Artificial Intelligence (AI) has changed the way businesses process and analyze data, allowing for new insights and making better-informed decisions. However, traditional AI solutions are often constrained by the need to transfer large amounts of data to a centralized data center or cloud infrastructure for processing, leading to an increase in latency and the high cost of network operations. Businesses are turning to edge computing/platforms for AI workloads to overcome these challenges. In this resource, ABI Research dives into the dynamic and emerging edge AI market and the next steps for stakeholders.
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Market Overview
Below are some notable definitions, trends, and demand forecasts ABI Research has compiled for the edge AI market:
- Shipments of on-premises and edge cloud AI servers are expected to reach more than 2 million annually by 2028 and grow at a Compound Annual Growth Rate (CAGR) of 56% from 2023 to 2028. The growth in cloud servers is driven by the increasing demand for low-latency networks and real-time processing capabilities.
- The emergence of intelligent accelerators that support AI processing, such as Data Processing Units (DPUs), Infrastructure Processing Units (IPUs), Neural Processing Units (NPUs), etc., also helps drive the viability for businesses to deploy edge AI solutions.
- AI is helping businesses transform and, increasingly, ABI Research is seeing the shift toward edge AI, with the installed base for on-premises and edge cloud AI servers increasing at a 63% CAGR from 2023 to 2028 (roughly a 5.5 million installed base in 2028).
- Shipments of worldwide edge AI gateways are expected to reach 42 million units by 2028, growing at a CAGR of 6% from 2023 to 2028.
- The total installed base for edge AI gateways is forecast to reach almost 200 million units by 2028, indicating strong demand for AI-specific hardware, especially for use cases like video analytics, computer vision, etc. that require high computing power to process the AI models and algorithms.
- Some of the key edge AI market trends and key drivers include low latency needs from businesses, greater privacy concerns, low cloud connectivity dependency, real-time decision-making, and the emphasis on sustainability.
- The main consumers of edge AI include retail, transportation, healthcare, manufacturing, and mining/industrial automation.
“While the traditional cloud approach still holds relevance in this ecosystem, we are seeing an increased appetite for new approaches to AI workloads, particularly from an edge AI perspective.” – Yih-Khai Wong, Senior Analyst at ABI Research
If you take a look at the chart below, you will see that the worldwide installed base for edge AI gateways is growing at a CAGR of 18% between 2021 and 2028.
Key Decision Items
Assess the Future of Edge AI
ABI Research envisions an edge AI world with the following:
- Heterogeneous AI: By using multiple types of AI models and algorithms, edge AI platforms can be designed to be flexible and support different types of solutions.
- Purpose-Built Edge AI Accelerators: Advancements in AI accelerators, such as Neural Processing Units (NPUs), will spur the growth of edge AI. The ability to have an AI processing model process data within an edge device instantly will enable businesses to operate more efficiently by being able to make informed decisions in real time.
- Proliferation of On-Device AI Capabilities: Devices built with AI processing capabilities will provide businesses with data collection and the ability to process and deploy information that can then be used as business outcomes (e.g., customer heatmaps or worker safety).
- Ethical Edge AI and Standardization Framework: The development of an ethical AI framework will be crucial, especially in industries where strict laws and frameworks protect the collection of personally identifiable data, health data, etc.
Identify the Business Opportunities for Implementing Edge AI in Your Operations
Below are some of the opportunities that businesses can look forward to by implementing an edge AI strategy:
- Improved Customer Experience: Edge AI software can assist businesses in giving customers more individualized experiences through tailored recommendations, targeted advertising, and specialized services.
- Data Privacy and Real-Time Decision-Making: By processing data locally and within the parameters of a private firewall, businesses can control the flow of data, ensuring data privacy. Businesses can also make faster and more accurate decisions based on real-time data, leading to an increase in efficiency.
- New Business Models: A well-thought-out edge AI strategy has the potential to be developed in new business models or revenue streams, with businesses able to diversify and provide new solutions or offerings to customers, leveraging edge AI platform implementations.
Enable Partnerships within the Edge AI Market
Partnerships and collaborations will be crucial in the complex edge AI market. ABI Research recommends that startups with niche software solutions partner with an established vendor.
An example of this partnership is how Skycatch partners with NVIDIA, creating a win-win situation for both parties, with Skycatch building its software platform on top of NVIDIA’s Jetson appliance. This, in turn, provides a compelling solution in the mining industry.
Vendors Need to Foster Innovation
Innovation will be the engine that drives the edge AI market forward. There is a lot of room for innovation and disruption in this dynamic and expanding market. New technological advancements have helped improve the speed and efficiency of edge AI processing, including energy-efficient processing chips, edge AI toolkits/software platforms, and thermal-efficient edge AI gateways. The proliferation of IoT devices, as well as the maturation of 5G networks, will continue to ensure the growth of edge AI worldwide.
Promote Edge-to-Cloud Orchestration
Cloud hyperscalers play an important role in the edge AI ecosystem, providing edge infrastructure for deploying and managing edge AI solutions. The emergence of distributed intelligence has also accelerated cloud hyperscalers’ focus on edge computing, with edge AI an important area of focus for these cloud hyperscalers.
ABI Research firmly believes that the ability to manage and orchestrate AI workflows locally across edge devices, as well as cloud infrastructure, will be crucial for edge AI software vendors. Service providers and System Integrators (SIs) play an important role in this area, providing a full end-to-end view of data orchestration and processing from the source to the edge.
Key Market Players to Watch
Dig Deeper for the Full Picture
Gain a more in-depth view of the edge AI market by downloading ABI Research’s Distributed Intelligence on the Edge: Building a Case For Edge AI research report. The report will help you:
- Understand how AI workloads are processed on the edge.
- Identify promising industry verticals and use cases that benefit from edge AI.
- Pinpoint the biggest growth drivers and challenges in the edge AI market.
- Predict the future of edge AI and the implications on business outcomes.
Get the report here.
Not ready for the report yet? Check out our following content:
- How Edge AI Enables Groundbreaking AI Applications [blog]
- Facing Enormous Workloads, Public Clouds Turn to SmartNICs, DPUs, and IPUs [Research Highlight]
- 6G’s Potential Lies with Distributed Compute and Edge-to-Cloud [Research Highlight]
This content is part of the company’s Distributed & Edge Computing Research Service.
Edge AI
