There’s a persistent fantasy about AI and content creation: that you type a single prompt and get the perfect landing page copy, the perfect ad campaign and the perfect headline. That moment of algorithmic intuition when the machine “just gets it.”
Pray that this time doesn’t come any time soon, because then we’re all fired.
The reality is that creating great content with AI follows almost exactly the same process we’d used before AI. The major difference is that now, we have someone else to do the labour-intensive parts.
AI can find competitors in seconds, summarize research I’d otherwise spend days on, and edit a version of my idea that looks surprisingly complete.
What it can’t do is decide what matters. If my strategy is vague or my positioning unfinished, it will simply mirror that confusion back to me — only more elegantly.
What AI Can and Can’t Do
AI can do the work I don’t enjoy: the searching, sorting, and structuring. And so much better than me. It can reveal patterns, organize information, and help me see what I’ve missed.
It can surprise me with a smart angle I hadn’t considered or a gap in my argument I hadn’t noticed.
But it can’t replace the part where I decide what actually matters. It won’t invent the missing value proposition I never defined. It won’t supply an original point of view if I haven’t bothered to form one. And it won’t build a process for me if I never had one.
The Familiar Process, Accelerated
When you strip it down, the process hasn’t really changed.
Step 1: Research
Before, I’d lose hours scrolling through competitor websites, taking screenshots and notes.
Now, I can ask AI to find competitors and compare their messaging, features, and positioning in a table.
Ten minutes later, I’m looking at an overview of the market and instead of wasting focus on gathering information, I can spend it interpreting what I see.
Prompt:
“List the main competitors for [product name] in [industry]: [website X], [website Y]. Summarize their positioning, target audiences, key messaging and differentiators in a table. Identify messaging gaps that [product] could use to stand out.”
Don’t forget to enable Deep Research if you’re using ChatGPT.
Step 2: Filling the Blanks
The handover from a technical team sometimes comes in the form of a feature list: precise, factual, and completely detached from how anyone would actually use the product. It tells what the technology does, but not why it matters, what problems it solves, or in which situations it becomes valuable.
Before AI, I had to dig deep to figure out what’s missing and how I can somehow fill the gaps.
Now, I can ask AI to explain how those features work, what problem they solve, and what outcome they create for the customer. It helps me see what’s missing, not just what’s there. And once I know what’s missing, my next conversation with the technical team has a clear direction.
Prompt:
“Explain what each of these features does for the customer. What problem does it solve, what outcome does it create, and why might that matter in their day-to-day work?”
Step 3: Structuring the Page
Before, I relied on a mix of instinct and experience, maybe an old layout that worked once, or a few templates I’d adjust for the next project.
Now, AI can draft a structure based on proven copywriting frameworks embedded in a custom GPT. Sometimes I use it in reverse, as a diagnostic tool: to look at a page I’ve already written and point out what’s missing. It can analyze flow, hierarchy, and clarity, and suggest where the argument weakens or the message loses focus. A kind of structural audit for my own copy.
Get a score on your page copy and suggestions for improvements here.
Step 4: Editing
Editing remains the most human part of the process — the one that separates the generic, read-them-a-hundred-times pieces from the ones that actually spread.
AI-generated text often reads as competent but hollow. It knows the form, not the intent. The sentences line up neatly, but the argument underneath is thin. This is where judgment and specificity matter most, where I connect the words to the product’s reality, the customer’s context, the company’s point of view, or my own experience.
Most of the clichés that appear in AI drafts don’t come from the model itself; they come from vague thinking in the prompt. The model can’t invent depth that isn’t there. When I refine an AI draft, I’m not just polishing its style. I look at every sentence and ask: Is this exactly what I want to say, or just an averaged version of it?
That averaging is the essence of AI’s “voice.” It doesn’t think, it predicts. It builds each next word by calculating what’s statistically most likely to appear, given everything it’s read before. The result is what I call flattening: language that’s factually correct but emotionally weightless. It sounds like everyone and no one at once.
Sometimes, this happens because the model is overfitting to consensus — repeating the safest phrasing that appears most often across its training data. The same phrases rise to the top not because they’re the best, but because they’re the most common. That’s why so much AI-generated writing feels strangely familiar: it’s a mirror of the average internet sentence.
Human writing, on the other hand, thrives on friction — the tension of a thought that tells a whole story already, the odd phrasing that reveals a mind behind the words, the one single paragraph that seems to read your mind. Specificity gives a text its texture. It’s what makes a reader pause and think, yes, that’s exactly how it feels.
At this stage, I either create multiple versions of the copy through different angles — outcome-led, benefit-led, feature-led (if it’s products content). Or I work with the AI on every single paragraph one by one.
I’ll push AI to explore each one separately, then read through the drafts, mixing and matching the best fragments. A paragraph from one version, a line from another. And then my edits. It sounds like a lot of work, and it is.
But it helps me do better work than I could have done alone.
What AI Really Changes
AI accelerates everything around those steps but never replaces them.
The big shift is that now, the distance between a vague idea and a tangible draft is almost zero. That sounds like a creative dream, but it also means our weaknesses appear faster. When the writing comes easily, the thinking has nowhere to hide.
The people and teams that get the most out of AI aren’t the ones chasing shortcuts. They’re the ones who already had a working system — the people who understand what good looks like and use AI to get there faster.
AI amplifies what’s already there: clarity, discipline, and curiosity. Without those, it only multiplies the noise.
The secret to creating great content with AI is the same as it was before AI: know what you’re trying to say. Everything else — the research, the drafts, the editing is just faster now.
