An agent researches a prospect and writes a "personal" first line, but the line would fit hundreds of other people, or it picks a shallow detail and pitches something the reader has no use for. Recipients spot it, ignore it or block the sending domain.
The agent is asked to personalise but not to judge fit. It finds one easy fact and builds the email around it. Bulk sending then adds deliverability damage on top of a weak message.
Make the agent score fit before it writes, and write nothing for poor fits. Require one specific fact tied to a problem your offer solves, with a source URL. Reject any draft that would still make sense sent to a different company. Keep send tools out of the agent's reach and send small batches yourself.
A recipient says an AI email named his blog's framework and looked real at first, but pitched a product that did not fit his setup.
The sender of that email reports spam placement, flagged volume, invalid addresses he forgot to screen, and recipients asking how he got their address.
The author found AI icebreakers generic enough to fit many prospects, and says checking them eats much of the time saved.
Recipients describe many AI-written cold emails a day and blocking whole sender domains in response.