Bridging the connectivity gap to power the next generation of AI

Digital transformation today is radically different from the prime years of cloud and SaaS, primarily due to the rise of AI.

To put a finer point, the demands of AI far exceed those of the early cloud. AI models are an entirely different beast from traditional software, often composed of millions, sometimes billions, of parameters and requiring large streams of real-time data.

Just think of AI applications like autonomous driving systems or real-time fraud detection. Training these models requires substantial experimentation, including testing different architectures, parameters, and training data subsets.

One consequence of AI acceleration is that deployments are being increasingly pushed to the edge, where data is produced, rather than in a central cloud or remote data center.

There are two main reasons for this shift.

First, AI requires network reliability to process data quickly and accurately (e.g. consistent network latency & performance) to process data quickly. Second, AI applications rely on a continuous exchange of real-time data with clients or endpoints, which means data sensitivity is paramount. For instance, an AI-driven drone must constantly communicate with its backend compute system to avoid crashing.

What both of these scenarios have in common is the need for a performant connectivity in order to function. Network mobility, network reliability & consistent throughput and latency are imperative.

Mobile connectivity Is often an afterthought

Ubiquitous mobile coverage, especially at the quality needed to support mobile AI use cases, is lacking across most enterprises. Walk into any building, facility, or manufacturing plant, and you’ll immediately see how far behind we are.

Historically, enterprises have relied on private slices through carrier networks, Wi-Fi, or other on-premises solutions. However, these approaches are insufficient to meet the unique demands of AI-powered applications.

While mobility is perceived as “table stakes” today, most connectivity solutions are static by design. Drones, robots, surveillance cameras, and other mobile AI endpoints require localized data processing at the source to deliver high throughput and low latency.

For example, in a security surveillance use case where cameras are scattered across an entire premises, running cabling is prohibitively expensive. Whether there’s an AI-powered system parsing video data or a live human analyzing feeds, mobile connectivity is required to power this infrastructure.

The future of enterprise connectivity is cloud native

The solution to this old age connectivity problem lies in a cloud-native model. Private mobile networks for edge AI provide much more resilient connectivity than Wi-Fi, which is critical for AI-based use cases. They also offer greater mobility, essential for use cases like drones and robotics that typically involve both indoor and outdoor workloads.

A mobile cloud model offers uniform connectivity, control, and orchestration of mobile devices across an organization. It incorporates a comprehensive stack of mobile services, including SIM management, radio access networks, packet core, orchestration, and life cycle management—all streamlined through simple cloud-based management. Most importantly, it integrates with existing enterprise infrastructure and security policies.

This model enables seamless operation across enterprise and mobile service providers and fosters a cloud-based ecosystem for automating and managing the mobile stack lifecycle. Simplified operations that align with IT standards reduce total cost of ownership compared to traditional deployments, and deep integration with enterprise security controls enhances security.

This model not only facilitates faster service deployment but also paves the way for a robust, secure, and agile mobile infrastructure capable of supporting the next generation of edge-based AI-driven applications and use cases.

Practical applications of AI at the edge

1. Drones and swarming coordination. AI-enabled drones must communicate continuously with backend computing systems to process video inferencing and navigation decisions. A fleet of drones, coordinating their positions in real time like those in an Intel light show, requires ultra-low latency to ensure seamless swarming coordination.
2. Robotics in manufacturing and supply chain. Factory automation relies heavily on AI-driven robots that navigate production lines and supply chains. Video inferencing allows them to detect defects or analyse inventory levels in real time, requiring seamless mobile connectivity.
3. Security surveillance. Surveillance cameras dispersed across large premises need to relay video data to a central system, whether for human operators or automated detection using AI. Localised video processing at the edge reduces the need for expensive ingress/egress charges in the cloud.
4. IoT devices and sensor networks. IoT devices gathering critical data in agriculture, energy, and other sectors need to send data to the edge for rapid processing. AI-powered analytics can then detect patterns or anomalies and make informed decisions.

And these examples just scratch the surface.

To sum up: a cloud-native model for mobile connectivity will be key to unlocking the next generation of AI. Private mobile networks provide the low-latency, high-throughput, and resilient connectivity these applications need. By integrating with existing enterprise infrastructure and security policies, this approach ensures seamless operation, reduces total cost of ownership, and enables robust, agile mobile infrastructure capable of supporting a diverse range of AI-driven applications at the edge.