AI orchestration is the management layer that coordinates the parts of an AI solution: models, tools, data sources, rules and people. It decides what runs in what order, passes information between steps and handles errors.
A simple orchestrator might first classify a customer message, then fetch the order from the POS, then ask a language model to write a reply, and finally check the reply against policy before sending. More advanced versions let the AI choose its own next step, while still enforcing limits, retries and logging.
Retail and hospitality businesses meet orchestration when an AI assistant handles a request that spans several systems, such as checking stock, applying a loyalty discount and booking a delivery. Most operators will not build this themselves, but it determines how dependable a vendor's AI product is.
Pitfalls include chains that are hard to debug, steps that fail silently and costs that grow because the AI makes many unnecessary calls. Ask how failures are handled, whether you can see a record of each step, and what happens when a connected system is down.
It matters because the difference between an impressive demo and a dependable tool is usually in the orchestration, not the model itself.