposes.

Finally, there is an increasing interest in generative AI for sustainability and social impact. Generative AI is employed in areas like renewable energy optimization, climate modeling, and healthcare research to address pressing global challenges.

Regional Analysis

North America is Dominant Region in the Global Generative AI in the Enterprise Market

North America is anticipated to be the most dominant region in the global generative AI in the enterprise market, with the largest market share of 40%. Generative AI has experienced significant expansion owing to major technology companies, research institutions, and an active startup ecosystem in North America. At the forefront of AI innovation and adoption, this region has witnessed wide use of generative AI across industries like marketing, entertainment, and e-commerce.

Asia, especially China, Japan, and South Korea, have invested significantly in research and development of all forms of artificial intelligence, including generative AI research & development efforts with impressive advances made in areas like natural language processing, computer vision systems, and autonomous systems.

Key Regions and Countries

North America

• US
• Canada
• Mexico

Western Europe

• Germany
• France
• The UK
• Spain
• Italy
• Portugal
• Ireland
• Austria
• Switzerland
• Benelux
• Nordic
• Rest of Western Europe

Eastern Europe

• Russia
• Poland
• The Czech Republic
• Greece
• Rest of Eastern Europe

APAC

• China
• Japan
• South Korea
• India
• Australia & New Zealand
• Indonesia
• Malaysia
• Philippines
• Singapore
• Thailand
• Vietnam
• Rest of APAC

Latin America

• Brazil
• Colombia
• Chile
• Argentina
• Costa Rica
• Rest of Latin America

Middle East & Africa

• Algeria
• Egypt
• Israel
• Kuwait
• Nigeria
• Saudi Arabia
• South Africa
• Turkey
• United Arab Emirates
• Rest of MEA

Market Share & Key Players Analysis

Generative AI for enterprise markets is an extremely competitive area, with several key players aggressive for market share.

OpenAI stands out as an exceptional player, offering API access to its cutting-edge GPT models that enable generative AI capabilities.

NVIDIA, well known for its powerful GPUs and deep learning frameworks, is widely adopted by enterprises for training and deploying generative AI models.

IBM Watson platform also provides AI solutions across industries, including training generative AI solutions.

Microsoft Azure provides businesses with a comprehensive selection of AI tools and services, making generative AI available for their applications.

Market share dynamics constantly shift as innovations appear, which drives competition among key players as well as advances in generative AI technologies.

Top Key Players in Generative AI in the Enterprise Market

• OpenAI
• NVIDIA Corp.
• IBM Corp.
• Microsoft Corp.
• Google LLC
• Amazon Web Services, Inc.
• Adobe Inc.
• Salesforce, Inc.
• Other Key Players

Recent Development

• In May 2023, IBM Consulting established a Center of Excellence for Generative AI to help clients drive productivity, improve customer experiences, and create new business models.
• In May 2023, NVIDIA is integrating its AI Enterprise software into Microsoft's Azure Machine Learning to help enterprises accelerate their AI initiatives. This provides an end-to-end cloud platform for developers to build, deploy and manage AI applications for large language models.

Report Scope