We are pleased to share Chapter 1 of the 2024 State of Edge AI Report with you.
This report represents months of hard work, research, and dedication to exploring the dynamic world of Edge AI applications across industries.
Report Highlights:
#1: Industry-specific insights– Each industry is covered by a dedicated chapter that provides industry-specific insights, descriptions, and examples. ,
#2: Real-life case studies – Get comprehensive insights from featured sections of real-world case studies.
#3: A look into the future - The report also provides a peek into the exciting generative AI technology and its convergence with edge computing before delving into the challenges still hindering Edge AI today.
Download the full report below.
The Evolution of AI: From Research to Edge Technology
Edge AI’s growing momentum over recent years has not been a stroke of luck or an unexpected turn of events. It’s been brewing in the research spheres for quite a while now. In fact, the way AI influences technology has been a discussion since the mid-1900s, ever since Alan Turing set the standards for the “thinking machine,” and Christopher Strachey wrote the first successful AI computer program, followed by Arthur Samuel of IBM pioneering machine learning. From then on, AI went on a long rollercoaster ride of rises and dips, going through hype cycles and significant interest and funding all the way down to not one but two “AI winters” before the turn of the millennium. Nonetheless, in recent years, especially going into the 2020s, AI has taken yet another leap, but this time, it has taken off.
With major developments in machine learning and the advent of technologies like cloud computing, edge computing, and fog computing, AI has significantly benefited from the data processing capabilities brought about by these technologies. Cloud computing’s ability to process massive amounts of data simultaneously and edge computing’s ability to process data locally and in real time are both crucial for AI’s mass adoption. This helps AI make informed decisions in fractions of the time that it would take humans, thus improving processes and systems across different industries and creating new and optimal ways of working. As a result, AI has witnessed significant growth in adoption rate across various large organizations, reaching 42% in 2023, according to the IBM Global AI Adoption Index 2023, with as many as 40% of organizations actively exploring the use of AI in their business operations. AI has once again captured the spotlight — and this resurgence has brought about a new wave of possibilities and opportunities to explore.
“Simplicity is key in the tech world: a solution is only as successful as it is widely accepted, adopted, and applied. That’s why Arduino’s mission is to democratize technologies like Edge AI, making it an accessible option for people with different backgrounds and in all industries to solve problems, create value, and grow.” – Fabio Violante, CEO of Arduino
Today, a lot of data processing is being decentralized from large cloud data centers to smaller localized data centers and edge devices. This has enabled the emergence of Edge AI, which processes data at or near the source of data generation. Many organizations are deploying edge functionalities, resulting in energy-efficient, low-latency applications with real-time performance. Edge AI offers significant data protection and security benefits, making it an attractive proposition for organizations across sectors to use edge computing features for various use cases. In the following sections, we take a look at the Edge AI market to see how it’s been responding to the technology’s potential, and we explore how different industries are adopting Edge AI into their workflows and systems.
Edge AI Market Landscape
For a technology that is moving quickly on the Gartner hype cycle for AI, Edge AI is constantly finding new use cases in various industries, and its adoption is expected to grow further and faster. In fact, Gartner analysts predict that edge computing technologies will gain traction and maturity in 2024, especially with the significant drop in the cost of developing and deploying edge systems thanks to technical innovation in this space. Such improvements in the technology have enabled the Edge AI market to witness remarkable growth over recent years. According to Market.US research, the global Edge AI market is expected to surpass the USD 140 billion mark by 2032, a considerable rise from just over USD 19.1 billion in 2023. That is a compound annual growth rate (CAGR) of almost 26% across nine years.
The Edge AI Market is expected to reach USD 143.6 Billion by 2032, an exponential rise from its 2023 value of USD 19.1 Billion (Credit: market.us)
The growth of the Edge AI market reflects the increasing integration of technology into various aspects of modern life. With the proliferation of IoT devices across industries, from manufacturing to healthcare, vast volumes of data are being generated continuously. This data holds significant potential for insights and optimization, but traditional centralized processing methods often struggle to handle real-time data processing without having to deal with latency issues.
Edge AI addresses this challenge by bringing AI and machine learning algorithms closer to where the data is generated, at the “edge” of the network. This localized approach allows for real-time processing and analysis, minimizing latency, reducing bandwidth usage, and enabling quicker decision-making. For instance, within the realm of autonomous vehicles, split-second responses to changing road conditions are crucial for safety. Edge AI enables these vehicles to process sensor data onboard, ensuring faster reaction times.
Moreover, advancements in semiconductor technology have played a crucial role in enabling more powerful and energy-efficient edge computing devices. These devices are capable of handling complex AI algorithms while remaining efficient enough to operate in resource-constrained environments, such as remote industrial sites or within wearable devices.
The rollout of 5G technology further amplifies the capabilities of Edge AI solutions. With its significantly enhanced connectivity and data transfer speeds, 5G facilitates seamless communication between edge devices and central systems, enabling faster data transmission and response times. This is particularly beneficial in scenarios such as healthcare monitoring, where timely analysis of patient data can have life-saving implications.
In essence, the growth of the Edge AI market represents a response to the evolving demands of industries and engineers in a data-driven world. It's about leveraging innovation to optimize processes, enhance efficiency, and ultimately improve the way industries operate.
Industry Adoption and Trends of Edge AI
“Under the radar thus far, edge is set to become a ubiquitous lever of scale and reinvention as artificial intelligence (AI)—including generative AI—driven applications become pervasive in enterprise functions and operations.” This is what researchers at Accenture articulated in their 2023 study on Leading with Edge Computing. This statement sums up the current state of Edge AI in a nutshell. With edge computing permeating a lot of industries and application areas, it is reaching a level of ubiquity that renders its implementation almost a necessity. In fact, Accenture’s survey uncovered that 83% of executives across multiple industries think that in order to stay competitive in the future, edge computing will be essential. And many are fearing missing out on all of Edge AI’s benefits if they do not act quickly and incorporate it into their workflows, products, and services.
However, the adoption of Edge AI is not uniform. While some companies regard edge as a key differentiator to bring AI into their core business, others still struggle with fully leveraging the technology’s benefits, mostly due to them considering it a standalone technology and using it in ad-hoc projects. Adopting edge computing strategically and integrating it with existing cloud strategies has shown the best outcomes: Advanced users of edge are four times more likely to achieve accelerated innovation, nine times more likely to increase efficiency, and seven times more likely to reduce costs (Accenture, 2023).
Edge moves computing closer to users and devices at the edge of the network, where it is the closest possible to data sources (Credit: Accenture)
Looking at Edge AI from the industry level, we see a similar distribution, with some industries already utilizing the technology almost ubiquitously and others still exploring its potential. The manufacturing industry seems to take the lion’s share of revenue with approximately 31% thanks to the integration of automation and real-time insights, capitalizing on Edge AI’s benefits in defect detection, reduced latency, real-time decision-making, cost efficiency, and data security. Following it is the automotive and transportation sector, especially with the recent ACES trends (autonomous driving, connectivity, electric vehicles, and shared mobility) leading the industry, all of which require Edge AI, albeit at varying levels. Furthermore, traffic management is seeing significant developments by integrating Edge AI into its sensors, cameras, and traffic management systems (TMS). While other industries like healthcare, retail, energy, and agriculture acknowledge the potential of Edge AI, they are yet to adopt it at the same level as manufacturing and automotive.
That being said, Edge AI is seemingly showing clear patterns and trends that are influencing the future of data science and machine learning (DSML) across many industries. As Gartner outlined in 2023, one trend that is leading the way is Edge AI as a promise of responsiveness. Edge AI promises quicker decision-making by executing AI algorithms locally, bypassing the need for the Cloud or remote data center connections. This reduces latency and enhances system responsiveness.
How organizations have been using AI in the past two years (Credit: IBM)
Converging AI and edge computing leads to more efficient and potentially energy-saving solutions. Gartner has forecasted that, by 2025, more than half of data analysis by deep neural networks will occur at the point of capture in an edge system, a significant increase from single-digit percentage points in 2021. This shows the significance of Edge AI in the years to come and how its implementation will continue to grow and penetrate various systems and workflows.
