The impact of AI on edge computing

Enterprises are moving computing resources closer to where data is created, making edge locations ideal for not only collecting and aggregating local data but also for consuming it as input for generative processes. AI, including Generative AI (GenAI), has emerged as a transformative technology, revolutionizing how machines learn, create, and adapt. IDC forecast shows that enterprise spending (which includes GenAI software, as well as related infrastructure hardware and IT/business services), is expected to more than double in 2024 and reach $151.1 billion in 2027 with a compound annual growth rate (CAGR) of 86.1% over the 2023-2027 forecast period¹.

And as organizations increasingly adopt edge computing for real-time processing and decision-making, the convergence of AI and edge computing presents unprecedented opportunities. 

The rise of edge computing

Edge computing has emerged as a strategic paradigm shift in the world of data processing. Unlike traditional centralized cloud computing, edge computing brings computation closer to the data source—whether it’s a fleet management, automated industrial machines, drone, or an autonomous vehicle. By processing data at or near the point of creation, edge computing reduces latency, improves real-time responsiveness, and minimizes the need for data transmission to centralized cloud servers. It’s like having a mini data center right where the action happens.IDC reports worldwide spending on edge computing is expected to reach $232 billion in 2024, an increase of 15.4% over 2023².

And according to a new forecast from the IDC worldwide Edge Spending Guide, combined enterprise and service provider spending across hardware, software, professional services, and provisioned services for edge solutions will sustain strong growth through 2027 when spending will reach nearly $350 billion.

The reasons behind the rise of edge computing are compelling:

• Reduced latency. In applications where real-time responsiveness is critical, minimizing latency is paramount. Edge computing ensures that data processing occurs locally, significantly reducing the time it takes for decisions to be made.
• Bandwidth optimization. Transmitting massive amounts of raw data to the cloud can strain network bandwidth. Edge devices preprocess data locally, sending only relevant information to the cloud. This optimization improves efficiency and reduces costs.
• Security and privacy. Edge processing keeps sensitive data local, addressing privacy concerns and ensuring compliance with data protection regulations. Critical data remains within the organization’s boundaries.
• Resilience. Edge devices continue functioning even during network outages or cloud downtime. This resilience is crucial for applications that cannot afford interruptions.

AI at the edge: A game-changer

AI at the edge offers real-time responsiveness, privacy compliance, cost efficiency, and edge autonomy, ensuring timely decisions, data protection, optimized infrastructure, and continuous functionality, including Computer vision. Edge devices equipped with cameras can leverage AI at the edge for object detection, image segmentation, and anomaly detection. Consider security cameras identifying intruders or drones inspecting infrastructure for defects. Learn more about this here. 

• Natural Language Processing (NLP). Chatbots, voice assistants, and language translation services can operate locally using NLP models. Edge-based NLP ensures privacy and reduces reliance on cloud servers.
• Predictive maintenance. When AI is brought to the edge the analysis of sensor data from industrial machinery can predict failures or maintenance needs. Edge-based predictive maintenance reduces downtime and improves operational efficiency. Read more about the impacts AI at the edge is predicted to have on the manufacturing industry in this recent blog. 
• Personalization. Retail stores and smart homes can use AI at the edge technology to personalize user experiences. Edge devices adapt content, recommendations, and advertisements based on individual preferences. But this is just the beginning. You can learn about more use cases that are finally in the realm of possibility within retail here. 

Dell Technologies edge portfolio: A perfect match for AI at the edge

• NativeEdge: A forward-thinking edge operations software platform, has an open design that works with any AI solution, software application, IoT framework, OT vendor solution, and multi cloud environment. NativeEdge leverages zero trust enabling technologies across data, application, and infrastructure layers to ensure integrity and safety of the entire edge estate. 
• Edge servers: Dell’s ruggedized and compact servers are purpose-built for edge deployments. These servers can host AI models directly, enabling real-time inference without relying on cloud connectivity. Whether it’s an autonomous vehicle making split-second decisions or a smart factory optimizing production, Dell’s edge servers play a crucial role.
• Edge gateways: Dell’s edge gateways serve as data aggregation points. AI algorithms can preprocess data at the gateway, reducing the volume of raw data sent to the cloud. By applying AI locally, organizations can achieve faster insights and minimize cloud costs.
• Edge storage solutions: AI-generated content—such as images, videos, or sensor data—requires reliable and scalable storage. Dell’s edge storage solutions provide the necessary capacity and performance for local applications. Whether it’s storing surveillance footage or maintaining historical sensor data, Dell’s storage infrastructure ensures seamless operations.