Generative AI Market by Offering (Transformer Models (GPT-1, GPT-2, GPT-3, GPT-4, LaMDA), Services), Modality (Text, Image, Video, Audio & Speech, Code), Application (Content Management, Search & Discovery), Vertical and Region - Global Forecast to 2030
[550 Pages Report] The Generative AI market is witnessing exponential expansion, with estimates pointing towards a monumental surge in market value, escalating from USD 20.9 billion in 2024 to an impressive USD 136.7 billion by 2030. This phenomenal growth trajectory, characterized by a substantial CAGR of 36.7% during the forecast period, is being propelled due to several converging factors. Firstly, continuous advancements in AI technologies, particularly in areas such as natural language processing (NLP), computer vision, and generative adversarial networks (GANs), are unlocking new possibilities for generating high-quality, human-like content across various domains. Secondly, increasing digitization across industries is driving the demand for AI-driven solutions to streamline processes, enhance creativity, and personalize user experiences. Moreover, expanding applications of Generative AI in diverse sectors like entertainment, healthcare, marketing, and design are fueling market growth, as businesses recognize its potential to revolutionize content creation, product development, and customer engagement.
Technology Roadmap of Generative AI Market
The generative AI market report covers the technology roadmap, with insights into the short-term and long-term developments.
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Short-term (1-5 Years):
- Continued advancements in transformer models, with incremental improvements in performance and capabilities.
- Incorporation of multimodal data, including text, images, video, and audio, to enable more diverse and engaging generative outputs.
- Scaling model size to trillions of parameters, leveraging enhanced hardware and infrastructure.
- Development of dedicated AI accelerators, such as Nvidia Hopper and Google TPU v4, to drive performance improvements.
- Expansion into more advanced text generation capabilities, including summarization, personalized content, and report/document generation.
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Long-term (5+ years):
- Exploration of fundamentally new model architectures beyond transformers, potentially incorporating neuro-symbolic approaches.
- Increased use of unsupervised and self-supervised learning techniques to reduce reliance on labeled data.
- Emergence of neuromorphic and quantum computing hardware specifically designed for Generative AI applications.
- Generative AI as a versatile, integrated content creation tool across various mediums, including text, images, video, and audio.
- Seamless integration of Generative AI capabilities with other AI systems, enabling new forms of creativity and productivity.
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Market Dynamics
Driver: The rising demand for automated content creation and curation
The surge in content creation and creative applications serves as a significant growth driver for the Generative AI market, catalyzing innovation, and demand for AI-powered solutions. With the proliferation of digital platforms and the internet, there is an insatiable appetite for diverse and engaging content across various industries, including media, advertising, gaming, and design. Generative AI technologies, such as deep learning models and generative adversarial networks (GANs), enable automated content generation with remarkable realism and creativity, empowering creators to produce vast quantities of high-quality multimedia content efficiently and cost-effectively. Moreover, Generative AI facilitates personalized content experiences tailored to individual preferences, driving user engagement and retention.
Restraint: High costs associated with training data preparation
The high costs associated with training data preparation pose a significant restraint for the Generative AI market. Training data is crucial for teaching AI models to generate accurate and realistic outputs, but obtaining and preparing large, diverse datasets can be both time-consuming and expensive. Generating high-quality training data often requires manual annotation, curation, and validation processes, which demand significant human resources and expertise. Moreover, sourcing relevant data that adequately represents the desired outcomes across various domains can be challenging and may entail additional expenses, especially for niche or specialized applications. These cost-intensive data preparation requirements can act as barriers to entry for smaller businesses and startups, limiting their ability to leverage Generative AI technologies effectively.
Opportunity: Increasing deployment of Large Language Models (LLMs)
The increasing deployment of Large Language Models (LLMs) presents a significant opportunity for the Generative AI market. LLMs, such as OpenAI's GPT (Generative Pre-trained Transformer) series, are revolutionizing natural language processing by enabling machines to understand and generate human-like text at an unprecedented scale and complexity. These powerful models have demonstrated remarkable capabilities in tasks such as text generation, translation, summarization, and question-answering, among others. As organizations across diverse sectors recognize the potential of LLMs to enhance productivity, automate processes, and deliver personalized experiences, the demand for Generative AI solutions leveraging these models is on the rise.
Challenge: Quality of output generated by Generative AI models
The quality of output generated by generative AI models poses a significant challenge for the Generative AI market. Despite substantial advancements, these models are susceptible to producing inaccurate, biased, or inappropriate content, undermining user trust and usability. For instance, instances of ChatGPT providing incorrect or nonsensical responses have been reported, highlighting the limitations and potential risks associated with relying solely on AI-generated outputs. Moreover, the phenomenon of model drift, as seen in GPT-4, where the model's performance deteriorates over time due to shifts in data distributions or unforeseen biases, further exacerbates concerns regarding the reliability and consistency of generative AI outputs. Similar challenges have been observed in other generative AI models where issues like hallucination, lack of coherence, and inappropriate content generation have been identified.
Generative AI Market Ecosystem
By offering, transformer models to register the fastest growth rate between 2024–2030.
Transformer models are experiencing unprecedented growth in the Generative AI market due to their unparalleled ability to process and generate complex sequences of data, particularly in natural language processing tasks. Transformers leverage self-attention mechanisms to capture long-range dependencies and relationships within input sequences, enabling them to generate highly coherent and contextually relevant outputs. This versatility makes transformer-based models like OpenAI's GPT series incredibly valuable across various applications, including text generation, translation, summarization, and question-answering. As a result, transformer models are rapidly becoming the go-to choice for generative AI applications, driving their unprecedented growth and dominance in the market.
By modality, text segment to capture the highest market share in 2024.
The dominance of text modality as the largest segment in the Generative AI market stems from its multifaceted appeal and widespread applicability across industries. Textual data's pervasive presence across digital platforms and communication channels provides a rich and easily accessible source for training and deploying generative AI models. This ubiquity aligns with the growing demand for text-based applications such as natural language understanding, generation, summarization, and sentiment analysis in sectors ranging from customer service to marketing and finance. The increasing need for personalized content, conversational interfaces, and automated text generation further drives the adoption of text-based generative AI solutions in various sectors.
By application, synthetic data management is slated to witness the highest growth rate during the forecast period.
Synthetic data management is emerging as the fastest-growing application segment in the generative AI market due to its ability to address critical data challenges faced by organizations across industries. Traditional methods of data collection and labeling are often time-consuming, expensive, and prone to biases or privacy concerns. Generative AI models, particularly those based on LLMs, can efficiently create high-quality synthetic data that mimics real-world data distributions while preserving privacy and addressing potential biases. This synthetic data can be used for a wide range of purposes, including training machine learning models, simulating rare or extreme scenarios, augmenting existing datasets, and enabling data anonymization. By leveraging synthetic data, organizations can accelerate model development, reduce costs associated with data acquisition, and enhance the performance and robustness of their AI systems. Additionally, synthetic data can help overcome regulatory hurdles and ethical concerns surrounding sensitive data, enabling broader adoption of AI solutions in domains such as healthcare, finance, and cybersecurity.
By vertical, media & entertainment segment will account for the largest market share in 2024.
The media and entertainment industry is expected to stand out as the largest vertical in the Generative AI market due to its unique reliance on creative content production and consumption. In this industry, where innovation and differentiation are paramount, generative AI solutions empower creators to generate vast quantities of high-quality multimedia content efficiently and cost-effectively. Whether it is generating lifelike characters for games and animations, composing music, creating visual effects, or generating personalized recommendations for content consumption, generative AI enables the development of immersive and engaging experiences that captivate audiences. As media consumption patterns evolve and demand for diverse and engaging content grows, the media and entertainment industry continues to invest heavily in generative AI technologies, driving innovation and shaping the future of content creation and consumption.
By region, Asia Pacific is set to experience the fastest growth rate during the forecast period.
Asia Pacific is set to emerge as the fastest-growing hub in the Generative AI market, fueled by substantial investments in research and development by both public and private sectors. Countries like China, Japan, South Korea, and India are at the forefront of AI innovation, with vibrant startup ecosystems and government initiatives supporting the development and adoption of emerging technologies. For instance, in China, ‘computing vouchers' are being offered by several city governments, including Shanghai, to AI startups to subsidize the training cost of their LLMs. On a similar note, South Korea’s Ministry of Science and ICT has earmarked USD 642.5 million through 2030 to invest in companies working on advanced AI chips. The investment will involve building new data centers and working with generative AI hardware companies and cloud service providers, among other projects.
Also, the sheer size and diversity of the population in the Asia-Pacific region provide a vast pool of data, a crucial ingredient for training and refining AI models. This abundance of data, coupled with the region's diverse cultural and linguistic landscapes, creates fertile ground for developing generative AI solutions that cater to local preferences and nuances. For instance, SB Intuitions, the subsidiary of Japanese multinational company SoftBank, is developing homegrown LLMs specialized for the Japanese language. SoftBank aims to complete the buildout of these homegrown LLMs with 350 billion parameters by end of 2024.
Key Market Players
The generative AI solution and service providers have implemented several types of organic and inorganic growth strategies, such as new product launches, product upgrades, 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 OpenAI (US), Microsoft (US), AWS (US), Google (US), Adobe (US), along with SMEs and startups such as Anthropic (US), Midjourney (US), Insilico Medicine (Hong Kong), Lumen5 (Canada), and AI21 Labs (Israel).
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Report Metrics |
Details |
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Market size available for years |
2019–2030 |
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Base year considered |
2023 |
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Forecast period |
2024–2030 |
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Forecast units |
USD (Billion) |
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Segments Covered |
Offering, Data Modality, Application, Vertical, and Region |
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Geographies covered |
North America, Europe, Asia Pacific, Middle East & Africa, and Latin America |
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Companies covered |
IBM (US), NVIDIA (US), Open AI (US), Anthropic (US), Meta (US), Microsoft (US), Google (US), AWS (US), Adobe (US), Accenture (Ireland), Capgemini (France), Insilico Medicine (Hong Kong), Simplified (US), Lumen5 (Canada), AI21 Labs (Israel), Hugging Face (US), Dialpad (US), Persado (US), Copy.ai (US), Synthesis AI (US), Hypernetuse AI (US), Viable (US), Together AI (US), Defog.ai (Singapore), Mistral AI (France), Adept (US), DeepSearch Labs (UK), Stability AI (UK), Lightricks (Israel), Cohere (Canada), Writesonic (US), Colossyan (UK), amberSearch (Germany), Mosaic ML (US), Inflection AI (US), Glean (US), Jasper (US), Runway (US), Inworld AI (US), Typeface (US), Paige.AI (US), Upstage (South Korea), PlayHT (US), Speechify (US), Midjourney (US), Fireflies (US), InstaDeep (UK), Synthesis (UK), Mostly AI (Austria), Forethought (US), Character.ai (US), GFP-GAN (China), Fontjoy (Italy), EtherAI (US), Starry AI (US), Magic Studio (US), Balchuan AI (China), Salesforce (US), Technology Innovation Institute (Abu Dhabi), Abacus.AI (US), and OpenLM (US). |
This research report categorizes the generative AI market based on offering, data modality, application, vertical, and region:
By Offering:
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Software
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Software, By Type
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Rule Based Models
- Knowledge Based Models
- Script Based Models
- Expert Systems
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Statistical Models
- Markov Models
- Hidden Markov Models
- Gaussian Mixture Models
- Conditional Random Fields
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Deep Learning
- Feedforward Neural Networks
- Recurrent Neural Networks
- Long Short-Term Memory (LSTM) Networks
- Gated Recurrent Units (GRUs)
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Generative Adversarial Networks (GANs)
- Conditional GANs
- Style GANs
- Cycle GANs
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Autoencoders
- Denoising Autoencoders
- Variational Autoencoders
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Convolutional Neural Networks (CNNs)
- Image Generating CNNs
- Video Generating CNNs
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Transformer Models
- Bidirectional Encoder Representations from Transformers (BERT)
- Generative Pre-trained Transformer 1 (GPT-1)
- Generative Pre-trained Transformer 2 (GPT-2)
- Generative Pre-trained Transformer 3 (GPT-3)
- Generative Pre-trained Transformer 4 (GPT-4)
- Language Model for Dialogue Applications (LaMDA)
- Other Transformer Models
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Rule Based Models
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Software, By Deployment Mode
- On-premises
- Cloud
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Software, By Type
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Services
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Professional Services
- Training and Consulting
- System Integration and Implementation
- Support and Maintenance
- Managed Services
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Professional Services
By Data Modality
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Text
- Text Generation
- Text-Based Chatbots
- Text Summarization
- Text Translation
- Others
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Image
- Image Generation
- Image Captioning
- Image Editing and Enhancement
- Others
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Video
- Video Generation
- Video Editing & Enhancement
- Video Annotation
- Others
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Audio and Speech
- Text to Speech
- Speech Recognition and Transcription
- Music Generation
- Others
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Code
- Code Generation
- Code Documentation
- Code Translation & Transpilation
- Others
By Application
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Business Intelligence and Visualization
- Sales Intelligence
- Marketing Intelligence
- Finance Intelligence
- Human Resource Intelligence
- Operations Intelligence
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Content Management
- Content Generation
- Content Curation, Tagging & Categorization
- Digital Marketing
- Media Editing
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Synthetic Data Management
- Synthetic Data Augmentation
- Synthetic Data Training
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Search and Discovery
- General Search
- Insight Generation
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Automation and Integration
- Personalization & Recommendation Systems
- Customer Experience Management
- Application Development & API Integration
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Cybersecurity Intelligence
- Fraud Detection & Prevention
- Risk & Compliance Management
- Automated Patch Management
- Digital Forensics & Incident Analysis
- Threat Simulation & Training
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Generative Design AI
- Design Exploration & Variation
- Modelling & Prototyping
- Product Renderings & Visual Collaterals
- Other Applications
By Vertical
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Media & Entertainment
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Media & Entertainment, By Text
- Script and Dialogue Generation
- Content Summarization
- Language Translation and Localization
- Interactive Storytelling
- Automated News Generation
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Media & Entertainment, By Video
- Video Synthesis
- Video Inpainting
- Video Summarization
- Visual Effects Enhancement
- Virtual Set Design
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Media & Entertainment, By Image
- Content Creation
- Style Transfer
- Face and Character Generation
- Image Super-Resolution
- Automated Thumbnail Generation
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Media & Entertainment, By Coding
- Procedural Content Generation
- Automated Animation Scripting
- Code Comment Generation
- Code Translation
- Code Refactoring Suggestions
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Media & Entertainment, By Audio & Speech
- Music Composition
- Voice Synthesis
- Sound Effects Generation
- Podcast Automation
- Audio Restoration
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Media & Entertainment, By Text
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BFSI
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BFSI, By Text
- Automated Customer Support
- Financial Document Summarization
- Sentiment Analysis
- Financial News Generation
- Fraudulent Mail Detection
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BFSI, By Video
- Automated Video Surveillance
- Virtual Financial Consultation
- Visual-Cognitive Credit Assessment
- Real Estate Property Evaluation
- Interactive Banking Tutorials
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BFSI, By Image
- Signature Verification
- Transaction Anomaly Detection
- Document Digitization
- Card Fraud Detection
- Customer Identity Verification
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BFSI, By Coding
- Algorithmic Trading Strategies
- Automated Report Generation
- Code Review and Optimization
- Fraud Detection Algorithm Development
- Automated Compliance Checks
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BFSI, By Audio & Speech
- Financial Data Audio Transcription
- Customer Support & Service
- Automated Meeting Summaries
- Financial Podcast Generation
- Voice-Enabled Financial Apps
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BFSI, By Text
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Healthcare & Life Sciences
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Healthcare & Life Sciences, By Text
- Electronic Health Records (EHR)
- Clinical Documentation Generation
- Drug Interaction Prediction
- Medical Literature Summarization
- Patient Communication and Education
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Healthcare & Life Sciences, By Video
- Surgical Procedure Simulation
- Rehabilitation Exercise Generation
- Gait Analysis and Rehabilitation
- Remote Patient Monitoring
- Medical Training Videos
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Healthcare & Life Sciences, By Image
- Synthetic Medical Imaging
- Tumor Detection and Segmentation
- Radiation Therapy Planning
- AR/VR Visualization For Anatomy
- Synthetic Molecular Structure Generation
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Healthcare & Life Sciences, By Coding
- Automated Medical Report Generation
- Algorithmic Medical Diagnosis
- Drug Dosage Calculation
- Healthcare Workflow Automation
- Clinical Decision Support Systems
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Healthcare & Life Sciences, By Audio & Speech
- Voice Assistants For Patients
- Speech Synthesis For Assistive Devices
- Medical Dictation and Transcription
- Speech-To-Text Medical Transcription
- Customized Audiograms
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Healthcare & Life Sciences, By Text
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Manufacturing
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Manufacturing, By Text
- Technical Documentation
- RFQ/RFP Generation
- Automated Email Communication
- Maintenance and Repair Manuals
- Demand Forecasting and Inventory Management
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Manufacturing, By Video
- Process Optimization
- Predictive Maintenance
- Robotic Process Control
- Worker Training and Safety
- Supply Chain Visualization
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Manufacturing, By Image
- Product Design/3d Prototyping
- Defect Detection
- Customized Manufacturing
- Generative Design
- Visual Inspection and Quality Control
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Manufacturing, By Coding
- Automated Script Generation
- Digital Twin Simulation
- Data Analysis and Visualization
- IoT Devices Integration
- CNC Machine G-Codes
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Manufacturing, By Audio & Speech
- Acoustic Quality Control
- Worker Assistance and Communication
- Real-Time Monitoring and Alerts
- Virtual Training and Simulations
- Voice-Controlled Interfaces
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Manufacturing, By Text
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Retail & Ecommerce
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Retail & Ecommerce, By Text
- Product Descriptions
- Chatbots and Customer Service
- Social Media Content Creation
- Email Marketing
- Personalized Shopping Recommendations
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Retail & Ecommerce, By Video
- Product Showcase Videos
- Video Advertisements
- Virtual Store Tours
- Instructional Videos
- User-Generated Content Compilation
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Retail & Ecommerce, By Image
- Product Image Generation
- Virtual Try-On
- Interior Design Simulation
- Customized Packaging Design
- Visual Search Enhancement
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Retail & Ecommerce, By Coding
- Automated Inventory Management
- Dynamic Pricing Optimization
- Fraud Detection and Prevention
- Supply Chain Optimization
- E-Commerce Platform Customization
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Retail & Ecommerce, By Audio & Speech
- Voice Assistants
- Personalized Audio Ads
- Audio Product Guides
- Voice Search Optimization
- Customer Escalation Management
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Retail & Ecommerce, By Text
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Transportation & Logistics
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Transportation & Logistics, By Text
- Automotive Content Creation
- Spare Parts Documentation
- Incident Reports
- Freight Documentation
- Vehicle Maintenance Logs
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Transportation & Logistics, By Video
- Surveillance and Security
- Driver Behavior Analysis
- Loading and Unloading Optimization
- Accident Reconstruction
- Parking Management
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Transportation & Logistics, By Image
- Virtual Test Environments
- Customized Vehicle Personalization
- Damage Inspection
- Route Optimization Visualization
- Traffic Flow Simulation
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Transportation & Logistics, By Coding
- Algorithm Development For Autonomous Vehicles
- Automated Data Processing For Logistics
- Vehicle Control Systems
- Predictive Analytics For Maintenance
- Intelligent Scheduling and Resource Allocation
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Transportation & Logistics, By Audio & Speech
- Emergency Response and Assistance
- Voice-Enabled Navigation
- Driver State Monitoring
- Speech Recognition For Driver Commands
- Hands-Free Data Entry
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Transportation & Logistics, By Text
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Construction & Real Estate
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Construction & Real Estate, By Text
- Automated Property Descriptions
- Legal Document Generation
- Project Planning Reports
- Client Communication
- Environmental Impact Assessments
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Construction & Real Estate, By Video
- Construction Progress Videos
- Virtual Reality Property Walkthroughs
- Safety Training Videos
- Quality Assurance Inspections
- Property Valuation Videos
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Construction & Real Estate, By Image
- Architectural Design Generation
- Facade Visualization
- Interior Layout Suggestions
- Site Progress Monitoring
- Virtual Staging
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Construction & Real Estate, By Coding
- Automated Building Information Modeling (Bim)
- Construction Workflow Automation
- Generative Design For Structures
- Energy Efficiency Simulations
- Maintenance and Repairs
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Construction & Real Estate, By Audio & Speech
- Construction Site Monitoring
- Voice-Activated Property Tours
- Automated Customer Support Calls
- Occupancy Monitoring
- Equipment Maintenance Alerts
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Construction & Real Estate, By Text
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Energy & Utilities
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Energy & Utilities, By Text
- Energy Consumption Forecasting
- Automated Reporting
- Customer Support & Resolution
- Regulatory Compliance
- Energy Trading Insights
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Energy & Utilities, By Video
- Safety Compliance Monitoring
- Renewable Energy Site Selection
- Real-Time Equipment Monitoring
- Energy Theft Detection
- Workforce Training
-
Energy & Utilities, By Image
- Infrastructure Inspection
- Facility Layout Optimization
- Environmental Monitoring
- Grid Management and Optimization
- GIS Data and Satellite Imagery Analysis
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Energy & Utilities, By Coding
- Optimized Energy Management Code
- Predictive Maintenance Algorithms
- Renewable Energy Simulation
- Energy Trading Algorithms
- Autonomous Grid Control
-
Energy & Utilities, By Audio & Speech
- Acoustic Anomaly Detection
- Emergency Response Coordination
- Voice-Activated Energy Control
- Multilingual Customer Support
- Predictive Maintenance Alerts
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Energy & Utilities, By Text
-
Government & Defense
-
Government & Defense, By Text
- E-Citizen Services
- Threat Intelligence
- Automated Translation
- Propaganda Detection
- Policy Document Generation
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Government & Defense, By Video
- Simulated Training Scenarios
- Surveillance Video Enhancement
- Activity Recognition and Anomaly Detection
- Drone and Uav Navigation
- Visual Storyboarding
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Government & Defense, By Image
- Satellite Image Analysis
- Facial Recognition
- Camouflage Design
- Object Detection Augmentation
- Anomaly Detection In Surveillance
-
Government & Defense, By Coding
- Automated Code Generation
- Vulnerability Simulation
- Code Obfuscation
- Reverse Engineering Aid
- Network Simulation and Optimization
-
Government & Defense, By Audio & Speech
- Audio Deepfake Detection
- Automated Speech Transcription
- Dialect Generation and Recognition
- Verbal Deception Detection
- Voice Biometrics and Authentication
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Government & Defense, By Text
-
IT & ITeS
-
IT & ITeS, By Text
- Natural Language Interfaces
- Automated Report Generation
- Data Annotation For ML
- Chatbots for IT Support
- API Documentation
-
IT & ITeS, By Video
- Video Training Modules
- Automated Video Editing
- Code Visualization
- IT Process Simulation
- Virtual IT Demos
-
IT & ITeS, By Image
- UI/UX Design Prototyping
- Logo and Branding Design
- Image Data Augmentation
- Fraud Detection
- Document Digitization
-
IT & ITeS, By Coding
- Code Auto-Completion
- Automated Code Review
- Bug Detection and Prevention
- Refactoring Assistance
- Code Comment Generation
-
IT & ITeS, By Audio & Speech
- Voice User Interfaces (VUI)
- Text-To-Speech Applications
- Voice-Activated Commands
- Voice Biometrics
- Automated Call Logging
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IT & ITeS, By Text
-
Telecommunications
-
Telecommunications, By Text
- Chatbots and Customer Support
- Automated Network Documentation
- Voice-To-Text Transcription
- Automatic Billing and Invoicing
- Content Generation For Marketing
-
Telecommunications, By Video
- Video Quality Enhancement
- Automated Video Summarization
- VR Content Generation
- Anomaly Detection In Video Feeds
- Content Personalization For Video Streaming
-
Telecommunications, By Image
- Network Infrastructure Visualization
- Automatic Fault Detection
- Antenna Placement Optimization
- Facial Recognition For Customer Service
- Real-Time Image Compression
-
Telecommunications, By Coding
- Code Auto-Completion
- Bug Detection and Repair
- Code Summarization and Documentation
- Code Translation
- Automated Script Generation
-
Telecommunications, By Audio & Speech
- Voice Authentication
- Language Generation For Ivr Systems
- Speech Emotion Recognition
- Automated Call Center Quality Assurance
- Voice Generation For Virtual Assistants
-
Telecommunications, By Text
- Other Verticals
By Region
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North America
- United States
- Canada
-
Europe
- UK
- Germany
- France
- Italy
- Spain
- Finland
- Rest of Europe
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Asia Pacific
- China
- India
- Japan
- South Korea
- Singapore
- Australia and New Zealand (ANZ)
- Rest of Asia Pacific
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Middle East and Africa
- Saudi Arabia
- UAE
- South Africa
- Turkey
- Rest of Middle East and Africa
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Latin America
- Brazil
- Mexico
- Argentina
- Rest of Latin America
Recent Developments:
- In March 2024, Microsoft and Adobe announced plans to bring Adobe Experience Cloud workflows and insights to Microsoft Copilot. The collaboration aims to leverage Microsoft 365 to help marketers overcome application and data silos and more efficiently manage everyday workflows.
- In March 2024, Adobe and NVIDIA, longstanding R&D partners, announced a new partnership to unlock the power of generative AI to further advance creative workflows. Adobe and NVIDIA will co-develop a new generation of advanced generative AI models with a focus on deep integration into applications the world’s leading creators and marketers use.
- In February 2024, the GSMA and IBM announced a new collaboration to support the adoption and skills of generative artificial intelligence (AI) in the telecom industry through the launch of GSMA Advance's AI Training program and the GSMA Foundry Generative AI program.
- In February 2024, OpenAI announced the introduction of Sora, a text-to-video generative AI model. Sora can generate videos for up to a minute long while maintaining visual quality and adherence to the user’s prompt. This model is not publicly available as of now and limited access has been granted to handful of red teamers, visual artists, designers, and filmmakers.
- In February 2024, Google unveiled Gemini 1.5, an updated generative AI model that comes with long context understanding across different modalities. In the same month, Google also announced the launch of Gemma, a new family of lightweight open-weight models. Starting with Gemma 2B and Gemma 7B, these new models were “inspired by Gemini” and are available for commercial and research usage.
- In January 2024, Capgemini and AWS expanded their strategic collaboration to enable broad enterprise generative AI adoption. Through this collaboration, Capgemini and AWS are focusing on helping clients realize the business value of adopting generative AI while navigating challenges, including cost, scale, and trust.
- In December 2023, Microsoft launched InsightPilot, an automated data exploration system powered by a Generative AI. This innovative system is specifically designed to simplify the data exploration process. InsightPilot incorporates a set of meticulously designed analysis actions to simplify data exploration.
- In December 2023, Google unveiled an unprecedented Generative AI named VideoPoet, which is multimodal and capable of generating videos. This groundbreaking model introduces video generation functionalities previously unseen in generative AI.
- In December 2023, Axel Springer and OpenAI announced a global partnership to strengthen independent journalism in the age of artificial intelligence (AI). The initiative will enrich users’ experience with ChatGPT by adding recent and authoritative content on various topics and explicitly values the publisher’s role in contributing to OpenAI’s products
- In November 2023, OpenAI announced the launch of GPT-4 Turbo, a next-generation model of GPT-4. GPT-4 Turbo is more capable and has knowledge of world events up to April 2023. It has a 128k context window to fit the equivalent of more than three hundred pages of text in a single prompt.
Frequently Asked Questions (FAQ):
What is a generative AI?
Generative AI refers to a subset of artificial intelligence techniques focused on creating data, content, or outputs that mimic or resemble human-generated content. Unlike traditional AI, which operates based on predefined rules or input-output mappings, generative AI employs models capable of understanding and generating new, original data based on patterns learned from existing data. This approach enables machines to autonomously produce diverse outputs, including images, text, audio, and video, often indistinguishable from human-created content. Generative AI techniques include generative adversarial networks (GANs), variational autoencoders (VAEs), and transformers, among others.
What is the total CAGR expected to be recorded for the generative AI market during 2024-2030?
The generative AI market is expected to record a CAGR of 36.7% from 2024-2030.
How is the large language model market shaping the broader Generative AI industry?
The large language model market is significantly shaping the broader Generative AI industry by driving innovation, setting benchmarks for model performance, and democratizing access to advanced AI capabilities. These models, such as GPT-3/3.5/4, are pushing the boundaries of what's possible in natural language understanding, generation, and manipulation. They are fueling the development of new applications across various sectors, from conversational agents and content generation to code completion and data analysis. As these models become more sophisticated and accessible, they are poised to revolutionize how we interact with technology and leverage generative AI-driven solutions in everyday life and business.
Which are the key drivers supporting the growth of the generative AI market?
The key factors driving the growth of the generative AI market include include advancements in deep learning algorithms and architectures, increased computational power and access to cloud resources, expanding applications across industries such as healthcare, finance, and entertainment, as well as rising demand for personalized and context-aware AI solutions.
Which are the top 3 verticals prevailing in the generative AI market?
BFSI, media & entertainment, and retail & eCommerce are leading in the generative AI market due to their data-centric operations and customer-focused strategies. BFSI relies on generative AI for risk assessment and customer service automation, while media & entertainment uses it for content creation and recommendation systems. Retail & eCommerce harness generative AI for personalized marketing and customer support, driving engagement and sales. These sectors' emphasis on data analytics and AI-driven innovation propels their market share in the generative AI landscape.
Who are the key vendors in the generative AI market?
Some major players in the generative AI market include IBM (US), NVIDIA (US), Open AI (US), Anthropic (US), Meta (US), Microsoft (US), Google (US), AWS (US), Adobe (US), Accenture (Ireland), Capgemini (France), Insilico Medicine (Hong Kong), Simplified (US), Lumen5 (Canada), AI21 Labs (Israel), Hugging Face (US), Dialpad (US), Persado (US), Copy.ai (US), Synthesis AI (US), Hypernetuse AI (US), Viable (US), Together AI (US), Defog.ai (Singapore), Mistral AI (France), Adept (US), DeepSearch Labs (UK), Stability AI (UK), Lightricks (Israel), Cohere (Canada), Writesonic (US), Colossyan (UK), amberSearch (Germany), Mosaic ML (US), Inflection AI (US), Glean (US), Jasper (US), Runway (US), Inworld AI (US), Typeface (US), Paige.AI (US), Upstage (South Korea), PlayHT (US), Speechify (US), Midjourney (US), Fireflies (US), InstaDeep (UK), Synthesis (UK), Mostly AI (Austria), Forethought (US), Character.ai (US), GFP-GAN (China), Fontjoy (Italy), EtherAI (US), Starry AI (US), Magic Studio (US), Balchuan AI (China), Salesforce (US), Technology Innovation Institute (Abu Dhabi), Abacus.AI (US), and OpenLM (US). .
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The generative AI market research study involved extensive secondary sources, directories, journals, and paid databases. Primary sources were mainly industry 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 software, hardware, services, technology, applications, warehouse sizes, 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 the supply and demand sides were interviewed to obtain qualitative and quantitative information on the market. The primary sources from the supply side included various industry experts, including Chief Experience Officers (CXOs); Vice Presidents (VPs); directors from business development, marketing, and generative AIs expertise; related key executives from generative AIs 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 in understanding 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 AIs 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.
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Market Size Estimation
Multiple approaches were adopted for estimating and forecasting the generative AI market. The first approach involves estimating the market size by summation of companies’ revenue generated through the sale of solutions and services.
Market Size Estimation Methodology-Top-down approach
In the top-down approach, an exhaustive list of all the vendors offering solutions and services in the generative AI market was prepared. The revenue contribution of the market vendors was estimated through annual reports, press releases, funding, investor presentations, paid databases, and primary interviews. Each vendor's offerings were evaluated based on breadth of software and services according to data types, business functions, deployment models, and verticals. The aggregate of all the companies’ revenue was extrapolated to reach the overall market size. Each subsegment was studied and analyzed for its global market size and regional penetration. The markets were triangulated through both primary and secondary research. The primary procedure included extensive interviews for key insights from industry leaders, such as CIOs, CEOs, VPs, directors, and marketing executives. The market numbers were further triangulated with the existing MarketsandMarkets’ repository for validation.
Market Size Estimation Methodology-Bottom-up approach
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 AIs 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 AIs providers, and organic and inorganic business development activities of regional and global players were estimated. With the data triangulation procedure and data validation through primary interviews, 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 and Top-Down Approach
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Data Triangulation
After arriving at the overall market size using the market size estimation processes as explained above, the market was split into several segments and subsegments. To complete the overall market engineering process and arrive at the exact statistics of each market segment and subsegment, data triangulation and market breakup procedures were employed, wherever applicable. The overall market size was then used in the top-down procedure to estimate the size of other individual markets via percentage splits of the market segmentation.
Market Definition
Generative AI refers to a subset of artificial intelligence techniques focused on creating data, content, or outputs that mimic or resemble human-generated content. Unlike traditional AI, which operates based on predefined rules or input-output mappings, generative AI employs models capable of understanding and generating new, original data based on patterns learned from existing data. This approach enables machines to autonomously produce diverse outputs, including images, text, audio, and video, often indistinguishable from human-created content. Generative AI techniques include generative adversarial networks (GANs), variational autoencoders (VAEs), and transformers, among others.
Stakeholders
- Generative AI software developers
- Large Language Model (LLM) vendors
- Business analysts
- Cloud service providers
- Consulting service providers
- Enterprise end-users
- Distributors and Value-added Resellers (VARs)
- Government agencies
- Independent Software Vendors (ISV)
- Managed service providers
- Market research and consulting firms
- Support & maintenance service providers
- System Integrators (SIs)/migration service providers
- Language service providers
- Technology providers
Report Objectives
- To define, describe, and predict the generative AI market by offering (software and services), data modality, application, vertical, 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 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)
Generating Response ...
Growth opportunities and latent adjacency in Generative AI Market