In plain English
AI orchestration is the discipline of running multiple AI components together reliably in production. The visible part of an AI feature is usually a single LLM call. The orchestration is everything around it: which model to use, how to construct the prompt, how to validate the response, what to do on failure, how to monitor quality over time.
Good orchestration matters because most real AI applications involve multiple steps. Invoice matching: extract structured data → validate against schema → match to PO → check tolerances → route exceptions → post to ERP. Each step has its own model choice, error modes, and retry logic. Without orchestration, the whole thing is fragile; with proper orchestration, it runs reliably enough to leave unsupervised over weekends.
The orchestration stack typically includes: a routing layer (different models for different tasks), a validation layer (does the output match the expected schema?), a retry layer (handle transient failures cleanly), a fallback layer (when the AI is uncertain, escalate to humans), a logging layer (every decision auditable), and a monitoring layer (accuracy drifts get caught).
For SME deployment, orchestration is what separates "we tried ChatGPT and it sort of worked" from "this has been in production for six months reliably." It's also what costs more to build than the AI itself, the integration, checks and recovery paths can take more work than the initial model call.
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