Set the benchmark before you judge anything
Around a 2% reply rate is good for cold outreach to US prospects in 2026. Not acceptable, not a floor to climb off. Good. If you are sitting at 2 to 3% you are doing better than most of the market, and anyone telling you otherwise is selling something with a 12% case study attached to it.
This matters because it changes what you ask AI to do. At 2%, sending twice as much email does not double replies. It doubles the number of people who now filter your domain. The lever that still moves is quality per send, and quality per send is bounded by how much you know about the person before you write.
That is the actual shift. A rep can research eight accounts properly before lunch. A model can read all forty, including the boring inputs: the support docs, the job specs, the ten-K risk factors, the changelog, the four-year-old conference talk. Most of it is worthless. One item in it is the email.
The one rule under everything below
Make it about them, not about you. Every rule that follows is a way of enforcing that one. First sentence about their situation. One idea per email. No product description until they have asked. Match the channel, because a message that reads well in an inbox reads like a press release on LinkedIn.
And a formatting rule worth putting in every prompt you write: no em dashes and no en dashes. They are now the fastest visual tell that a human did not type the sentence. Use a full stop. The sentence is usually better for it.
First touch
One specific fact, one email
Here is what I know about {contact} at {company}:
{paste the job post, the podcast transcript, the
support doc, the pricing page, the earnings call
section, whatever you actually have}
I sell {product} to {buyer type}.
Pick the ONE fact in there that a competitor of mine
would not have bothered to read. Build the whole email
on that fact.
Rules:
- First sentence is about them, not me, not my company
- 70 words maximum
- No em dashes, no en dashes
- No "I hope this finds you well", no "I noticed"
- One ask, at the end, and make it small
- Do not describe my product. Describe the problem the
fact implies they already have.
Then tell me which fact you picked and why you picked
it over the others.
The line doing the work is “a fact a competitor would not have bothered to read”. It pushes the model past the funding announcement and the homepage headline, which every other rep in the inbox already used that week. Asking it to justify the choice at the end is your check on whether it actually read anything.
Sequences
Rewrite a sequence so it is about them
Here are the five emails in my current sequence:
{paste all five}
Rewrite each one so the subject of every sentence is
the prospect or their situation, never my product.
Constraints per email:
- One idea only
- Under 90 words
- One ask, at the end
- No em dashes or en dashes anywhere
- Email 3 must be readable on a phone in under
eight seconds
Then, before the rewrites, answer this: which two of
these five would you delete entirely, and what is the
sequence trying to say that it is not saying?
“Which two would you delete” is the load-bearing line. Ask for improvements and you get five improved emails. Ask what to cut and the model has to form a view on what the sequence is for. Most five-email sequences are three emails with padding.
Self-critique
Make it name its own weakest line
Here is the email you just wrote:
{paste it back}
Now do three things.
1. Name the single weakest sentence in it and say
exactly why it is weak.
2. Tell me which sentence a busy {buyer title} would
skip on a phone screen.
3. Tell me what a sceptical version of this person
would think when they hit the ask.
Do not rewrite anything yet. Just answer.
“Do not rewrite anything yet” is the whole trick. If you let it rewrite in the same turn it will paper over the weak line instead of naming it, and you learn nothing. Getting the critique first means the second draft fixes a diagnosed problem rather than reshuffling adjectives.
LinkedIn
Same fact, different register
Here is the cold email I am sending {contact}:
{paste}
Write the LinkedIn version. This is a different
channel, so treat it as one:
- It sits next to their friends and their old
colleagues, so a formal opening reads as a sales
robot
- Under 400 characters
- No subject line thinking, no sign-off, no title
- Lowercase is fine. Sentence fragments are fine.
- It should read like something a human typed on a
phone between meetings
- No em dashes or en dashes
Keep the same specific fact. Lose everything else.
The line about sitting next to their friends and old colleagues is what changes the output. Without it you get the email with the greeting removed, which is the single most obvious tell that a message was recycled. The character cap does the rest.
Follow-up
The honest bump when nothing has changed
I emailed {contact} twice about {topic}. No reply.
Both emails:
{paste}
Here is what has happened since:
{paste anything new, or write "nothing"}
Write a third email that:
- Does not reference the earlier two
- Does not ask if they saw my last email
- Does not invent urgency or a fake deadline
- Gives them a reason to reply that did not exist
when I sent email two
If there is genuinely no new reason in what I gave
you, do not write the email. Say so, and tell me what
single piece of information would make a third touch
worth sending.
The last paragraph is the one people delete, and it is the only one that matters. Left to itself the model will always produce the email, because producing the email is what it was asked to do. Giving it an explicit exit means the times it refuses are real signal about the account.
Personalisation at volume, without the tell
Personalisation at volume fails in a specific way. The variable slot gets filled and the sentence around it stays identical, so a prospect who forwards your email to a peer at another company sees the same skeleton twice. The fix is not a better merge field. It is asking for the whole email to be rebuilt from the fact rather than wrapped around it.
- Batch by trigger, not by list. Twenty accounts that all posted the same kind of role get one prompt run. Twenty random accounts get twenty runs and no reusable judgement.
- Feed real source material, not summaries. A model given a job post writes differently than a model given “they are hiring engineers”.
- Read three of the twenty before any of them send. If two share a sentence, the prompt was too prescriptive. Loosen it.
- Keep the ask constant and let everything above it vary. A varying ask is how you lose the ability to tell what is working.
When you find yourself pasting the same account context every morning, the problem has stopped being your prompt. It is data access. That transition is covered in this walkthrough of using Claude for sales, and the same source material works across models if your team is not standardised, which this comparison of Claude, ChatGPT, Gemini and DeepSeek gets into.
What not to hand to AI
The test is simple. If being wrong is expensive, a human writes it. Three categories fail that test every time.
Pricing
A model will confidently state a discount structure it inferred from your website. You will honour it or lose the deal arguing about it.
Compliance claims
Security, data residency, certifications. A near-miss answer here is a legal problem, not a productivity one. Draft the question, never the answer.
Apologies
An angry customer can tell. A generated apology reads as a second insult, and it is the one email where the extra ten minutes is obviously worth it.
Everything else is fair game as a draft. The default should be that AI output lands in your drafts folder rather than the send queue for at least the first month. If you want the fuller version of that boundary, the AI sales checklist lays out where the line usually gets crossed.
What to measure
Change one thing at a time. Rewriting the whole sequence and the subject lines and the send window in the same week means you learn nothing from the result, good or bad.
Next
Outreach fills the top. What happens after the reply is a different set of habits: qualifying honestly, forecasting without optimism, and knowing which deals to stop working.