Artificial Neural Networks (ANN) Market is segmented By Type (Feedback Artificial Neural Network, Feedforward Artificial Neural Network), By Component (Solutions, Platform/API, Services), By Deployment (On-premises, Cloud), By Application (Image Recognition, Signal Recognition, Data Mining, Others), By End-User (Banking, Financial Services, Insurance, Retail and E-commerce, Healthcare and Life Sciences, Others) and By Region (North America, Latin America, Europe, Asia Pacific, Middle East, and Africa) – Share, Size, Outlook, and Opportunity Analysis, 2024-2031
Report Overview
Global Artificial Neural Networks (ANN) Market reached US$ 164.3 million in 2022 and is expected to reach US$ 600.3 million by 2030 growing with a CAGR of 17.6% during the forecast period 2024-2031.
The Artificial Neural Networks (ANN) market has turned out to show rapid growth within the field of artificial intelligence (AI) and machine learning. The growth of the market is due to advancements in computing power, the availability of large datasets, and increased demand for AI applications across various industries.
Furthermore, the market has shown increased demand in diverse fields, including healthcare, finance, retail, manufacturing, transportation, and more. The technology is used for various tasks such as pattern recognition, image and speech recognition, data analysis, predictive modeling, and decision-making.
The ANN market is characterized by the presence of both established tech giants and emerging startups across the globe. Companies such as IBM, Amazon, Google, Microsoft, and NVIDIA are actively involved in the development and deployment of ANN technologies and which has made them cover more than 69.8% in 2022 globally. Additionally, numerous specialized AI startups focus on providing ANN-based solutions for specific industries and use cases.
Market Summary
| Metrics | Details |
| CAGR | 17.6% |
| Size Available for Years | 2021-2030 |
| Forecast Period | 2024-2031 |
| Data Availability | Value (US$) |
| Segments Covered | Type, Component, Deployment, Application, End-User and Region |
| Regions Covered | North America, Europe, Asia-Pacific, South America and Middle East & Africa |
| Fastest Growing Region | Asia-Pacific |
| Largest Region | North America |
| Report Insights Covered | Competitive Landscape Analysis, Company Profile Analysis, Market Size, Share, Growth, Demand, Recent Developments, Mergers and Acquisitions, New Product Launches, Growth Strategies, Revenue Analysis, Porter’s Analysis, Pricing Analysis, Regulatory Analysis, Supply-Chain Analysis and Other key Insights. |
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Market Dynamics
Rising Demand For Predictive Analysis
The rising penetration of predictive analysis in various industries such as healthcare, banking, financial services, insurance and retail and e-commerce, is boosting the artificial neural networks (ANN) market in several ways. Predictive analysis involves using data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. Artificial Neural Networks are particularly useful for predictive analysis, as they are capable of learning patterns and relationships from large datasets, and using this information to make accurate predictions.
Furthermore, predictive analytics can be used to forecast future trends, identify potential risks and opportunities, and optimize business processes. As the volume of data generated by businesses and organizations continues to grow, the need for accurate and effective predictive analytics solutions is becoming more critical. For instance, ANN-based predictive analysis has significant applications in healthcare, including disease diagnosis, patient monitoring, and drug discovery. ANN models trained on medical data can identify patterns and predict disease progression, enabling early intervention and personalized treatment plans.
Aggressive Strategies From Market Players
Market players, including technology giants, and startups, such as IBM, Amazon, Google, Microsoft and research institutions, are actively driving advancements in ANN technology and its applications. Market players are heavily investing in research and development activities to enhance ANN algorithms, architectures, and training methodologies. These efforts aim to improve the accuracy, efficiency, and scalability of ANN models, enabling them to tackle more complex tasks and handle larger datasets.
SAP Labs India has announced collaboration news with IIM Bangalore to launch AI for Managers program. The program intends to provide a pool of interested managers who aspire to be skilled Decision Makers with access to knowledge of Artificial Intelligence and its components, including Statistical Learning, Machine Learning, and Deep Learning. It is a 16-month certification program that launches on April 22, 2022, and it consists of 11 online modules that are layered together according to the order in which they appear on a learning curve.
Lack Of Standardization
The lack of standardization and static approach is a major restraint on market growth. ANN solutions vary widely in terms of their architectures, algorithms, and implementation, which can make it difficult for businesses and organizations to compare and evaluate different solutions. Additionally, it can result in compatibility issues and interoperability problems when integrating ANN solutions with other systems.
Furthermore, it also creates a challenge for developing and deploying ANN solutions at scale. ANN solutions typically require significant investment in hardware, software, and personnel, and can add to the complexity and cost of development and deployment. It can be a significant barrier for small and medium-sized businesses with limited resources.
Market Segment Analysis
The global artificial neural networks (ANN) market is segmented based on type, component, deployment, application, end-user and region.
Growing Demand For A Network With Great Adaptability And Learning Features
Feedback artificial neural networks dominate the global market covering nearly 1/3rd of the market. Risk management and fault detection are critical components of artificial neural networks (ANN). Feedback Artificial Neural Network (FBANN) is a type of neural network that includes feedback connections between neurons, allowing information to flow in both forward and backward directions. It enables the network to learn and adapt over time by adjusting its connections and weights in response to feedback signals.
FBANNs are commonly used in applications where the input data is dynamic and changing over time, such as in time series analysis, speech recognition, and image processing. FBANNs can also be used in control systems, where they can adjust the output of the system based on feedback signals.
Source: DataM Intelligence Analysis (2023)
Market Geographical Share
North America’s Increasing Demand For Advanced Technological Infrastructure, High Research And Development Investments
North America is a significant market for artificial neural networks (ANN) due to the region's advanced technological infrastructure, high research and development investments, and the presence of leading technology companies. The market is expected to cover nearly 45.5% in the forecast period due to the increasing adoption of ANN in various industries, including healthcare, finance, automotive, and retail.
The healthcare industry in North America is expected to drive the growth of the ANN market in the region. The increasing demand for accurate and efficient diagnosis, treatment planning, and drug discovery is driving the adoption of ANN in the healthcare industry. ANN can analyze large amounts of medical data to provide accurate diagnoses and predict potential health issues. Additionally, the increasing adoption of wearable devices and electronic health records is expected to further drive the growth of ANN in the healthcare industry.
Source: DataM Intelligence Analysis (2023)
Market Keyplayers
The major global players include IBM Corporation, Qualcomm Technologies, Inc, Intel Corporation, Oracle, nDimensional, Alyuda Research, LLC, Microsoft, SAP SE, Starmind, Afiniti, Ward Systems Group, Inc, Google LLC, NeuralWare.
COVID-19 Impact Analysis
The COVID-19 pandemic has had both positive and negative impacts on the artificial neural networks (ANN) market. As the pandemic has posed challenges to various industries, it has also created tremendous opportunities for the adoption and development of ANN technologies. The healthcare industry is among the dominant industry which has witnessed a significant surge in demand for AI and ANN technologies during the pandemic.
ANN models have been used for various applications, such as drug discovery, disease diagnosis, and patient monitoring. ANN-based predictive models have also played a crucial role in analyzing patient data, identifying patterns, and predicting disease outcomes, aiding in effective healthcare management and decision-making leading to create positive growth conditions during the pandemic phase.
Russia-Ukraine War Impact
The conflict has severely disrupted global supply chains, especially because it affects the production and distribution of critical components and technologies required for ANN systems. The disruptions in the supply chain lead to delays in the production and deployment of ANN-related hardware and infrastructure. Furthermore, the Russia-Ukraine conflict has also created geopolitical uncertainty, which has impacted the investment climate and business confidence in the region. Uncertainty and instability lead to cautious decision-making and reduced investments in AI technologies, including ANN.
Key Developments
- On November 3, 2021, Oracle Corporation announced the launch of new AI services on Oracle's cloud infrastructure. Developers can train the new OCI AI services using data specific to their organizations or utilize pre-trained, out-of-the-box models on business-related data.
- On October 29, 2021, Google LLC announced the launch of a new AI solution that includes various capabilities of multiple ML solutions on a single AI system.
- On September 20, 2022, At GTC, NVIDIA revealed the large language model (LLM) framework's open beta, which clients can select to run on OCI's accelerated cloud instances. LLMs are being built by customers for a variety of AI applications, including chatbots, code development, text summarization, and content generation.
Why Purchase the Report?
- To visualize the global artificial neural networks (ANN)- market segmentation based on type, component, deployment, application, end-user and region, as well as understand key commercial assets and players.
- Identify commercial opportunities by analyzing trends and co-development.
- Excel data sheet with numerous data points of artificial neural networks (ANN) market-level with all segments.
- PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
- Product mapping available as excel consisting of key products of all the major players.
The global artificial neural networks (ANN) market report would provide approximately 77 tables, 78 figures and 199 Pages.
Target Audience 2024
- Manufacturers/ Buyers
- Industry Investors/Investment Bankers
- Research Professionals
- Emerging Companies
