AI writes fast. That part is solved.
The harder question is whether anyone wants to read what it produces, and whether the email sounds like you or sounds like everyone else using the same tool with the same prompts.
Most AI-generated email copy lands somewhere between forgettable and obviously synthetic. The good news is that the gap between raw output and something worth sending is narrower than you might expect, if you know where to focus your editing time and what to fix first.
Why AI Email Copy Falls Flat
The email you generate in thirty seconds probably sounds exactly like the email your competitor generated in thirty seconds. Same rhythm. Same structure. Same vocabulary. Same emotional flatness.
As one content strategist noted, the core issue is that AI copy “lacks rhythm, emotion, and intent. It’s predictable. It’s clean to the point of sterile.”
That sterility shows up in specific ways. Every paragraph follows the same beat. Transitions feel mechanical. The language stays relentlessly neutral, avoiding anything that might be memorable or distinct.
James Milsom describes what happens when this content reaches inboxes: “The emails are often polished but flat. Lifeless.”
Recipients notice. Maybe not consciously, but they notice. The email gets deleted or ignored because nothing in it demanded attention, and nothing felt like it came from a person worth talking to.
The Editing Gap
Raw AI output almost never ships unchanged. Industry research from Litmus shows that while AI has dramatically sped up email production, human judgment remains central to the process.
Rafael Viana, Senior Email Marketing Strategist at Validity, puts it directly: “You can’t dump AI onto your emails and say it’ll fix everything without thinking about your strategy.”
The numbers support this. Teams using AI cut production time from weeks to days. But somewhere in that process, someone still reads the output, spots the problems, and fixes them. The speed comes from drafting, not from skipping the edit.
What takes the time is teaching AI your voice, spotting the patterns that mark content as synthetic, and knowing which emotional registers AI simply cannot hit on its own.
Voice Is Not a Setting
You can tell an AI tool to write “in a friendly tone.” You cannot tell it to understand your brand’s specific flavor of friendly. Is it coffee-shop friendly or customer-service friendly? Neighborhood friendly or LinkedIn friendly? These distinctions matter, and AI mostly guesses at them.
The fix involves reference material. Show, don’t tell. Include two or three of your best-performing emails as examples in your prompt. Specify words you always use and words you never touch. Describe your sentence length preferences. Note whether you use contractions, how you handle exclamation points, whether you start sentences with “And” or “But.”
Even then, expect drift. AI trends toward the average of its training data, which means it trends toward generic. Your specificity gets diluted with each generation if you do not actively maintain it.
Copy.ai observed that even after extensive brand voice setup, outputs sometimes remained “overly promotional” and required “human polishing for topics that are nuanced.” The tool helps. It does not replace the judgment about what sounds right.
Where AI Struggles Most
Emotional writing exposes the limits fastest.
Nick Usborne, a copywriter who has been analyzing AI writing since its early days, identified the core weakness: “The writing of ChatGPT is devoid of emotion. Not surprising. But as any experienced copywriter will tell you, this is a problem. It doesn’t make us feel anything.”
He noticed something else: “The copy has no rhythm or pace. It’s monotonous. There is no punch at the beginning or peak in energy towards the close.”
Rhythm is harder to fake than vocabulary. Humans speed up and slow down, create tension and release it, break patterns for emphasis. AI produces smooth, consistent output because it optimizes for coherence, not for the deliberate disruptions that make writing memorable.
The practical implication: if your email needs to make someone feel something, plan to do substantial rewriting. AI can give you structure and cover the expected points, but the emotional contour needs human hands.
What Actually Works
AI performs best when the task is pattern-based and the goal is speed.
Subject lines are the sweet spot. Short form, testable, pattern-driven. Generate twenty options in seconds, pick the strongest three, test them against each other. The economics work because you need volume for testing anyway, and AI delivers volume trivially.
HubSpot’s testing found that AI tools produced usable subject lines and reasonable body copy, though email length became an issue. One tool generated a 430-word email where best practice suggests keeping promotional emails between 50 and 125 words. Another tool stayed under 120 words and performed better.
First drafts for standard email types work similarly. Welcome sequences, order confirmations, appointment reminders. These emails have clear structures and predictable content. AI handles the baseline competently, and your edits add brand flavor without starting from blank.
Where AI adds less value: apology emails, crisis communications, anything requiring cultural timing or awareness of recent events. These demand judgment that AI lacks.
The Fake Personalization Problem
Cold email tools now offer AI-powered personalization at scale. They scrape LinkedIn, analyze company websites, and generate opening lines that mention something specific about the recipient.
The results range from impressive to disastrous.
GMass tested several personalization tools and found output like this from one AI: “I read in your article about the Turtle With The Golden Gun, that you are a 80s movie buff.” The problem? No such article or movie exists. The AI fabricated a detail that sounded personal.
Another tool generated: “Not sure if you’re much of a foodie, but have you been to Salar Restaurant and Lounge?” The attempt at connection felt awkward and obviously algorithmic.
The lesson is that personalization at scale is hard because genuine personalization requires actually knowing something about the person. AI can approximate this with available data, but approximation fails when recipients detect the pattern, and they increasingly do.
Hunter’s State of Cold Email 2025 report found that “two-thirds of decision makers didn’t mind if AI was used to help write a cold email, as long as the email still felt human.” The standard is feel, not perfection. But feeling human is precisely what generic AI personalization fails to achieve.
Editing for Authenticity
The editing process has a specific shape. Cut first. Then adjust voice. Then check flow. Then verify the ask.
Cutting is straightforward. AI pads. Look for phrases like “In order to” (just “to” works), “It’s important to note that” (delete), sentences that restate what you just said. A 200-word AI draft often becomes 150 words after trimming, and the shorter version usually reads better.
Voice adjustment means scanning for mismatches. Words you never use. Formality levels that feel off. The AI’s idea of “friendly” versus your actual brand personality. Swap in your vocabulary. Break up sentences that run too smooth.
Flow check means reading aloud. Where do you stumble? Those spots need rewriting. AI tends toward medium-length sentences stacked evenly. Humans vary more. Short punch. Longer explanation with multiple clauses that build toward something. Fragment for effect. Mix it up.
The CTA needs its own pass. AI-generated calls to action default to “Learn More” and “Get Started.” Competent but uninspiring. Ask whether the button text says something specific about what happens next. Does it match your voice? Would you click it?
Speed Without Sameness
The production timeline has genuinely changed. Litmus data shows that only 6% of email teams now require more than two weeks to produce a single email, compared to 62% in 2024. AI accelerated that shift.
But speed creates its own problem. When everyone can produce more email faster, inbox competition intensifies. The advantage goes to emails that stand out, and standing out requires the opposite of what AI naturally produces.
One analysis framed it sharply: “Your audience isn’t rejecting AI. They’re rejecting safety. AI writing will always default to safe copy.”
Safe copy blends in. It covers the expected points in the expected way. It does not make the reader pause or think or feel something unexpected. And in an inbox where everyone has access to the same acceleration tools, safe copy disappears.
The question becomes whether you use AI to produce more of the same, or whether you use the time savings to invest in making each email actually distinct. The former is easier. The latter performs better.
The Human Element
Email marketing ultimately depends on one person deciding to engage with a message from another person, or at least believing they are engaging with a person.
James Milsom offers a useful frame: “AI is a first draft. Nothing more.”
That characterization sets the right expectations. The blank page problem is solved. Structure and coverage are handled. What remains is the work of making the output yours, which means injecting voice, rhythm, perspective, and the kind of specificity that makes a reader think “This feels real. This feels like someone worth replying to.”
The tools keep improving. Brand voice features get more sophisticated. Personalization gets better at pulling relevant details without fabricating them. But the gap between competent output and genuinely effective email copy persists, because effectiveness depends on things AI does not possess: a point of view, emotional intuition, understanding of what this specific reader needs to hear right now.
For now, and likely for a while, the job is collaboration. AI handles the parts it does well. You handle the parts that require being human. The email that results can be faster to produce and still sound like it came from someone real.
Where that balance lands for you depends on your volume, your brand voice complexity, and your tolerance for editing. But the emails that perform best will always be the ones where someone cared enough to make them feel like they mattered.
For more on AI in email marketing, see AI for email marketing: what actually works. For building automated sequences with AI, check out AI email sequence building.
