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The global artificial intelligence market size was valued at USD 196.63 billion in 2023 and is projected to expand at a compound annual growth rate (CAGR) of 37.3% from 2023 to 2030. The continuous research and innovation directed by tech giants are driving adoption of advanced technologies in industry verticals, such as automotive, healthcare, retail, finance, and manufacturing. For instance, in December 2023, Google LLC launched ‘Gemini’, a large language AI model. Google's new Gemini model would come with three variants Gemini Nano, Gemini Pro, and Gemini Ultra. Gemini stands from its competitors out due to its native multimodal characteristic.
AI has proven to be a significant revolutionary element of the upcoming digital era. Tech giants like Amazon.com, Inc.; Google LLC; Apple Inc.; Facebook; International Business Machines Corporation; and Microsoft are investing significantly in research and development of AI, thus increasing the artificial intelligence market cap. These companies are working to make AI more accessible for enterprise use cases. Moreover, various companies adopt AI technology to provide a better customer experience and improve their presence in the artificial intelligence industry 4.0. For instance, in March 2020, McDonald’s made its most significant tech investment of USD 300 million to acquire an AI start-up in Tel Aviv to provide a personalized customer experience using artificial intelligence.
The essential fact accelerating the rate of innovation in AI is accessibility to historical datasets. Since data storage and recovery have become more economical, healthcare institutions and government agencies build unstructured data accessible to the research domain. Researchers are getting access to rich datasets, from historic rain trends to clinical imaging. The next-generation computing architectures, with access to rich datasets, are encouraging information scientists and researchers to innovate faster.
Furthermore, progress in profound learning and ANN (Artificial Neural Networks) has also fueled the adoption of AI in several industries, such as aerospace, healthcare, manufacturing, and automotive. ANN works in recognizing similar patterns and helps in providing modified solutions. Tech companies like Google Maps have been adopting ANN to improve their route and work on feedback received using ANN. ANN is substituting conventional machine learning systems to evolve precise and accurate versions. For instance, recent advancements in computer vision technology, such as GAN (Generative Adversarial Networks) and SSD (Single Shot MultiBox Detector), have led to digital image processing techniques. For instance, images and videos taken in low light, or low resolution, can be transformed into HD quality by employing these techniques. Continuous research in computer vision has built the foundation for digital image processing in security & surveillance, healthcare, and transportation, among other sectors. Such emerging methods in machine learning are anticipated to alter the manner AI versions are trained and deployed.
COVID-19 outbreak is expected to stimulate market growth of next-generation tech domains, including artificial intelligence, owing to mandated WFH (work-from-home) policy due to the pandemic. For instance, LogMeIn, Inc., a U.S.-based company that provides Software-as-a-Service (SaaS) and cloud-based customer engagement and remote connectivity & collaboration services, has experienced a significant increase in new sign-ups across its product portfolios amid the pandemic. Also, tech companies are expanding their product offerings and services to widen availability across the globe. For instance, in April 2020, Google LLC launched an AI-enabled chatbot called Rapid Response Virtual Agent for call centers. This chatbot is built to respond to issues customers might be experiencing due to coronavirus (COVID-19) outbreak over voice, chat, and other social channels.
The market growth stage in the artificial intelligence market is high, and the pace of the market growth is accelerating. The AI market is characterized by a high degree of innovation owing to the rapid technological advancements driven by factors such as advancements in machine learning algorithms, availability of big data, and increasing computing power. Subsequently, innovative AI applications are constantly emerging, disrupting existing industries and creating new ones.
The AI market is also characterized by a high level of merger and acquisition (M&A) activity by the leading players. This is due to several factors, including the desire to gain access to new AI technologies and talent, the need to consolidate in a rapidly growing market, and increasing strategic importance of AI.
The AI market is also subject to increasing regulatory scrutiny due to concerns about the potential negative impacts of AI, such as algorithmic bias, privacy violations, and job displacement. As a result, governments around the world are developing regulations to govern the development and use of AI. These regulations could have a significant impact on the AI market, affecting the development and adoption of AI technologies.
There are a limited number of direct product substitutes for AI. However, several technologies can be used to achieve similar outcomes to AI, such as automation, rule-based systems, and expert systems. These technologies can be used as substitutes for AI in certain applications, but they typically do not offer the same level of performance or flexibility as AI.
End-user concentration is a significant factor in the AI market. Since there are a number of end-user industries that are driving demand for AI solutions. The concentration of demand in a small number of end-user industries creates opportunities for companies that focus on developing AI solutions for these industries. However, it also creates challenges for companies that are trying to compete in a crowded market.
The introduction of big data is projected to accelerate the expansion of the artificial intelligence market as a large volume of data needs to be stored, captured, and analyzed. End-users are increasingly concerned about the need to manage and improve the computational model of such data. This is encouraging companies to introduce artificial intelligence solutions at a faster pace and increase the implementation of artificial intelligence in business. Several private and public organizations have gathered tasks or application-specific information comprising issues, such as medical informatics, fraud detection, national intelligence, marketing, and cybersecurity. Artificial Intelligence algorithms enable automated analysis of unsupervised and unorganized data by continuously improving each set of data.
Artificial intelligence is becoming increasingly important in big data because it enables the extraction of high-level and complex abstractions through a centralized learning process. The need for extracting and mining meaningful patterns from large amounts of data propels the growth of artificial intelligence in big data analytics. Furthermore, the technology aids in overcoming big data analytics challenges such as data analysis trustworthiness, raw data format variation, imbalanced input data, and highly distributed input sources. Another issue is a lack of efficient storage and information retrieval, as data is collected in large quantities and made available across multiple domains. These difficulties are overcome by using semantic indexing, which improves comprehension and knowledge discovery.
Software solutions led the market and accounted for more than 36.7% of the global revenue in 2022. This high percentage can be attributed to prudent advances in information storage capacity, high computing power, and parallel processing capabilities to deliver high-end services. Furthermore, the ability to extract data, provide real-time insight, and aid decision-making has positioned this segment to capture a significant portion of the market. Artificial intelligence software solutions include libraries for designing and deploying artificial intelligence applications, such as primitives, linear algebra, inference, sparse matrices, video analytics, and multiple hardware communication capabilities. The need for enterprises to understand and analyze visual content to gain meaningful insights is expected to spur the adoption of artificial intelligence software over the forecast period.
Companies adopt AI services to reduce their overall operational costs, yielding more profit. Artificial Intelligence as a Service, or AIaaS, is being used by companies to obtain a competitive advantage over the cloud, thus assisting the growth of the mobile AI market. Artificial intelligence services include installation, integration, maintenance, and support undertakings. The segment is projected to grow significantly over the forecast period. AI hardware includes chipsets such as GPU (Graphics Processing Unit), CPU, application-specific integrated circuits (ASIC), and field-programmable gate arrays (FPGAs). GPUs and CPUs currently dominate the artificial intelligence hardware market due to their high computing capabilities required for AI frameworks. For instance, in September 2020, Atomwise partnered with GC Pharma to offer AI-based services to the former and help develop more effective novel hemophilia therapies.
On the back of its growing prominence because of its complicated data-driven applications, including text/content or speech recognition, deep learning segment led the market and accounted for around 36.4% share of global revenue in 2022. Deep learning offers lucrative investment opportunities as it helps overcome the challenges of high data volumes. For instance, in July 2020, Zebra Medical Vision collaborated with TELUS Ventures to enhance the availability of the former’s deep learning solutions in North America and expand AI solutions to clinical care settings and new modalities.
Machine learning and deep learning cover significant investments in AI. They include both AI platforms and cognitive applications, including tagging, clustering, categorization, hypothesis generation, alerting, filtering, navigation, and visualization, which facilitate the development of advisory, intelligent, and cognitively enabled solutions. Growing deployment of cloud-based computing platforms and on-premises hardware equipment for the safe and secure restoration of large volumes of data has paved a way for the expansion of the analytics platform. Rising investments in research and development by leading players will also play a crucial role in increasing the uptake of artificial intelligence technologies. During the forecast period, NLP segment is expected to gain momentum. NLP is becoming increasingly widely used in various businesses to understand client preferences, evolving trends, purchasing behavior, decision-making processes, and more, in a better manner.
The advertising & media segment led the market and accounted for more than 19.5% of the global revenue share in 2022. This high share is attributable to growing AI marketing applications with significant traction. For instance, in January 2022, Cadbury started an initiative to let small business owners create their AD for free using the face and voice of a celebrity, with the help of an AI tool. However, the healthcare sector is anticipated to gain a leading share by 2030. Healthcare segment has been segregated based on use cases such as robot-assisted surgery, dosage error reduction, virtual nursing assistants, clinical trial participant identifier, hospital workflow management, preliminary diagnosis, and automated image diagnosis. The BFSI segment includes financial analysis, risk assessment, and investment/portfolio management solicitations.
Artificial intelligence has witnessed a significant share in the BFSI sector due to high demand for risk & compliance applications along with regulatory and supervisory technologies (SupTech). By using AI-based insights in Suptech tools in financial markets, the authorities are increasingly examining FinTech-based apps used for regulatory, supervisory, and oversight purposes for any potential benefits. In a similar vein, regulated institutions are creating and implementing FinTech applications for reporting and regulatory and compliance obligations. Financial institutions are using AI applications for risk management and internal controls as well. The combination of AI technology with behavioral sciences enables large financial organizations to prevent wrongdoing, moving the emphasis from ex-post resolution to proactive prevention.
Other verticals for artificial intelligence systems include retail, law, automotive & transportation, agriculture, and others. Increasing government regulations play a crucial role in driving the growth of the automotive artificial intelligence market. Governments worldwide are becoming increasingly concerned about road safety and enacting strict safety measures. Additionally, conversational AI platform is also one of the most used artificial intelligence app in every vertical. Retail segment is anticipated to witness a substantial rise owing to the increasing focus on providing an enhanced shopping experience. An increasing amount of digital data in text, sound, and images from different social media sources is driving the need for data mining and analytics. In the entertainment and advertising industry, AI has been creating a positive impact, and companies are using AI techniques to promote their products and connect to the customer base.
North America dominated the market and accounted for over 36.8% share of global revenue in 2022. This high share is attributable to favorable government initiatives to encourage the adoption of artificial intelligence (AI) across various industries. Governments in North America are investing in AI research and development, establishing specialized research institutes and centers, and funding AI-related projects. They also utilize AI in many fields, such as enhancing public safety and transportation and promoting healthcare innovation.
The regional market in Asia Pacific is anticipated to witness significant growth in artificial intelligence market. This growth owes to the significantly increasing investments in artificial intelligence. The artificial intelligence market in China is diverse and covers various applications, such as natural language processing, computer vision, robotics, autonomous vehicles, and virtual assistants. With its substantial population and extensive data resources, China provides a fertile environment for developing and implementing AI technologies.
In addition, the government in Saudi Arabia is actively promoting artificial intelligence through a range of initiatives, policies, and financial support programs. For instance, the Saudi Data and AI Authority (SDAIA) is a government agency to help Saudi Arabia create an AI ecosystem of government and private sector entities. SDAIA deploys innovative AI solutions, which comprise strategies for combining data and AI into key domains.
Vendors in the market are focusing on increasing customer base to gain a competitive edge in the industry. Therefore, key players are taking several strategic initiatives, such as mergers and acquisitions, and partnerships with other major companies. For instance, in April 2020, Advanced Micro Devices announced a strategic alliance with Oxide Interactive LLC, a video game developer company, to develop graphics technologies for cloud gaming market space. Both companies have planned to create a set of tools & techniques to address the real-time demands of cloud-based gaming. Also, in October 2023, NVIDIA Corporation and Oracle enter into a partnership agreement to assist customers in overcoming business difficulties using accelerated computing and AI. The partnership is aimed to help speed up customer adoption of artificial intelligence services.
Open AI is developing an innovative artificial general intelligence (AGI) model code-named Project Q-Star. The model could have an immense impact on the overall AI market and provide breakthroughs in how one interacts with technology, automate processes, and solve a few of the world’s most pressing issues.
In November 2023, The University of Cambridge, along with Intel Corporation and Dell Technologies announced the implementation of the co-designed fastest AI supercomputer ‘Dawn Phase 1’. Leading technical teams built the supercomputer that mobilize the power of both high-performance computing (HPC) and artificial intelligence (AI) for solving some of the world’s most critical challenges. This is projected to accelerate the future technology leadership and inward investment into the U.K. technology sector.
In March 2023, Enlitic introduced the latest release of Enlitic Curie, a platform that makes it easy for radiology departments to manage their workflow. The platform hosts Curie|ENDEX, which utilizes NLP and computer vision for the analysis & processing of medical images; and Curie|ENCOG, which leverages AI to identify and protect Protected Health Information.
In June 2023, AMD unveiled its AI Platform strategy with the introduction of the AMD Instinct MI300 Series accelerator family, which included a first look at the AMD Instinct MI300X accelerator. The accelerator has been developed for the purpose of large language model training and inference for generative AI workloads.
In June 2023, IBM announced that it would be partnering with The All England Lawn Tennis Club at the 2023 Wimbledon Championship. The company would be leveraging IBM watsonx’s generative AI technology to product commentary for video highlights during the tournament. Additionally, the IBM AI Draw Analysis will offer insights regarding how favorable the draws would be for every singles player.
In April 2023, H20.ai announced a strategic partnership with GeoTechnologies, a Japan-based provider of map data & location information solutions for vehicle navigation systems. The company has leveraged H20.ai’s H2O AI Cloud to develop an AI-powered platform that uses on-board camera footage for gauging sidewalk safety.
In January 2023, Iris.ai announced that it had received the EIC Accelerator Blended finance, which is EIC’s flagship startup funding program. The funding includes a €2.4 million grant as well as up to €12 million in investments from the EIC and the European Investment Grant.
In September 2022, AiCure launched its clinical site services program that partners with sponsors and sites through the course of research and offers data-driven, actionable insights to minimize study risks and optimize the workflow.
In August 2022, Atomwise announced an exclusive, strategic research collaboration with Sanofi for AI-powered drug discovery. As part of the deal, Sanofi is leveraging Atomwise’s AtomNet platform for the purpose of computational discovery & research of up to 5 drug targets.
In July 2022, Clarifai announced the launch of its ‘Clarifai Community’ free service for enabling everyone to share, create, and use The World’s AI. Moreover, it also announced the development of the ‘AI Lake’ product category, which collects and centralizes every AI resource of an enterprise, and offers tools for sharing across the enterprise.
In June 2022, Francisco Partners announced that it had acquired the healthcare analytics and data assets that formed a part of Watson Health business of IBM. As part of this development, the new standalone company was named Merative, with its products organized in 6 product categories.
In April 2022, Sensely and Keralty S.A.S, along with its American affiliate Sanitas USA, Inc., announced a multi-year partnership. Through this collaboration, Sanitas aims to power its next-gen mySanitas application by leveraging Sensely’s advanced visual UI and multilingual symptom assessment tool.
Report Attribute |
Details |
Market size value in 2023 |
USD 196.63 billion |
Revenue forecast in 2030 |
USD 1,811.75 billion |
Growth rate |
CAGR of 37.3% from 2023 to 2030 |
Actual data |
2017 - 2023 |
Forecast period |
2024 - 2030 |
Quantitative units |
Revenue in USD billion and CAGR from 2023 to 2030 |
Report coverage |
Revenue forecast, company ranking, competitive landscape, growth factors, and trends |
Segments covered |
Solution, technology, end-use, function, region |
Regional scope |
North America; Europe; Asia Pacific; South America; MEA |
Country scope |
U.S.; Canada; Mexico; Germany; U.K.; China; Japan; India; Brazil |
Key companies profiled |
Advanced Micro Devices; AiCure; Arm Limited; Atomwise, Inc.; Ayasdi AI LLC; Baidu, Inc.; Clarifai, Inc; Cyrcadia Health; Enlitic, Inc.; Google LLC; H2O.ai.; HyperVerge, Inc.; International Business Machines Corporation; IBM Watson Health; Intel Corporation; Iris.ai AS.; Lifegraph; Microsoft; NVIDIA Corporation; Sensely, Inc.; and Zebra Medical Vision, Inc. |
Customization scope |
Free report customization (equivalent up to 8 analysts working days) with purchase. Addition or alteration to country, regional & segment scope. |
Pricing and purchase options |
Avail customized purchase options to meet your exact research needs. Explore purchase options |
This report forecasts revenue growth at global, regional, and country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2017 to 2030. For this study, Grand View Research has segmented the global artificial intelligence market report based on solution, technology, function, end-use, and region:
Solution Outlook (Revenue, USD Billion, 2017 - 2030)
Hardware
Accelerators
Processors
Memory
Network
Software
Services
Professional
Managed
Technology Outlook (Revenue, USD Billion, 2017 - 2030)
Deep Learning
Machine Learning
Natural Language Processing (NLP)
Natural Language Understanding (NLU)
Natural Language Generation (NLG)
Machine Vision
Generative AI
Generative Adversarial Networks (GANs)
Transformers
Variational Auto-encoders
Diffusion Networks
Function Outlook (Revenue, USD Billion, 2017 - 2030)
Cybersecurity
Finance and Accounting
Human Resource Management
Legal and Compliance
Operations
Sales and Marketing
Supply Chain Management
End-use Outlook (Revenue, USD Billion, 2017 - 2030)
Healthcare
Robot Assisted Surgery
Virtual Nursing Assistants
Hospital Workflow Management
Dosage Error Reduction
Clinical Trial Participant Identifier
Preliminary Diagnosis
Automated Image Diagnosis
BFSI
Risk Assessment
Financial Analysis/Research
Investment/Portfolio Management
Others
Law
Retail
Advertising & Media
Automotive & Transportation
Agriculture
Manufacturing
Others
Regional Outlook (Revenue, USD Billion, 2017 - 2030)
North America
U.S.
Canada
Mexico
Europe
Germany
U.K.
Asia Pacific
China
Japan
India
South America
Brazil
Middle East and Africa (MEA)
b. The global artificial intelligence market size was estimated at USD 136.6 billion in 2022 and is expected to reach USD 196.6 billion in 2023.
b. The global artificial intelligence market is expected to grow at a compound annual growth rate of 37.3% from 2023 to 2030 to reach USD 1,811.8 billion by 2030.
b. North America dominated the AI market and accounted for over 36.8% share of global revenue in 2021.
b. Some key players operating in the AI market include Atomwise, Inc.; Lifegraph; Sense.ly, Inc.; Zebra Medical Vision, Inc.; Baidu, Inc.; H2O ai; IBM Watson Health; NVIDIA; Enlitic, Inc.; Google LLC; Intel Corporation; and Microsoft Corporation.
b. Key factors that are driving the artificial intelligence market growth include a rise in the adoption of big data, analytics, and the increasing potential of R&D in developing AI systems and technological innovations across the globe.
Table of Contents
Chapter 1. Methodology and Scope
1.1. Market Segmentation and Scope
1.2. Market Definitions
1.3. Research Methodology
1.3.1. Information Procurement
1.3.2. Information or Data Analysis
1.3.3. Market Formulation & Data Visualization
1.3.4. Data Validation & Publishing
1.4. Research Scope and Assumptions
1.4.1. List of Data Sources
Chapter 2. Executive Summary
2.1. Market Outlook
2.2. Segment Outlook
2.3. Competitive Insights
Chapter 3. Artificial Intelligence Market Variables, Trends, & Scope
3.1. Market Introduction/Lineage Outlook
3.2. Market Size and Growth Prospects (USD Billion)
3.3. Industry Value Chain Analysis
3.4. Market Dynamics
3.4.1. Market Drivers Analysis
3.4.2. Market Restraints Analysis
3.4.3. Industry Opportunities
3.4.4. Industry Challenges
3.5. Artificial Intelligence Market Analysis Tools
3.5.1. Porter’s Analysis
3.5.1.1. Bargaining power of the suppliers
3.5.1.2. Bargaining power of the buyers
3.5.1.3. Threats of substitution
3.5.1.4. Threats from new entrants
3.5.1.5. Competitive rivalry
3.5.2. PESTEL Analysis
3.5.2.1. Political landscape
3.5.2.2. Economic and Social landscape
3.5.2.3. Technological landscape
3.5.2.4. Environmental landscape
3.5.2.5. Legal landscape
Chapter 4. Artificial Intelligence Market: Solution Estimates & Trend Analysis
4.1. Segment Dashboard
4.2. Artificial Intelligence Market: Solution Movement Analysis, 2022 & 2030 (USD Million)
4.3. Hardware
4.3.1. Hardware Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
4.4. Software
4.4.1. Software Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
4.5. Services
4.5.1. Services Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
Chapter 5. Artificial Intelligence Market: Technology Estimates & Trend Analysis
5.1. Segment Dashboard
5.2. Artificial Intelligence Market: Technology Movement Analysis, 2022 & 2030 (USD Million)
5.3. Deep Learning
5.3.1. Deep Learning Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
5.4. Machine Learning
5.4.1. Machine Learning Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
5.5. Natural Language Processing (NLP)
5.5.1. Natural Language Processing (NLP) Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
5.6. Machine Vision
5.6.1. Machine Vision Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
5.7. Generative AI
5.7.1. Generative AI Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
Chapter 6. Artificial Intelligence Market: Function Estimates & Trend Analysis
6.1. Segment Dashboard
6.2. Artificial Intelligence Market: Function Movement Analysis, 2022 & 2030 (USD Million)
6.3. Cybersecurity
6.3.1. Cybersecurity Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
6.4. Finance and Accounting
6.4.1. Finance and Accounting Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
6.5. Human Resource Management
6.5.1. Human Resource Management Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
6.6. Legal and Compliance
6.6.1. Legal and Compliance Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
6.7. Operations
6.7.1. Operations Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
6.8. Sales and Marketing
6.8.1. Sales and Marketing Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
6.9. Supply Chain Management
6.9.1. Supply Chain Management Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
Chapter 7. Artificial Intelligence Market: End-user Estimates & Trend Analysis
7.1. Segment Dashboard
7.2. Artificial Intelligence Market: End-user Movement Analysis, 2022 & 2030 (USD Million)
7.3. Healthcare
7.3.1. Healthcare Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
7.4. BFSI
7.4.1. BFSI Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
7.5. Law
7.5.1. Law Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
7.6. Retail
7.6.1. Retail Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
7.7. Advertising & Media
7.7.1. Advertising & Media Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
7.8. Automotive & Transportation
7.8.1. Automotive & Transportation Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
7.9. Agriculture
7.9.1. Agriculture Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
7.10. Manufacturing
7.10.1. Manufacturing Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
7.11. Others
7.11.1. Others Market Revenue Estimates and Forecasts, 2017 - 2030 (USD Million)
Chapter 8. Artificial Intelligence Market: Regional Estimates & Trend Analysis
8.1. Artificial Intelligence Market Share, By Region, 2022 & 2030, USD Million
8.2. North America
8.2.1. North America Artificial Intelligence Market Estimates and Forecasts, 2017 - 2030 (USD Million)
8.2.2. U.S.
8.2.2.1. U.S. Artificial Intelligence Market Estimates and Forecasts, 2017 - 2030 (USD Million)
8.2.3. Canada
8.2.3.1. Canada Artificial Intelligence Market Estimates and Forecasts, 2017 - 2030 (USD Million)
8.2.4. Mexico
8.2.4.1. Mexico Artificial Intelligence Market Estimates and Forecasts, 2017 - 2030 (USD Million)
8.3. Europe
8.3.1. Europe Artificial Intelligence Market Estimates and Forecasts, 2017 - 2030 (USD Million)
8.3.2. U.K.
8.3.2.1. U.K. Artificial Intelligence Market Estimates and Forecasts, 2017 - 2030 (USD Million)
8.3.3. Germany
8.3.3.1. Germany Artificial Intelligence Market Estimates and Forecasts, 2017 - 2030 (USD Million)
8.3.4. France
8.3.4.1. France Artificial Intelligence Market Estimates and Forecasts, 2017 - 2030 (USD Million)
8.4. Asia Pacific
8.4.1. Asia Pacific Artificial Intelligence Market Estimates and Forecasts, 2017 - 2030 (USD Million)
8.4.2. China
8.4.2.1. China Artificial Intelligence Market Estimates and Forecasts, 2017 - 2030 (USD Million)
8.4.3. Japan
8.4.3.1. Japan Artificial Intelligence Market Estimates and Forecasts, 2017 - 2030 (USD Million)
8.4.4. India
8.4.4.1. India Artificial Intelligence Market Estimates and Forecasts, 2017 - 2030 (USD Million)
8.5. South America
8.5.1. South America Artificial Intelligence Market Estimates and Forecasts, 2017 - 2030 (USD Million)
8.5.2. Brazil
8.5.2.1. Brazil Artificial Intelligence Market Estimates and Forecasts, 2017 - 2030 (USD Million)
8.6. Middle East and Africa
8.6.1. Middle East and Africa Artificial Intelligence Market Estimates and Forecasts, 2017 - 2030 (USD Million)
Chapter 9. Competitive Landscape
9.1. Recent Developments & Impact Analysis by Key Market Participants
9.2. Company Categorization
9.3. Company Market Positioning
9.4. Company Market Share Analysis
9.5. Company Heat Map Analysis
9.6. Strategy Mapping
9.6.1. Expansion
9.6.2. Mergers & Acquisition
9.6.3. Partnerships & Collaborations
9.6.4. New Product Launches
9.6.5. Research And Development
9.7. Company Profiles
9.7.1. Advanced Micro Devices
9.7.1.1. Participant’s Overview
9.7.1.2. Financial Performance
9.7.1.3. Product Benchmarking
9.7.1.4. Recent Developments
9.7.2. AiCure
9.7.2.1. Participant’s Overview
9.7.2.2. Financial Performance
9.7.2.3. Product Benchmarking
9.7.2.4. Recent Developments
9.7.3. Arm Limited
9.7.3.1. Participant’s Overview
9.7.3.2. Financial Performance
9.7.3.3. Product Benchmarking
9.7.3.4. Recent Developments
9.7.4. Atomwise, Inc.
9.7.4.1. Participant’s Overview
9.7.4.2. Financial Performance
9.7.4.3. Product Benchmarking
9.7.4.4. Recent Developments
9.7.5. Ayasdi AI LLC
9.7.5.1. Participant’s Overview
9.7.5.2. Financial Performance
9.7.5.3. Product Benchmarking
9.7.5.4. Recent Developments
9.7.6. Baidu, Inc.
9.7.6.1. Participant’s Overview
9.7.6.2. Financial Performance
9.7.6.3. Product Benchmarking
9.7.6.4. Recent Developments
9.7.7. Clarifai, Inc
9.7.7.1. Participant’s Overview
9.7.7.2. Financial Performance
9.7.7.3. Product Benchmarking
9.7.7.4. Recent Developments
9.7.8. Cyrcadia Health
9.7.8.1. Participant’s Overview
9.7.8.2. Financial Performance
9.7.8.3. Product Benchmarking
9.7.8.4. Recent Developments
9.7.9. Enlitic, Inc.
9.7.9.1. Participant’s Overview
9.7.9.2. Financial Performance
9.7.9.3. Product Benchmarking
9.7.9.4. Recent Developments
9.7.10. Google LLC
9.7.10.1. Participant’s Overview
9.7.10.2. Financial Performance
9.7.10.3. Product Benchmarking
9.7.10.4. Recent Developments
9.7.11. H2O.ai.
9.7.11.1. Participant’s Overview
9.7.11.2. Financial Performance
9.7.11.3. Product Benchmarking
9.7.11.4. Recent Developments
9.7.12. HyperVerge, Inc.
9.7.12.1. Participant’s Overview
9.7.12.2. Financial Performance
9.7.12.3. Product Benchmarking
9.7.12.4. Recent Developments
9.7.13. International Business Machines Corporation
9.7.13.1. Participant’s Overview
9.7.13.2. Financial Performance
9.7.13.3. Product Benchmarking
9.7.13.4. Recent Developments
9.7.14. IBM Watson Health
9.7.14.1. Participant’s Overview
9.7.14.2. Financial Performance
9.7.14.3. Product Benchmarking
9.7.14.4. Recent Developments
9.7.15. Intel Corporation
9.7.15.1. Participant’s Overview
9.7.15.2. Financial Performance
9.7.15.3. Product Benchmarking
9.7.15.4. Recent Developments
9.7.16. Iris.ai AS.
9.7.16.1. Participant’s Overview
9.7.16.2. Financial Performance
9.7.16.3. Product Benchmarking
9.7.16.4. Recent Developments
9.7.17. Lifegraph
9.7.17.1. Participant’s Overview
9.7.17.2. Financial Performance
9.7.17.3. Product Benchmarking
9.7.17.4. Recent Developments
9.7.18. Microsoft
9.7.18.1. Participant’s Overview
9.7.18.2. Financial Performance
9.7.18.3. Product Benchmarking
9.7.18.4. Recent Developments
9.7.19. NVIDIA Corporation
9.7.19.1. Participant’s Overview
9.7.19.2. Financial Performance
9.7.19.3. Product Benchmarking
9.7.19.4. Recent Developments
9.7.20. Sensely, Inc.
9.7.20.1. Participant’s Overview
9.7.20.2. Financial Performance
9.7.20.3. Product Benchmarking
9.7.20.4. Recent Developments
9.7.21. Zebra Medical Vision, Inc.
9.7.21.1. Participant’s Overview
9.7.21.2. Financial Performance
9.7.21.3. Product Benchmarking
9.7.21.4. Recent Developments
List of Tables
Table 1 Global AI Market by Solution, 2017 - 2030 (USD Million)
Table 2 Global AI Market by Technology, 2017 - 2030 (USD Million)
Table 3 Global AI Market by function, 2017 - 2030 (USD Million)
Table 4 Global AI Market by End Use, 2017 - 2030 (USD Million)
Table 5 Global AI Market by Region, 2017 - 2030 (USD Million)
Table 6 North America AI Market by country, 2017 - 2030 (USD Million)
Table 7 Europe AI Market by country, 2017 - 2030 (USD Million)
Table 8 Asia Pacific AI Market by country, 2017 - 2030 (USD Million)
Table 9 South America AI Market by country, 2017 - 2030 (USD Million)
Table 10 MEA AI Market by country, 2017 - 2030 (USD Million)
Table 11 Key companies launching new products/services
Table 12 Key companies engaged in mergers & acquisition
Table 13 Key companies engaged in Research & development
Table 14 Key Companies engaged in expansion
List of Figures
Fig. 1 AI Market segmentation
Fig. 2 Market research process
Fig. 3 Information procurement
Fig. 4 Primary research pattern
Fig. 5 Market research approaches
Fig. 6 Market formulation & validation
Fig. 7 AI Market snapshot
Fig. 8 AI Market segment snapshot
Fig. 9 AI Market competitive landscape snapshot
Fig. 10 Market driver impact analysis
Fig. 11 Market restraint impact analysis
Fig. 12 AI Market, solution outlook key takeaways (USD Million)
Fig. 13 AI Market: solution movement analysis 2022 & 2030 (USD Million)
Fig. 14 Hardware market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 15 Software market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 16 Services market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 17 AI Market: technology outlook key takeaways (USD Million)
Fig. 18 AI Market: technology movement analysis 2022 & 2030 (USD Million)
Fig. 19 Deep Learning market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 20 Machine Learning market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 21 Natural Language Processing market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 22 Machine Vision market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 23 Generative AI market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 24 AI Market: Function outlook key takeaways (USD Million)
Fig. 25 AI Market: Function movement analysis 2022 & 2030 (USD Million)
Fig. 26 Cybersecurity market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 27 Finance and Accounting market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 28 Human Resource Management market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 29 Legal and Compliance market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 30 Operations market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 31 Sales and Marketing market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 32 Supply Chain Management market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 33 AI Market: End-Use outlook key takeaways (USD Million)
Fig. 34 AI Market: End-Use movement analysis 2022 & 2030 (USD Million)
Fig. 35 Healthcare market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 36 BFSI market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 37 Law market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 38 Legal market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 39 Retail market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 40 Advertising & Media market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 41 Automotive & Transportation market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 42 Agriculture market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 43 Manufacturing market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 44 Others market revenue estimates and forecasts, 2017 - 2030 (USD Million)
Fig. 45 Regional marketplace: Key takeaways
Fig. 46 AI Market: Regional outlook, 2022 & 2030 (USD Million)
Fig. 47 North America AI Market estimates and forecasts, 2018 - 2030 (USD Million)
Fig. 48 U.S. AI Market estimates and forecasts, 2018 - 2030 (USD Million)
Fig. 49 Canada AI Market estimates and forecasts, 2018 - 2030 (USD Million)
Fig. 50 Mexico AI Market estimates and forecasts, 2018 - 2030 (USD Million)
Fig. 51 Europe AI Market estimates and forecasts, 2018 - 2030 (USD Million)
Fig. 52 UK AI Market estimates and forecasts, 2018 - 2030 (USD Million)
Fig. 53 Germany AI Market estimates and forecasts, 2018 - 2030 (USD Million)
Fig. 54 France AI Market estimates and forecasts, 2018 - 2030 (USD Million)
Fig. 55 Asia Pacific AI Market estimates and forecasts, 2018 - 2030 (USD Million)
Fig. 56 China AI Market estimates and forecasts, 2018 - 2030 (USD Million)
Fig. 57 Japan AI Market estimates and forecasts, 2018 - 2030 (USD Million)
Fig. 58 India AI Market estimates and forecasts, 2018 - 2030 (USD Million)
Fig. 59 South America AI Market estimates and forecasts, 2018 - 2030 (USD Million)
Fig. 60 Brazil AI Market estimates and forecasts, 2018 - 2030 (USD Million)
Fig. 61 MEA AI Market estimates and forecasts, 2018 - 2030 (USD Million)
Fig. 62 Strategy framework
Fig. 63 Company Categorization
Market Segmentation
The artificial intelligence sector has predominantly been driven by software, with a limited number of companies producing hardware components like CPUs, ASICs, FPGAs, and GPUs. However, recent advancements in design tools have made FPGAs more compatible with intricate software methodologies, making them more accessible to those who design and construct algorithm models. To address issues related to power consumption, slow processing, and inefficiency, hardware-based AI solutions are being introduced. As the market matures, there is a growing demand for a new business model that leverages predictive, efficient automation and scalable parallel processing capabilities. The requirement for hardware-based AI products emerged as end-use applications called for lower power and higher performance. In the past few years, only a handful of companies have ventured into the development of these components. To gain a competitive edge, vendors like IBM Corporation and Intel Corporation have begun manufacturing AI chipsets to achieve high performance in scaling dynamic parallel processes.
Artificial Intelligence employs layers of algorithms to recognize objects visually, interpret human speech, and process data. These algorithms are utilized for calculations, data processing, and automated reasoning tasks. There’s a growing demand to refine these algorithms to deliver improved and efficient solutions for various end-use applications. AI researchers are continually striving to enhance algorithms across different domains. Traditional algorithms often fall short in terms of accuracy and efficiency, prompting manufacturers and technology developers to concentrate on developing standardized algorithms. For example, NVIDIA’s traditional GPUs used a machine learning algorithm for voice recognition and image labeling. However, this approach had limitations concerning accuracy and solution time. Consequently, the company revised its algorithm by integrating big data and computational power, altering these dynamics. This has enabled machines and devices to operate with increased accuracy, thereby propelling the growth of AI computing and artificial intelligence.
A significant challenge hindering the growth of the industry is the need for vast amounts of data to train AI systems for character and image recognition. Additionally, storing such large volumes of data contributes to the issue of data traceability. Companies like Google, Inc., and Facebook employ artificial intelligence in image recognition applications, which necessitates access to a substantial amount of data. In the healthcare sector, the data needed for identifying tumors in X-rays is extremely limited. The primary issue that emerges in artificial intelligence due to the lack of data availability is making effective decisions using the data at hand. Moreover, the development of networks that can be trained using less data is ongoing and is anticipated to be commercialized in the next 10 to 12 years.
This section will provide insights into the contents included in this artificial intelligence market report and help gain clarity on the structure of the report to assist readers in navigating smoothly.
Industry overview
Industry trends
Market drivers and restraints
Market size
Growth prospects
Porter’s analysis
PESTEL analysis
Key market opportunities prioritized
Competitive landscape
Company overview
Financial performance
Product benchmarking
Latest strategic developments
Market size, estimates, and forecast from 2018 to 2030
Market estimates and forecast for product segments up to 2030
Regional market size and forecast for product segments up to 2030
Market estimates and forecast for application segments up to 2030
Regional market size and forecast for application segments up to 2030
Company financial performance
A three-pronged approach was followed for deducing the artificial intelligence market estimates and forecasts. The process has three steps: information procurement, analysis, and validation. The whole process is cyclical, and steps repeat until the estimates are validated. The three steps are explained in detail below:
Information procurement: Information procurement is one of the most extensive and important stages in our research process, and quality data is critical for accurate analysis. We followed a multi-channel data collection process for artificial intelligence market to gather the most reliable and current information possible.
Analysis: We mine the data collected to establish baselines for forecasting, identify trends and opportunities, gain insight into consumer demographics and drivers, and so much more. We utilized different methods of artificial intelligence market data depending on the type of information we’re trying to uncover in our research.
Market Research Efforts: Bottom-up Approach for estimating and forecasting demand size and opportunity, top-down Approach for new product forecasting and penetration, and combined approach of both Bottom-up and Top-down for full coverage analysis.
Value-Chain-Based Sizing & Forecasting: Supply-side estimates for understanding potential revenue through competitive benchmarking, forecasting, and penetration modeling.
Demand-side estimates for identifying parent and ancillary markets, segment modeling, and heuristic forecasting.
Qualitative Functional Deployment (QFD) Modelling for market share assessment.
Market formulation and validation: We mine the data collected to establish baselines for forecasting, identify trends and opportunities, gain insight into consumer demographics and drivers, and so much more. We utilize different methods of data analysis depending on the type of information we’re trying to uncover in our research.
Market Formulation: This step involves the finalization of market numbers. This step on an internal level is designed to manage outputs from the Data Analysis step.
Data Normalization: The final market estimates and forecasts are then aligned and sent to industry experts, in-panel quality control managers for validation.
This step also entails the finalization of the report scope and data representation pattern.
Validation: The process entails multiple levels of validation. All these steps run in parallel, and the study is forwarded for publishing only if all three levels render validated results.
The artificial intelligence market was categorized into four segments, namely solution (Hardware, Software, Services), technology (Deep Learning, Machine Learning, Natural Language Processing, Machine Vision), end-use (Healthcare, BFSI, Law, Retail, Advertising & Media, Automotive & Transportation, Agriculture, Manufacturing), regions (North America, Europe, Asia Pacific, South America, Middle East and Africa).
The artificial intelligence market was segmented into solution, technology, end-use, and regions. The demand at a segment level was deduced using a funnel method. Concepts like the TAM, SAM, SOM, etc., were put into practice to understand the demand. We at GVR deploy three methods to deduce market estimates and determine forecasts. These methods are explained below:
Demand estimation of each product across countries/regions summed up to from the total market.
Variable analysis for demand forecast.
Demand estimation via analyzing paid database, and company financials either via annual reports or paid database.
Primary interviews for data revalidation and insight collection.
Used extensively for new product forecasting or analyzing penetration levels.
Tool used invoice product flow and penetration models Use of regression multi-variant analysis for forecasting Involves extensive use of paid and public databases.
Primary interviews and vendor-based primary research for variable impact analysis.
The artificial intelligence market was analyzed at a regional level. The globe was divided into North America, Europe, Asia Pacific, South America, Middle East and Africa, keeping in focus variables like consumption patterns, export-import regulations, consumer expectations, etc. These regions were further divided into nine countries, namely, the U.S.; Canada; Mexico; Germany; the UK; China; Japan; India; Brazil.
All three above-mentioned market research methodologies were applied to arrive at regional-level conclusions. The regions were then summed up to form the global market.
The artificial intelligence market was analyzed via companies operating in the sector. Analyzing these companies and cross-referencing them to the demand equation helped us validate our assumptions and conclusions. Key market players analyzed include:
Advanced Micro Devices. - Advanced Micro Devices (AMD) is a multinational corporation known for its semiconductor production. The company’s offerings include Graphics Processing Units (GPUs), Accelerated Processing Units (APUs), and System-on-Chip (SoC) solutions. They also provide technology for gaming consoles, chipsets, and processors for embedded systems and servers. AMD operates under two main business divisions: Computing and Graphics, and Enterprise, Embedded and Semi-custom. The Computing and Graphics segment focuses on GPUs, APUs, and chipsets, while the Enterprise, Embedded and Semi-custom segment concentrates on server and embedded processors, semi-custom SoC products, and technology for game consoles. AMD’s clientele is diverse and extensive. It includes large direct data centers, which are primarily cloud service providers. Original Design Manufacturers (ODMs), Original Equipment Manufacturers (OEMs), and online retailers are also part of AMD’s customer base. Additionally, the company serves add-in-board manufacturers (AIBs), PC OEMs, independent distributors, and system integrators. This wide range of customers underscores AMD’s significant presence in the global semiconductor industry.
AiCure - Established in 2010, AiCure is a company based in the United States that designs and implements AI technologies that have been clinically validated to enhance medication compliance and patient behavior. The company provides a software-as-a-service (SaaS) model that incorporates motion detection and facial recognition technology. AiCure is in the process of creating a mobile technology platform that integrates the latest advancements in artificial intelligence, including machine learning, predictive analytics, deep learning, and computer vision. This platform leverages AI on mobile devices to verify medication intake in high-risk groups and clinical trials. The company’s areas of expertise include artificial intelligence, risk management, clinical research, population health, health outcomes, and randomized clinical trials. This wide range of specializations highlights AiCure’s commitment to leveraging advanced technology to improve health outcomes.
Arm Limited - Arm Limited, a subsidiary of ARM Holdings Plc, is a global player in the semiconductor Intellectual Property (IP) sector. The company licenses its technology to semiconductor and systems companies, and designs these technologies both in the UK and internationally. Original Equipment Manufacturers (OEMs) utilize Arm’s technology for a variety of applications, including digital set-top boxes, mobile handsets, network routers, and car braking systems. Arm offers a comprehensive suite of solutions encompassing processors, graphics and multimedia, physical IP, development tools, IoT solutions, system IP, and wireless IP. With a presence in North America, Europe, the Middle East, and the Asia Pacific, Arm has a truly global footprint.
Atomwise Inc. - Atomwise, Inc. is a company that utilizes deep learning technology for the purpose of drug discovery. It has formed partnerships with several biotechnology and pharmaceutical corporations, such as Jiangsu Hansoh Pharmaceutical Group Co., Ltd. in China, the Drugs for Neglected Diseases initiative (DNDi) in Switzerland, and BridgeBio Therapeutics in the U.S., to create superior medicines using its AI platform. The company’s solution is driven by AtomNet technology, a deep learning neural network designed specifically for structure-based drug design and discovery. This technology leverages insights gathered from a multitude of experimental affinity measurements and protein structures to predict the binding of small molecules to proteins.
Ayasdi AI LLC - Established in 2008, Ayasdi AI LLC is an analytics firm based in California, U.S. The company specializes in the development and provision of machine intelligence platforms. These platforms offer applications and software to organizations that aim to construct and scrutinize predictive models using big data and high-dimensional data sets. Ayasdi’s machine intelligence platform integrates substantial data infrastructure and scalable computing with topological data analysis, statistical and geometric algorithms, and machine learning to enhance productivity. The software provided by Ayasdi has been implemented by governments and organizations for various purposes. These include anomaly detection, national security applications, information security, drug development, disease research, oil & gas well development, anti-money laundering, customer segmentation, fraud detection, trading strategies, and the creation of clinical pathways for hospitals.
Baidu, Inc. - Baidu, Inc., founded in 2000 and headquartered in Beijing, China, is a leading internet search provider in the country. The company offers a variety of channels for information access and primarily operates within China. Baidu’s operations are divided into two segments: online marketing services and others. Its search services employ a keyword-based approach to user queries, akin to the Google search engine. Baidu also offers transaction services, including Baidu Wallet, Baidu Nuomi, Baidu Maps, among others. iQiyi, a platform comparable to YouTube, is an online video service that hosts movies, videos, songs, and more. Baidu caters to four main participant types: Users, Customers, Baidu Union Members, and Content Providers.
Clarifai, Inc. - Clarifai, Inc. is a company specializing in artificial intelligence, offering solutions in visual recognition. It provides sophisticated image recognition systems capable of detecting visual searches and near-duplicates. The company’s image recognition system can identify objects, tags, and various categories in images, and can also search for similar images. Catering to both enterprises and the developer community, Clarifai provides visual recognition and computer vision AI. Its products have a wide range of applications, including custom face recognition, demographics, and moderation.
Cyrcadia Health - Cyrcadia Health, a U.S.-based medical biosensor company, specializes in surgical and medical instruments. The company leverages predictive analytics to facilitate early detection of breast cancer through personalized wearable devices. Lifeline Biotechnologies, Inc., the parent company, holds a 30% stake in Cyrcadia Health. The company’s business encompasses medical and hospital equipment. The Cyrcadia Health solution is a user-friendly system committed to enhancing patient outcomes, refining surgical decision-making processes, and curbing healthcare costs via early detection and screening. Clinical tests of its patented wearable technology are conducted at The Ohio State University and El Camino Hospital in Mt. View, CA. These tests aim to detect breast cancer using circadian predictive analytic analysis and dynamic testing. The current development and design of the company’s technology are undertaken by Flextronics in Haifa, Israel, a global leader in the manufacture of wearable medical-grade technology.
Enlitic, Inc. - Enlitic, Inc. is a company that specializes in the application of artificial intelligence (AI) in the healthcare sector. The firm is known for creating software solutions that offer valuable insights into treatment planning, early detection, and disease monitoring. A significant part of Enlitic’s work involves the development of tools that enable physicians to effectively utilize collected medical data. In addition to this, the company also creates clinical decision support solutions and products. To develop these AI solutions, Enlitic has formed partnerships with various academic research institutions, healthcare providers, and pharmaceutical solution providers. These collaborations have been instrumental in advancing the company’s mission to revolutionize healthcare through AI.
Google LLC - Google LLC is a multinational internet and computer software solutions provider that specializes in offering internet-related services and products. It is a wholly-owned subsidiary of Alphabet, Inc. The company operates through three business segments: Google Services, Google Cloud, and Other Bets. Google’s products and services include Application Programming Interfaces (APIs), enterprise products, productivity tools, search engines, social networking applications, mobile devices, and advertisement services. Google’s voice recognition product portfolio includes Google Now and application programming interfaces such as Google Cloud Search API. The company’s primary clients are ARM, Intel, Snapchat, Twitter, and Uber. TensorFlow, Google Inc.'s artificial intelligence solution, was developed by researchers and engineers working within a machine intelligence research organization. Google LLC operates its business from 70 offices in over 40 countries across the globe.
Supply Side Estimates
Company revenue estimation via referring to annual reports, investor presentations, and Hoover’s.
Segment revenue determination via variable analysis and penetration modeling.
Competitive benchmarking to identify market leaders and their collective revenue shares.
Forecasting via analyzing commercialization rates, pipelines, market initiatives, distribution networks, etc.
Demand side estimates
Identifying parent markets and ancillary markets
Segment penetration analysis to obtain pertinent
revenue/volume
Heuristic forecasting with the help of subject matter experts
Forecasting via variable analysis
Understanding market dynamics (in terms of drivers, restraints, & opportunities) in the countries.
Understanding trends & variables in the individual countries & their impact on growth and using analytical tools to provide high-level insights into the market dynamics and the associated growth pattern.
Understanding market estimates and forecasts (with the base year as 2022, historic information from 2017 to 2021, and forecast from 2023 to 2030). Regional estimates & forecasts for each category are available and are summed up to form the global market estimates.
The report provides market value for the base year 2022 and a yearly forecast till 2030 in terms of revenue/volume or both. The market for each of the segment outlooks has been provided on region & country basis for the above-mentioned forecast period.
The key industry dynamics, major technological trends, and application markets are evaluated to understand their impact on the demand for the forecast period. The growth rates were estimated using correlation, regression, and time-series analysis.
We have used the bottom-up approach for market sizing, analyzing key regional markets, dynamics, & trends for various products and end-users. The total market has been estimated by integrating the country markets.
All market estimates and forecasts have been validated through primary interviews with the key industry participants.
Inflation has not been accounted for to estimate and forecast the market.
Numbers may not add up due to rounding off.
Europe consists of EU-8, Central & Eastern Europe, along with the Commonwealth of Independent States (CIS).
Asia Pacific includes South Asia, East Asia, Southeast Asia, and Oceania (Australia & New Zealand).
Latin America includes Central American countries and the South American continent
Middle East includes Western Asia (as assigned by the UN Statistics Division) and the African continent.
GVR strives to procure the latest and unique information for reports directly from industry experts, which gives it a competitive edge. Quality is of utmost importance to us, therefore every year we focus on increasing our experts’ panel. Primary interviews are one of the critical steps in identifying recent market trends and scenarios. This process enables us to justify and validate our market estimates and forecasts to our clients. With more than 8,000 reports in our database, we have connected with some key opinion leaders across various domains, including healthcare, technology, consumer goods, and the chemical sector. Our process starts with identifying the right platform for a particular type of report, i.e., emails, LinkedIn, seminars, or telephonic conversation, as every report is unique and requires a differentiated approach.
We send out questionnaires to different experts from various regions/ countries, which is dependent on the following factors:
Report/Market scope: If the market study is global, we send questionnaires to industry experts across various regions, including North America, Europe, Asia Pacific, Latin America, and MEA.
Market Penetration: If the market is driven by technological advancements, population density, disease prevalence, or other factors, we identify experts and send out questionnaires based on region or country dominance.
The time to start receiving responses from industry experts varies based on how niche or well-penetrated the market is. Our reports include a detailed chapter on the KoL opinion section, which helps our clients understand the perspective of experts already in the market space.
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