What breaks first when scaling Intercom AI?

Both. As traffic grows, AI responses, assignment rules, automation triggers, and CRM lookups can overlap. Small timing differences become visible issues.

That’s typically a race condition between automation and human routing. When ownership logic isn’t centralized, multiple responders can activate simultaneously.

Often it’s structural. Many AI setups rely on channel-native automation. As complexity increases, configuration alone can’t prevent overlapping triggers.

That’s latency amplification. If each message triggers multiple downstream API calls, those milliseconds compound under load.

Correct. Scaling conversational AI is mostly about orchestration. Who owns the conversation? When do humans override AI? How are retries handled? Those are architectural questions.

Yes. Many teams introduce an external orchestration layer that governs conversation state and decision logic, while Intercom remains the user-facing channel.