인공지능 시장 보고서 : 유형, 오퍼링, 기술, 시스템, 최종사용자 산업, 지역별(2024-2032년)

The global artificial intelligence market size reached US$ 92.6 Billion in 2023. Looking forward, IMARC Group expects the market to reach US$ 737.1 Billion by 2032, exhibiting a growth rate (CAGR) of 24.9% during 2024-2032. The increasing adoption of cloud-based solutions, rising sales of autonomous vehicles (AVs), and the growing prevalence of chronic diseases represent some of the key factors driving the market.

Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, particularly computer systems. It involves the creation of algorithms and systems that enable machines to perform tasks that typically require human cognitive functions, such as learning, reasoning, problem-solving, and decision-making. AI encompasses a wide range of technologies, including machine learning, natural language processing, and computer vision. Machine learning, a subset of AI, involves the development of algorithms that allow computers to learn from and make predictions or decisions based on data without explicit programming. Natural language processing enables machines to understand and interpret human language, facilitating communication between humans and computers. Additionally, computer vision empowers machines to interpret and analyze visual information from the world, similar to the way humans perceive and understand images.

The exponential increase in data generation and the availability of high-performance computing resources have enabled AI systems to process and analyze massive datasets, fostering the development of advanced AI algorithms and models. Additionally, AI-driven automation has the potential to streamline operations and enhance efficiency in various sectors, from manufacturing to logistics. Businesses are leveraging AI to optimize processes, reduce costs, and improve overall productivity. Other than this, AI-powered algorithms enable businesses to offer personalized experiences to customers, enhancing engagement and satisfaction. Recommendation systems, chatbots, and virtual assistants use AI to understand customer preferences and deliver tailored solutions. Besides this, AI is revolutionizing healthcare by aiding in diagnostics, drug discovery, and treatment development. Medical imaging analysis, predictive analytics, and genomics research are benefiting from AI-driven insights. In line with this, smart devices and the Internet of Things (IoT) are integrating AI for voice recognition, facial recognition, and predictive maintenance, contributing to the growth of AI in consumer electronics. Furthermore, AI is reshaping the financial industry with algorithmic trading, fraud detection, and risk assessment. Machine learning algorithms analyze market trends and predict investment opportunities. Moreover, breakthroughs in machine learning techniques, particularly deep learning, have enhanced the capabilities of AI systems in tasks such as image recognition, natural language understanding, and predictive analytics, leading to broader applications across industries.

Artificial Intelligence Market Trends/Drivers:

Data Proliferation and Processing Power

The digital age has generated an unprecedented amount of information, ranging from customer behavior data to sensor data from IoT devices. This wealth of data serves as the foundation for training AI algorithms and models. Moreover, the availability of high-performance computing resources, including GPUs and specialized AI hardware, has empowered researchers and developers to process and analyze these vast datasets at remarkable speeds. This synergy between data availability and processing power has unlocked the potential to develop sophisticated AI systems capable of recognizing patterns, making predictions, and deriving insights that were previously inaccessible.

Advancements in Machine Learning

Deep learning algorithms, inspired by neural networks, have shown exceptional performance in tasks such as image recognition, natural language understanding, and speech synthesis. These algorithms excel at automatically learning hierarchical features from raw data, enabling them to perform complex tasks with remarkable accuracy. Transfer learning, a technique within deep learning, allows models pre-trained on one task to be fine-tuned for another, reducing the need for extensive labeled data. Such advancements have lowered the barriers to entry for AI development and expanded its applicability in various domains, driving the integration of AI solutions into everyday technologies and processes.

Automation and Efficiency

Automation powered by AI technologies, such as robotic process automation (RPA) and autonomous systems, is eliminating repetitive and mundane tasks that previously consumed valuable human resources. This shift allows human workers to focus on higher-value tasks that require creativity, critical thinking, and problem-solving. Industries like manufacturing, logistics, and customer service are deploying AI-powered robots and systems to handle tasks ranging from inventory management to customer interactions. The result is not only increased productivity but also improved accuracy and consistency. Businesses are recognizing the potential of AI to transform their operations, leading to the widespread adoption of automation solutions that augment human capabilities and drive business growth.

Artificial Intelligence Industry Segmentation:

IMARC Group provides an analysis of the key trends in each segment of the global artificial intelligence market report, along with forecasts at the global, regional and country levels from 2024-2032. Our report has categorized the market based on type, offering, technology, system and end-use industry.

Breakup by Type:

• Narrow/Weak Artificial Intelligence
• General/Strong Artificial Intelligence

Narrow/weak artificial intelligence dominates the market

The report has provided a detailed breakup and analysis of the market based on the type. This includes narrow/weak artificial intelligence and general/strong artificial intelligence. According to the report, narrow/weak artificial intelligence represented the largest segment.

Unlike general or strong AI, which aims to replicate human-like cognitive abilities across a wide range of tasks, narrow AI is designed to excel in specific, well-defined tasks. This focused approach allows for more efficient development and deployment. Narrow AI technologies, such as machine learning and natural language processing, have demonstrated remarkable proficiency in tasks like image recognition, language translation, virtual assistants, and recommendation systems. This effectiveness has led to rapid adoption in sectors like healthcare, finance, e-commerce, and manufacturing, where the immediate benefits of automation, efficiency, and data-driven insights are highly valued. Additionally, the development of narrow AI systems often requires less complex algorithms and data, making it more accessible for businesses to implement and integrate into their existing workflows.

Breakup by Offering:

• Hardware
• Software
• Services

Software holds the largest share in the market

A detailed breakup and analysis of the market based on the offering has also been provided in the report. This includes hardware, software, and services. According to the report, software accounted for the largest market share.

In the industry of artificial intelligence (AI), software plays a pivotal role as it serves as the foundation for developing, deploying, and managing AI applications. The accessibility of AI software frameworks, libraries, and tools has lowered the entry barriers for businesses, researchers, and developers to experiment with and integrate AI capabilities into their operations. Software offerings encompass a wide array of AI applications, from machine learning platforms to natural language processing APIs. This flexibility allows organizations to tailor AI solutions to their specific needs, whether it's optimizing supply chains, personalizing customer experiences, or automating decision-making processes. Furthermore, the cloud-based nature of many AI software solutions enables seamless scalability and updates, ensuring that businesses can stay abreast of the latest advancements without significant infrastructure investments.

Breakup by Technology:

• Machine Learning
• Natural Language Processing
• Context-Aware Computing
• Computer Vision
• Others

Machine learning is the most widely used technology

The report has provided a detailed breakup and analysis of the market based on technology. This includes machine learning, natural language processing, context-aware computing, computer vision, and others. According to the report, machine learning represented the largest segment.

AI software serves as the intellectual core, housing algorithms, models, and data processing mechanisms that empower machines to simulate human cognitive functions. This software-driven approach offers unparalleled flexibility, enabling developers and businesses to tailor AI solutions to a diverse range of applications across industries. The accessibility of AI software tools and platforms has democratized AI development, allowing organizations to integrate AI functionalities into existing workflows and applications without extensive hardware investments. This accessibility, coupled with the rapid advancements in AI algorithms and techniques, ensures that software remains at the forefront of AI innovation. Moreover, the cloud-based deployment of AI software solutions facilitates scalability and updates, ensuring that businesses can stay current with the evolving AI landscape.

Breakup by System:

• Intelligence Systems
• Decision Support Processing
• Hybrid Systems
• Fuzzy Systems

Intelligence systems hold the largest share in the market

A detailed breakup and analysis of the market based on the system has also been provided in the report. This includes intelligence systems, decision support processing, hybrid systems, and fuzzy systems. According to the report, intelligence systems accounted for the largest market share.

Intelligence systems encompass a wide array of applications, ranging from virtual assistants and chatbots to advanced recommendation engines and predictive analytics platforms. These systems leverage AI algorithms to process vast amounts of data, derive meaningful insights, and facilitate informed decision-making. The appeal of intelligence systems lies in their ability to enhance efficiency, accuracy, and customer engagement across various sectors. Virtual assistants streamline customer interactions, while recommendation engines personalize user experiences, both of which contribute to improved satisfaction and retention. Furthermore, intelligence systems enable predictive analytics, enabling businesses to anticipate trends and optimize strategies.

Breakup by End-Use Industry:

• Healthcare
• Manufacturing
• Automotive
• Agriculture
• Retail
• Security
• Human Resources
• Marketing
• Financial Services
• Transportation and Logistics
• Others

Manufacturing represents the largest segment

The report has provided a detailed breakup and analysis of the market based on end-use industry. This includes healthcare, manufacturing, automotive, agriculture, retail, security, human resources, marketing, financial services, transportation and logistics, and others. According to the report, manufacturing represented the largest segment.

AI technologies are revolutionizing manufacturing by enhancing operational efficiency, quality control, and product innovation. AI-driven automation optimizes production processes, leading to reduced downtime, higher productivity, and improved resource utilization. Manufacturing involves intricate tasks such as predictive maintenance, where AI algorithms analyze equipment data to predict maintenance needs, preventing costly breakdowns. Quality control benefits from AI-powered visual inspection systems that detect defects with exceptional accuracy. Additionally, AI-driven analytics aid in demand forecasting and supply chain optimization, ensuring efficient inventory management and minimizing production bottlenecks.

Breakup by Region:

• North America
  • United States
  • Canada
• Asia Pacific
  • China
  • Japan
  • India
  • South Korea
  • Australia
  • Indonesia
  • Others
• Europe
  • Germany
  • France
  • United Kingdom
  • Italy
  • Spain
  • Russia
  • Others
• Latin America
  • Brazil
  • Mexico
  • Others
• Middle East and Africa

North America exhibits a clear dominance in the market

The report has also provided a comprehensive analysis of all the major regional markets, which include North America (the United States and Canada); Europe (Germany, France, the United Kingdom, Italy, Spain, Russia, and others); Asia Pacific (China, Japan, India, South Korea, Australia, Indonesia, and others); Latin America (Brazil, Mexico, and others); and the Middle East and Africa. According to the report, North America was the largest market for artificial intelligence.

North America possesses a dense concentration of tech giants, research institutions, and startups, facilitating knowledge exchange and collaboration. Silicon Valley, in particular, serves as a global hub for AI research and development. The availability of venture capital funding in North America accelerates AI-driven entrepreneurship and disruptive solutions. Additionally, a skilled workforce proficient in AI technologies contributes to the region's competitive advantage. Major industries, including technology, healthcare, finance, and manufacturing, heavily invest in AI to gain a competitive edge, driving the market's growth. Moreover, North American governments promote AI research through funding and supportive policies. Collaboration between academia and industry propels AI advancements, while a receptive consumer base encourages AI adoption in products and services.

Competitive Landscape:

Major technology companies like Google, Microsoft, and IBM invest significantly in AI research and development, contributing to breakthroughs in algorithms, natural language processing, and machine learning frameworks. Additionally, tech leaders such as Facebook, OpenAI, and NVIDIA contribute to open-source AI projects, democratizing access to AI tools and accelerating development by fostering collaboration among developers. Other than this, organizations such as Apple, Amazon, and Intel acquire AI startups to harness their expertise and innovative solutions. Partnerships with research institutions and other industry players foster knowledge exchange and technology sharing. Besides this, financial institutions like JPMorgan Chase utilize AI for risk assessment, fraud detection, and trading algorithms, enhancing accuracy and decision-making.

The market research report has provided a comprehensive analysis of the competitive landscape in the market. Detailed profiles of all major companies have also been provided. Some of the key players in the market include:

• Amazon Web Services Inc.
• Apple Inc.
• Baidu
• Cisco Systems Inc.
• Facebook Inc.
• General Electric Company
• Google LLC (Alphabet Inc.)
• International Business Machines
• Intel Corporation
• Micron Technology Inc.
• Microsoft Corporation
• Nvidia Corporation
• Oracle Corporation
• Rockwell Automation Inc.
• Samsung Electronics Co. Ltd.
• SAP SE
• Siemens AG

Recent Developments:

• Google LLC revealed the introduction of two advanced AI-powered solutions: the Target and Lead Identification Suite, along with the Multiomics Suite, designed specifically for the life sciences sector. These innovative offerings are designed to address the intricate challenges faced within the industry. The Target and Lead Identification Suite is engineered to streamline the process of identifying potential targets and leads, contributing to more efficient drug discovery and development. On the other hand, the Multiomics Suite, a sophisticated tool, facilitates comprehensive analysis by integrating diverse omics data, thereby enhancing insights into complex biological systems.
• International Business Machines introduced its latest security solution known as the IBM Security QRadar Suite by integrating powerful analytics and threat detection capabilities, to fortify their security posture and safeguard sensitive data. This comprehensive suite is strategically designed to address the evolving landscape of cybersecurity challenges. With a focus on bolstering security measures, the IBM Security QRadar Suite combines advanced technologies and methodologies to provide a robust defense against modern threats. This suite encompasses a range of tools and features that enable organizations to proactively identify, analyze, and respond to potential security breaches.
• Cisco Systems Inc. recently unveiled a series of AI-powered innovations for its Webex collaboration platform. These advancements represent Cisco's commitment to enhancing the collaborative experience and efficiency of its users. By integrating artificial intelligence, the Webex collaboration platform aims to elevate virtual interactions to a new level of effectiveness.

Key Questions Answered in This Report

• 1. What was the size of the global artificial intelligence market in 2023?
• 2. What is the expected growth rate of the global artificial intelligence market during 2024-2032?
• 3. What are the key factors driving the global artificial intelligence market?
• 4. What has been the impact of COVID-19 on the global artificial intelligence market?
• 5. What is the breakup of the global artificial intelligence market based on the type?
• 6. What is the breakup of the global artificial intelligence market based on the offering?
• 7. What is the breakup of the global artificial intelligence market based on the technology?
• 8. What is the breakup of the global artificial intelligence market based on the system?
• 9. What is the breakup of the global artificial intelligence market based on the end-use industry?
• 10. What are the key regions in the global artificial intelligence market?
• 11. Who are the key players/companies in the global artificial intelligence market?

Table of Contents

1 Preface

2 Scope and Methodology

• 2.1 Objectives of the Study
• 2.2 Stakeholders
• 2.3 Data Sources
  • 2.3.1 Primary Sources
  • 2.3.2 Secondary Sources
• 2.4 Market Estimation
  • 2.4.1 Bottom-Up Approach
  • 2.4.2 Top-Down Approach
• 2.5 Forecasting Methodology

3 Executive Summary

4 Introduction

• 4.1 Overview
• 4.2 Key Industry Trends

5 Global Artificial Intelligence Market

• 5.1 Market Overview
• 5.2 Market Performance
• 5.3 Impact of COVID-19
• 5.4 Market Forecast

6 Market Breakup by Type

• 6.1 Narrow/Weak Artificial Intelligence
  • 6.1.1 Market Trends
  • 6.1.2 Market Forecast
• 6.2 General/Strong Artificial Intelligence
  • 6.2.1 Market Trends
  • 6.2.2 Market Forecast

7 Market Breakup by Offering

• 7.1 Hardware
  • 7.1.1 Market Trends
  • 7.1.2 Market Forecast
• 7.2 Software
  • 7.2.1 Market Trends
  • 7.2.2 Market Forecast
• 7.3 Services
  • 7.3.1 Market Trends
  • 7.3.2 Market Forecast

8 Market Breakup by Technology

• 8.1 Machine Learning
  • 8.1.1 Market Trends
  • 8.1.2 Market Forecast
• 8.2 Natural Language Processing
  • 8.2.1 Market Trends
  • 8.2.2 Market Forecast
• 8.3 Context-Aware Computing
  • 8.3.1 Market Trends
  • 8.3.2 Market Forecast
• 8.4 Computer Vision
  • 8.4.1 Market Trends
  • 8.4.2 Market Forecast
• 8.5 Others
  • 8.5.1 Market Trends
  • 8.5.2 Market Forecast

9 Market Breakup by System

• 9.1 Intelligence Systems
  • 9.1.1 Market Trends
  • 9.1.2 Market Forecast
• 9.2 Decision Support Processing
  • 9.2.1 Market Trends
  • 9.2.2 Market Forecast
• 9.3 Hybrid Systems
  • 9.3.1 Market Trends
  • 9.3.2 Market Forecast
• 9.4 Fuzzy Systems
  • 9.4.1 Market Trends
  • 9.4.2 Market Forecast

10 Market Breakup by End-Use Industry

• 10.1 Healthcare
  • 10.1.1 Market Trends
  • 10.1.2 Market Forecast
• 10.2 Manufacturing
  • 10.2.1 Market Trends
  • 10.2.2 Market Forecast
• 10.3 Automotive
  • 10.3.1 Market Trends
  • 10.3.2 Market Forecast
• 10.4 Agriculture
  • 10.4.1 Market Trends
  • 10.4.2 Market Forecast
• 10.5 Retail
  • 10.5.1 Market Trends
  • 10.5.2 Market Forecast
• 10.6 Security
  • 10.6.1 Market Trends
  • 10.6.2 Market Forecast
• 10.7 Human Resources
  • 10.7.1 Market Trends
  • 10.7.2 Market Forecast
• 10.8 Marketing
  • 10.8.1 Market Trends
  • 10.8.2 Market Forecast
• 10.9 Financial Services
  • 10.9.1 Market Trends
  • 10.9.2 Market Forecast
• 10.10 Transportation and Logistics
  • 10.10.1 Market Trends
  • 10.10.2 Market Forecast
• 10.11 Others
  • 10.11.1 Market Trends
  • 10.11.2 Market Forecast

11 Market Breakup by Region

• 11.1 North America
  • 11.1.1 United States
    • 11.1.1.1 Market Trends
    • 11.1.1.2 Market Forecast
  • 11.1.2 Canada
    • 11.1.2.1 Market Trends
    • 11.1.2.2 Market Forecast
• 11.2 Asia Pacific
  • 11.2.1 China
    • 11.2.1.1 Market Trends
    • 11.2.1.2 Market Forecast
  • 11.2.2 Japan
    • 11.2.2.1 Market Trends
    • 11.2.2.2 Market Forecast
  • 11.2.3 India
    • 11.2.3.1 Market Trends
    • 11.2.3.2 Market Forecast
  • 11.2.4 South Korea
    • 11.2.4.1 Market Trends
    • 11.2.4.2 Market Forecast
  • 11.2.5 Australia
    • 11.2.5.1 Market Trends
    • 11.2.5.2 Market Forecast
  • 11.2.6 Indonesia
    • 11.2.6.1 Market Trends
    • 11.2.6.2 Market Forecast
  • 11.2.7 Others
    • 11.2.7.1 Market Trends
    • 11.2.7.2 Market Forecast
• 11.3 Europe
  • 11.3.1 Germany
    • 11.3.1.1 Market Trends
    • 11.3.1.2 Market Forecast
  • 11.3.2 France
    • 11.3.2.1 Market Trends
    • 11.3.2.2 Market Forecast
  • 11.3.3 United Kingdom
    • 11.3.3.1 Market Trends
    • 11.3.3.2 Market Forecast
  • 11.3.4 Italy
    • 11.3.4.1 Market Trends
    • 11.3.4.2 Market Forecast
  • 11.3.5 Spain
    • 11.3.5.1 Market Trends
    • 11.3.5.2 Market Forecast
  • 11.3.6 Russia
    • 11.3.6.1 Market Trends
    • 11.3.6.2 Market Forecast
  • 11.3.7 Others
    • 11.3.7.1 Market Trends
    • 11.3.7.2 Market Forecast
• 11.4 Latin America
  • 11.4.1 Brazil
    • 11.4.1.1 Market Trends
    • 11.4.1.2 Market Forecast
  • 11.4.2 Mexico
    • 11.4.2.1 Market Trends
    • 11.4.2.2 Market Forecast
  • 11.4.3 Others
    • 11.4.3.1 Market Trends
    • 11.4.3.2 Market Forecast
• 11.5 Middle East and Africa
  • 11.5.1 Market Trends
  • 11.5.2 Market Breakup by Country
  • 11.5.3 Market Forecast

12 SWOT Analysis

• 12.1 Overview
• 12.2 Strengths
• 12.3 Weaknesses
• 12.4 Opportunities
• 12.5 Threats

13 Value Chain Analysis

14 Porters Five Forces Analysis

• 14.1 Overview
• 14.2 Bargaining Power of Buyers
• 14.3 Bargaining Power of Suppliers
• 14.4 Degree of Competition
• 14.5 Threat of New Entrants
• 14.6 Threat of Substitutes

15 Price Indicators

16 Competitive Landscape

• 16.1 Market Structure
• 16.2 Key Players
• 16.3 Profiles of Key Players
  • 16.3.1 Amazon Web Services Inc.
    • 16.3.1.1 Company Overview
    • 16.3.1.2 Product Portfolio
  • 16.3.2 Apple Inc.
    • 16.3.2.1 Company Overview
    • 16.3.2.2 Product Portfolio
    • 16.3.2.3 Financials
    • 16.3.2.4 SWOT Analysis
  • 16.3.3 Baidu
    • 16.3.3.1 Company Overview
    • 16.3.3.2 Product Portfolio
  • 16.3.4 Cisco Systems Inc.
    • 16.3.4.1 Company Overview
    • 16.3.4.2 Product Portfolio
    • 16.3.4.3 Financials
    • 16.3.4.4 SWOT Analysis
  • 16.3.5 Facebook Inc.
    • 16.3.5.1 Company Overview
    • 16.3.5.2 Product Portfolio
    • 16.3.5.3 Financials
    • 16.3.5.4 SWOT Analysis
  • 16.3.6 General Electric Company
    • 16.3.6.1 Company Overview
    • 16.3.6.2 Product Portfolio
    • 16.3.6.3 Financials
    • 16.3.6.4 SWOT Analysis
  • 16.3.7 Google LLC (Alphabet Inc.)
    • 16.3.7.1 Company Overview
    • 16.3.7.2 Product Portfolio
    • 16.3.7.3 SWOT Analysis
  • 16.3.8 International Business Machines
    • 16.3.8.1 Company Overview
    • 16.3.8.2 Product Portfolio
    • 16.3.8.3 Financials
    • 16.3.8.4 SWOT Analysis
  • 16.3.9 Intel Corporation
    • 16.3.9.1 Company Overview
    • 16.3.9.2 Product Portfolio
    • 16.3.9.3 Financials
    • 16.3.9.4 SWOT Analysis
  • 16.3.10 Micron Technology Inc.
    • 16.3.10.1 Company Overview
    • 16.3.10.2 Product Portfolio
    • 16.3.10.3 Financials
    • 16.3.10.4 SWOT Analysis
  • 16.3.11 Microsoft Corporation
    • 16.3.11.1 Company Overview
    • 16.3.11.2 Product Portfolio
    • 16.3.11.3 Financials
    • 16.3.11.4 SWOT Analysis
  • 16.3.12 Nvidia Corporation
    • 16.3.12.1 Company Overview
    • 16.3.12.2 Product Portfolio
    • 16.3.12.3 Financials
    • 16.3.12.4 SWOT Analysis
  • 16.3.13 Oracle Corporation
    • 16.3.13.1 Company Overview
    • 16.3.13.2 Product Portfolio
    • 16.3.13.3 Financials
    • 16.3.13.4 SWOT Analysis
  • 16.3.14 Rockwell Automation Inc.
    • 16.3.14.1 Company Overview
    • 16.3.14.2 Product Portfolio
    • 16.3.14.3 Financials
    • 16.3.14.4 SWOT Analysis
  • 16.3.15 Samsung Electronics Co. Ltd.
    • 16.3.15.1 Company Overview
    • 16.3.15.2 Product Portfolio
    • 16.3.15.3 Financials
    • 16.3.15.4 SWOT Analysis
  • 16.3.16 SAP SE
    • 16.3.16.1 Company Overview
    • 16.3.16.2 Product Portfolio
    • 16.3.16.3 Financials
    • 16.3.16.4 SWOT Analysis
  • 16.3.17 Siemens AG
    • 16.3.17.1 Company Overview
    • 16.3.17.2 Product Portfolio
    • 16.3.17.3 Financials
    • 16.3.17.4 SWOT Analysis