There is a question that comes up more and more in conversations with senior technology and operations leaders: “Do we need to solve connectivity before we can scale AI?”

The honest answer is nuanced, and the nuance matters. AI can function without a persistent connection to the broader network. Edge models run on devices. Cameras detect anomalies. Sensors process data locally. In a contained environment, AI works.

But contained is not scaled, or orchestrated, and certainly not mission-critical.

The moment your AI needs to act, to alert a command center, coordinate a response across teams, or make a decision that affects people beyond a single device, connectivity becomes the determining factor between intelligence and impact.

The global edge AI market is valued at over $25.65 billion today and projected to reach $165.05 billion by 2035 (Precedence Research). And according to Deloitte’s 2026 State of AI in the Enterprise report, 58% of business and IT leaders are already using physical AI, with 80% expecting to begin within two years. The investment is real. The question is whether the network can carry it.

The lonely robot problem

If a tree falls in a forest with no one around, does it make a sound? Here’s the modern version of that question: if a robot calls for help and there’s no network to carry the signal, does anyone hear it?

Consider a surveillance camera equipped with an AI model capable of detecting a weapon or identifying a security threat in real time. In isolation, that is an impressive capability. But if the camera cannot reach central command, if it cannot orchestrate a response, dispatch a unit, or trigger a protocol, the detection means nothing.

The AI saw the threat. Nobody heard it.

This is what happens when organizations treat connectivity as an afterthought. The intelligence is there, but it is effectively a robot calling out in silence: capable of observation, but unable to communicate actionable intelligence to the people and systems that need it.

90% of law enforcement agencies now support AI use, a 55% increase year over year (2025 U.S. Public Safety Trends Report). In high-stakes environments, a connectivity gap is not an inconvenience. It is a liability.

Two Perspectives on AI Deployment

Most enterprise AI deployments sit between two approaches:

  • edge-first (models on devices, fast and private but isolated) and
  • cloud-first (centralized intelligence at scale, but connectivity-dependent).

The leading organizations run both, a hybrid architecture where edge devices handle local processing while staying connected to centralized systems for orchestration and model updates.

That hybrid only works when connectivity is a first-class requirement.

“96% of industrial organizations call wireless reliability critical for enabling AI. 97% expect AI workloads to significantly increase their connectivity requirements.” — Cisco Industrial AI & Security Report, 2026

When the link between edge and cloud is unreliable, the hybrid becomes fragmented, and the people who most need the intelligence are making decisions with incomplete information.

Connectivity as Critical Infrastructure for AI

Think about how organizations view electricity. Nobody deploys sophisticated equipment and then figures out the power supply later. Connectivity is becoming the same thing for AI, and when it fails, the cost is real:

“Over 90% of mid-size and large enterprises report that a single hour of downtime costs more than $300,000, with 41% citing costs exceeding $1 million per hour.” — ITIC 2024 Hourly Cost of Downtime Survey

For organizations operating in remote environments or across distributed infrastructure, this is not a theoretical concern. It is a present operational risk.

Connectivity is no longer just infrastructure. It is part of the AI stack itself.

 

What Dejero Sees in the Field

At Dejero, our Smart Blending Technology™ aggregates multiple network paths (cellular, broadband, satellite, and Wi-Fi) simultaneously, creating a resilient “super-pipe” that adapts in real time. What we are increasingly seeing is that this infrastructure isn’t just enabling video transport or field communications. It is also enabling AI.

One priority network service now serves more than 6.1 million connections across nearly 30,000 public safety agencies. When the network fails, everything built on top of it fails too, and that lesson applies to AI just as directly as it applies to live video.

Connectivity planning is not a downstream technical decision. It is a strategic prerequisite.

“51% of organizations cite reliable connectivity as the #1 gap in their networks’ ability to support AI at scale.” — Cisco Industrial AI & Security Report, 2026

The organizations that will lead in AI are not simply the ones that invest the most in models. They are the ones that build the infrastructure to carry those models into the real world, reliably, resiliently, everywhere their operations take them.