Edge AI Software Market

[330 Pages Report] The global Edge Al Software Market is projected to grow from USD 1.1 billion in 2023 to USD 4.1 billion by 2028, at a CAGR of 30.5% during the forecast period. The adoption of Edge AI software globally is being driven by several factors, including the increasing adoption of IoT devices, the need for real-time data processing, and the emergence of 5G networks. The market is expected to continue to grow as more organizations adopt edge computing and AI technologies to improve their operations and gain insights from data collected by edge devices. The growth of the Edge AI market is also driven by increased usage of social media and e-commerce platforms, integration of AI into edge ecosystems, and growing enterprise workloads on the cloud.

Technology Roadmap of Edge AI Software till 2028

The Edge AI software market report covers the technology roadmap till 2028, with insights into short-term, mid-term, and long-term developments.

Short-term roadmap (2023-2025)

• Focus on lower latency to enable real-time decision-making for critical applications, reducing delays and enhancing efficiency.
• Significant advancements in technologies leading to a 10 times boost in edge device processing power, enabling faster real-time AI at the edge.
• Ethical AI ensures fairness, accountability, and transparency in AI decision-making.

Mid-term roadmap (2025-2028)

• Quantum computing is set to drastically enhance AI processing power and capabilities..
• Compliance with ethical AI standards ensures responsible AI development and deployment.
• Seamless integration between edge and cloud services, enabling the coexistence of local and cloud AI.

Long-term roadmap (2029-2030)

• Cognitive AI represents AI systems with human-like adaptability and learning.
• Fully autonomous AI systems capable of making decisions independently, marking a significant milestone in AI maturity.
• 6G integration will enable faster and more reliable edge AI communication.

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Market Dynamics

Driver: Rising Introduction of 5G Network to Boost Market Growth

The increasing adoption of 5G network technology across the globe is expected to drive market growth significantly in the upcoming years. The implementation of 5G networks is set to revolutionize the way IT and telecommunications converge, opening new avenues for high-end applications and reducing computational complexity. With the deployment of 5G networks, data centres can be organized at edge configurations, and industry-specific platforms can be implemented in standard ecosystems using software-defined connectivity fundamentals and virtual machines. The high speed and low latency of 5G networks offer tremendous opportunities for businesses to develop innovative solutions and services, thereby increasing their competitiveness in the market.

Restraint: Complexity and cost of deploying edge AI software

The complexity and cost of deploying edge AI software is a major challenge that is hindering the adoption of edge AI technologies. Edge AI software requires specialized hardware and software infrastructure to deploy and operate, which can be complex and expensive, especially for small and medium-sized businesses. Additionally, there is a lack of standardized tools and frameworks for deploying edge AI software, which can make it difficult to manage and scale deployments. Edge AI software can be computationally demanding, requiring specialized hardware such as GPUs and FPGAs, which can be a major barrier for businesses that do not have the necessary IT infrastructure in place.

Opportunity: Exploring Emerging Applications in Various Fields

Edge AI software is the driving force behind transformative applications in autonomous vehicles, smart cities, and industrial automation. In autonomous vehicles, it enable real-time decision-making and heightened safety through the analysis of sensor data. Edge AI also plays a critical role in smart cities, reducing latency by processing data at the edge and enabling intelligent traffic management, energy-efficient lighting, and more. As this technology advances, it continues to shape the future of these sectors by enhancing efficiency and innovation, ultimately revolutionizing transportation, urban living, and industrial processes.

Challenge: Integration complexities with existing IT infrastructure and systems

Integrating edge AI software with existing IT infrastructure and systems is a complex and challenging task. Edge AI systems are often heterogeneous, storing data in silos, and raising security and privacy concerns. Existing IT systems are typically more homogenous and centralized, with stricter security and privacy controls in place. There are no standard ways to integrate edge AI software with existing IT infrastructure and systems, which can make it difficult and expensive for businesses to adopt edge AI solutions.

Edge AI software market Ecosystem

By vertical, BFSI accounts for the largest market size during the forecast period.

Edge AI software is rapidly being adopted by the BFSI industry to improve efficiency, security, and customer experience. It enables real-time data processing and decision-making on devices at the edge of the network, rather than relying on centralized cloud computing. This can significantly reduce latency and improve performance, particularly in areas such as fraud detection, risk management, and customer personalization.

Based on the data type, audio data to hold the highest CAGR during the forecast period.

Edge AI software is the implementation of artificial intelligence in an edge computing environment, which allows computations to be done close to where data is created. Audio data is one type of data that can be processed using edge AI software. Audio data is being used in edge AI software for generative AI for audio and music, environmental sounds and sound effects, speech recognition, and audio processing for IoT devices. By processing data locally, edge AI can reduce latency, increase privacy, and improve the efficiency of AI applications.

By deployment, cloud accounts for the largest market size during the forecast period.

Cloud is expected to account for the largest market share in edge AI software due to its scalability, flexibility, cost-effectiveness, performance, reliability, and security features. Additionally, cloud providers offer a variety of edge AI services and platforms that make it easier for businesses to get started with edge AI without having to invest in their own infrastructure. Some of the leading cloud providers that support edge AI deployments include AWS, Microsoft, and Google.

North America to account for the largest market size during the forecast period.

North America is expected to account for the largest market size in the edge AI software market due to its early adoption of edge AI technology, the presence of leading-edge AI software vendors, and the strong demand for edge AI solutions in key North American industries. The increasing adoption of IIoT devices, the growing demand for real-time data processing and decision-making, the need to improve operational efficiency and productivity, and the rising concerns about data privacy and security are some of the key factors driving the growth of the edge AI software market in North America.

Key Market Players

The Edge AI software vendors have implemented various types of organic and inorganic growth strategies, such as partnerships and agreements,  new product launches, product upgrades, business expansions, and mergers and acquisitions to strengthen their offerings in the market. The major vendors in the global market for Edge AI software include Microsoft (US), IBM (US), Google (US), AWS (US), Nutanix (US), Synaptics (US), TIBCO (US), Octonion (Switzerland), Imagimob (Sweden), Anagog (Israel), Veea (US), Gorilla Technology (China), Azion (US), Bragi (Germany), Tact.ai (US), Ekinops (France), Clearblade (US), Alef Edge (US), Adapdix (US), byteLAKE (Poland), Reality Al (US), Deci (Israel), Edgeworx (US), Swim (US), Invision Al (Canada), Horizon Robotics (China), Kneron (US), DeepBrainz (India), Johnson Controls(Ireland), and Blaize(US).

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Scope of the Report

This research report categorizes the edge AI software market based on Offering, Data Type, Verticals, and Region.

By Offering:

• Solutions
  • By Type:
    • Standalone
    • Integrated
  • By Deployment:
    • Cloud
    • On-premises
• Services
  • Professional Services
    • Consulting Services
    • Deployment and Integration
    • Support and Maintenance
  • Managed Services

By Data Type:

• Video and Image Data
• Audio Data
• Text and Language Data
• Environmental and Location Data
• Biometric Data
• Multi-Modal Data

By Vertical:

• Banking, Financial Services, and Insurance
• Government & Public Sector
• Healthcare & Life Sciences
• Telecommunications
• Energy & Utilities
• Manufacturing
• Automotive, Transportations, and Logistics
• Media & Entertainment
• Other Verticals

By Region:

• North America
  • US
  • Canada
• Europe
  • UK
  • Germany
  • France
  • Italy
  • Spain
  • Beneux
  • Rest of Europe
• Asia Pacific
  • China
  • Japan
  • India
  • ANZ
  • ASEAN
  • Rest of Asia Pacific
• Middle East & Africa
  • UAE
  • Kingdom of Saudi Arabia
  • Qatar
  • Turkey
  • South Africa
  • Rest of the Middle East & Africa
• Latin America
  • Brazil
  • Mexico
  • Argentina
  • Rest of Latin America

Recent Developments:

• Microsoft has partnered with Fungible to accelerate datacenter innovation. Fungible is a company that develops data processing units (DPUs) that are optimized for AI workloads. Microsoft plans to use Fungible's DPUs to accelerate the performance of Azure IoT Edge and other edge AI solutions.
• Cisco and Nutanix have formed a global strategic partnership to accelerate hybrid multicloud deployments by offering a hyperconverged IT modernization and business transformation solution.
• IBM and Bharti Airtel have formed a partnership. The partnership will enable Indian enterprises to access secured edge cloud services, which will help them to accelerate innovation, improve efficiency, and reduce costs.
• Google and Amazon Web Services (AWS) have partnered to make it easier for developers to build and deploy edge AI applications. The two companies have integrated their edge AI platforms, so that developers can use the same tools and APIs to build and deploy edge AI applications on both Google Cloud and AWS.
• Energous and Veea have partnered to enable complete wireless power deployments, integrating Energous PowerBridges and Veea's Edge Platform with Wiliot IoT Pixels. The partnership aims to enable the next generation of real-time asset tracking applications in industrial, logistics, and retail applications.

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