An AI email writer is a language model applied to marketing email: you describe the campaign, it produces subject lines, body copy and calls to action. Every major email platform now ships one, including ours, and they are all roughly as good as each other because they are all built on similar models.
Which means the difference between output you can send and output you delete is entirely in how you use it. Here is what actually works.
What AI is good at, precisely
Compression. Give it 400 words and ask for 150 without losing the argument, and it will do a better job than most people do on their own copy, because it has no attachment to any particular sentence.
Variants. Ten subject lines, five opening paragraphs, three different angles on the same offer. The value is breadth, not quality — you are buying options to choose from.
Structure. Turning a rough set of notes into a sequenced email with a clear ask is mechanical work that models handle reliably.
Boilerplate. Shipping confirmations, receipt copy, the paragraph under the button explaining what happens next. Nobody's brand rests on these and they take real time to write.
What it is bad at
Specifics it does not have. The model does not know your customer said the onboarding was confusing, or that your best-performing email last quarter was the plain one with no images. It fills those gaps with plausible generalities, and plausible generalities are exactly what makes AI copy feel like AI copy.
Having an opinion. Persuasive email takes a position. Models hedge, because hedging is the safe average of everything they were trained on. Ask one to argue that most email newsletters should be shorter and it will produce a balanced overview of when newsletters should be longer or shorter.
Your voice. It can imitate a described tone. It cannot reproduce the specific way you build a sentence, and the gap is most obvious in exactly the emails where voice matters most.
The division of labour that works
Write the argument yourself. That means: what you are claiming, the specific evidence for it, and what you want the reader to do. Three or four bullet points is enough — this is the part that carries the information the model cannot invent.
Then hand it over for structure, compression and variants. Then edit, with a specific list of things to remove.
The prompt that actually produces something usable
Most AI email output is bad because the prompt was three words. A prompt worth writing has five parts:
- Audience, specifically. Not "our customers" — "people who bought once in the last 90 days and have not opened the last three emails."
- The single action. One. Emails with two asks convert worse whoever writes them.
- The evidence. The actual facts, numbers, quotes or product details. This is the most important part and the one people skip.
- Constraints. Length in words, reading level, whether images are allowed, what you must not claim.
- Voice sample. Paste 200 words of an email you were happy with. This does more than any adjective you could supply.
Then ask for three versions rather than one, because the second is usually better than the first and the third is usually a different idea entirely.
The edit pass
Every model reaches for the same constructions. Delete them on sight:
- Opening with a rhetorical question the reader did not ask.
- "In today's fast-paced world" and its cousins.
- "Unlock", "elevate", "seamless", "game-changing", "dive in".
- Tricolons where the third item adds nothing — "faster, simpler, and more powerful".
- A closing paragraph that summarises the email you just read.
- Em-dashes used as a substitute for a decision about sentence structure.
Then check three things the model gets wrong structurally: the first line does not depend on the subject line (many clients show the subject and preview text together, so repeating is wasteful — see email preview text); there is exactly one call to action; and the specific claims are things you can actually stand behind.
Where AI copy quietly goes wrong
Volume, again. AI makes drafting fast, which makes sending more feel free, and send frequency is the strongest predictor of unsubscribes and complaints. If you adopt an AI writer, hold your cadence fixed for a quarter so you can tell what changed.
There is also a persistent worry that AI-written copy trips spam filters. It does not, directly — filters score reputation, authentication and engagement rather than authorship. But generic copy earns fewer clicks, fewer clicks weaken engagement signals, and weak engagement does move you toward the spam folder. The route is indirect and real. We tested the specifics in does AI-written email hurt deliverability.
A realistic workflow
Notes → AI draft → your edit → subject line variants → test the subject line → send with per-subscriber timing. That is maybe fifteen minutes for an email that used to take an hour, and the fifteen minutes are spent on the parts that need you.
All of it runs inside IGSendMail on every plan including the free one, and through the MCP server if you would rather work in Claude or ChatGPT and have the draft land in your account.
Frequently asked questions
Is AI-written email as effective as human-written email?
For transactional and administrative email, effectively yes. For persuasive email, an unedited AI draft consistently underperforms because it lacks the specifics and the point of view that make people act. An AI draft with a human edit performs about as well as human-written copy and takes considerably less time.
Will AI-written emails get flagged as spam?
Not for being AI-written. Spam filtering scores sender reputation, authentication and recipient engagement. The indirect risk is that generic copy gets fewer clicks, and weak engagement over time does hurt inbox placement.
What is the best prompt for writing marketing emails?
One that supplies the audience precisely, the single action, the actual evidence, hard constraints on length and claims, and a 200-word sample of your own writing. The voice sample changes the output more than any instruction about tone.
Should I let AI write my subject lines?
Generate with AI, choose yourself. Models are good at producing twelve options quickly and mediocre at knowing which one fits your audience. Feeding the tool your past campaign performance improves the suggestions substantially over a generic generator.
