Artificial Neural Network Market Report Scope & Overview:

The Artificial Neural Network Market size was USD 272.56 Million in 2023 and is expected to Reach USD 1.14 Billion by 2031 and grow at a CAGR of 19.6% over the forecast period of 2024-2031. 

An Artificial Neural Network (ANN) is a computational model that mimics the capability and structure of the neuron framework in the human cerebrum. The Artificial Neural Network Market is being driven by the advancement of artificial intelligence and the growing acceptance of the Artificial Neural Network organization in the medical services sector. Factors like as rising medical care use, rising hospitalizations, and rising prevalence of chronic illnesses all contribute to the Artificial Neural Network Industry Outlook's growth.

Artificial Neural Network Market Revenue Analysis

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Furthermore, counterfeit neural organizations provide significant benefits in clinical research, which is expected to drive the Artificial Neural Network Market growth throughout the forecast year. Furthermore, many businesses are working on artificial neuron network-based programming as a result of the growing popularity of artificial neural networks. For example, Microsoft's MAQ programming uses an Artificial Neural Network to extract information from the past and predict future features.



  • Targeted customer behavior and predicted sales are depended upon to effectively boost the Artificial Neural Network Market over the forecast period.

  • The structure of Artificial Neural Network phases is predicted to be aided by rapid digitalization.


  • Because the security and privacy of video content transmitted across several platforms can be a major problem for organizations, 

  • The biggest Artificial Neural Network Market restraint is a lack of competent professionals to manage the artificial neural network.


  • Interest in geographical information and logical apparatuses is growing, as is interest in market forecasting solutions, thanks to cloud-based arrangements.

  • Profitable Artificial Neural Network Industry chances are based on expanded application areas for profound neural organizations.


  • Dealing with the encryption issues

  • Less number of employees willing to take up work due to uncertainty


The growing phenomenon of the COVID-19 virus resulted in the collapse of global economies. The governments' mandated lockdown to stop the virus from spreading had an impact on industrial progress. Only a few industries, like as healthcare and e-commerce, were able to withstand the pandemic crisis. The market is driven by the usage of artificial neural network systems in healthcare for tracking patient behaviours and in the e-commerce sector for marketing.

During the pandemic, rapid digitalization is projected to promote the implementation of artificial neural network platforms. The government's endeavor to incorporate cutting-edge artificial neural network technology into the vaccination process in order to improve the efficacy of vaccination campaigns, as well as the use of this technology to detect an existing COVID-19 therapy medicine.


Market, By Application:

Applications are used to segment ANN. Image recognition, signal recognition, data mining, and other verticals are among them (recommender system and drug discovery). Because of the increased need for active data mining to transform raw data into meaningful information, the data mining segment is the fastest-growing category in the Artificial Neural Network Market.

Market, By Deployment:

To maximize profitability and effectively automate the equipment maintenance process, the majority of suppliers in the Artificial Neural Network Market provide cloud-based maintenance solutions. Cloud-based ANN solutions are predicted to become more popular as a result of its advantages, which include easy data upkeep, cost-effectiveness, scalability, and effective administration.


By Component

  • Hardware

  • Solution

  • Services

By Deployment Mode

  • Cloud

  • On-premises

By Application

  • Image Recognition

  • Signal Recognition

  • Data Mining

  • Others

By Enterprise

  • Large Enterprises

  • Small and Medium Enterprises

By End-User

  • BFSI

  • Retail

  • E-Commerce

  • IT & Telecom

  • Manufacturing

  • Healthcare

  • Logistics

  • Others


In the worldwide ANN market, North America is predicted to have the greatest market size, while Asia Pacific (APAC) is expected to develop at the fastest CAGR during the forecast period. The biggest growth rate in APAC may be ascribed to enormous expenditures made by both the commercial and public sectors to improve their business solutions, leading in greater demand for ANN solutions used to train vast volumes of data sets with less oversight.

North America accounts for the largest share of revenue in the worldwide Artificial Neural Network Market. The ANN market in the region is undergoing substantial changes. Many North American ANN solution vendors are experimenting in the ANN industry by adding AI and deep learning capabilities into their existing ANN solutions. They are also pursuing various growth initiatives in order to increase their market position.


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  • North America

    • The USA

    • Canada

    • Mexico

  • Europe

    • Germany

    • The UK

    • France

    • Italy

    • Spain

    • The Netherlands

    • Rest of Europe

  • Asia-Pacific

    • Japan

    • south Korea

    • China

    • India

    • Australia

    • Rest of Asia-Pacific

  • The Middle East & Africa

    • Israel

    • UAE

    • South Africa

    • Rest of Middle East & Africa

  • Latin America

    • Brazil

    • Argentina

    • Rest of Latin America


The key players in the Artificial Neural Network Market are Google, IBM, Oracle, Microsoft, Intel, Qualcomm, Alyuda, Ward Systems, GMDH, LLC, Starmind & Other Players

Oracle - Company Financial Analysis

Company Landscape Analysis

Artificial Neural Network Market Report Scope:
Report Attributes Details
Market Size in 2023  US$ 272.56 Mn
Market Size by 2031  US$ 1.14 Bn
CAGR   CAGR of 19.6% From 2024 to 2031
Base Year  2023
Forecast Period  2024-2031
Historical Data  2020-2022
Report Scope & Coverage Market Size, Segments Analysis, Competitive  Landscape, Regional Analysis, DROC & SWOT Analysis, Forecast Outlook
Key Segments • by Component (Software and Services)
• by Deployment Model (On-Premises and Cloud)
• by Enterprise Size (Large Enterprises and Small & Medium Enterprises)
• by Application (Image Recognition, Signal Recognition, Data Mining, and Others)
• by End User (BFSI, Retail, E-Commerce, IT & Telecom, Manufacturing, Healthcare, Logistics, and Others)
Regional Analysis/Coverage North America (USA, Canada, Mexico), Europe
(Germany, UK, France, Italy, Spain, Netherlands,
Rest of Europe), Asia-Pacific (Japan, South Korea,
China, India, Australia, Rest of Asia-Pacific), The
Middle East & Africa (Israel, UAE, South Africa,
Rest of Middle East & Africa), Latin America (Brazil, Argentina, Rest of Latin America)
Company Profiles Google, IBM, Oracle, Microsoft, Intel, Qualcomm, Alyuda, Ward Systems, GMDH, LLC, Starmind.
Key Drivers • Targeted customer behavior and predicted sales are depended upon to effectively boost the Artificial Neural Network Market over the forecast period.
• The structure of Artificial Neural Network phases is predicted to be aided by rapid digitalization.
Market Opportunities • Interest in geographical information and logical apparatuses is growing, as is interest in market forecasting solutions, thanks to cloud-based arrangements.


Frequently Asked Questions

Ans: The growth rate of the Artificial Neural Network Market is 21.4% over the forecast period 2023-2030.

Ans: The market size for the Artificial Neural Network Market was valued at USD 216.48 million in 2022.

Ans: The Covid-19 pandemic impacted the Artificial Neural Network Market significantly. The detailed analysis is included in the final report.

Ans: The key players of the Artificial Neural Network Market are Google, IBM, Oracle, Microsoft, Intel, Qualcomm, Alyuda, Ward Systems, GMDH, LLC, and Starmind.

Ans: North America region dominated the Artificial Neural Network Market.

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

4.2 Impact of Ukraine- Russia war

4.3 Impact of ongoing Recession

4.3.1 Introduction

4.3.2 Impact on major economies US Canada Germany France United Kingdom China Japan South Korea Rest of the World

5. Value Chain Analysis

6. Porter’s 5 forces model


7. PEST Analysis


8. Artificial Neural Network Market, by Component

8.1 Software

8.2 Services

9. Artificial Neural Network Market, by Deployment Mode

9.1 Cloud

9.2 On-premises

10. Artificial Neural Network Market, by Application

10.1 Image Recognition

10.2 Signal Recognition

10.3 Data Mining

10.4 Others

11. Artificial Neural Network Market, by Enterprise

11.1 Large Enterprises

11.2 Small and Medium Enterprises

12. Artificial Neural Network Market, by End User

12.1 BFSI

12.2 Retail

12.3 E-Commerce

12.4 IT & Telecom

12.5 Manufacturing

12.6 Healthcare

12.7 Logistics

12.8 Others


13. Regional Analysis

13.1 Introduction

13.2 North America

13.2.1 the USA

13.2.2  Canada

13.2.3  Mexico

13.3 Europe

13.3.1  Germany

13.3.2  the UK

13.3.3  France

13.3.4  Italy

13.3.5  Spain

13.3.6  The Netherlands

13.3.7  Rest of Europe

13.4 Asia-Pacific

13.4.1  Japan

13.4.2  South Korea

13.4.3  China

13.4.4  India

13.4.5  Australia

13.4.6  Rest of Asia-Pacific

13.5 The Middle East & Africa

13.5.1  Israel

13.5.2  UAE

13.5.3  South Africa

13.5.4  Rest

13.6 Latin America

13.6.1  Brazil

13.6.2  Argentina

13.6.3  Rest of Latin America

14. Company Profiles 

14.1 Google

14.1.1 Financial

14.1.2 Products/ Services Offered

14.1.3 SWOT Analysis

14.1.4 The SNS view

14.2 IBM

14.3 Oracle

14.4 Microsoft

14.5 Intel

14.6 Qualcomm

14.7 Alyuda

14.8 Ward Systems

14.9 GMDH

14.10 Starmind

15. Competitive Landscape

15.1 Competitive Benchmarking

15.2 Market Share Analysis

15.3 Recent Developments

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:

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