Macros aren’t the problem,
Multi part questions are.

Canned responses are not a relic.

A well maintained macro library is fast, predictable, free to run, and auditable in a way no model is. Most teams evaluating AI already have one, and the honest question is not whether macros work. It is whether they still work on the questions customers are actually sending.

So we tested both properly.

Across 10,000 tickets from four support teams, half were handled with each team’s existing macro library and half with ResolveAI. Same agents, same escalation rules, same measurement window, matched by intent and volume.

How we measured it

Speed on its own would have been a useless comparison, so we tracked what happened after the reply was sent.

• Whether the customer came back with the same question.

• How long each conversation took from first message to close.

• How often an agent rewrote the suggested reply before sending it.

The last one turned out to be the most revealing, because it measures what the agent thought of the answer rather than what the customer said about it.

Where macros held their own

On narrow, high frequency questions the templates were essentially unbeatable.

Password resets, store hours, return windows, warranty length. When a question has exactly one shape and exactly one correct answer, a sentence written once by someone who knew what they were doing is very hard to improve on.

Macros matched or beat AI on:

• Single intent questions

• Policy statements that must not vary

• Legal and compliance wording

• Anything requiring an exact figure

• Raw speed to send

For those tickets, the case for changing anything is weak.

Where the gap opened

The difference appeared the moment a customer asked for two things at once, which happened in roughly a third of the sample.

Follow up contacts - Down 34% on multi intent tickets.

Agent edit rate - Fell from 41% to 12%.

Handle time - Improved by under a minute on simple tickets, and by more than four on compound ones.

Satisfaction - Within a point on single questions, and clearly ahead once more than one issue was involved.

The edit rate explains the rest. Macros were rewritten because the template could not know the order number, the delivery date, or what the customer asked in their second sentence.

The real comparison

This was never a contest between AI and templates. It is a question of whether your volume is shaped like your macro library. Teams with narrow, repetitive queues gain the least from switching and should be honest with themselves about that. Teams whose customers write messy, multi part messages gain the most, because that is exactly where a template stops being an answer and becomes a starting point.

Most teams should run both

The strongest setups in the sample did not choose. They kept macros for the answers that must never vary and let the AI handle anything that requires reading the account, combining two questions, or working out what the customer actually meant. The macro library ends up smaller and better maintained, because it only holds the things worth writing by hand.

Ready to see which half is yours?

The quickest way to find out is to count how many of last month’s tickets contained more than one question. ResolveAI reads the account and the conversation together, so compound questions get one coherent answer instead of two pasted templates.

Book a personalized demo to see how ResolveAI fits into your existing workflow.