The wrong starting question is whether an AI receptionist can answer the phone. It can. The useful question is whether your calls are predictable enough to handle safely and valuable enough to justify the work.
Start with the calls, not the software
Review a representative sample of inbound calls. Group them by what the caller needed, what information was required and how the call ended. Patterns matter more than volume alone.
Routine questions, message capture and straightforward bookings are different from calls that require diagnosis, negotiation or judgement. Treating them as one queue produces a poor design.
Measure the current cost
Record missed calls, abandoned voicemails, interruption time and the number of enquiries that arrive outside business hours. If possible, trace how many become bookings or qualified conversations.
A baseline prevents an interesting demonstration from being mistaken for a useful business result.
Write down the handover rules
A receptionist is useful when it knows its boundaries. Decide which words, topics, customer types or confidence levels should trigger a transfer, message or urgent notification.
If the team cannot agree on the escalation path, the workflow is not ready to automate.
Test one narrow call type
Begin with a contained flow such as after-hours quote capture or routine appointment changes. Listen to outcomes, review failures and ask staff whether the information received was actually useful.
Expand only when the narrow flow is dependable. A smaller system that staff trust beats a broad one they work around.