AI for Customer Support

Look at a month of tickets and the distribution is usually lopsided. A handful of question types — order status, hours, delivery, password resets, what is included — make up a large share of volume, and none of them require anyone to think. Meanwhile the conversations that genuinely need a person wait behind them.

That is the case for automation here. Not replacing support, but changing what your team spends the day on, and getting first-response times down to something that does not itself generate a complaint.

1. 1

   Answering documented questions

   Responding from your help centre and policies rather than general knowledge, so answers match what you actually offer.

2. 2

   Triage and tagging

   Reading an incoming message, working out the topic and urgency, and ordering the queue before anyone opens it.

3. 3

   Drafting replies for agents

   A middle ground that many teams prefer: the tool writes, a person approves and sends. Faster than typing, with nothing going out unreviewed.

4. 4

   Out-of-hours acknowledgement

   Handling what it can overnight and at weekends, so nobody waits until Monday for a first reply.

5. 5

   Surfacing knowledge gaps

   Flagging questions that come up repeatedly with no article behind them — often the most useful output, because it improves your documentation permanently.

Deflection rate is the metric providers like to quote and the easiest one to make look good. A conversation counts as deflected if the customer did not reach a human — including when they gave up, went to a competitor, or complained on social media instead.

The measures worth tracking are resolution without escalation, first-response time, reopen rate on closed tickets, and customer satisfaction on automated conversations specifically. If satisfaction on automated threads is materially worse than on human ones, the deflection number is hiding a problem rather than describing a win.

• Pull a month of tickets and count the top question types. That list is your scope.
• Check those answers are actually documented somewhere the tool can read.
• Start in draft mode — the tool writes, an agent sends — before letting anything reply directly.
• Set escalation to trigger on uncertainty, not just on keywords.
• Make reaching a person easy and obvious. Hiding it is the fastest way to turn a small problem into a public one.

What percentage of support tickets can AI handle?

It varies enormously by business, and any provider quoting a single number across all customers is guessing. What determines it is how concentrated your question mix is and how well documented the answers are. Counting your own top question types is a far better estimate than any benchmark.

Should AI reply to customers directly or draft for an agent?

Drafting is the safer starting point and many teams stay there for anything non-routine. Direct replies make sense once you have seen enough output on a well-defined question set to trust it, and with escalation configured to hand over on uncertainty rather than push through.