AI in customer service

What it is genuinely good at

• Drafting. Turning three notes into a polite, complete reply. This is where the time actually goes, and where the saving is real.
• Summarising. A forty-message thread into a paragraph, so whoever takes it over does not read for ten minutes.
• Rewriting. Shorter, friendlier, clearer – on a reply a person already wrote.
• Finding. Suggesting the knowledge base article that fits this case.

What it is bad at

Anything requiring it to know something it was not told. It does not know your contract with this customer, it does not know the thing your colleague said on the phone, and it will produce a fluent, confident answer regardless.

That is why unattended replies to customers are a much bigger commitment than drafting – the failure mode is not "no answer", it is "a wrong answer, beautifully written".

Human in the loop

The arrangement that works: the AI proposes, a person sends. It costs a click and removes the entire category of failure above. It also means the team stays practised at answering, which matters the day something unusual arrives.

The data protection question

An AI that drafts has to read the ticket. If the model runs at a third-party provider, ticket content leaves your processor chain – a separate processing operation with its own agreement and its own transfer question.

A model running on the same server as the data avoids that entirely. It is worth asking a vendor precisely where their AI runs. More in GDPR and your help desk.