The Artificial General Intelligence (AGI) Market Size is valued at USD 3.2 billion in 2023 and is predicted to reach USD 26.9 billion by the year 2031 at a 31.3% CAGR during the forecast period for 2024-2031.
Artificial General Intelligence (AGI) is a type of AI that can understand, learn, and perform any intellectual task that a human can. Unlike narrow AI, which is specialized for specific tasks, AGI would have the ability to generalize knowledge across different areas, function autonomously, and improve itself over time. AGI could revolutionize industries but also raises significant ethical, social, and existential concerns. The advancement of AGI is also being fueled by significant investments made by industry participants in scientific research and development (R&D) programs. Additionally, a major factor in the growth of the artificial intelligence sector is the broad use of AI in a variety of industries.
However, the biggest obstacles in the AGI industry are technical difficulty. In contrast with conventional AI, which is intended for specific uses, AGI needs to be as flexible and adaptive as human intellect. This calls for advances in domains like automatic learning capacities, abstraction, contextual awareness, and linguistic recognition. Even with sophisticated learning algorithms, modern artificial intelligence (AI) systems find it difficult to extend information among domains without requiring extensive remediation. Furthermore, developing a system that can comprehend and handle enormous volumes of unorganized data in a way that is compatible with the human mind is an enormous undertaking. The actual deployment of artificial general intelligence (AGI) is hampered by the AI field's ongoing struggles with constraints in technical capacity, analyzing data, and the inclusion of sophisticated neural networks.
The artificial general intelligence (AGI) market is segmented based on offering, technology, deployment, processing, application, company size, end user, and business model. Based on the offering, the market is segmented into hardware, software, and services. According to the technology segment, the market is segmented into machine learning (ML), natural language processing, deep learning, and robotics. By deployment, the market is divided into cloud-based, on-premise, and Web-based. By processing, the market is segmented into image processing, text processing, and voice processing. By application, the market is segmented into cybersecurity, financial trading, healthcare research, manufacturing, online search, personal security, predictive analytics, robot-assisted surgery, smart cars, telehealth, virtual nursing assistance, and others. By company size, the market is segmented into small and medium-sized enterprises (SMEs), large enterprises. By end-user, the market is further segmented into agriculture, automotive, financial services and insurance (BFSI), banking, educational institutes, government and defense, healthcare, IT and telecom, manufacturing, media and entertainment, retail and eCommerce, transportation and logistics, and others. By Business Model, the market is segmented into B2B, B2C, and B2B2C.
The natural language processing (NLP) artificial general intelligence (AGI) market is expected to hold a major global market share in 2023. BFSI is the dominant category. Businesses providing banking services, including commercial, retail, and expenditures, as well as investment management services, financial services, and protection services for both economic and life risks, make up the BFSI sector. To control operational issues, ensure profitable development, and provide optimum efficiency, financial institutions like banks and insurance companies need to use innovative information technology.
According to market research, the software category is currently the industry leader, having obtained the highest part of the AGI market. The segment's prominence can be due to the increasing need for advanced algorithms, machine learning frameworks, and natural language processing software and tools. These technologies enable innovation in AGI systems. The increasing popularity of software and tools can be attributed to their ability to enhance fundamental intelligence and decision-making capabilities in AGI applications across many industries.
The North American artificial general intelligence (AGI) market is expected to note the highest market share in revenue in the near future. This can be attributed to its robust manufacturing foundation, highly developed technological bases, and extensive use of the Internet of Things (IoT) devices across numerous industries. The area encourages artificial intelligence adoption and ecological expansion with its favorable legislative environment, excellent investing environment, and dynamic and driven culture. In addition, Asia Pacific is likely to grow rapidly in the artificial general intelligence (AGI) market because of the expanding embrace of NLP (natural language processing) technologies. In addition, the growing online retail sector is increasing demand for AI-driven solutions that improve customer service, streamline operations, and target advertising.
| Report Attribute | Specifications |
| Market Size Value In 2023 | USD 3.2 Bn |
| Revenue Forecast In 2031 | USD 26.9 Bn |
| Growth Rate CAGR | CAGR of 31.3% from 2024 to 2031 |
| Quantitative Units | Representation of revenue in US$ Million and CAGR from 2024 to 2031 |
| Historic Year | 2019 to 2023 |
| Forecast Year | 2024-2031 |
| Report Coverage | The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
| Segments Covered | By Offering, Technology, Deployment, Processing, Application, Company Size, End User, And Business Model |
| Regional Scope | North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
| Country Scope | U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
| Competitive Landscape | Ada Support, AIBrain, Albert Technologies, Alibaba, Amazon, Anki, Apple, Astute Solutions, Baidu, Brainasoft, Brighterion, Cambricon, Google, H2O.ai, Huawei, IBM, IDEAL, IFlyTek, Intel, Ipsoft, Megvii Technology, Meta, Microsoft, NanoRep (LogMeIn), Salesforce, SAP, SenseTime, Sogou, SoundHound, Tencent, Thunder Software, Wipro, Yseop, and Zebra Medical Vision. |
| Customization Scope | Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
| Pricing And Available Payment Methods | Explore pricing alternatives that are customized to your particular study requirements. |
Artificial General Intelligence (AGI) Market By Offering-
Artificial General Intelligence (AGI) Market By Technology-
Artificial General Intelligence (AGI) Market By Deployment-
Artificial General Intelligence (AGI) Market By Processing-
Artificial General Intelligence (AGI) Market By Application-
Artificial General Intelligence (AGI) Market By Company Size-
Artificial General Intelligence (AGI) Market By End User-
Artificial General Intelligence (AGI) Market By Business Model-
Artificial General Intelligence (AGI) Market By Region-
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
This study employed a multi-step, mixed-method research approach that integrates:
This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.
Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.
Secondary data for the market study was gathered from multiple credible sources, including:
These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.
Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.
Primary interviews for this study involved:
Interviews were conducted via:
Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.
All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.
The data validation process included:
This ensured that the dataset used for modelling was clean, robust, and reliable.
The bottom-up approach involved aggregating segment-level data, such as:
This method was primarily used when detailed micro-level market data were available.
The top-down approach used macro-level indicators:
This approach was used for segments where granular data were limited or inconsistent.
To ensure accuracy, a triangulated hybrid model was used. This included:
This multi-angle validation yielded the final market size.
Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.
Given inherent uncertainties, three scenarios were constructed:
Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.