AI Generated Copy: How To Do Different Types RIght

Email Copy, Website Copy, Product Descriptions, Landing Pages, Real World Examples

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TL;DR

Use AI for efficiency: generating first drafts, optimizing copy, A/B testing, and automating. AI-generated copy can speed up website content, emails, product descriptions, and landing pages – but there are AI resistant tasks that still require humans. Human editing keeps it on-brand and aligned with the overall funnel. Always refine it with human oversight to ensure accuracy, persuasion, and audience connection. The best results come from pairing AI’s speed with a copywriter’s strategic thinking – humans direct, AI drafts, and humans polish for maximum conversions.

Will Copywriters Be Replaced By AI?

No, it’s not replacing copywriters who know how to use it – and over 80% of marketers already use AI in their online marketing (Statista, 2024).

Are you leveraging the speed of AI generated copy yet?

Most of us marketers are drawn by promises of speed, efficiency, and data-driven insights. Creating website copy and marketing emails isn’t taking the hours of manpower that it used to.

It’s predicted that large companies will AI-generate 30% of outbound marketing messages in 2025 (Gartner, 2023).

But if AI writes copy at scale, where do human copywriters fit in?

They handle AI-resistant tasks – turning broad, low-converting copy into targeted, high-converting content. AI generated copy plays it safe, but great marketing speaks directly to the audience.

Great copy works because it doesn’t speak to everyone. It singles out the ideal customer and intentionally excludes others. It brings in experience-based empathy, real testimonials, case studies, and niche expertise.

In other words, the elements that make it work come from a human touch.

AI can generate the words, but human expertise is needed to polish those words into impactful marketing messages.

“There is no more B2B or B2C: It’s H2H, Human to Human.” – Bryan Kramer

In the following sections, we’ll explore AI’s role in four key types of marketing copy – website pages, email marketing, product descriptions, and landing pages.

For each, we’ll break down:

How AI can help
Where it falls short
How the human copywriter can practically apply AI
Real-world examples and case studies
Best practices for using AI in copywriting ethically and effectively

We built out this guide to help copywriters explore practical ways to use AI in marketing copy creation.

Successful marketers pair the efficiency of AI with the savvy of human editors. Freelance copywriters and editors are having to adapt quickly, but their jobs aren’t disappearing.

If you let it, AI can be an amazing employee.

Let’s begin.

Can AI Write Website Content?

Yes – to do it, generate a draft that uses key company info, then edit for context, brand voice, and user experience.

A website – where your homepage, services page, about, etc. live on the internet – is your virtual storefront.

Your website is like having an on-call 24/7 sales representative that takes care of every visitor at once. It’s no small task. Can AI handle these pages that handle so much of your brand impression?

Not alone. But as a collaborative tool, yes.

AI generated copy is essentially being used for website content on-demand. For example, you can ask an AI to brainstorm a dozen headline options for your homepage hero section, or to draft an SEO-friendly meta description for a new page in seconds.

Many businesses – from scrappy startups to large enterprises – are already using AI in these ways. In fact, 43% of marketers using AI tools leverage them specifically for content creation tasks like writing website copy (HubSpot, 2024).

Small businesses benefit by getting instant drafts without hiring extra writers, while larger companies use AI to generate first drafts that their content teams can then refine.

Here’s What AI For Website Copywriting Looks Like

The options are endless – but some popular uses include:

Outlining a page – organizing sections of a homepage or landing page
Suggesting microcopy – giving you options for call-to-action buttons, form fields, and tooltips
Generating paragraphs – writing content like an “About Us” blurb or product descriptions
Generating entire website pages – creating drafts for service pages, blog posts, or landing pages
Translating and localizing web copy – adapting content to different languages and cultural nuances to expand your reach
Optimizing for SEO – suggesting keywords, meta descriptions, and improving readability
A/B testing variations – generating multiple headline or copy versions for conversion optimization
Adapting tone and style – analyzing your copy to ensure consistency across pages
Summarizing content – condensing long-form copy into concise, engaging snippets or TL:DRs
Generating FAQs – pulling common customer questions for both human readers and AI web crawlers

This can be done with a general LLM (like ChatGPT or Claude), or with a tool made for generating copy.

Some content management systems and website builders now integrate AI assistants to help fill in webpage text. For example, HubSpot’s content assistant can generate webpage copy suggestions, and Jasper or Copy.ai can spit out copy variations. WordPress and Squarespace have built-in AI copy tools, letting you generate content directly in your website builder.

This AI generated copy often serves as a starting point – giving human writers raw material to work with.

It’s also useful for tedious bits like meta tags or boilerplate descriptions.

In essence, AI is becoming a handy web copy sidekick: it can churn out the basics at lightning speed and even analyze data (like SEO keywords or past conversion data) to propose “data-driven” copy elements (e.g. headlines it predicts might perform well).

Unfortunately, There’s Issues With Using AI to Write Website Copy

AI struggles with elements that persuade visitors to take action – like emotional connection, user experience, relevancy, originality, and maintaining a consistent brand voice.

AI models learned from broad internet text, which means the copy they generate tends to sound generic.

“A lot of [AI-generated content] sounds the same. As humans, we crave authenticity and originality. We can spot AI-generated content from a mile away.” – Luke Joyce, Expert Copywriter at Kyyte

AI doesn’t intuitively pick up brand voice. That’s a major issue, since with website copy, brand voice does heavy lifting – and its consistency builds trust and signals to readers whether they’re the right audience.

Brand voice is also extremely nuanced, which is why AI doesn’t pick it up quickly. A single prompt or guide doesn’t capture it well, just like a quick description or a social media profile doesn’t fully define a person.

Brand voice is shaped by things like:

Customer interactions, seen in testimonials and customer support
Communication themes and devices, like how you share stories, what you emphasize in them, and where you tend to integrate them into the copy
Delivery, like how copy is formatted, how it tends to flow, and what kind of value it typically delivers.

When brand voice is inconsistent, it makes readers disconnected. But when the brand voice is consistent, but not aligned with your brand, you draw in the wrong audience.

Storytelling in particular is another challenge for AI.

It can tell a good story. But it doesn’t intuitively spin it in the way that your audience likes to hear.

It’s like having a story that you tell differently when it’s your friends versus your grandma. Same story, but you know all the little details to tell that make it interesting for the respective person.

Humans are naturally great at weaving stories that inspire and build trust. AI, by contrast, has no lived experience or genuine empathy. It can mimic a story structure, but it’s prone to just stringing together clichés.

It won’t know the nuanced pain points of your specific audience or the subtle cues that resonate with them – unless a human feeds in that insight.

And while AI can access facts from its training data, it lacks context and reasoning. It might fill a page with generic benefit statements, but miss the one thing your customers really care about.

Additionally, humans bring in outside knowledge. Maybe it’s a new industry trend or a recent testimonial that would enrich the copy beyond what the AI pulled from its training data.

A human expert looks at the page strategically and asks:

Is this addressing our audience’s actual needs and objections?
Is there a clear journey for the reader that leads them to our call-to-action?

If not, they revise.

You might rearrange sections, add a more compelling hook to the intro, or spice up a dull AI-written headline with a fresher angle. Essentially, the copywriter adds the conversion-focused polish.

An experienced web copywriter also knows credibility is more valuable than AI’s efficiency. They will fact-check any claims the AI made and integrate real data or proof points (e.g. case study results, awards, customer numbers) to strengthen the page. AI won’t automatically do that.

Many teams now position writers as “editors” of AI content, but that editing is not a trivial task – AI creates a draft, and the human elevates it from passable to high-converting.

The Human Copywriter’s Role Is Both Editor And Strategist.

AI can give you a rough draft, but a human turns that into a compelling page. That means refining AI-generated drafts heavily –

Injecting brand personality
Clarifying the messaging
Fact-checking claims and sources
Providing outside knowledge
Ensuring the flow is cohesive and user-friendly
Adding the persuasive elements that drive action.

“People think in stories, not statistics, and marketers need to be master storytellers,” – Arianna Huffington, founder of the Huffington Post

Real World Examples – AI vs. Human in Web Copy

In August 2023, conversion optimization company VWO ran a competition where brands tested AI-written website copy against their original human-written versions.

The results were mixed – AI alone clearly wasn’t a magic bullet.

In the competition, Booking.com tested an AI-generated call-to-action on their hotel booking page against two human-written variations. The human copy won, yielding a 1.7% higher conversion rate on the CTA (VWO, 2024).

In such a high-traffic scenario, 1.7% is a meaningful lift – and it was the nuance in the human-crafted text that eked out that win.

On the other hand, some tests showed AI can contribute useful ideas in the funnel.

An e-commerce site Schneider’s saw an AI-written homepage banner lead to a 7.06% increase in clicks.
An insurance company’s landing page headlines generated by AI boosted CTA clicks by as much as 15.77% compared to the original text.

Those are sizable improvements – but the AI didn’t work in isolation. Human teams selected and implemented the best AI suggestions and likely adjusted them.

The insurance company’s team created three AI variants and chose the top performer; the AI headlines still had to align with the campaign’s context (which humans oversaw).
Meanwhile, many other tests in the competition were inconclusive or ties – meaning the AI copy was “okay” but not better than what humans already had.

The overarching takeaway from these real-world A/B tests is that AI can generate workable web copy – and sometimes even improve metrics – but it’s inconsistent.

Without human creative direction and iteration, AI copywriting might underperform or simply match baseline.

In high-stakes pages like homepages, companies are finding the best results by using AI for ideas and drafts, then relying on their human copywriters to craft the final high-converting version.

Can AI Do Email Copywriting?

Yes – use AI to analyze data from past campaigns, identify what worked, and generate options for A/B testing. You can use this for subject lines, email body text, and elements within the email, like placements of a button.

Emails – those messages you send regularly and during campaigns – are for nurturing. Sometimes it’s to sell something, most of the time it’s just to keep up the conversation, but all of the time they should be interesting or helpful to the recipient.

Understandably, keeping up the ongoing conversations takes a ton of content.

AI and email marketing are the perfect match marketers have been waiting for. At a $36 return for each $1 you put in, email has the highest ROI (OptinMonster, 2025) – but demands strong strategy and a lot of copy.

The big promise here is efficiency – AI can churn out subject lines, schedule perfect send times, and even write segments of emails faster than a human.

On the other hand, AI generated copy struggles to replicate personalization and genuine connection – a major driver of email marketing success.

Here’s what AI in email marketing looks like:

Using AI here looks like subject line optimization, automating email sequences and followups, tweaking messages based on segments, and handling first drafts.

Email sequence and follow up automation – making sure a lead doesn’t get lost
Segment-based messaging – tailoring one email for different audiences with small adjustments
Generating first drafts – allowing writers to focus on strategy and editing.

However, 100% AI generated copy is especially poor low quality when it comes to marketing emails.

Unlike web content, blogs, or product descriptions – where AI has access to massive amounts of training data – emails exist within the “deep web” (password-protected inboxes, CRM systems, and internal databases).

AI doesn’t have the same level of exposure to high-performing marketing emails as it does to publicly available content. That means AI’s working with a smaller and less representative dataset when generating email copy.

This makes your original data, like a company’s best emails, and human editing even more valuable.

For now, let’s discuss AI strengths with email marketing.

Optimize Subject Lines

Crafting a short, punchy subject line that compels opens is both art and science – and AI has proved quite adept at the science side. By analyzing heaps of data on what words get high open rates, AI tools can offer subject line options that are likely to perform well.

Some brands use AI platforms (like Persado or Jacquard) to generate dozens of subject line variations and test them.

The results can be impressive: in one experiment, sales emails using AI-generated subject lines saw a 37.6% open rate, compared to 28% with human-written subject lines (Lead411, 2023). The AI-driven lines, which often included engaging phrases and even personalized tokens, clearly resonated more in that case.

Automate Follow-Ups And Sequences

Losing leads due to missed follow-ups or under-messaging is a major funnel leak. Hot leads cool down quickly when you’re slow to reach out.

So what does this automation practically look like?

A common use is to program AI to send a series of onboarding emails to new subscribers, adjusting the content slightly based on user behavior (if they clicked the last email or not).

This means marketers can set up complex drip campaigns without manually writing every single email variation. Time-triggered or behavior-triggered emails (like cart abandonment reminders, re-engagement emails, etc.) can be largely machine-handled once you provide templates.

The automation setup is a common feature within email marketing platforms, such as Klaviyo, HubSpot, Mailchimp, ActiveCampaign, and ConvertKit.

For platforms without built-in AI, Zapier can connect them to AI tools – like Flodesk to Zapier to ChatGPT.

Micro-Segment For Better Personalization

Traditionally, you might segment your email list by demographics or past purchases and then craft different emails for each segment. Now AI can do ultra-micro-segmentation – analyzing customers’ browse and purchase data to send highly targeted content.

For example, an AI system might identify a group of customers who only buy during discounts and automatically insert an extra coupon in their emails. Or it might notice a segment that always clicks on subject lines with certain words (like “exclusive” or “webinar”) and tailor subject lines for them accordingly.

This level of dynamic personalization is difficult and time-consuming for humans, but AI can handle the data crunching instantly. It’s essentially personalization at scale: AI algorithms can customize email content blocks (product recommendations, for instance) for each recipient based on their unique history.

Not to mention, AI can optimize send times for each user (by learning when you tend to open your emails) and even adjust email frequency. All of this leads to potentially better engagement metrics with less manual labor.

It’s no wonder a survey found 87% of organizations are using AI to enhance their email marketing efforts (Loopex, 2024), given how it can boost productivity.

But There’s Some Problems With AI Generated Copy In Email Marketing

Emotional resonance, deep personalization, and combining brand voice with context are not AI strong suits – but are critical for email marketing success.

The efficiency gains are real, but they come with major trade-offs

AI generated copy in emails can read as formulaic or impersonal – because, at the end of the day, they are formulas derived from data.

AI can plug your name into a sentence (“Hey John, check out our new features…”), but true personalization is more than using a first name or mentioning a past purchase.

It’s things like:

Understanding the emotions and motivations of your audience. AI doesn’t truly understand emotion; it predicts what a human might say. We can often tell when a person is faking emotion – why would AI be better at it?
Understanding context. AI won’t know the latest meme your audience is talking about (unless someone explicitly updated its prompt), nor can it easily improvise a witty aside or empathetic comment about, say, a holiday season or a local sports team victory.
Having emotional intelligence. When a customer has a complaint, AI might produce a grammatically correct apology email, but will it convey empathy? It may use stilted phrases that don’t feel authentic, which makes the customer’s frustration worse.
Grasping a brand’s story or values, unless you teach it. If your brand is quirky and known for puns, an out-of-the-box AI won’t naturally include that humor. The humor can still sound forced or out of place, even when it aligns with how you trained it.
“Getting” the audience. AI might churn out recommendations (“You bought a diaper bag, you might like a stroller”), but it won’t understand why a person bought X or what problem they’re trying to solve. A human marketer can spot demographic and activity clues, realizing the user likely bought it as a gift – making the AI recommendations more annoying than helpful.

Over-relying on AI’s logic can lead to tone-deaf communications, like promoting something the customer already said they’re not interested in, just because some data pattern suggested it.

In short, AI currently struggles with the human aspect of email marketing – the heartfelt storytelling, the conversational tone tailoring, and the nuanced understanding of customer feelings.

This can show up in the metrics. For example, AI generated copy for subject lines might get decent open rates (thanks to optimization) but then underperform on clicks or replies because the content doesn’t quite spark the audience’s interest or trust.

The Human Copywriter’s Role Is Pairing AI’s Efficiency With Genuine Personalization

First and foremost, humans need to refine the tone and messaging of AI-composed emails.

We can do that by:

Adding warmth & personality – like breaking up sentences to make them more conversational or adding a personal story from the founder.
Ensuring brand alignment – like switching out words that aren’t quite on brand, and adding mention to recent niche news
Following brand guidelines – like removing (or adding) expletives, or bolding certain parts.
Infusing strategy – making sure the individual email aligns with where it lives in a segment, and making sure it has a good persuasion arc.
Refining & highlighting key benefits – such as trimming fluff and bringing the most persuasive points forward
Bringing emotion & trust – like adding testimonials, personal anecdotes, and empathetic lines AI wouldn’t naturally include.

There’s evidence that these human refinements pay off.

One analysis showed that while AI generated copy could boost some metrics like open rates, emails that combined AI efficiency with human creativity saw the best engagement. Open rates and click-throughs improved when human writers adjusted AI content to better speak to customer emotions (leading to more conversions down the line) (Stripo, 2023).

In this same study, email open rates and click rates were almost equal between AI-written and human-written content – but only after humans thoroughly edited the AI’s output for clarity and correctness

The final success depended on human refinement. That’s why, in practice, many teams now use AI for drafting and humans for editing in email marketing.

For example, a team might have AI generate 5 versions of a promotional email. The copywriter then picks the most promising one and edits it heavily – adding a catchy first line that the AI missed, correcting any awkward phrasing, and double-checking facts or links.

They may also A/B test a human-written subject line against the AI’s suggestion, or vice versa, to make sure they’re not missing a creative angle. And if the AI ever suggests something that doesn’t feel right for the audience, the human has the final veto.

Humans also need to monitor performance and feedback, and make adjustments based on it. AI alone wouldn’t pick up on qualitative cues, like a frustrated passive aggressive response. It also may not have access to important metrics, like positive and negative replies from customers.

Overall, the human copywriter ensures that efficiency never comes at the price of authenticity.

Real World Example – AI Efficiency Plus Human Personalization

Novo Nordisk, a global pharmaceutical company, needed to send email campaigns targeted to physicians – who were already frustrated by pharma’s digital interactions since the pandemic. Novo turned to AI specifically for help with subject lines and content optimization (Endpoints News, 2022).

Novo Nordisk implemented an AI tool (Phrasee, now Jaquard) to generate and test subject lines at scale. They also had their medical-legal team make sure the content remained sensitive and within guidelines.

But it wasn’t just plug-and-play – the human team guided the AI by defining the proper tone. The emails had to be encouraging and informative, not fear-mongering or too salesy, given the medical context. They also had to make sure everything complied with strict FDA regulations for pharmaceutical communications.

The results were telling: the AI-optimized emails increased open rates by 24% and click rates by 14%.

Plus, the subject lines didn’t need to pass through so many hands for review.

That’s a substantial lift in engagement and a lot of time saved.

The AI handles the heavy lifting of data and volume – optimizing send times, generating subject line options, segmenting lists – and humans handle the finesse – crafting a relatable narrative, maintaining brand voice, and ensuring the message truly speaks to readers.

Can You Use AI To Write Product Description?

Yes – to do it, give it as many relevant specifications and details as possible – AI will handle the structure and wording.

Product descriptions – those blurbs with the specifications, details, descriptions, and reviews of a product – give the customer the information they need to buy (or not buy). Since the focus isn’t on nurturing, they tend to act as landing pages – small chunks of direct-response copy, where the CTA is to “add to cart.”

Shoppers want details. If they can’t find them, they’ll buy from someone who has them.

If you’ve written for an e-commerce business or manage an online catalog, you know the pain of writing all these detailed product descriptions – especially in bulk.

Understandably, AI generated copy for product descriptions is a lifesaver for many businesses looking to scale up content creation.

But can AI-written product descriptions actually engage and convert customers?

Here’s What AI For Product Descriptions Look Like

AI is commonly used for generating descriptions, optimizing for SEO, and doing bulk updates.

The most obvious benefit of AI in product descriptions is speed and volume. What used to take a copywriter 15–30 minutes to write (a single product description) can now be drafted by an AI in moments.

For example, Describ, an e-commerce performance tool, integrated an AI solution that used image recognition and GPT-style text generation to create product descriptions. They found it reduced the time to produce each description by 96%, from about 30 minutes to under 1 minute per product (DevelopsBay).

The scalability of AI generated copy for product descriptions is unmatched.

Small businesses can use AI tools (like Shopify’s built-in AI “Magic” writer or description generators like Jasper) to instantly get decent descriptions for all their products.

Likewise, large e-commerce platforms can automate the creation of descriptions for the long tail of their catalog (think of a marketplace like Amazon or Alibaba with millions of listings).

The main benefits of AI product generation are:

Consistency – it can follow a template or style you give it, so every item in a category has a similar format (which can be good for user experience).
SEO optimization – it can suggest and add relevant keywords.
Translation – generating descriptions in multiple languages at scale dramatically broadens your reach.
Scalability – have a description for every single stock keeping unit, even if you add hundreds of new products overnight
Bulk updates – new features or a fresh tone can be implemented instantly.

How are these done, practically?

It depends on the size and needs of a business. For many businesses, there’s AI integration within the tools they use.

With smaller businesses, built in tools create AI generated copy for descriptions (like with Shopify).
For larger companies, AI tools inside content management systems (CMS) or PIM software (like Pimcore or Akeneo) generate descriptions at scale.
Many large enterprise companies, like Amazon and Alibaba, have developed their own proprietary AI technologies.
Companies that don’t want AI-generated descriptions (but still use AI for support) often use it to research competitor product descriptions, helping them find gaps and stand out.

Product descriptions are the final push before a shopper adds to cart, so nothing should cause hesitation. Unfortunately, key buying triggers, like a product’s measurements, aren’t always in the AI’s data.

There’s Some Problems With AI Generated Copy For Product Descriptions

Besides sounding generic, AI can invent product details or display error messages.

Ever seen an Amazon product with low ratings or high returns—not because it was bad, but because the color, size, or quality was misrepresented?

Shoppers need exact details to make a purchase, not flowery AI-generated descriptions. No storytelling or persuasion will sell a table if key details like dimensions and materials are missing. Especially when they’re dealbreakers.

Here are several major issues that come up with unreviewed AI generated copy for product descriptions:

Missing information. Product specs are often dealbreakers. Missing measurements and materials are enough to turn customers away.
Inaccurate information. Product details are product-specific, not AI-generated estimates. Misrepresented products lead to returns, frustrated customers, and negative word-of-mouth marketing.
No focus on unique selling points. AI doesn’t inherently know what makes your product different from a competitor’s unless you feed that info in every time.
Overdone descriptions. Shoppers need clear features to make a purchase, not flowery AI-generated descriptions. Clumsy storytelling gives a product description a strong “AI sound.”
Focusing on benefits over features. Many product’s features are their benefits. A work boot description needs less persuasion than a high-ticket product on a landing page.
Lack of context. This leads to an unclear unique selling point. The customer wants to know if the product was made for someone like them with problems like theirs. They don’t care about a bouquet of benefits.
Displayed errors. If AI can’t generate something, it might default to the error message, like when an Amazon vendor’s product title was a ChatGPT message (Digivate, 2024).
Keyword stuffing. It can help your listing rank, but it cheapens the presentation of a product – and may be penalized, depending on the platform.

The upshot is that AI alone can churn out a lot of product content. However, that content risks being generic, incomplete, or including errors.

Copywriters Quality Control The Brand Personality And Accuracy – AI Handles The Scale

A recent study comparing AI vs. human-written product descriptions found that:

Both AI and humans effectively convey sentiment.
Humans and some AI models produce the most accessible (readable) descriptions.
Advanced AI (like ChatGPT-4) and humans perform best when it comes to SEO and persuasiveness.
Simpler AI models score higher on clarity, but clarity depends on context, not just word length.
Humans still lead with emotional appeal & call-to-action, as these require deeper language understanding. (Arxiv, 2024)

It makes sense – in the end, humans are selling to other humans, not to machine learning systems. Humans add the quality control and creative enhancement layer to AI output.

Here’s a hypothetical (but typical) illustration of this hybrid approach.

Imagine an online home décor store that has thousands of items. The company decides to use AI to help handle description writing – keeping up with inventory churn was overwhelming their small content team. They implement an AI tool that generates a basic paragraph for each product.

Immediately, they save a huge amount of time. The descriptions include all the must-have details (dimensions, materials, etc.) and read clearly, but somewhat blandly.

The content manager assesses and course corrects.

They categorize products by importance and value. For the majority of low-cost, utilitarian items (generic picture frames, simple storage bins), they deploy the AI descriptions with only a light proofread – the customers mainly care about specs for these, and it’s not worth heavy editing each one.
For their high-value items, the copywriter steps in to refine those descriptions heavily, like with a $2,000 Italian leather sofa. They use the AI draft as a base for factual info, and add a backstory about the craftsmanship
They also spot-check categories – for instance, they notice the AI wrote very similar descriptions for five different throw blankets, since all are soft and warm. That’s not very helpful for differentiation. So the copywriter edits each to highlight what’s unique

A few months after this hybrid approach, the store reviews its analytics: products with the enriched, human-touched descriptions show a higher conversion rate on their pages compared to the ones where they left the AI text mostly as-is.

They also notice fewer customer questions coming in asking for details (because the descriptions are more thorough and clear) and even a slight uptick in organic search traffic to product pages (likely because the human-edited ones had more unique, search-friendly phrasing).

The content manager concludes that AI generated copy enabled them to scale rapidly, but human creativity and judgment made the difference in conversion. They continue to review analytics and adjust their process, always including at least a quick human review of AI outputs, and a deep edit for hero products.

Is There An AI That Creates Landing Pages?

Yes – many AI tools can generate both landing page copy and design. But since landing pages are high-stakes, use AI for what it does best: analyzing success, generating test options, suggesting layouts, and automating personalization.

Landing pages – those standalone pages designed for specific marketing campaigns or lead generation – are all about conversion.

Whether the goal is getting a visitor to sign up for your emails, get a lead magnet, or buy something, landing pages need to be punchy and persuasive. It isn’t for SEO or nurturing content – this copy needs a logical flow that nudges the user to take an action immediately.

Given the high stakes, can AI generate high converting landing pages? Not usually.

Can it optimize landing pages? Absolutely.

Improving landing pages relies on past campaign insights and testing – and that’s AI’s specialty.

AI offers major landing page support through:

Data-driven content creation
Generating versions to test
Planning layout and element placement
Multivariate testing
Personalized automation

AI can quickly generate multiple versions of core landing page elements, like headlines and calls-to-action, which can then be A/B tested to see which performs best.

The speed is an obvious benefit – but the real advantage is how it can base what it generates on highly relevant data. That is, analytics and pages from previous campaigns.

Traditionally, a copywriter might write two or three headline options for a landing page and test them sequentially. Now, AI can spit out 10 headline variations – and the options are even more relevant if you trained it on previous campaigns.

This gives you a larger pool to experiment with, or simply helps fight writer’s block.

You can manually do this with an LLM by compiling past landing pages in a doc, then uploading it to Claude or ChatGPT for context before requesting suggestions for your new landing page. Some advanced conversion platforms (like Unbounce and VWO) have integrated AI to facilitate this.

For example, VWO integrated OpenAI’s GPT-3.5 to suggest alternative copy for any element on your page with a click (VWO, 2024). Meaning, if you want to test a different value proposition in your hero section, AI can instantly generate options based on the context of your existing copy. This greatly speeds up the iteration cycle.

Beyond content creation, how else can AI practically help build landing pages?

Layout And Placement Recommendations

Emerging AI tools can analyze your copy user behavior (where people click, how far they scroll) and propose changes – such as moving a CTA button higher, or shortening the form if drop-off is high.

Some AI-driven landing page builders even claim to assemble entire page layouts optimized for conversion: they might select a template, arrange text blocks, and choose stock images that align with the content. This is still early-stage, but it’s a hint of where things are heading – AI not just writing copy, but helping design the page for you.

Automated Personalization

Landing pages can be dynamic, changing content based on the visitor (like their industry, or keywords they searched). AI (like Personyze) can manage this at scale, generating on-the-fly variations.

For example, if you have a landing page for a software product, AI could tailor the headline to each industry: a visitor from healthcare might see “Secure & Efficient Software for Healthcare Teams,” while a visitor from finance sees “Secure & Efficient Software for Finance Teams.”

Multivariate Testing

Humans might test two versions at a time, but AI can juggle many variables and combinations. There are AI-driven experimentation systems (like Optimizley) that will create numerous page variants (mixing and matching headlines, images, body text) and use algorithms to direct traffic optimally (multi-armed bandit approaches).

For instance, an AI might run 10 versions in parallel, detect early which ones are performing poorly, and automatically allocate more traffic to the winners to reach conclusions faster. This accelerates the optimization process and can uncover non-intuitive combinations that work well together.

Generating Microcopy Options

Those snippets on CTA buttons and testimonial excerpts can be the difference between a click. These elements are especially useful for A/B testing and multivariate testing.

For example, say a landing page was for a webinar. Instead of defaulting to “Sign Up Now,” AI generated copy variations could be like “Save My Seat” or “Claim Your Spot” or “Say Less – Sign Me Up!” which can then be tested.

Companies using AI for conversion optimization report gains like reduced cost-per-lead and faster time-to-optimization. In one case study, combining AI with human-led A/B testing saved over $1.3 million ([24]7.ai, 2024). The new efficiency improved conversion-lifting revenue and reduced labor in running tests.

In summary, AI helps landing pages by providing quick content generation, enabling extensive testing (including automated A/B tests), and leveraging data to optimize wording and layout. It takes a lot of the grunt work and some guesswork out of landing page optimization, making it more scientific and fast-paced.

However, AI is limited in deep audience understanding, context, and creative coherence – which are all critical elements to landing page success.

Here’s What Causes AI To Create A Bad Landing Page

Conversion is as much an art as a science – here’s how its AI’s shortcomings often show up in landing page creation:

AI has poor grasp on contextual relevance and messaging strategy. AI doesn’t inherently know the deeper context of your campaign or the psychology of your specific audience. It might generate a technically “correct” headline that sounds good, but misses the emotional core that would really drive your audience to act.

For instance, AI might propose “Improve Team Collaboration with Our Tool” as a headline because it sounds positive and includes a keyword. But maybe your audience cares less about “team collaboration” and more about “reducing email overload”. A human who knows the pain points might craft a very different, more resonant headline. AI won’t catch those subtleties without being explicitly told.

AI generated copy plays average with by staying safe and generic. High-converting landing pages hook users with a clever angle or story – something that stands out. AI can’t yet reliably produce truly creative, out-of-the-box angles.

It can do the same thing with layout and length. For example, AI might pull together a decent 1,000 word landing page for a low-ticket offer – but the human copywriter knows that much copy isn’t necessary or helpful for a small offer.

AI doesn’t have emotional intelligence (even though it can mimic it). Great landing pages often hit an emotion – excitement, urgency, FOMO (fear of missing out), relief, etc. AI lacks genuine empathy – it can’t put itself in the customer’s shoes the way a human marketer can after talking to real customers.

Without the skills to read the room of the target audience, AI generated copy often makes emotional language sound overdone, clunky, or manipulative.

AI won’t put unique selling propositions at the forefront. It won’t know what makes your offer special if you don’t tell it, and it certainly won’t put it front and center. Plus, if your offering has a nuance, AI might not emphasize it correctly.

For example, if your product is slightly more expensive than competitors but far more durable, the AI might mistakenly tout “affordable” in an attempt to use common selling points. A human strategist would avoid that angle and focus on durability/value. AI lacks judgment – it doesn’t know which selling points are actually differentiators versus which might misposition the product.

Doesn’t handle objections and trust elements as well. Good landing pages preempt customer objections (“Don’t worry, there’s a 30-day free trial”) and build trust (testimonials, guarantees, etc.).

An AI might not automatically include those, or if it does, it could fabricate a testimonial – which you obviously can’t use. Incorporating real social proof and handling likely questions often requires human insight.

Can optimize in the wrong direction. AI might find a variant that gets lots of clicks on the CTA – but those leads don’t convert down the funnel because the messaging attracts people with slightly wrong expectations.

For example, a high-ticket fitness coaching page might get the most sign-ups from leads who mistake it for an influencer’s offer — who then turn out to be the most likely to request refunds and leave bad reviews. Just because it “converted” well initially doesn’t mean it was well optimized.

A human needs to ensure the landing page conversion is also quality conversion – AI wouldn’t discern that nuance just from immediate metrics.

There’s also technical limitations. Sometimes AI suggests changes that aren’t practical due to design constraints, or it doesn’t fully grasp brand guidelines (like certain words or claims you avoid).

For instance, an AI might inadvertently use language that is off-tone or even risky compliance-wise (like “guaranteed results” when you legally shouldn’t guarantee). Without human control, these things slip through, and can cause major issues later.

AI lacks the deep audience insight, creativity, and fine-tuned persuasive touch needed to craft a high-converting landing page from scratch. It can generate and test variations, but it might optimize superficially (for clicks, not sentiment), and it won’t know how to truly sell your product to your audience in the most effective way.

That’s where human marketers and copywriters shine.

Human Copywriters Are The Architects Of Persuasion – AI Is Just A Power Tool

The human role is to devise the core messaging strategy: What is the main hook of this page? What pain are we solving, and what promise are we making?

Those decisions require understanding the target audience deeply, which comes from research, experience, and creativity. Once the strategy is set, humans can use AI to explore variations, but they have to curate which variations align with the strategy.

A copywriter will often craft the key message (or at least choose it from AI generated copy suggestions) and make sure the landing page copy follows a logical narrative.

This typically involves:

A strong headline and subheadline that grab attention and state a compelling benefit or unique offer.
A supporting paragraph or bullet points that expand on how the user benefits.
Social proof elements (testimonials, logos of clients) to build credibility.
A clear call-to-action that tells the user exactly what to do and what they’ll get.
Perhaps some additional sections like a brief “How it works” or a few sentences addressing common concerns or questions.

AI can help fill in some of these pieces, but a human will sequence them and edit them to flow naturally. For instance, a human might notice that an AI-generated bullet list is ordered poorly (maybe the most important point is buried last) – the copywriter will reorder and tweak wording so the strongest points hit first.

If the page is for a specific ad campaign, the copywriter usually writes a transition phrase to connect the ad to the landing page.

For example, if the ad is about productivity, the landing page might start with, “Looks like productivity’s on the top of your to-do list” followed by a headline that reinforces the message. AI wouldn’t know what ad or email the user came from unless it’s integrated into a system.

Humans also need to interpret test results and make the final call on informed adjustments.

If an AI test finds Variation B had a higher click rate, a human analyst will dig in:

Why might that be?
Did Variation B’s headline resonate more?
Did it appeal to a different emotion?
Are these clicks coming from the kind of people we want to continue down the funnel?

This deeper understanding then informs – not just that page – but broader marketing efforts. AI can show you what, but humans often figure out the why.

Moreover, human designers and copywriters ensure the page remains user-friendly. Maybe AI suggests a lot of text because it thinks more info is good, and lots of high-performing landing pages are long. But a human knows visually it’s too much and will trim to keep the page concise and scannable.

Also, humans will integrate visuals and multimedia – AI might not automatically say “include a video walkthrough here” if it only focuses on text. But a human team might know that a video demo significantly boosts conversion and design the page accordingly.

A case in point: a lead generation company might use AI to draft several versions of a landing page aimed at capturing webinar sign-ups.

The AI comes up with different headlines and bullet points. The marketing team reviews them and finds one AI headline “Join Our Webinar to Improve Your Marketing” is too bland, another “Triple Your Marketing ROI – Free Webinar” might draw more interest but feels a tad hype-y.
The human copywriter then crafts a refined headline: “Your Webinar Invite: How I Doubled My Marketing ROI In Half The Time (And How You Can Too).” It’s a blend of AI’s idea and human moderation. They pair it with a subhead that speaks to the specific audience: “For financial coaches marketing themselves, learn tactics to work smarter, not harder.” The AI wouldn’t know the audience is “financial coaches marketing themselves” unless told – the human infers that from context.
They let AI generate some bullet points for what the webinar covers, then edit those to be punchier and align with what the webinar truly will deliver (cross-checking with the presenter). For example, AI wrote “Learn about automation tools,” and the human edits to “The 3 automation tools that save you hours each week”. Now it’s benefit-driven. The designer on the team makes sure the sign-up form is prominent and that the page isn’t too cluttered; they may remove an AI-suggested extra section that wasn’t adding value.
After launching a multivariate test of a few versions (using AI to juggle them), the human team notices differences in lead quality. One version is getting lots of sign-ups with business emails (good leads), whereas another got many sign-ups but mostly personal emails (less qualified leads). They trace this back to the wording differences and realize one version sounded a bit “too good to be true,” attracting curiosity-seekers rather than serious prospects. That insight is something a human marketer catches – AI alone might have declared the latter version the winner just on sheer volume of sign-ups, not understanding lead quality.
The marketers then adjust the copy to better qualify the audience (perhaps adding “for financial coaches marketing themselves” in the headline or text to signal who it’s for, which might reduce sign-ups slightly but increase the proportion of quality leads).

To give a concrete outcome, let’s say initially the landing page converted 5% of visitors. After the AI-human collaboration and multiple rounds of tuning, the page converts 8% of visitors.

With the original landing page converting at 5%, 1,000 visitors would have resulted in 50 webinar attendees. If 10% of those attendees purchased a $5,000 coaching package, that’s $25,000 in revenue.

After the AI-human collaboration boosted the conversion rate to 8%, those same 1,000 visitors now generate 80 attendees—and at the same 10% purchase rate, $40,000 in revenue.

That’s a $15,000 increase just by refining AI generated copy with strategic human input.

In essence, a final optimized landing page is often the product of many iterations and insights that go beyond what AI could autonomously do.

Can AI Do Copywriting?

Yes. AI for optimization + Human for strategy = Higher conversions.

AI supports the process, but the vision and final optimization steps are driven by experienced (human) marketers. They get their audience deeply and can make the nuanced adjustments that turn ok copy into great copy.

Artificial intelligence creates incredible efficiency, the ability to scale content production, and data-driven optimization. But that’s not enough to make a high-converting landing page.

In every area – website copy, emails, product descriptions, landing pages – AI can support the writing process (generating drafts, suggesting variations, personalizing at scale), but it can’t yet replicate the judgment calls, context-driven strategic thinking, and emotional intelligence of a human copywriter.

Successful AI generated copy almost always has a human editor or strategist behind it. AI can do lots of heavy lifting, but humans provide the heart and direction.

As a marketer or copywriter, you remain the critical decision-maker who knows your audience and brand best.

Your clients can buy AI copywriting software, but they need your critical thinking to do quality control.

For many of these clients, they simply don’t know what they don’t know. They’ll spend thousands or tens of thousands on marketing software and AI… and feel defeated when it doesn’t help their conversions. Or when it helps their conversions, but not their revenue.

There’s a major opportunity for copywriters and marketers to bridge that gap.

AI won’t replace your intuition and insight. Instead, it augments your capabilities – freeing you from some tedious tasks so you can focus more on the creative and strategic parts of copywriting.

Those who learn to ride the AI wave (with guardrails) will greatly amplify their output and results, while those who rely on AI blindly or entirely could end up with off-brand, ineffective content.

Practical AI Starting Points For Copywriters

If you want to harness AI for your marketing copywriting (while maintaining high standards), here are some practical steps to get started:

Experiment With AI For First Drafts, Then Refine With Human Oversight

Pick a piece of content (say a blog intro or a product description) and use an AI tool to generate a draft. Treat that draft as a starting point – then edit it heavily. Add your brand voice, fix any inaccuracies, and inject creativity where needed.

This lets you gauge how much time AI can save you and where human touches are still needed. You might find that, even with heavy edits, the structure of AI generated copy still saves you time.

Over time, you’ll develop prompting techniques to get better first drafts from AI, but you should plan to always have a human revision stage. For example, you might use AI to generate 5 tagline ideas for a campaign, then you shortlist the best and tweak them to perfection.

Establish Clear Quality Control Processes To Maintain Brand Voice And Accuracy

Put in place a review checklist for any AI-generated or AI-assisted content before it goes live. This checklist can include:

Voice/tone check (does it sound like us?)
Fact check (are all claims and figures verified?)
Compliance/legal check (are we honoring claims/privacy laws?)
Plagiarism check (are we unintentionally copying another source?)

Essentially, treat AI content with the same rigor as content from a junior copywriter – it might be mostly fine, but nothing should bypass editorial review. The goal is to create a safety net that catches mistakes or off-brand elements in AI output. This might also involve having a designated editor or experienced writer give final approval on AI-involved copy until you’re confident in the consistency.

Identify Which Parts Of Your Copywriting Process Are Repetitive Or Formulaic

Those are great candidates for AI assistance.

For example, generating meta descriptions, populating product specs, formulating A/B test options, creating outlines, or personalizing email greetings. Automate or semi-automate those with AI to free up your time.

Identify Which Tasks Are AI-Resistant

When it comes to brainstorming campaign concepts, crafting your brand story, or developing key messaging, bring in the humans.

You can then use AI generated copy to flesh out the execution once the core idea is set. Think of it like this: a human should outline what needs to be communicated (and how), and AI can help fill in the blanks or suggest improvements.

AI is only intimidating if you try overhauling everything at once. You don’t need to do that – and you shouldn’t.

Thankfully, even a small, low risk step can give you a lot of momentum.

The rise of AI in copywriting isn’t a threat to human copywriters – it’s a call to evolve our roles. Copywriters who effectively manage and collaborate with AI will likely replace those who don’t.

They’ll be the ones who can output twice the work, maintain stellar quality, and adapt quickly to marketing needs. Rather than feel like AI is out to get you, add it to your toolkit.

Keep your copywriting fundamentals strong – your understanding of audiences, your creativity, your storytelling prowess – and let AI take care of the grunt work and data-driven tweaks.

Who Can Help Me Figure Out What Kind Of AI-Generated Copy Is Worth Using?

I build a content strategy that uses AI-generated copy where it fits, and avoids it where it doesn’t. The goal: AI visibility for your website that leads to bookings. Start with a consult call.

Types of AI Generated Copy | 2025 Guide

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