January 24, 2024 | Posted in News
A Focus on Reducing Latency, and the Imperative to Address Privacy and Security Concerns in an Increasingly Connected World are the Drive Behind Edge AI Software Market’s Meteoric Growth.
Edge AI Software Market Scope and Overview
The Edge AI Software Market, valued at USD 730.95 million in 2022, is projected to soar, reaching a staggering USD 2979 million by 2030. This growth trajectory, boasting a compound annual growth rate (CAGR) of 19.2%, is underpinned by several pivotal factors driving the adoption of Edge AI Software globally.
Edge AI, an amalgamation of edge computing and AI, has emerged as a critical solution empowering artificial intelligence algorithms to operate on local devices with computing capacity. This technology enables real-time data processing sans internet connectivity, finding widespread applications across automotive, manufacturing, healthcare, energy, consumer goods, IT, telecom, and other sectors.
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Some of the Key Players Studied are:
Alphabet Microsoft Corporation IBM Corporation Amazon Web Services Nutanix Synaptics Incorporated SWIM.AI Imagimob AB Anagog Ltd TIBCO Software Others
Market Analysis
The market’s growth is significantly bolstered by the expanding realm of cloud computing, witnessing a substantial CAGR. As businesses increasingly adopt cloud computing, daily data transfers to the cloud surge, amplifying cloud workloads. Edge nodes and devices facilitate the storage and retrieval of data that necessitates real-time reactions, a feat accomplished by edge AI software solutions. Moreover, the burgeoning utilization of artificial intelligence across diverse contexts further augments this growth. AI’s demand for substantial computing power finds a fitting solution in edge AI, especially in tasks like real-time data processing and capture, enabling offline functionality and low-latency operations.
Segment Analysis
In the vertical domain, BFSI spearheads the market due to its adoption of Edge AI Software for enhanced efficiency, security, and customer experience. Real-time data processing at the edge diminishes latency, proving pivotal in areas like fraud detection, risk management, and personalized customer services.
Regarding data types, audio data takes center stage with the highest projected CAGR. Edge AI Software efficiently processes audio data for diverse applications, from generative AI for audio and music to speech recognition, leveraging local processing to reduce latency and bolster privacy.
In deployment, the cloud segment dominates owing to its scalability, flexibility, and cost-effectiveness. Cloud providers offering robust edge AI services ease entry into the domain without significant infrastructure investment.
Key Drivers of the Edge AI Software Market
The Edge AI Software market is flourishing, propelled by a convergence of transformative factors at the intersection of artificial intelligence and edge computing. Key contributors to its dynamic growth include:
Edge Computing Proliferation:The rise of edge computing architectures, driven by the need for real-time processing, fuels the demand for AI solutions at the edge, ensuring data processing closer to the source for faster insights and reduced latency. Increasing IoT Adoption:The proliferation of Internet of Things (IoT) devices generates vast amounts of data at the edge. Edge AI software leverages machine learning algorithms to process this data locally, facilitating quicker and more intelligent decision-making in IoT applications. Latency Reduction Requirements:Industries, especially those reliant on mission-critical applications, demand reduced latency for swift decision-making. Edge AI addresses this need by processing data locally, minimizing the time taken for data to travel to centralized servers. Bandwidth Efficiency:Edge AI optimizes bandwidth usage by processing data on-site, reducing the need to transmit large volumes of raw data to central servers. This not only conserves bandwidth but also enhances the efficiency of network resources. Privacy and Security Concerns:Edge AI meets the growing demand for data privacy by processing sensitive information locally, mitigating the risks associated with transmitting confidential data over networks, thus addressing security and compliance requirements. Scalability in Edge Deployments:As edge computing environments expand, the scalability of Edge AI Software becomes crucial. Solutions that can seamlessly integrate with diverse edge devices and environments contribute to the market’s robust growth. Industry-Specific Applications:Edge AI finds application in various industries, from healthcare and manufacturing to retail and smart cities. Tailored solutions addressing industry-specific challenges and requirements are driving the adoption of Edge AI Software across diverse sectors. Advancements in Edge AI Algorithms:Continuous advancements in machine learning algorithms optimized for edge devices enhance the capabilities of Edge AI Software. These improvements contribute to more accurate and efficient processing of data at the edge.
Key Regional Developments
North America experiences significant market growth due to its robust focus on adopting advanced technologies, including AI and machine learning. The U.S., in particular, emphasizes nationwide AI adoption, propelling market expansion.
The Asia Pacific region, boasting the highest expected CAGR, witnesses key players expanding their reach across developing nations. Japan, notably, focuses on developing edge AI solutions for automotive and manufacturing sectors.
Key Takeaways
BFSI Dominance: The BFSI sector leads in Edge AI Software adoption, leveraging real-time processing for enhanced security and customer-centric services. Audio Data’s Surge: Audio data processing emerges as a pivotal growth area, fueling innovation in generative AI and speech recognition. Cloud Deployment’s Triumph: The cloud segment reigns supreme, offering scalability, flexibility, and ease of adoption for edge AI solutions.
Recent Developments
Microsoft and Fungible’s collaboration aims to enhance Azure IoT Edge and other edge AI products’ performance through specialized Data Processing Units (DPUs). Cisco and Nutanix’s global partnership targets accelerating hybrid multi-cloud deployments for modernizing IT landscapes. IBM’s alliance with Bharti Airtel promises secured edge cloud services for Indian businesses, fostering innovation and cost reduction.
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Table of Contents – Major Key Points
1. Introduction
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 on Major Economies
5. Value Chain Analysis
6. Porter’s 5 Forces Model
7. PEST Analysis
8. Edge AI Software Market Segmentation, by Industry Vertical
8.1. Government & Public
8.2. Manufacturing
8.3. Automotive
8.4. Energy & Utilities
8.5. Telecom
8.6. Healthcare
8.7. Others
9. Edge AI Software Market Segmentation, by Component
9.1. Solutions
9.2. Services
10. Edge AI Software Market Segmentation, by Data Source
10.1. Speech Recognition
10.2. Biometric Data
10.3. Sensor Data
10.4. Mobile Data
11. Edge AI Software Market Segmentation, by Application
11.1. Autonomous Vehicles
11.2. Access Management
11.3. Video Surveillance
11.4. Remote Monitoring & Predictive Maintenance
11.5. Telemetry
11.6. Energy Management
11.7. Others
12. Regional Analysis
12.1. Introduction
12.2. North America
12.3. Europe
12.4. Asia-Pacific
12.5. The Middle East & Africa
12.6. Latin America
13. Company Profile
14. Competitive Landscape
14.1. Competitive Benchmarking
14.2. Recent Developments
14.3. Market Share Analysis
15. USE Cases and Best Practices
16. Conclusion
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