Why is email marketing like being in a long-term relationship? Because when you ask your partner how they’re feeling, and they respond, “Fine,” you know that doesn’t always mean things are good.

In the same vein, AI can produce campaigns that look fine. But on closer inspection, you might find it doesn’t have the juice to motivate a customer reader to act. 

AI has made email marketing easier. But “easier” doesn’t mean “better.”

AI isn’t all bad

We have learned AI’s power over the five years since AI tools went mainstream. 

With marketer guidance, AI can generate subject lines, write and revise copy drafts, summarize customer reviews, and rewrite product copy. Beyond this day-to-day drudgery, AI can handle the behind-the-scenes work we often sacrifice, such as suggesting segmentation ideas, creating testing angles, and sketching out lifecycle journeys.

AI can turn one idea into 10 campaign variations while your team is still arguing over the brief. And that’s great. This helps email teams that are historically overworked and under-resourced. We are the classic “Do more with less” people. 

So, it’s easy to see why marketers succumb to AI’s siren song of speed, scale, and instant creative support. But that creates a new problem: The way we use AI is producing more average email faster. 

‘Average’ is easier to produce

AI is skilled at producing competent marketing output that looks good on the surface. 

The writing is clear, grammatically correct, structured, polished, and generally comprehensible — a Grammarly editor’s dream! It follows recognizable email formats, suggests benefit-led subject lines, and rewrites copy to sound warmer, punchier, shorter, clearer, or more urgent. 

With the right brief, your AI tool can produce a welcome email, abandoned-basket email, reactivation email, product-launch email, and a Black Friday email that look perfectly acceptable.

And that is exactly why AI can be so dangerous.

If your AI tool generated a big pile of slop, you’d recognize it immediately and reject it. But what if the copy isn’t so obviously bad that it’s worth challenging? 

You might think, “That’s fine. Let’s send it.” But there’s that word again. It’s “fine.” And “fine” Is seductive when your deadline looms.

Now that more brands are using similar tools, asking similar questions, accepting similar outputs, and following similar best-practice structures, we aren’t seeing better email. 

Instead, the inbox becomes a sea of competent sameness, and your message might not stand out anymore. 

Speed is not a strategy

One reason why email marketers have adopted AI so quickly is that the channel is perpetually hungry.

Email needs constant feeding. Fill this campaign calendar! Refresh that automated program! Support your promotional windows! Launch these new products! Repurpose your content! Chase your commercial targets! 

It’s exhausting, but the inbox never gives you a week off because you’re tired.

So, it is tempting to see AI as the answer to the volume problem.

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If you can create more copy faster, that helps, sometimes. But speed is an advantage only if you’re moving in the right direction. If you have a weak strategy, AI simply helps you execute it more efficiently.

It can help you send more messages without questioning whether you should send them. It will generate a reactivation email, but not diagnose why customers went inactive.

AI can suggest five subject lines without indicating which emotional trigger matters to the audience. It can help you build a lifecycle sequence without connecting it to your customers’ real-life decision journeys.

AI can accelerate execution, but it can’t refine the thinking process that guides your email marketing program.

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AI cannot rescue a weak brief

AI output is only as good as the thinking that guides it.

A vague prompt will produce vague marketing. A generic brief will produce generic copy. A shallow understanding of the customer will lead to shallow messaging.

This is not the tool’s fault. The marketer fails by not giving it direction. 

If a marketer asks AI to write an email promoting a 20% discount, AI will do that. But it doesn’t ask discerning questions like these:

Is this discount the right message? Does the audience need it? Could the promotion damage our brand’s perceived value? Does this audience segment need reassurance rather than urgency? Is this email solving the wrong problem? 

AI will not say, “Before we write this campaign, should we talk about whether training customers to wait for discounts damages your margin and long-term behavior?”

It’s your job as the marketer in charge to raise those questions.

This applies to lifecycle marketing, too. AI can map out a welcome journey, but if the brief says only, “Create a five-email welcome sequence for a skincare brand,” the output will probably look like many other skincare brands’ welcome sequences. 

It might include a brand introduction, bestsellers, social proof, educational content, and an incentive. Fine (ugh), but it’s not necessarily the right strategy.

The strategy comes when marketers ask and answer customer-centered questions like these:

• Who is subscribing? 
• Where did they come from? 
• What do they already know? 
• What are they trying to solve? 
• What anxieties might they have? 
• What stage of awareness are they in? 
• What does our brand need them to believe? 
• What behavior do we want to encourage? 
• What makes our brand different from our competitors?

These questions matter more than the ability to quickly generate five emails.

The competitive advantage is shifting

Companies usually reward their email teams for outputs like campaign count, turnaround time, variants created, automations launched, segments targeted, and the biggest number of all: how much revenue they can attribute to email.

But if every team can generate subject lines quickly or create a basic welcome journey in a day, it devalues the competitive advantage of getting your message into the inbox faster than everyone else. 

Teams will regain the advantage when they improve the quality of their thinking before they create the email, in areas like these:

• How well they understand their customers and interpret data.
• How clearly they define the commercial problem and identify behavioral barriers.
• How well team members can synthesize what they know to choose the right message, build the right journey, protect the brand voice, and design the right test.
• If they can judge whether the AI-generated output is genuinely good or merely acceptable.

In other words, the advantage is not the tool. The advantage comes from the marketer’s judgment when using it. 

That’s also why you can’t delegate the work to junior marketers who don’t have your knowledge and experience. They might know which buttons to push, but not why or why not.

More email is not always the answer

One of my concerns with AI is that it can easily lead us to solve the wrong problem.

If a team believes their email program needs more campaigns, AI will help produce them. If the team believes they need more content, AI will help create it. If the team believes they need more personalization, AI will generate more ideas.

But does the program really need more of anything? Or does it need sequencing, or fewer but more useful emails? 

Perhaps it needs clearer positioning or stronger lifecycle logic. Should it stop over-mailing low-intent subscribers or address hesitation earlier in the journey? Maybe it needs to fix the post-purchase experience before pushing another acquisition offer.

Customers are already scanning, filtering, deleting, and ignoring their email and using inbox tools to manage the overload. Producing more competent but undifferentiated email will not break through the noise.

Personalization and persuasion still need expert judgment

AI can identify patterns, generate dynamic content ideas, create product recommendations, and develop different messages for different segments. Used well, email becomes more relevant and useful.

Used badly, it makes generic marketing feel artificially specific.

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Personalization is valuable when it uses customer data to help customers make better decisions, reduce friction, improve timing, increase relevance, or make the experience feel easier and more useful.

A customer does not care that a brand has personalized an email. They care whether the message is relevant, timely, respectful, and helpful. They care whether the brand understands what they need or is simply using data because it can.

Personalization can be powerful. But it can also be creepy, irrelevant, or an empty gesture. 

AI can help marketers brainstorm persuasive angles, explore objections, write for different motivations, suggest behavioral triggers, and generate testing hypotheses. But it does not automatically know which psychological lever is appropriate, ethical, and relevant in the specific context.

This matters because persuasion is not about sprinkling cognitive biases over a campaign like salt and pepper. It is about understanding how people make decisions and using that knowledge responsibly.

AI can support that work, but human judgment must drive it.

The danger of outsourcing the thinking

The real risk is not that AI will replace email marketers overnight. It’s that marketers outsource their judgment to their tools, along with the knowledge that makes those tools valuable.

The marketer should bring customer understanding, commercial context, ethical decision-making, brand awareness, strategic prioritization, and the ability to connect the email to a bigger business goal.

If those skills are weak, AI can create the illusion of competence to compensate for that weakness. It can lead marketers to create “just fine” campaigns based on sketchy strategy. 

This is why training and professional development matter more in the age of AI, not less. As tools grow more capable, marketers need deeper knowledge to use them intelligently.

‘Easier’ is not the same as ‘better’

The first phase of AI adoption in email has largely been about productivity. It’s time to shift into the next phase, which will focus on the quality of thinking. 

We need to ask how AI can help us with these goal-oriented quests:

• Understand customers better.
• Challenge assumptions.
• Support better testing.
• Diagnose journey gaps. 
• Explore different motivations.
• Make stronger strategic decisions. 
• Improve the relevance, usefulness, and distinctiveness of our email programs.

This is where AI becomes genuinely interesting. It’s not a machine that produces more average email, but a tool that helps skilled marketers think more deeply, explore more widely, and execute more intelligently.

AI has made email marketing easier. But “easier” doesn’t automatically lead to “better.”

Better email still requires strategy, customer understanding, and knowledge of persuasion. It requires brand distinctiveness, testing discipline, and judgment.

AI can help marketers do the work. But it can’t decide what work is worth doing.

That is where the advantage lies. Not in producing the most email in the shortest time. But in producing email that is more relevant, useful, distinctive, persuasive, and worthy of the customer’s attention.

Now that the average email is easier than ever to create, “average email” is exactly what we marketers need to surpass.

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