image

Report Scope & Overview:

The Composite AI Market size was valued at USD 680 Million in 2022 and is expected to grow to USD 7948.9 Million by 2030 and grow at a CAGR of 35.98% over the forecast period of 2023-2030.

Composite AI integrates multiple artificial intelligence (AI) technologies, machine learning algorithms, and data processing methodologies to create advanced systems capable of understanding, analyzing, and solving complex problems. These applications are found across various industries including healthcare, finance, retail, manufacturing, automotive, and telecommunications. The increasing demand for innovative AI-driven solutions that can address the limitations of traditional industry-specific approaches is driving the growth of the composite AI market. However, concerns regarding data privacy and regulatory compliance emerge, particularly when dealing with sensitive information such as personal or financial data. Additionally, integrating multiple AI technologies into a composite solution may pose challenges in terms of compatibility issues, increased complexity in system management, and potential technical failures. Despite these challenges, continuous advancements in AI algorithms and machine learning techniques have enabled businesses to integrate multiple AI capabilities into one comprehensive solution.

Composite AI Market Revenue Analysis

Market Dynamics

Drivers

  • Exponential growth in investments In AI Technologies can be attributed to the market growth.

  • The increasing complexity of AI applications for enhanced performance and accuracy

AI applications are becoming increasingly intricate, necessitating the integration of multiple AI technologies and models to address complex problems. Presently, organizations are confronted with the reality that training a massive neural network using machine learning (ML) does not always scale to solve problems of heightened complexity. While the pure ML approach proves effective for many classification and recognition tasks, it falls short when it comes to solving problems that require a deeper level of understanding. Additionally, ML creates an insatiable demand for training data and computational power. Composite AI solutions offer a means to harness the strengths of different AI algorithms and components, empowering organizations to tackle complex challenges and achieve superior performance. Moreover, composite AI solutions can capitalize on the advantages of various AI models and algorithms, resulting in enhanced performance and accuracy compared to standalone AI approaches. By combining different techniques, organizations can attain more robust and precise results, thereby bolstering their decision-making and problem-solving capabilities.

Restrains

  • Concerns related to data privacy and security have become increasingly prominent in today's digital age.

Opportunities

  • Enhancing Real-Time Decision-Making through Integration with Edge Computing and IoT

  • Rapid adoption of digital transformation across industries has created lucrative opportunities for market growth.

Challenges

  • Challenges related to Data availability and quality are crucial factors in the development of composite AI solutions.

These solutions heavily rely on large and diverse datasets to train and optimize models. However, organizations often encounter challenges when trying to acquire high-quality, labeled, and relevant data, particularly for specific use cases or industries. Additionally, data privacy concerns and regulatory restrictions further complicate the process of accessing and sharing data. The availability of high-quality data is considered critical because it is used to train AI algorithms. When developing AI applications that can deliver value, the quality of the data fed into these algorithms becomes of utmost importance. It is essential to ensure that the data used for training is accurate, reliable, and representative of the real-world scenarios the AI application will encounter.

To overcome these challenges, organizations must invest in robust data collection and labeling processes. They should prioritize data quality and ensure that the data used for training is diverse, representative, and free from biases. Additionally, organizations must navigate the complex landscape of data privacy regulations and ensure compliance while still accessing the necessary data for AI development.

Impact of Russia Ukraine war

Geopolitical tensions can lead to global market uncertainty. Uncertainty often affects investor confidence, leading to volatility in financial markets. This can impact funding for AI startups, research initiatives, and technology companies working on Composite AI solutions. Investors might become more cautious, affecting the availability of capital for innovation and growth. The war could impact the regional markets in Russia, Ukraine, and neighboring countries. Economic instability, currency fluctuations, and government policies responding to the conflict can influence local businesses' ability to invest in new technologies like Composite AI. Companies in these regions might delay or reevaluate their AI-related projects due to economic uncertainties.

Geopolitical conflicts can lead to changes in trade policies, alliances, and regulations. These changes might affect global technology collaborations, research partnerships, and intellectual property rights. It could potentially alter the competitive landscape for Composite AI companies and impact their international operations and strategies. Geopolitical tensions often lead to increased concerns about security, both in cyberspace and in physical domains. This could potentially drive the demand for AI-powered security and surveillance solutions, such as those utilizing computer vision or anomaly detection, as nations and businesses seek to enhance their security measures. The conflict may draw attention to the ethical use of AI technologies, particularly in warfare and defense. It might prompt discussions and debates about responsible AI development, leading to potential shifts in regulations or public perceptions about the use of AI in sensitive areas.

Impact of recession

During a recession, businesses tend to tighten their budgets, leading to reduced spending on new technologies, including AI. Composite AI solutions often require investments in various technologies, including machine learning models, data infrastructure, and integration tools. Companies might delay or scale back their adoption of Composite AI due to financial constraints. The adoption of new technologies generally slows down during economic downturns as businesses focus more on survival and cost-cutting measures rather than investing in innovation. This can affect the rate at which Composite AI solutions are implemented across industries. Different industries may experience varying effects on Composite AI adoption during a recession. For example, industries heavily reliant on consumer spending, such as retail, might cut back on AI investments. However, sectors like healthcare or cybersecurity, where AI solutions can optimize operations or enhance security, might continue investing in Composite AI despite the economic downturn. Companies may reevaluate their priorities during a recession. They might prioritize short-term gains over long-term technological investments, leading to a temporary slowdown in Composite AI adoption. This shift in focus can impact the development and deployment of advanced AI systems. Despite challenges, recessions can also foster innovation. Companies may seek more cost-effective or efficient ways to implement Composite AI solutions. This might lead to the development of new tools, platforms, or approaches that make Composite AI more accessible or affordable.

Key Market Segmentation

By Technique

  • Conditioned Monitoring

  • Pattern Recognition

  • Data Processing

  • Proactive Mechanism

  • Data Mining & Machine Learning

  • Others

By offering

  • Hardware

  • Software

  • Service

By Application

  • Product Design & Development

  • Quality Control

  • Predictive Maintenance

  • Security & Surveillance

  • Customer Service

  • Other

By Industry Vertical

  • BFSI

  • Retail and eCommerce

  • Manufacturing

  • Energy and Utilities

  • Transportation and Logistics

  • Healthcare and Life Sciences

  • Media and Entertainment

  • Government and Defense

  • Telecom

  • Other

Composite AI Market Segmentation Analysis

By Application, Product design and development are projected to account for the largest market size during the forecast period. The business applications of product design and development play a crucial role in implementing composite AI solutions in the market. These applications provide tools and functionalities that generate innovative ideas and concepts, enabling businesses to explore new product possibilities and identify areas where composite AI can add value. Incorporating composite AI in product design and development offers significant benefits for companies seeking to enhance their product development capabilities, reduce costs, and bring innovative products to market more efficiently.

Regional Analysis 

North America stands at the forefront of adopting and expanding composite AI solutions. The region's advanced AI technology companies, robust research and development capabilities, and mature market ecosystem all contribute to the rapid growth of composite AI solutions. Industries such as healthcare, BFSI, retail, and manufacturing are embracing composite AI to fuel innovation, elevate customer experiences, and optimize operational efficiency. These factors are driving the widespread adoption of composite AI solutions across the region. Furthermore, various industry verticals, including telecom, healthcare, media and entertainment, retail and eCommerce, and BFSI, are leveraging composite AI solutions to boost productivity and achieve superior performance.

The Asia Pacific segment is expected to experience the highest compound annual growth rate (CAGR) during the forecast period. This growth can be attributed to the rapid advancements in artificial intelligence (AI) technologies, the increasing availability of data, and the growing digital transformation initiatives across various industries. As AI technologies continue to evolve and organizations increasingly embrace digital transformation, the adoption of Composite AI solutions is projected to further expand in the region.

REGIONAL COVERAGE:

North America

  • US

  • Canada

  • Mexico

Europe

  • Eastern Europe

    • Poland

    • Romania

    • Hungary

    • Turkey

    • Rest of Eastern Europe

  • Western Europe

    • Germany

    • France

    • UK

    • Italy

    • Spain

    • Netherlands

    • Switzerland

    • Austria

    • Rest of Western Europe

Asia Pacific

  • China

  • India

  • Japan

  • South Korea

  • Vietnam

  • Singapore

  • Australia

  • Rest of Asia Pacific

Middle East & Africa

  • Middle East

    • UAE

    • Egypt

    • Saudi Arabia

    • Qatar

    • Rest of Middle East

  • Africa

    • Nigeria

    • South Africa

    • Rest of Africa

Latin America

  • Brazil

  • Argentina

  • Colombia

  • Rest of Latin America

Key Players:

The Major players in the market are Google, NVIDIA, DataRobot, SAS Institute, SAP, Microsoft, AWS, IBM, BlackSwan Technologies, Oracle, Pega, Salesforce, OpenText, HPE, Squirro, SparkCognition, Intel, UiPath, and others in the final report.

Google-Company Financial Analysis

Recent development

In April 2023, Amazon SageMaker introduced Collections, a new feature that allows for the organization of machine learning models within the Amazon SageMaker Model Registry. Collections enable the gathering and hierarchical organization of relevant registered models, enhancing the discoverability of models on a larger scale.

In July 2022, AWS partnered with Hugging Face to simplify the utilization of cutting-edge machine learning models and expedite the implementation of advanced NLP features for companies. This collaboration entails Hugging Face leveraging Amazon Web Services as its preferred cloud provider to deliver exceptional customer services.

In May 2022, BlackSwan Technologies and Refinitiv entered into a strategic agreement aimed at revolutionizing customer risk assessment. This agreement facilitates the development of an advanced compliance solution that incorporates comprehensive financial crime data and groundbreaking AI technologies for KYC, transaction monitoring, and screening.

Composite AI Market Report Scope:
Report Attributes Details
Market Size in 2022  US$ 680  Million
Market Size by 2030  US$ 7948.9 Million
CAGR   CAGR of 35.98 % From 2023 to 2030
Base Year 2022
Forecast Period  2023-2030
Historical Data  2019-2021
Report Scope & Coverage Market Size, Segments Analysis, Competitive  Landscape, Regional Analysis, DROC & SWOT Analysis, Forecast Outlook
Key Segments • By Technique (Conditioned Monitoring, Pattern Recognition, Data Processing, Proactive Mechanism, Data Mining & Machine Learning, Others)
• By Offering (Hardware, Software, Service)
• By Application (Product Design & Development, Quality Control, Predictive Maintenance, Security & Surveillance, Customer Service, Other)
• By Industry Vertical (BFSI, Retail and eCommerce, Manufacturing, Energy and Utilities, Transportation and Logistics, Healthcare and Life Sciences, Media and Entertainment, Government and Defense, Telecom, Other)
Regional Analysis/Coverage North America (US, Canada, Mexico), Europe (Eastern Europe [Poland, Romania, Hungary, Turkey, Rest of Eastern Europe] Western Europe] Germany, France, UK, Italy, Spain, Netherlands, Switzerland, Austria, Rest of Western Europe]), Asia Pacific (China, India, Japan, South Korea, Vietnam, Singapore, Australia, Rest of Asia Pacific), Middle East & Africa (Middle East [UAE, Egypt, Saudi Arabia, Qatar, Rest of Middle East], Africa [Nigeria, South Africa, Rest of Africa], Latin America (Brazil, Argentina, Colombia, Rest of Latin America)
Company Profiles Google, NVIDIA, DataRobot, SAS Institute, SAP, Microsoft, AWS, IBM, BlackSwan Technologies, Oracle, Pega, Salesforce, OpenText, HPE, Squirro, SparkCognition, Intel, UiPath
Key Drivers • Exponential growth in investments In AI Technologies can be attributed to the market growth.
• The increasing complexity of AI applications for enhanced performance and accuracy
Market Restrain • Concerns related to data privacy and security have become increasingly prominent in today's digital age.

 

Frequently Asked Questions

Ans: The CAGR of the Composite AI Market is 35.98 %.

 

Ans:  USD 680 Million in 2022 is the market share of the Composite AI Market.

Ans:

  • Exponential growth in investments In AI Technologies can be attributed to the market growth.
  • The increasing complexity of AI applications for enhanced performance and accuracy

Ans. The forecast period for the Composite AI Market is 2022-2030.

Ans: There are three options available to purchase this report,A. Single User License USD 4650Features: A non-printable PDF to be accessed by just one user at a time 1. No Excel would be delivered along with the PDF 2. 1 complimentary analyst briefing session of 30 minutes to be provided post-purchase and delivery of the study 3. 1 complimentary update to be provided after 6 months of purchase 4. Additional 80 analyst hours of free customization to add extra slices of information that might be missing from the study B. Enterprise User License: USD 6,450 Features: 1. A printable/ sharable and downloadable PDF 2. No limit over the number of users 3. An Excel spreadsheet would be delivered along with the PDF 4. 2 complimentary analyst briefing sessions of 30 minutes each to be provided post-purchase and delivery of the study 5. 2 complimentary updates to be provided within 1 year of purchase 6. Additional 100 analyst hours of free customization to add extra slices of information that might be missing from the study.C: Excel Datasheet: USD 2,3251.  ME sheet is provided in Excel format.2. 1 complimentary analyst briefing session of 30 minutes to be provided post-purchase and delivery of the study.

Table of Contents

1.Introduction
1.1 Market Definition
1.2 Scope
1.3 Research Assumptions

2. Research Methodology

3. Market Dynamics
3.1 Drivers
3.2 Restraints
3.3 Opportunities
3.4 Challenges

4. Impact Analysis
4.1 COVID-19 Impact Analysis
4.3 Impact of Ongoing Recession
4.3.1 Introduction
4.3.2 Impact on major economies
4.3.2.1 US
4.3.2.2 Canada
4.3.2.3 Germany
4.3.2.4 France
4.3.2.5 United Kingdom
4.3.2.6 China
4.3.2.7 japan
4.3.2.8 South Korea
4.3.2.9 Rest of the World

5. Value Chain Analysis

6. Porter’s 5 forces model

7. PEST Analysis

8. Composite AI Market Segmentation, By Technique
8.1 Conditioned Monitoring
8.2 Pattern Recognition
8.3 Data Processing
8.4 Proactive Mechanism
8.5 Data Mining & Machine Learning
8.6 Others

9. Composite AI Market Segmentation, By offering
9.1 Hardware
9.2 Software
9.3 Service

10. Composite AI Market Segmentation, By Application
10.1 Product Design & Development
10.2 Quality Control
10.3 Predictive Maintenance
10.4 Security & Surveillance
10.5 Customer Service
10.6 Other

11.  Composite AI Market Segmentation, By Industry Vertical
11.1 BFSI
11.2 Retail and eCommerce
11.3 Manufacturing
11.4 Energy and Utilities
11.5 Transportation and Logistics
11.6 Healthcare and Life Sciences
11.7 Media and Entertainment
11.8 Government and Defense
11.9 Telecom
11.10 Others

12. Regional Analysis
12.1 Introduction
12.2 North America
12.2.1 North America Composite AI Market By Country
12.2.2 North America Composite AI Market By Technique
12.2.3 North America Composite AI Market By offering
12.2.4 North America Composite AI Market By Application
12.2.5 North America Composite AI Market By Industry Vertical
12.2.6 USA
12.2.6.1 USA Composite AI Market By Technique
12.2.6.2 USA Composite AI Market By offering
12.2.6.3 USA Composite AI Market By Application
12.2.6.4 USA Composite AI Market By Industry Vertical
12.2.7 Canada
12.2.7.1 Canada Composite AI Market By Technique
12.2.7.2 Canada Composite AI Market By offering
12.2.7.3 Canada Composite AI Market By Application
12.2.7.4 Canada Composite AI Market By Industry Vertical
12.2.8 Mexico
12.2.8.1 Mexico Composite AI Market By Technique
12.2.8.2 Mexico Composite AI Market By offering
12.2.8.3 Mexico Composite AI Market By Application
12.2.8.4 Mexico Composite AI Market By Industry Vertical
12.3 Europe
12.3.1 Eastern Europe
12.3.1.1 Eastern Europe Composite AI Market By Country
12.3.1.2 Eastern Europe Composite AI Market By Technique
12.3.1.3 Eastern Europe Composite AI Market By offering
12.3.1.4 Eastern Europe Composite AI Market By Application
12.3.1.5 Eastern Europe Composite AI Market By Industry Vertical
12.3.1.6 Poland
12.3.1.6.1 Poland Composite AI Market By Technique
12.3.1.6.2 Poland Composite AI Market By offering
12.3.1.6.3 Poland Composite AI Market By Application
12.3.1.6.4 Poland Composite AI Market By Industry Vertical
12.3.1.7 Romania
12.3.1.7.1 Romania Composite AI Market By Technique
12.3.1.7.2 Romania Composite AI Market By offering
12.3.1.7.3 Romania Composite AI Market By Application
12.3.1.7.4 Romania Composite AI Market By Industry Vertical
12.3.1.8 Hungary
12.3.1.8.1 Hungary Composite AI Market By Technique
12.3.1.8.2 Hungary Composite AI Market By offering
12.3.1.8.3 Hungary Composite AI Market By Application
12.3.1.8.4 Hungary Composite AI Market By Industry Vertical
12.3.1.9 Turkey
12.3.1.9.1 Turkey Composite AI Market By Technique
12.3.1.9.2 Turkey Composite AI Market By offering
12.3.1.9.3 Turkey Composite AI Market By Application
12.3.1.9.4 Turkey Composite AI Market By Industry Vertical
12.3.1.10 Rest of Eastern Europe
12.3.1.10.1 Rest of Eastern Europe Composite AI Market By Technique
12.3.1.10.2 Rest of Eastern Europe Composite AI Market By offering
12.3.1.10.3 Rest of Eastern Europe Composite AI Market By Application
12.3.1.10.4 Rest of Eastern Europe Composite AI Market By Industry Vertical
12.3.2 Western Europe
12.3.2.1 Western Europe Composite AI Market By Country
12.3.2.2 Western Europe Composite AI Market By Technique
12.3.2.3 Western Europe Composite AI Market By offering
12.3.2.4 Western Europe Composite AI Market By Application
12.3.2.5 Western Europe Composite AI Market By Industry Vertical
12.3.2.6 Germany
12.3.2.6.1 Germany Composite AI Market By Technique
12.3.2.6.2 Germany Composite AI Market By offering
12.3.2.6.3 Germany Composite AI Market By Application
12.3.2.6.4 Germany Composite AI Market By Industry Vertical
12.3.2.7 France
12.3.2.7.1 France Composite AI Market By Technique
12.3.2.7.2 France Composite AI Market By offering
12.3.2.7.3 France Composite AI Market By Application
12.3.2.7.4 France Composite AI Market By Industry Vertical
12.3.2.8 UK
12.3.2.8.1 UK Composite AI Market By Technique
12.3.2.8.2 UK Composite AI Market By offering
12.3.2.8.3 UK Composite AI Market By Application
12.3.2.8.4 UK Composite AI Market By Industry Vertical
12.3.2.9 Italy
12.3.2.9.1 Italy Composite AI Market By Technique
12.3.2.9.2 Italy Composite AI Market By offering
12.3.2.9.3 Italy Composite AI Market By Application
12.3.2.9.4 Italy Composite AI Market By Industry Vertical
12.3.2.10 Spain
12.3.2.10.1 Spain Composite AI Market By Technique
12.3.2.10.2 Spain Composite AI Market By offering
12.3.2.10.3 Spain Composite AI Market By Application
12.3.2.10.4 Spain Composite AI Market By Industry Vertical
12.3.2.11 Netherlands
12.3.2.11.1 Netherlands Composite AI Market By Technique
12.3.2.11.2 Netherlands Composite AI Market By offering
12.3.2.11.3 Netherlands Composite AI Market By Application
12.3.2.11.4 Netherlands Composite AI Market By Industry Vertical
12.3.2.12 Switzerland
12.3.2.12.1 Switzerland Composite AI Market By Technique
12.3.2.12.2 Switzerland Composite AI Market By offering
12.3.2.12.3 Switzerland Composite AI Market By Application
12.3.2.12.4 Switzerland Composite AI Market By Industry Vertical
12.3.2.13 Austria
12.3.2.13.1 Austria Composite AI Market By Technique
12.3.2.13.2 Austria Composite AI Market By offering
12.3.2.13.3 Austria Composite AI Market By Application
12.3.2.13.4 Austria Composite AI Market By Industry Vertical
12.3.2.14 Rest of Western Europe
12.3.2.14.1 Rest of Western Europe Composite AI Market By Technique
12.3.2.14.2 Rest of Western Europe Composite AI Market By offering
12.3.2.14.3 Rest of Western Europe Composite AI Market By Application
12.3.2.14.4 Rest of Western Europe Composite AI Market By Industry Vertical
12.4 Asia-Pacific
12.4.1 Asia Pacific Composite AI Market By Country
12.4.2 Asia Pacific Composite AI Market By Technique
12.4.3 Asia Pacific Composite AI Market By offering
12.4.4 Asia Pacific Composite AI Market By Application
12.4.5 Asia Pacific Composite AI Market By Industry Vertical
12.4.6 China
12.4.6.1 China Composite AI Market By Technique
12.4.6.2 China Composite AI Market By offering
12.4.6.3 China Composite AI Market By Application
12.4.6.4 China Composite AI Market By Industry Vertical
12.4.7 India
12.4.7.1 India Composite AI Market By Technique
12.4.7.2 India Composite AI Market By offering
12.4.7.3 India Composite AI Market By Application
12.4.7.4 India Composite AI Market By Industry Vertical
12.4.8 Japan
12.4.8.1 Japan Composite AI Market By Technique
12.4.8.2 Japan Composite AI Market By offering
12.4.8.3 Japan Composite AI Market By Application
12.4.8.4 Japan Composite AI Market By Industry Vertical
12.4.9 South Korea
12.4.9.1 South Korea Composite AI Market By Technique
12.4.9.2 South Korea Composite AI Market By offering
12.4.9.3 South Korea Composite AI Market By Application
12.4.9.4 South Korea Composite AI Market By Industry Vertical
12.4.10 Vietnam
12.4.10.1 Vietnam Composite AI Market By Technique
12.4.10.2 Vietnam Composite AI Market By offering
12.4.10.3 Vietnam Composite AI Market By Application
12.4.10.4 Vietnam Composite AI Market By Industry Vertical
12.4.11 Singapore
12.4.11.1 Singapore Composite AI Market By Technique
12.4.11.2 Singapore Composite AI Market By offering
12.4.11.3 Singapore Composite AI Market By Application
12.4.11.4 Singapore Composite AI Market By Industry Vertical
12.4.12 Australia
12.4.12.1 Australia Composite AI Market By Technique
12.4.12.2 Australia Composite AI Market By offering
12.4.12.3 Australia Composite AI Market By Application
12.4.12.4 Australia Composite AI Market By Industry Vertical
12.4.13 Rest of Asia-Pacific
12.4.13.1 Rest of Asia-Pacific Composite AI Market By Technique
12.4.13.2 Rest of Asia-Pacific Composite AI Market By offering
12.4.13.3 Rest of Asia-Pacific Composite AI Market By Application
12.4.13.4 Rest of Asia-Pacific Composite AI Market By Industry Vertical
12.5 Middle East & Africa
12.5.1 Middle East
12.5.1.1 Middle East Composite AI Market By Country
12.5.1.2 Middle East Composite AI Market By Technique
12.5.1.3 Middle East Composite AI Market By offering
12.5.1.4 Middle East Composite AI Market By Application
12.5.1.5 Middle East Composite AI Market By Industry Vertical
12.5.1.6 UAE
12.5.1.6.1 UAE Composite AI Market By Technique
12.5.1.6.2 UAE Composite AI Market By offering
12.5.1.6.3 UAE Composite AI Market By Application
12.5.1.6.4 UAE Composite AI Market By Industry Vertical
12.5.1.7 Egypt
12.5.1.7.1 Egypt Composite AI Market By Technique
12.5.1.7.2 Egypt Composite AI Market By offering
12.5.1.7.3 Egypt Composite AI Market By Application
12.5.1.7.4 Egypt Composite AI Market By Industry Vertical
12.5.1.8 Saudi Arabia
12.5.1.8.1 Saudi Arabia Composite AI Market By Technique
12.5.1.8.2 Saudi Arabia Composite AI Market By offering
12.5.1.8.3 Saudi Arabia Composite AI Market By Application
12.5.1.8.4 Saudi Arabia Composite AI Market By Industry Vertical
12.5.1.9 Qatar
12.5.1.9.1 Qatar Composite AI Market By Technique
12.5.1.9.2 Qatar Composite AI Market By offering
12.5.1.9.3 Qatar Composite AI Market By Application
12.5.1.9.4 Qatar Composite AI Market By Industry Vertical
12.5.1.10 Rest of Middle East
12.5.1.10.1 Rest of Middle East Composite AI Market By Technique
12.5.1.10.2 Rest of Middle East Composite AI Market By offering
12.5.1.10.3 Rest of Middle East Composite AI Market By Application
12.5.1.10.4 Rest of Middle East Composite AI Market By Industry Vertical
12.5.2. Africa
12.5.2.1 Africa Composite AI Market By Country
12.5.2.2 Africa Composite AI Market By Technique
12.5.2.3 Africa Composite AI Market By offering
12.5.2.4 Africa Composite AI Market By Application
12.5.2.5 Africa Composite AI Market By Industry Vertical
12.5.2.6 Nigeria
12.5.2.6.1 Nigeria Composite AI Market By Technique
12.5.2.6.2 Nigeria Composite AI Market By offering
12.5.2.6.3 Nigeria Composite AI Market By Application
12.5.2.6.4 Nigeria Composite AI Market By Industry Vertical
12.5.2.7 South Africa
12.5.2.7.1 South Africa Composite AI Market By Technique
12.5.2.7.2 South Africa Composite AI Market By offering
12.5.2.7.3 South Africa Composite AI Market By Application
12.5.2.7.4 South Africa Composite AI Market By Industry Vertical
12.5.2.8 Rest of Africa
12.5.2.8.1 Rest of Africa Composite AI Market By Technique
12.5.2.8.2 Rest of Africa Composite AI Market By offering
12.5.2.8.3 Rest of Africa Composite AI Market By Application
12.5.2.8.4 Rest of Africa Composite AI Market By Industry Vertical
12.6. Latin America
12.6.1 Latin America Composite AI Market By Country
12.6.2 Latin America Composite AI Market By Technique
12.6.3 Latin America Composite AI Market By offering
12.6.4 Latin America Composite AI Market By Application
12.6.5 Latin America Composite AI Market By Industry Vertical
12.6.6 Brazil
12.6.6.1 Brazil Composite AI Market By Technique
12.6.6.2 Brazil Composite AI Market By offering
12.6.6.3 Brazil Composite AI Market By Application
12.6.6.4 Brazil Composite AI Market By Industry Vertical
12.6.7 Argentina
12.6.7.1 Argentina Composite AI Market By Technique
12.6.7.2 Argentina Composite AI Market By offering
12.6.7.3 Argentina Composite AI Market By Application
12.6.7.4 Argentina Composite AI Market By Industry Vertical
12.6.8 Colombia
12.6.8.1 Colombia Composite AI Market By Technique
12.6.8.2 Colombia Composite AI Market By offering
12.6.8.3 Colombia Composite AI Market By Application
12.6.8.4 Colombia Composite AI Market By Industry Vertical
12.6.9 Rest of Latin America
12.6.9.1 Rest of Latin America Composite AI Market By Technique
12.6.9.2 Rest of Latin America Composite AI Market By offering
12.6.9.3 Rest of Latin America Composite AI Market By Application
12.6.9.4 Rest of Latin America Composite AI Market By Industry Vertical

13 Company Profile
13.1 Google
13.1.1 Company Overview
13.1.2 Financials
13.1.3 Product/Services/Offerings
13.1.4 SWOT Analysis
13.1.5 The SNS View
13.2 NVIDIA.
13.2.1 Company Overview
13.2.2 Financials
13.2.3 Product/Services/Offerings
13.2.4 SWOT Analysis
13.2.5 The SNS View
13.3 DataRobot.
13.3.1 Company Overview
13.3.2 Financials
13.3.3 Product/Services/Offerings
13.3.4 SWOT Analysis
13.3.5 The SNS View
13.4 SAS Institute.
13.4.1 Company Overview
13.4.2 Financials
13.4.3 Product/Services/Offerings
13.4.4 SWOT Analysis
13.4.5 The SNS View
13.5 SAP.
13.5.1 Company Overview
13.5.2 Financials
13.5.3 Product/Services/Offerings
13.5.4 SWOT Analysis
13.5.5 The SNS View
13.6 Microsoft.
13.6.1 Company Overview
13.6.2 Financials
13.6.3 Product/Services/Offerings
13.6.4 SWOT Analysis
13.6.5 The SNS View
13.7 AWS.
13.7.1 Company Overview
13.7.2 Financials
13.7.3 Product/Services/Offerings
13.7.4 SWOT Analysis
13.7.5 The SNS View
13.8 IBM.
13.8.1 Company Overview
13.8.2 Financials
13.8.3 Product/Services/Offerings
13.8.4 SWOT Analysis
13.8.5 The SNS View
13.9 BlackSwan Technologies.
13.9.1 Company Overview
13.9.2 Financials
13.9.3 Product/Services/Offerings
13.9.4 SWOT Analysis
13.9.5 The SNS View
13.10 Oracle.
13.10.1 Company Overview
13.10.2 Financials
13.10.3 Product/Services/Offerings
13.10.4 SWOT Analysis
13.10.5 The SNS View

14. Competitive Landscape
14.1 Competitive Benchmarking
14.2 Market Share Analysis
14.3 Recent Developments
14.3.1 Industry News
14.3.2 Company News
14.3 Mergers & Acquisitions

15. USE Cases and Best Practices

16. Conclusion

An accurate research report requires proper strategizing as well as implementation. There are multiple factors involved in the completion of good and accurate research report and selecting the best methodology to compete the research is the toughest part. Since the research reports we provide play a crucial role in any company’s decision-making process, therefore we at SNS Insider always believe that we should choose the best method which gives us results closer to reality. This allows us to reach at a stage wherein we can provide our clients best and accurate investment to output ratio.

Each report that we prepare takes a timeframe of 350-400 business hours for production. Starting from the selection of titles through a couple of in-depth brain storming session to the final QC process before uploading our titles on our website we dedicate around 350 working hours. The titles are selected based on their current market cap and the foreseen CAGR and growth.

 

The 5 steps process:

Step 1: Secondary Research:

Secondary Research or Desk Research is as the name suggests is a research process wherein, we collect data through the readily available information. In this process we use various paid and unpaid databases which our team has access to and gather data through the same. This includes examining of listed companies’ annual reports, Journals, SEC filling etc. Apart from this our team has access to various associations across the globe across different industries. Lastly, we have exchange relationships with various university as well as individual libraries.

Secondary Research

Step 2: Primary Research

When we talk about primary research, it is a type of study in which the researchers collect relevant data samples directly, rather than relying on previously collected data.  This type of research is focused on gaining content specific facts that can be sued to solve specific problems. Since the collected data is fresh and first hand therefore it makes the study more accurate and genuine.

We at SNS Insider have divided Primary Research into 2 parts.

Part 1 wherein we interview the KOLs of major players as well as the upcoming ones across various geographic regions. This allows us to have their view over the market scenario and acts as an important tool to come closer to the accurate market numbers. As many as 45 paid and unpaid primary interviews are taken from both the demand and supply side of the industry to make sure we land at an accurate judgement and analysis of the market.

This step involves the triangulation of data wherein our team analyses the interview transcripts, online survey responses and observation of on filed participants. The below mentioned chart should give a better understanding of the part 1 of the primary interview.

Primary Research

Part 2: In this part of primary research the data collected via secondary research and the part 1 of the primary research is validated with the interviews from individual consultants and subject matter experts.

Consultants are those set of people who have at least 12 years of experience and expertise within the industry whereas Subject Matter Experts are those with at least 15 years of experience behind their back within the same space. The data with the help of two main processes i.e., FGDs (Focused Group Discussions) and IDs (Individual Discussions). This gives us a 3rd party nonbiased primary view of the market scenario making it a more dependable one while collation of the data pointers.

Step 3: Data Bank Validation

Once all the information is collected via primary and secondary sources, we run that information for data validation. At our intelligence centre our research heads track a lot of information related to the market which includes the quarterly reports, the daily stock prices, and other relevant information. Our data bank server gets updated every fortnight and that is how the information which we collected using our primary and secondary information is revalidated in real time.

Data Bank Validation

Step 4: QA/QC Process

After all the data collection and validation our team does a final level of quality check and quality assurance to get rid of any unwanted or undesired mistakes. This might include but not limited to getting rid of the any typos, duplication of numbers or missing of any important information. The people involved in this process include technical content writers, research heads and graphics people. Once this process is completed the title gets uploader on our platform for our clients to read it.

Step 5: Final QC/QA Process:

This is the last process and comes when the client has ordered the study. In this process a final QA/QC is done before the study is emailed to the client. Since we believe in giving our clients a good experience of our research studies, therefore, to make sure that we do not lack at our end in any way humanly possible we do a final round of quality check and then dispatch the study to the client.

Start a Conversation

Hi! Click one of our member below to chat on Phone