Generative AI Market by Offering (Software (Transformer Models (GPT-1, GPT-2, GPT-3, GPT-4, LaMDA)), Services), Application (Computer Vision, Synthetic Data Generation (Medical Imaging, Cybersecurity)), Vertical and Region - Global Forecast to 2028
[385 Pages Report] The market for generative AI is anticipated to increase from USD 11.3 billion in 2023 to USD 51.8 billion by 2028, at a CAGR of 35.6% over the course of the forecast period. The generative AI market is expected to grow at a significant rate during the forecast period, owing to various business drivers. Some factors driving the growth of the generative AI market include evolution of AI and deep learning, rise in the era of content creation and creative applications and innovation of cloud storage enabling easy access to data.
Generative AI Market Technology Roadmap till 2030
The generative AI market report covers generative AI technology roadmap till 2030, with insights around initiation, development, and commercialization of technologies across text, code, images, and video/3D/gaming based generative AI. Some of the key findings from the technology roadmap include:
Generative AI Market Short term technology roadmap (2023-2025)
- Technological improvements in language modeling
- Improvements in generative adversarial networks (GANs)
Generative AI Market Mid-term technology roadmap (2026-2028)
- Advances in meta-learning and few-shot learning techniques
- Development of generative AI systems that can learn from multi-modal data
Generative AI Market Long term technology roadmap (2029-2030)
- The emergence of fully autonomous generative AI systems
- Increased use of generative AI in scientific research
Generative AI business models across use cases have also been included in the report.
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Generative AI Market Dynamics
Driver: Innovation of cloud storage enabling easy access to data
The innovation of cloud storage has enabled easy access to otherwise locked data that wasn’t made available to the public. Before cloud storage became mainstream, accessing data was a costly affair for data scientists in need of data for research, but now governments, research institutes, businesses are unlocking data that were once confined to tape cartridges and magnetic disks. To train machine learning models used in generative AI, data scientists need enough data for precise accuracy and efficiency. With the easy availability of data, research facilities now could train ML models to solve complex problems with data available to them. One of the specific advantages and applications of generative AI is that it can levrage cloud storage to generate new data in a more time-efficient and cost-effective manner. This is useful for accomplishing tasks such as data organization, data processing, data augmentation, data synthesis, and data generation for unrepresented or underrepresented groups. Furthermore, it could help in analyzing data and understanding complex systems. Some of its notable applications include converting satellite images to map views to investigate new locations, transforming medical images into photo-realistic images, and generating marketing data based on collected data about the target market or consumer behaviors.
Restraints: Risk associated with data breaches and sensitive information leaks
Data security concerns while outsourcing generative AI projects have been marring the expansion of generative AI market. With data confidentiality regulations increasingly becoming stringent across the globe, data safety becomes even more paramount in generative tech projects. Unstructured data used for labeling includes personal data such as faces, vehicle license plates, and even sensitive medical information – which can cause serious data breaches if not appropriately secured.
Issues often arise when enterprises deal with outsourcing of generative AI projects, with many freelancers working on the data from multiple locations. Data security firm Trustwave estimates that nearly 63% of data thefts are caused by lack of due diligence while outsourcing data to third party service providers. Often, crowdsourcing based data training platforms do not lay stress on data security policies, and data annotators might access data through unsecure personal devices, download/transfer it to an unknown storage location, or work on data using public Wi-Fi networks where it can be easily stolen.
Opportunity: Acceleration in deployment of Large Language Models (LLMs)
Advances in Large Language Models, or “LLMs,” and other generative ML tooling are streamlining content creation. LLMs are complex neural networks that can generate text. They underpin systems like OpenAI’s GPT-3 (text) and Google’s LaMDA (conversational dialogue) and helped inspire OpenAI’s DALL-E and Midjourney (text-to-image). LLMs have been increasing an average of 10x per year in size and sophistication. The result: Modern AI can autonomously generate content—be it text, visual, audio, code, data, or multimedia—on par with human benchmarks.
Today, less than 1% of online content is generated using AI. Within the next ten years, it is predicted that at least 50% of online content will be generated by or augmented by AI.Generative AI and LLMs are the foundation of an important paradigm shift in content creation, communication, and knowledge generation. Just as cloud computing and smartphones transformed industries and created entirely new ones, so too will generative AI. In ten years, cloud computing grew from less than 5% of software spend to approximately 30%. Similarly, US smartphone penetration went from 1% to 55%. Generative AI has broad application across media and communications to software to life sciences and beyond. In many use cases, it is both lower cost and higher value, and it is estimated that adoption could be even faster.
Challenge: Complexity and technical challenges with generative AI
Generative AI is a technology that can become difficult to understand. A lot of people do not know how it works and how to use or implement it. This is counterproductive to its advantages and applications. A small business might refuse to implement it in its operation because it is a complex and unfamiliar technology. Free services such as ChatGPT and Dall-E have limitations. For example, during peak usage, ChatGPT tends to suffer from downtimes. Dall-E is also free, but a particular user can generate up to 50 free images in the first month and its usage will be limited further to 15 images per month. Paid services offer more guarantees and flexibility. However, because numerous AI companies and AI services have mushroomed since 2020, it is difficult to choose the most reliable ones or those that provide the best value for requirements. Implementing in-house Generative Ai capabilities has technical challenges because models can be computationally expensive and inefficient.
Generative AI market Ecosystem
Software segment to account for larger market size during forecast period
Generative AI software are currently used in various applications, including natural language processing, image generation, and generative design. As generative AI becomes more powerful through robust ML models, generative AI software are expected to play a significant role in various industries and sectors, including entertainment, fashion, and transportation. For instance, H&M uses generative AI software to create personalized clothing designs for customers based on their unique preferences and style.
Synthetic Data Generation to register highest CAGR during forecast period
The synthetic data generation segment is projected to witness a higher growth rate during the forecast period. The growth is due to the increasing demand for high-quality training data to build robust and accurate AI models. This technology helps organizations generate large volumes of diverse, labeled, and accurate synthetic data that can be used for a wide range of applications in various industries.
North America to have largest market size during forecast period
North America is expected to lead the generative AI market in 2023. The US is the largest market for generative AI in North America. The North American market for generative AI is being driven by several factors, including increasing demand for AI-generated content in industries such as media and entertainment, the growing use of AI in healthcare and other industries, and the availability of large amounts of data for training generative models.
In addition, North America has a strong ecosystem of startups and venture capitalists focused on AI, which is helping to drive innovation in the field. Many of the leading companies in the generative AI market are based in North America, including OpenAI, Nvidia, and Google.
Key Market Players
The generative AI solution and service providers have implemented various types of organic and inorganic growth strategies, such as new product launches, product upgradations, partnerships, and agreements, business expansions, and mergers and acquisitions to strengthen their offerings in the market. Some major players in the generative AI market include Microsoft (US), IBM (US), Google (US), AWS (US), META (US), Adobe (US), OpenAI (US), and Insilico Medicine (Hong Kong).
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Report Metrics |
Details |
Market size available for years |
2019–2028 |
Base year considered |
2022 |
Forecast period |
2023–2028 |
Forecast units |
USD (Billion) |
Segments covered |
Offering, Application, Vertical, and Region |
Geographies covered |
North America, Asia Pacific, Europe, the Middle East & Africa, and Latin America |
Companies covered |
Microsoft (US), IBM (US), Google (US), AWS (US), META (US), Adobe (US), OpenAI (US), Simplified (US), Insilico Medicine (Hong Kong), Genie AI (UK), Lightricks (Israel), Lumen5 (Canada), GIPHY (US), Dialpad (US), Persado (US), Codacy (Portugal), Paige.AI (US), Riffusion (US), Play.ht (India), Speechify (US), Media.io (France), Midjourney (US), FireFlies (US), Brandmark.io (Netherlands), Morphis Technologies (Portugal), Synthesia (UK), Mostly AI (Austria), Veesual (France), Deep AI (US), Galileo (US), Excel Formula Bot (Florida), JetBrains (Czech Republic), Character.AI (US), GFP-GAN (US), Fontjoy (Italy), Eleuther AI (US), Starry AI (US), and Magic Studio (US). |
This research report categorizes the generative AI market based on offering, application, vertical, and region.
By Offering:
- Software
- Services
By Application:
-
Natural Language Processing (NLP)
- Automated content creation
- Product Description
- Marketing Copy
- Sentiment Analysis
- Language Translation
-
ML-based Predictive Modeling
- Predictive Analytics
- Personalized Recommendations
- Others
-
Computer Vision
- Object Recognition
- Image & Video Analysis
- Surveillance
-
Robotics and Automation
- Assembly Line Production
- Material Handling
- Other
-
Speech Recognition
- Speech-to-Text Conversion
- Call Center Automation
- Other
-
Music and Art Generation
- Automated Music Composition
- Visual Art
- Graphics Design
- Education and Training
-
Finance and Accounting
- Fraud Detection and Risk Assessment
- Investment Analysis and Financial Forecasting
- Compliance Monitoring
-
Legal
- Contact Analysis and Review
- Legal Research and Analysis
- Automated Document Summarization
- Others
-
Customer Service and Support
- Chatbots
- Voice Assistants
- Automated Ticket Resolution
-
Augmentaed Reality (AR) and Virtual Reality (VR)
- Virtual Object Creation
- Virtual Environment Creation
- Personalized Avatar Creation
- Training and Simulation
-
Synthetic Data Generation
- Autonomous System Training
- Medical Imaging
- Cybersecurity
- Product Design
- Precision Agriculture
- Environmental Sustainibility
-
Video Editing/Generation
- Automated Video Editing
- Virtual Set Design
- Special Effects and Animation
-
3D Modeling and Reconstruction
- Image and Texture Synthesis
- 2D to 3D Model Generation
- 3D Model Simulations
-
Game Design and Character Production
- Procedural Content Generation
- Non-Player Character Generation
By Vertical:
-
Media and Entertainment
- Gaming
- Publishing Agencies
- Print and Electronic Media
- Others
-
Transportation and Logistics
- Air
- Road
- Rail
- Maritime
- Third Party Logistics (3PL)
-
Manufacturing
- Discrete Manufacturing
- Process Manufacturing
-
Healthcare and Life Sciences
- Healthcare Institutes
- Healthcare Device Manufacturing
- Pharmaceutical and Lifesciences
- Medical Research
- IT and ITES
- Construction and Real Estate
-
BFSI
- Banking
- Financial Services and Fintech
- Insurance
-
Energy and Utilities
- Oil and Gas
- Utilities
- Mining
- Retail and Ecommerce
-
Government and Defense
- Public Sector
- Military and Defense
- Other Verticals
By Region:
-
North America
- US
- Canada
-
Europe
- UK
- Germany
- France
- Italy
- Spain
- Finland
- Rest of Europe
-
Asia Pacific
- China
- India
- Japan
- South Korea
- Singapore
- Australia and New Zealand
- Rest of Asia Pacific
-
Middle East and Africa
- Saudi Arabia
- UAE
- South Africa
- Israel
- Turkey
- Rest of Middle East and Africa
-
Latin America
- Brazil
- Mexico
- Argentina
- Colombia
- Rest of Latin America
Recent Developments:
- In March 2023, ChatGPT and Whisper models have been made available on API, giving developers access to cutting-edge language and speech-to-text capabilities.
- In February 2023, Microsoft introduced new Microsoft Dynamics 365 "Copilot" artificial intelligence (AI) capabilities to help sales teams.
- In February 2023, IBM and NASA's Marshall Space Flight Center announced a collaboration to use IBM's artificial intelligence (AI) technology to discover new insights in NASA's massive trove of Earth and geospatial science data.
- In January 2023, Microsoft announced the general availability of Azure OpenAI Service as part of Microsoft’s continued commitment to democratizing AI, and ongoing partnership with OpenAI.
Frequently Asked Questions (FAQ):
What is generative AI?
Generative AI as a new type of AI that learns from data and creates new data based on what it learns. It's therefore capable of creating entirely new entities in many different forms, including text, images, audio, and video. Generative AI works by using deep learning to build models from a given set of training data.
What is the total CAGR expected to be recorded for the generative AI market during 2023-2028?
The market is expected to record a CAGR of 35.6% from 2023-2028.
Which are the key drivers supporting the growth of the generative AI market?
Some factors driving the growth of the generative AI market include the innovation of cloud storage enabling easy access to data, evolution of AI and deep learning and rise in the era of content creation and creative applications.
Which are the key technology trends prevailing in the generative AI market?
The three key technologies gaining foothold in the generative AI market are generative pre-trained transformers (GPTs), reinforcement learning, and natural language processing. It is also anticipated that meta-learning - which involves training a model to learn how to learn – will substantially improve the efficiency of generative AI systems by allowing them to learn from fewer data points.
Who are the key vendors in the generative AI market?
Some major players in the generative AI market include Microsoft (US), IBM (US), Google (US), AWS (US), META (US), Adobe (US), OpenAI (US), Simplified (US), Insilico Medicine (Hong Kong), Genie AI (UK), Lightricks (Israel), Lumen5 (Canada), GIPHY (US), Dialpad (US), Persado (US), Codacy (Portugal), Paige.AI (US), Riffusion (US), Play.ht (India), Speechify (US), Media.io (France), Midjourney (US), FireFlies (US), Brandmark.io (Netherlands), Morphis Technologies (Portugal), Synthesia (UK), Mostly AI (Austria), Veesual (France), Deep AI (US), Galileo (US), Excel Formula Bot (Florida), JetBrains (Czech Republic), Character.AI (US), GFP-GAN (US), Fontjoy (Italy), Eleuther AI (US), Starry AI (US), and Magic Studio (US).
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The research study for the generative AI market involved extensive secondary sources, directories, journals, and paid databases. Primary sources were mainly Interviews with Experts from the core and related industries, preferred generative AI providers, third-party service providers, consulting service providers, end users, and other commercial enterprises. In-depth interviews were conducted with various primary respondents, including key industry participants and subject matter experts, to obtain and verify critical qualitative and quantitative information and assess the market’s prospects.
Secondary Research
In the secondary research process, various sources were referred to for identifying and collecting information for this study. Secondary sources included annual reports, press releases, and investor presentations of companies; white papers, journals, and certified publications; and articles from recognized authors, directories, and databases. The data was also collected from other secondary sources, such as journals, government websites, blogs, and vendors’ websites. Additionally, Generative AI spending of various countries was extracted from the respective sources. Secondary research was mainly used to obtain key information related to the industry’s value chain and supply chain to identify key players based on solutions, services, market classification, and segmentation according to offerings of major players, industry trends related to solutions, services, deployment modes, business functions, applications, verticals, and regions, and key developments from both market- and technology-oriented perspectives.
Primary Research
In the primary research process, various primary sources from both supply and demand sides were interviewed to obtain qualitative and quantitative information on the market. The primary sources from the supply side included various Interviews with Experts, including Chief Experience Officers (CXOs); Vice Presidents (VPs); directors from business development, marketing, and generative AI expertise; related key executives from generative AI solution vendors, SIs, professional service providers, and industry associations; and key opinion leaders.
Primary interviews were conducted to gather insights, such as market statistics, revenue data collected from solutions and services, market breakups, market size estimations, market forecasts, and data triangulation. Primary research also helped understand various trends related to technologies, applications, deployments, and regions. Stakeholders from the demand side, such as Chief Information Officers (CIOs), Chief Technology Officers (CTOs), Chief Strategy Officers (CSOs), and end users using generative AI solutions, were interviewed to understand the buyer’s perspective on suppliers, products, service providers, and their current usage of generative AI solutions and services, which would impact the overall generative AI market.
The breakup of Primary Research:
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COMPANY NAME |
DESIGNATION |
SYNTHESIA |
Senior Advisor |
ExcelFormulaBot |
CEO and Co-Founder |
IBM |
Senior Data Scientist |
|
Senior Technology Expert |
Generative AI Market Size Estimation
In the bottom-up approach, the adoption rate of generative AI solutions and services among different end users in key countries with respect to their regions contributing the most to the market share was identified. For cross-validation, the adoption of generative AI solutions and services among industries, along with different use cases with respect to their regions, was identified and extrapolated. Weightage was given to use cases identified in different regions for the market size calculation.
Based on the market numbers, the regional split was determined by primary and secondary sources. The procedure included the analysis of the generative AI market’s regional penetration. Based on secondary research, the regional spending on Information and Communications Technology (ICT), socio-economic analysis of each country, strategic vendor analysis of major generative AI providers, and organic and inorganic business development activities of regional and global players were estimated. With the data triangulation procedure and data validation through primaries, the exact values of the overall generative AI market size and segments’ size were determined and confirmed using the study.
Global Generative AI Market Size: Bottom-Up Approach:
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Global Generative AI Market Size: Top-down Approach
Data Triangulation
Based on the market numbers, the regional split was determined by primary and secondary sources. The procedure included the analysis of the generative AI market’s regional penetration. Based on secondary research, the regional spending on Information and Communications Technology (ICT), socio-economic analysis of each country, strategic vendor analysis of major generative AI providers, and organic and inorganic business development activities of regional and global players were estimated. With the data triangulation procedure and data validation through primaries, the exact values of the overall generative AI market size and segments’ size were determined and confirmed using the study.
Market Definition
Generative AI refers to unsupervised and semi-supervised machine learning algorithms that enable computers to use existing content such as text, audio and video files, images, and even code to create new organic content, such as marketing mails, social media advertisements, legal contracts, and musical symphonies. Generative AI allows computers to abstract the underlying patterns related to input data so that the machine learning model can generate new content
Stakeholders
- Generative Al vendors
- Generative Al service vendors
- Managed service providers
- Support and maintenance service providers
- System integrators (Sis)/migration service providers
- Value-added resellers (VARs) and distributors
- Independent software vendors (ISVS)
- Third-party providers
- Technology providers
Report Objectives
- To define, describe, and predict the generative AI market by component (solutions and services), deployment mode, generative model type, application, business function, organization size, verticals, and region
- To provide detailed information related to major factors (drivers, restraints, opportunities, and industry-specific challenges) influencing the market growth
- To analyze the micro markets with respect to individual growth trends, prospects, and their contribution to the total market
- To analyze the opportunities in the market for stakeholders by identifying the high-growth segments of the generative AI market
- To analyze opportunities in the market and provide details of the competitive landscape for stakeholders and market leaders
- To forecast the market size of segments for five main regions: North America, Europe, Asia Pacific, Middle East and Africa, and Latin America
- To profile key players and comprehensively analyze their market rankings and core competencies
- To analyze competitive developments, such as partnerships, new product launches, and mergers and acquisitions, in the generative AI market
- To analyze the impact of recession across all the regions across the generative AI market
Available Customizations
With the given market data, MarketsandMarkets offers customizations as per your company’s specific needs. The following customization options are available for the report:
Product Analysis
- Product quadrant, which gives a detailed comparison of the product portfolio of each company.
Geographic Analysis
- Further breakup of the North American generative AI market
- Further breakup of the European generative AI market
- Further breakup of the Asia Pacific generative AI market
- Further breakup of the Middle Eastern & African generative AI market
- Further breakup of the Latin America generative AI market
Company Information
- Detailed analysis and profiling of additional market players (up to five)
Growth opportunities and latent adjacency in Generative AI Market