Top Growth Trends in the Artificial Neural Network Market: Key Insights and Opportunities for 2025-2034
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How Does the Projected Growth of the Artificial Neural Network Market Compare Over the Forecast Period?
The size of the artificial neural network market has seen rapid expansion in the past few years. The market is projected to rise from $0.49 billion in 2024 to $0.58 billion in 2025, with a compound annual growth rate (CAGR) of 17.80%. This surge during the historic period is rooted in the escalation of cyber attacks, augmented demand for AI and automation, the development of autonomous systems, rising traction for the Internet of Things (IoT), and wider cloud integration.
Expectations are that the artificial neural network market will experience swift expansion in the coming years. By 2029, projections suggest a market worth of $1.11 billion, reflecting a compound annual growth rate (CAGR) of 17.44%. This growth trajectory during the forecast period can be linked to the escalating demand for artificial intelligence (AI) across various sectors, a surge in demand for cloud-based solutions, growing usage of voice assistants and conversational AI, increased application in drug development and healthcare, and a rise in big data volume. The approaching period is expected to witness significant trends such as advancements in machine learning and neural networks, amalgamation with big data and analytics, interconnectivity with IoT and edge computing, integration of hybrid cloud and edge, and interplay with 5G networks.
Which Macro and Microeconomic Factors Are Accelerating the Growth of the Artificial Neural Network Market?
The surge in demand for cloud-based solutions is anticipated to drive the expansion of the artificial neural network market in the future. Defined as services or applications that function on remote servers accessed through the internet rather than local servers or devices, the demand for such solutions is growing owing to features like scalability, cost-effectiveness, improved data access, and facilitation of remote collaboration, catering to the changing requirements of contemporary businesses. Artificial neural networks elevate the utility of these cloud-based solutions by providing superior data processing, predictive analytics, and smart decision-making facilities for heightened performance and scalability. For example, in December 2023, it was announced by Eurostat, the Luxembourg-based government agency in the EU, that there had been a growth of 4.2 percentage points in 2023 in the rate of enterprises acquiring cloud computing services, in comparison to 2021. Consequently, this rising demand for cloud-based solutions is a significant contributor to the development of the artificial neural network (ANN) market.
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Who Are the Key Players Steering the Development of the Artificial Neural Network Market?
Major companies operating in the artificial neural network market are:
• Google Inc._x000D_
• Microsoft Corporation_x000D_
• Amazon Web Services Inc._x000D_
• Intel Corporation_x000D_
• IBM Corporation_x000D_
How Are Consumer Trends Shaping the Current and Future Landscape of the Artificial Neural Network Market?
Leading companies in the artificial neural network industry are prioritizing the creation of novel platforms like AI-based networking systems. These aim to bolster data processing capacities, refine predictive analytics, and streamline machine learning models, thereby facilitating more efficient and scala ble solutions in sectors such as healthcare, finance, and automotive. AI-based networking systems encompass sophisticated structures that merge artificial intelligence with networking technologies to enhance data transmission, boost network administration, and elevate overall performance. For example, in February 2022, the International Telecommunication Union, headquartered in Switzerland, introduced the AI for Good neural network – an innovative platform designed to promote sustainable growth via artificial intelligence. This platform incorporates high-level AI algorithms to address worldwide issues like climate change, poverty, and health inequities. Its pivotal function involves processing and examining massive datasets from varied sources, providing crucial insights that aid governments, organizations, and researchers in making knowledgeable decisions. The platform also promotes cooperation, rallying experts from disparate sectors to engineer inventive, expansive solutions that support sustainable development and align with the UN’s Sustainable Development Goals (SDGs).
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Which Primary Segments of the Artificial Neural Network Market Are Driving Growth and Industry Transformations?
The artificial neural network market covered in this report is segmented –
1) By Component: Solutions, Platform Or Application Programming Interface (API), Services
2) By Deployment Mode: On-Premises, Cloud
3) By Enterprise: Large Enterprises, Small And Medium Enterprises
4) By Application: Image Recognition, Signal Recognition, Data Mining
5) By End-User: Banking, Financial Services, and Insurance (BFSI), Retail And E-Commerce, IT And Telecom, Manufacturing, Healthcare, Automotive
Subsegments:
1) By Solutions: Software Solutions, Hardware Solutions, Deployment Solutions, Data Management Solutions
2) By Platform Or Application Programming Interface (API): Cloud-Based Platforms, On-Premise Platforms, Open-Source APIs, Proprietary APIs
3) By Services: Consulting Services, Integration And Deployment Services, Support And Maintenance Services, Managed Services
Which Geographical Regions Are Shaping the Artificial Neural Network Market Growth?
North America was the largest region in the artificial neural network market in 2024. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the artificial neural network market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.
What Are the Defining Aspects of the Artificial Neural Network Market Landscape?
An artificial neural network (ANN) refers to a machine learning architecture that mimics the human brain’s neural structure to process data and recognize patterns. It consists of layers of interconnected nodes, including input, hidden, and output layers, which work together to transform input data into meaningful outputs through weighted connections.
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