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How to spot AI in emails — the patterns that hurt reply rate.

Email is where most hidden AI lives in 2026. Cold outreach, customer service replies, inbound sales notes, candidate communication, vendor pitches, recruiter messages. Anyone screening inbound mail at volume needs a fast read on which messages a model drafted, because that single signal changes how you reply, who you trust, and how much time you spend on a thread. This guide is the manual screen. Five email tells, a five-step workflow, the four inbox categories where the screen works, and when to paste the message into TextSight instead of guess.

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Why this matters

AI email reads cheap, and cheap kills reply rate.

Anyone reading inbound at scale has seen the same patterns across hundreds of messages. Recognition is automatic now, and substance never gets a chance to land once the opener fires the AI flag.

One sentence sets the register for the whole message

An 80-word cold email with one "I hope this email finds you well" reads fully AI because that single sentence is twenty percent of the message. The recipient's pattern-match fires on the opener and never recovers, even if the rest of the email is concrete and specific. Long-form content can absorb a templated sentence; short-form email cannot.

Reply rate is the honest metric

A cold pitch with the wrong register loses replies before the ask is read. A vendor reply with no specifics loses trust before the resolution is read. A candidate email that reads as a JD echo loses interview slots before the resume is opened. The cost of AI tells is not stylistic; it is conversion.

Manual spotting works above 150 words

The honest ceiling on eye-only screening sits around 75 to 85 percent on messages above 150 words, dropping fast on three-line replies. That is enough to triage an inbox and route high-stakes messages into a tool for confirmation. The five tells below carry most of that screening weight.

Five tells

The five email patterns to screen for first.

Any single tell can show up in a real email written by a careful human. The signal is when two or more cluster in the same message. The first and last tells carry the most weight because they bookend the read.

1. "I hope this email finds you well"

The single most reliable email tell. Every major model defaults to this opener when asked for a polite professional message. Almost no human types it in 2026. A single sighting is enough to raise an eyebrow in cold outreach. Paired with the templated closer in tell 5, the verdict is settled. Real senders open with a specific reference, a shared connection, or the ask itself.

2. "I am reaching out to..." framing

Variants include "Just wanted to reach out about," "I wanted to follow up on," "I am writing to inquire about," and "Just checking in to see." The past-tense distancing softener is an AI fingerprint, especially in cold sales sequences and recruiter pitches. Humans usually open with the actual ask. The opener pattern lands in roughly half of ChatGPT-drafted business emails alongside tell 1.

3. Generic enthusiasm with no specific context

"I came across your recent work in this space," "great fit for your team," "innovative solution that aligns with your needs," "I have been admiring the work you are doing." Sentences that praise the recipient without naming a single concrete thing. Real prospects cite a specific post, talk, feature, customer, or announcement. AI fills the slot with abstract flattery that could be sent to any company in the sector.

4. Uniform three-paragraph structure

AI loves a fixed email shape: polite opener paragraph, capability-or-context paragraph, closing paragraph with the ask. Three roughly balanced paragraphs in a 200-word message is the canonical pattern. Humans vary. A real sender might write one short paragraph, or a four-paragraph note with a separate why-now line, or one long anecdote followed by a one-line ask. Structural uniformity is the structural tell.

5. No specific personalisation plus templated closer

"Looking forward to hearing back," "Please let me know if you have any questions," "Would love to hop on a quick 15-minute call to explore how we can support your goals." The closer cluster is the second bookend. Combined with tell 1 in the opener and zero specific references anywhere in the body, the message is almost certainly AI. The specific-reference test is the cleanest single screen for any inbound email.

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5-step workflow

The five-step inbox screen — under a minute per email.

A workflow you can run on every inbound message without slowing the inbox down. Two eye steps, one phrase scan, one tool check on the borderline ones, one response decision.

Step 1 — check the opener line

Read the first sentence in isolation, before the body has a chance to set context. If the opener is a polite formula like "I hope this email finds you well" or "I am reaching out to," the message is already at risk. Real senders open with a specific reason, a shared reference, or the ask itself. The opener carries roughly forty percent of the manual-screening weight on its own.

Step 2 — look for specific context

Scan the body for anything only a real reader could write. A name, a number, a project, a recent post of yours, a feature you shipped, a customer you have, an announcement from last month. AI emails describe value in the abstract. A single concrete reference in the first 100 words is the strongest single human signal. Zero specific references in 200 words is the inverse verdict.

Step 3 — identify the generic phrases

Run the message past the five tells. Polite opener, vague enthusiasm, uniform three-paragraph structure, no specific personalisation, templated closer with a quick-call ask. Two or more tells clustering inside one email is a verdict. One tell on its own is a yellow flag worth a second read.

Step 4 — scan with TextSight when stakes matter

For high-stakes emails, paste the message into app.textsight.ai for sentence-level highlights and a calibrated Authenticity Score. A 20-second scan returns a verdict that confirms or overrides your gut. The free plan covers 10,000 characters per day, which is a long workday of inbox screening, and Pro at $19.99 a month removes the daily cap for high-volume desks.

Step 5 — respond accordingly

Treat the AI verdict as inbox metadata. For cold sales and recruiters, an AI-drafted pitch is fair grounds to skip. For internal comms or vendor replies, ask a follow-up only a real reader could answer. A real sender replies in one line; AI scaffolding stumbles on the specifics. For candidate communication, route the borderline messages to a phone screen instead of guessing on prose alone.

Inbox categories

Four email types where the screen pays off.

Each inbound category has its own version of the same five tells and its own response decision. Sales, customer service, and hiring screens benefit most from a fast manual read backed by a tool scan on the borderline messages.

Cold outreach

The easiest category by far. AI use is near-universal in modern outbound, and the templates are dense with tells 1, 3, and 5. A trained eye hits 85 percent or better on cold pitches above 100 words. The response decision is usually straightforward: an obviously AI pitch is a signal of low personalisation, low effort, and low fit. Most of those messages are safe to skip without a tool scan.

Customer support inbound

Harder. Support templates have always been formal and structured, and AI fits the existing register. Tells 1 and 5 still fire, but customer support agents have written this way for years. Weight the manual read down for known support contexts and lean on the specific-context test: a real customer always names a product, an order number, or a recent change. AI-generated complaints are abstract.

Inbound sales replies and vendor pitches

Mixed. A small vendor often drafts in AI to sound corporate, which lights up tells 2, 4, and 5. A large vendor with a real account manager usually writes shorter and more specific replies. The screen here works best as a routing signal: AI-flavoured pitches go to a backlog, specific replies move to a real read. Vendor messages above 200 words give the screen enough material to fire reliably.

Candidate communication

The highest-stakes category for hiring teams. A candidate who pasted the JD into ChatGPT writes generically about driving growth and unlocking value. A real candidate names a feature, a customer, your last funding round, or a recent announcement. The specific-reference test outperforms most detectors on candidate emails. For borderline messages between two finalists, the TextSight scan in step 4 settles the call inside a minute.

Annotated example

A real AI cold email, tell by tell.

A cold sales outreach drafted by ChatGPT with the prompt "write me a 100-word cold email about TextSight." Read it cold, then read the callouts.

The message itself

"Hi Dipak, I hope this email finds you well. I wanted to reach out about TextSight and the impressive work you have been doing in the AI detection space. I have been following what your team is building and would love to explore a potential collaboration. Our platform helps teams streamline their workflows, scale their operations, and unlock new growth opportunities. Would you be open to a quick 15-minute call next week to discuss further? Looking forward to hearing back from you soon. Best regards, Alex."

What the five tells caught

"I hope this email finds you well" is tell 1. "I wanted to reach out about" is tell 2, the past-tense distancing softener. "The impressive work you have been doing" plus "following what your team is building" is tell 3, vague reference with zero specifics about what the work actually is. The roughly balanced three-sentence shape with a polite open, capability-claim middle, and ask-plus-closer footer is tell 4. "Quick 15-minute call" and "Looking forward to hearing back from you soon" close tell 5, the templated closer cluster. Five tells in 95 words; a real sender might trip one.

What an actual human cold email would look like

"Dipak, saw the streaming SSE release on TextSight last week. We run AI screening for a hiring team that hits 200 candidate emails a day, and your sentence-level highlights are the right shape for that workflow. Worth fifteen minutes Tuesday to compare notes on API rate limits? Happy to share what we ship if useful. Alex." Sixty-five words. One specific reference (the SSE release), one specific use case (200 candidate emails a day), one specific ask (Tuesday), one specific offer (share what we ship). Zero tells.

FAQ

Spotting AI in emails frequently asked.

What is the single biggest tell that an email was written by AI?
The opener. "I hope this email finds you well" and "I am reaching out to" show up in roughly seventy percent of ChatGPT-drafted professional emails. A single sighting in cold outreach is enough to flag the message. Pair the opener with no specific personalisation in the first 100 words and the verdict is settled.
Can I spot AI in a three-line email?
Sometimes. A short reply that combines a polite opener with a templated closer like "Looking forward to hearing back" is suggestive. Most other tells need a paragraph or two to fire. Treat anything below 150 words as inconclusive unless two tells cluster in the same message.
Do the same tells work on Claude and Gemini emails?
Yes, with small shifts. Claude leans on the polite-opener phrasing and softer hedging. Gemini favours numbered or bulleted structure inside the body. ChatGPT is the most templated of the three. The five tells overlap across all model families because they share training data on business email corpora.
Are cold sales emails the easiest to spot?
By far. Cold outreach is the most templated email category and the one where senders use AI most aggressively. If a stranger writes a five-paragraph note with three bullet points, a vague compliment, and a quick-call ask, the probability it was drafted by a model is high.
How do I screen a candidate communication email?
Look for specifics about your job posting, your team, your product, or your last funding round. A candidate who pasted the JD into ChatGPT writes generically about driving growth and unlocking value. A real candidate names a feature, a customer, or a recent announcement. The specific-reference test outperforms most detectors on candidate emails.
When should I use a detector instead of my eyes?
When the stakes are high, when two tells fire but the email is short, when the sender is internal and the call matters, or when you are tied between two finalist candidates. Pasting an email into TextSight takes 20 seconds and returns an Authenticity Score plus per-sentence flags that confirm or override your gut read.
Why do AI emails hurt reply rates so much?
Recipients pattern-match the opener and closer inside a second. The rest of the email reads with that frame already in place. Substance does not get a chance to land. Cold outreach loses more reply rate to AI register than to any other factor short of bad targeting. The same effect plays out on internal comms and vendor messages, just with lower stakes.
What is the fastest screen if I am triaging 200 inbox messages?
Read the first sentence and the last sentence only. If the opener is a polite formula and the closer has a quick-call ask, the message is almost certainly AI. Skip and save the minute. The middle paragraph rarely changes the verdict when the bookends are this templated.
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