NETGEAR’s Richard Jonker contends that to bridge the AV accountability gap, the industry must move beyond siloed management platforms to AI-driven resolution. True AI in AV must integrate network-layer telemetry—like VLAN and QoS data—as a primary source to diagnose root causes that disparate systems miss, ultimately unifying AV experience and IT infrastructure.
Talk to enough people in the AV industry, and you will find broad agreement on one thing: the word AI has been stretched so far it has lost most of its meaning. Automation gets called AI. Rule-based alerting gets called AI. A chatbot bolted onto a monitoring dashboard gets called AI. The enthusiasm is understandable since the technology is genuinely moving fast, but the labeling has gotten ahead of reality, and the industry knows it.
But let's not throw out the baby with the bathwater. We should question inflated claims, but we should also be willing to recognize where AI can genuinely add value. Especially when it comes to operational intelligence, because the accountability gap between AV, IT, and the network is exactly where AI delivers.
Platforms Everywhere, But the Complete Picture is Missing
InfoComm 2026 made clear that the industry has moved on from boxes. A decade ago, the conversation was about whether AV over IP would work. Today, the question is how platforms work together. Every major vendor now has a management layer: room control platforms, audio networking platforms, collaboration platforms, AV management platforms. The market has answered the device fragmentation problem of the last decade by building around software and platforms rather than hardware and point products. That is the right direction.
A New Problem Has Been Created
Each platform sees its own slice of the environment. The collaboration platform knows whether the call is live. The control system knows whether the display is on. The audio platform knows whether the DSP is responding. The network switch knows whether the port is up. But none of them, individually, can tell you why the room is not working. Craig Durr, Chief Analyst and founder of The Collab Collective, put it plainly after walking the InfoComm floor: the single-pane-of-glass pitch, he observed, usually just aggregates noise into one place. That is the "phantomization" problem in a sentence.
The Accountability Gap
When something fails in a converged AV environment, the investigation must cross platform boundaries that no single tool was designed to span. IT owns the network. AV owns the experience. The collaboration platform vendor owns their stack. When something fails, the executive audience does not care which layer caused the issue. They only know the stream glitched, the room did not work, or the CEO could not be heard.
That gap, the space between platforms where operational problems live, is the specific challenge that AI in AV needs to address to be worth the name. Not AI that adds a feature to an existing platform. AI that sits above the platforms and reasons across all of them. Durr's observation from the show floor cuts to it directly: "Monitoring was never the job. Resolution is."
What the Network Sees
The network is the connective tissue between every platform in the room. It is also the layer that most operational AI in AV does not cover. Magic in the room, boring on the network. That is still the goal. But boring depends on active intelligence, not passive infrastructure.
The root cause of an AV failure is often not in the device layer at all. It is upstream: a VLAN misconfiguration, a port that dropped, a Quality of Service (QoS) policy that changed. Even though every platform reports green, the room does not work. Without network-layer visibility, the investigation stays manual, and the blame game thrives.
That is one of the reasons NETGEAR partners with companies like Utelogy, Providius, XYTE, and NetSpeek. We believe network-layer visibility belongs inside the AI's operational context, not alongside it. These kinds of platforms are going to use an AI-native AV operations product to treat the network as a first-class data source.
The industry solved device fragmentation by moving to platforms. That was the right answer to the right problem. The next problem is both holistic and operational: how do you manage an environment that no single platform can fully see? That is the question AI in AV should be answering. When it does, it earns its name.
