AI customer assistants: when they make sense and how to introduce them safely

Start from the questions, not the technology

Before you choose a model or a platform, collect the real questions your customers ask. If a large share of them repeat — delivery, returns, stock, sizes — the assistant has a clear task and a measurable benefit.

If the questions are rare, highly individual or require judgment, a better information page or a better reply process may be the smarter move.

Answers only from verified sources

The biggest risk with language models is a confident but wrong answer. Retrieval-augmented generation (RAG) restricts the assistant to your documents and product data and makes it possible to cite the source of every answer.

• Keep delivery, returns and warranty policies up to date.
• Connect the assistant to real stock levels, not to static text.
• Set clear rules for when the assistant must decline or hand over the conversation.

Escalation to a person

The assistant must recognise when it is unsure, when the customer is unhappy and when the question involves payment or personal data. In those cases the conversation is handed to a staff member with the full context.

Privacy and data

Send the model only what it needs. Remove emails, phone numbers and other personal data when they are not required for the answer, and sign a data processing agreement with the provider. Let customers know they are talking to an automated assistant.

Measuring quality

Before launch, prepare a test set of real questions with expected answers. After launch, track the escalation rate, customer ratings and the cases where the assistant was wrong — and use them to improve it.