How Can Businesses Benefit from the Edge AI Boom?
Embracing Edge AI can be a game-changer for businesses, propelling them toward a future where real-time data processing and decision-making are at the forefront of innovation. Edge AI is a term that refers to the deployment of artificial intelligence (AI) models closer to users and devices, either on-premises — such as in a retail store or bank branch — or on edge computing platforms. With this approach, AI processing occurs near where data is created, on the “edge” of the network, in a decentralized manner, instead of using cloud-based solutions or centralized computing. This results in reduced latency, improved performance, and enhanced privacy and security.
Many industries, including retail, finance, manufacturing, healthcare, automotive, and telecommunications, are investing in Edge AI to increase efficiency in their operations — essentially by providing a large set of automation possibilities — and to improve their customer experiences. Demand for this technology is growing, and the overall state of Edge AI adoption is gaining traction.
According to Gartner, “More than 55% of all data analysis by deep neural networks will occur at the point of capture in an edge system by 2025, up from less than 10% in 2021.” And according to IDC, by “2023 more than 70% of organizations will run varying levels of data processing at the IoT edge.”
AIThority Insights:
The Role of AI in Super-empowering Customer Service Agents
The Benefits of an Edge AI Approach
One of the main benefits of Edge AI over running AI in centralized computing is improved efficiency. By processing data locally rather than sending it to a central server, Edge AI can drastically reduce latency and remove bandwidth constraints, leading to faster decision-making and improved operational efficiency.
This allows for real-time or near-real-time processing of data, a critical feature in many applications, such as autonomous vehicles, manufacturing processes, and healthcare monitoring. This type of processing is also a key advantage when the goal is to improve the customer experience.
Businesses also save on bandwidth and data storage costs by minimizing the need to transmit vast amounts of raw data back to the cloud or a data center; instead, only the relevant preprocessed information is sent.
This is much more scalable and sometimes can enable solutions that otherwise wouldn’t be technically or economically feasible, and it also brings increased resiliency: Edge AI systems can continue to process data even when they lose connectivity, enhancing the reliability of weak communication links and optimizing costs.
Last, but not least, the Edge AI approach can offer enhanced privacy and security: since data is processed locally, Edge AI reduces the risk of data breaches or loss during transmission. This makes it suitable for industries with stringent privacy regulations because sensitive data does not need to leave the premises.
Edge AI in Action
It has been said that “data is the new oil,” which means that any connected source of information is also a juicy target for criminals. AI is increasingly important in these scenarios as the attacks become more advanced.
Top IT Leadership Article: What Financial Services Organizations Need to Know About Zero Trust Maturity
Edge AI security measures, like access control, can be delegated to edge nodes, which can run sophisticated AI-based algorithms for inference and behavioral analysis that can detect and stop suspicious activity from cyberattacks — including the feared “zero-day” attacks — before they even enter business networks and cause any harm.
In retail, Edge AI can enable smarter customer experiences, such as personalized recommendations based on real-time in-store behavior, including monitoring self-checkouts to reduce losses or running real-time inventory management. In manufacturing, it can assist in predictive maintenance by processing data from numerous sensors to predict equipment failures and suggest timely maintenance, minimizing downtime.
In healthcare, patient monitoring devices can use Edge AI to process health data in real-time, alert healthcare providers of any immediate risks, and ensure patient privacy by keeping sensitive health data localized. Security systems equipped with Edge AI can process video footage in real-time for facial recognition, anomaly detection, or immediate threat analysis and alerting.
Telecom operators can use Edge AI to optimize network operations by analyzing network traffic in real-time and dynamically managing network resources. Similarly, Edge AI can be used for managing smart power grids, detecting anomalies, and predicting energy demand to optimize power generation and distribution.
Steps for Preparing to Adopt Edge AI
Organizations looking to adopt Edge AI will first need to identify the specific use cases or areas in their business where Edge AI could provide a considerable benefit. This should be followed by an evaluation of the technical requirements since Edge AI involves different technology than traditional cloud-based AI.
The next step is to ensure your team has the necessary skill set or that your organization has a mechanism in place for training. Teams will need to be educated on edge computing, AI model development, and edge node orchestration.
At this stage, a partnership with edge computing providers and companies that provide ready-to-use AI models is all it takes to start collecting results, as your company will be able to leverage an integrated solution and vendor expertise.
Edge AI aligns with strategic growth initiatives, seamlessly integrates with change management practices, and may even catalyze the emergence of groundbreaking business models. The adoption of Edge AI signifies a commitment to staying ahead of the curve, ensuring that your enterprise remains competitive in a rapidly evolving digital landscape.
Comments are closed.