Why Intelligence Needs to Be Distributed, Not Centralized

Webinar Overview

For a decade, the industrial playbook for turning data into insight has followed one pattern: collect it, move it to a central repository, then analyze it. That model worked for business analytics. But it's becoming a real obstacle for industrial AI at scale.

Operational data is continuous, time-sensitive, and often safety-critical. By the time it's centralized, the moment to act on it has often passed, and the context that people closest to operations understand best gets lost along the way. Every movement of that data also raises security and governance questions that only grow as volumes scale.

Join us to explore why intelligence needs to move to where operational data already lives, instead of forcing that data into a central system before anything useful can be done with it.

You'll learn:

• Why the centralize-first model struggles to keep up with real-time, regulated operations

• What it looks like to bring intelligence closer to the edge while still maintaining enterprise-wide visibility and governance

• A clearer lens for evaluating your own data architecture

• Where centralization still makes sense, and where it's holding your AI initiatives back

This session is designed for the leaders and teams driving AI and data strategy across industrial operations, from business and operations executives to plant leaders, industrial data and AI architects, and the OT/IT teams responsible for scaling AI across distributed environments.