Does AI-written outreach get more replies?

The short answer

If your team uses AI to write outreach, measure the conversations it starts, not the number of replies alone. In Lineer’s sample, fully AI-written campaigns received more human replies than templated campaigns over 14 days: 2.02% versus 1.20%. Yet templates had the highest rate of positive first replies. A reply that declines the offer still counts as a reply.

These campaigns reached different customers and audiences. The result is a reason to test AI-written sequences with your own prospects, not a forecast of what your team will gain.

The sample is not verified as exclusively cold outreach. Use these findings to frame a test of your own writing approach, rather than as a cold-email response-rate target.

What we found

What changes when AI writes more?

Compare human replies and positive first replies across the observed sequence window.

Human replies, 14 days. 14-day observation window.

Templated

1.20% 45 of 3,751

Half-AI

1.61% 148 of 9,197

Fully AI

2.02% 47 of 2,327

0%2.5% response rate

First human replies / eligible outreach starts.

Human replies, 14 days, 14-day window

Group	Replies	Eligible starts	Reply rate
Templated	45	3,751	1.20%
Half-AI	148	9,197	1.61%
Fully AI	47	2,327	2.02%

Each window uses a different eligible cohort, not a cumulative response curve. These are observed rates, not causal effects. The 95% campaign-bootstrap interval for the fully AI minus templated difference was −0.45 to +2.51 percentage points; for fully AI minus half-AI, −0.84 to +2.12 points. Both span zero.

Fully AI-written campaigns received about eight more human replies per 1,000 starts than templates at the observed rates. The positive first-reply rates tell a different story: templates led that measure, though the number of positive replies was small.

The apparent winner also changed when we removed individual customers from the comparison. Excluding one workspace brought the fully AI reply rate down to 1.50%, below the half-AI rate of 1.61%. That workspace belongs in the main result; its effect shows how much customer mix matters.

How to calculate outreach reply rates

In this study, each rate uses eligible outreach starts in that writing group as its denominator. Each start counts once, even when its sequence includes follow-ups. The figures are not replies divided by the total number of emails sent.

• Human reply rate: starts with a human reply within 14 days divided by eligible starts. For fully AI-written campaigns, that is 47 of 2,327, or 2.02%.
• Positive first-reply rate: starts whose first human reply welcomed a conversation or requested relevant information, divided by eligible starts. For fully AI-written campaigns, that is 12 of 2,327, or 0.52%.

The positive-reply denominator is still 2,327 starts, not the 47 human replies. Templates had 22 positive first replies from 3,751 starts, or 0.59%. Comparing those rates answers a different question from comparing raw reply counts between groups of different sizes.

A positive first reply is an expression of interest. It does not establish that a meeting took place or that the prospect became a customer.

What this means for a sales team

Compare complete outreach sequences over the same period. A first-email report misses replies that arrive after follow-ups. Then separate positive interest, referrals, objections, and opt-outs. Any-reply rate can tell you whether people engage; positive replies tell you more about whether the campaign is opening sales conversations.

If you test writing approaches, use comparable prospect segments, offers, senders, and follow-up schedules. Decide in advance what counts as success. A booked meeting is more useful than a reply count when your systems can track it reliably.

Review a sample of the actual messages as well as the rates. Check whether the prospect detail is accurate, whether the offer fits the recipient’s responsibilities, and whether the proposed next step is reasonable. Keep your reply classifications consistent across writing groups, including negative replies and opt-outs.

Our follow-up statistics examine when replies arrived in this same cohort. Use that timing context when choosing a reporting window; it is not independent evidence that AI-written outreach performs better.

If you want to review how research becomes a draft, explore Lineer’s agent workflow. You can inspect prospect context and configure which proposed actions require your approval.

Data and method

We reviewed 15,275 outreach starts across 65 campaigns and 11 workspaces. Opening emails were sent from June 15 through August 27, 2026, leaving every included start a full 14 days for a response before the September 10 data snapshot. Campaigns were labeled templated, half-AI, or fully AI in a supplied classification workbook. We did not verify the authorship of every sent email or follow-up.

Each outreach start counted once. We excluded bounces, out-of-office messages, and automated acknowledgments from human replies. One analyst reviewed first responses and marked a reply positive when it welcomed a conversation or requested relevant information. A positive reply is not a verified meeting or sale.

This is an observational comparison. Customers chose their own writing approach, audience, and offer. Differences between those choices may explain the rates. The estimate for fully AI versus templated human replies was uncertain enough that a campaign-level resampling interval included no advantage. Some historical records lacked the evidence needed for inclusion, and the sample is not verified as exclusively cold outreach.

Lineer operates the platform studied and has a commercial interest in its use. This article shares its cohort with the other two insights; they are not independent studies. Customer and recipient identities are omitted.