Physical AI is moving intelligence beyond dashboards into the factory, where robots, cameras, sensors, and other autonomous systems must sense, process data, and act in real time. But as manufacturers move from promising pilots to production-scale deployment, the demands on the underlying network increase significantly.

Autonomous systems require consistent performance, real-time visibility, and coordinated connectivity across devices and locations. Best-effort networks can quickly become a barrier to uptime, safety and performance.

In this webinar, Ericsson will explore why the network is critical part of the physical AI control loop, and how private 5G can provide the predictable mobility, high-quality uplink performance, traffic prioritization and secure device connectivity these environments require. We’ll also examine how edge and cloud intelligence can work together to support physical AI at scale.

Attendees will leave with a practical framework for assessing network readiness, aligning IT and OT priorities, and identifying a focused first use case that can demonstrate measurable operational value.

Learning Objectives:

• Identify the connectivity requirements that distinguish production-scale physical AI from digital AI and isolated pilots.
• Evaluate how private 5G can support reliable mobility, predictable performance, uplink-intensive vision and sensor workloads, and secure device access.
• Assess where edge, cloud and on-device compute should work together across robotics, machine vision and autonomous operations.
• Apply a practical starting framework to select a high-value factory use case, define success criteria, and align IT and OT stakeholders.