Two production lines don't create twice the problems. They create four times the coordination failures. Heat dissipation changes. Manual workarounds that one shift handled intuitively become impossible across three. Equipment quirks that veterans compensated for silently now break new operators. The complexity curve bends upward, always.

First, users report problems. Then you add basic alerts. Eventually you correlate data sources. Full-stack observability comes later, AI-driven prevention last. Nobody skips steps. Each stage unlocks only when distributed complexity forces you to see deeper into system behavior. The path is remarkably consistent.

Consensus algorithms. Leader election. Event sourcing. Saga patterns. Teams building distributed systems independently arrive at the same answers. Not because they copy each other, but because the problems are fundamental. State management in distributed environments has a limited solution space. The patterns repeat.

Pilot in non-critical systems first. Small deployment parallel to legacy. Wider rollout only after trust accumulates. When downtime has physical consequences and systems run continuously, proving reliability takes years. This caution looks like resistance to innovation. It's actually survival instinct encoded in operational practice.

Warehousing falls behind production. Supply chains strain. Cross-department coordination that worked through hallway conversations collapses under scale. Legacy systems reject integration with new automation. Bottlenecks invisible at small volumes become obvious constraints. Scale doesn't create these dependencies. It exposes them.

Your core team knows which valve sticks, which sensor reads high, which sequence needs manual intervention. Add a second shift and that knowledge evaporates. Manual compensations become impossible to replicate. Scaling fails precisely where processes depend on undocumented expertise. Control systems and documentation aren't bureaucracy. They're scaling prerequisites.