As manufacturers push AI into live operations, centralized architectures are hitting their limits. Latency, reliability, and lack of context become execution risks—not technical details.
This guide explores what breaks when industrial AI moves to production—and what it takes to execute AI reliably at the edge.
Inside, you’ll learn:
Why centralized, cloud-first AI architectures break down at scale
How industrial AI is shifting from analytics to real-time execution
What “distributed intelligence” really means in plant-floor environments
Why data context and orchestration matter as much as models
What it takes to operationalize Edge AI reliably across sites
