A colleague and I each used AI to write a blog post recently. We hadn’t talked about it, hadn’t shared notes, hadn’t seen each other’s work in progress.

We got back nearly identical posts.

Not “similar in theme.” Nearly identical: same structure, same points, same phrasing in places. Two different people, same tool, same basic prompt, same generic result.

And honestly, I wasn’t that surprised.

I’ve been using AI extensively in my marketing work, and one of the biggest things I’ve learned is that the quality of what you get out is directly tied to the quality of what you put in.

The more I use AI, the more convinced I am that the biggest mistake marketers can make isn’t using too much AI. It’s using it without giving it enough of themselves.

Why This Happens

AI tools are trained to produce the statistically likely answer to a prompt. If two people give the tool similar, generic instructions, they get a similar, generic output; the tool has no reason to do anything else.

It doesn’t know your voice, your specific clients, your actual experience, the mistakes you’ve made, the lessons you’ve learned, or the opinions you’ve developed through doing the work.

Left without that input, it defaults to the most average version of the answer.

That’s the real risk of AI content; not that it’s necessarily bad, but that it’s interchangeable.

And interchangeable content doesn’t just read as generic. It can, apparently, collide with someone else’s “unique” post word-for-word.

This Happen With More Than Blog Posts

The same thing has happened to me with video scripts.

When I ask AI to write a script about a broad marketing topic without giving it much context, I can usually predict what I’m going to get: a strong hook, three or four familiar talking points, a conclusion, and language that sounds perfectly polished but could have come from almost any marketer.

There’s nothing technically wrong with it.

There’s just nothing particularly mine about it.

I’ve found that the AI results become dramatically better when I first give AI my own perspective. Tell the tool what I’ve actually seen with clients, what I believe, what surprised me, what I disagree with, and what I would tell someone based on my experience.

Then I’m not asking AI to come up with my perspective.

I’m asking it to help me articulate my perspective.

That distinction is huge.

An Example of Unique AI Content from My Blog

One example is a blog post I wrote about why a $100,000 website might not generate a single lead.

Instead of asking AI something like, “Write me a blog post about why expensive websites don’t generate leads,” I fed it the actual details behind the post.

I gave it my experience. The patterns I’ve seen. The mistakes companies make. The lessons I’ve learned from working on websites and lead generation. The specific points I wanted to make.

Then I edited the output to make sure it actually reflected what I believe and how I communicate.

The result was much stronger because AI wasn’t inventing the expertise.

I supplied the expertise. AI helped me turn it into content.

That’s increasingly how I think about AI-assisted marketing.

The tool shouldn’t be responsible for bringing the insight to the table.

You should

What This Means for Your Content

If you’re using AI to help with blogs, emails, video scripts, or social posts, genericness isn’t just a quality problem; it’s a differentiation problem.

And now, potentially, it’s a duplication problem, too.

Google doesn’t need another version of the same article that already exists 10,000 times online. Your audience doesn’t either.

And readers can tell when something sounds like it could have been written by, and for, anyone.

But more importantly, your content is one of the few places where your actual experience can become an asset.

The client problem you solved.

The campaign that completely failed.

The number that surprised you.

The opinion you’ve developed after doing something 50 times.

The mistake you wouldn’t make again.

The thing everyone in your industry says that you don’t actually agree with.

Those are the things AI can’t authentically manufacture on your behalf.

The Fix Isn’t “Don’t Use AI.” It’s “Don’t Use It Without Feeding It You.”

AI is a genuinely useful tool for marketing.

I use it for messaging drafts, reporting, brainstorming, first-pass email copy, content development, research organization, and plenty of other things.

But I’ve found that the best results come when AI has something specific to work with.

Give it the raw material.

Give it your stories.

Give it your numbers.

Give it your opinions.

Give it the weird detail that happened to you that no generic AI prompt would ever produce.

Edit and Personalize the AI Output

Then challenge the output.

Edit it. Push back on it. Tell it what’s wrong. Add the things it missed. Remove the things that don’t sound like you.

The goal shouldn’t be to have AI create something for you.

It should be to use AI to help you create something you couldn’t have created as quickly on your own.

There’s a big difference.

The Takeaway

AI isn’t the risk; genericness is.

The marketers who get the most value from AI won’t necessarily be the ones who write the most prompts or automate the most content.

They’ll be the ones who understand that their competitive advantage isn’t the AI itself.

It’s the experience, perspective, judgment, and point of view they bring to it.

Because everyone has access to the same tools now.

The differentiator is what you put into them.

And the fastest way to make sure your content doesn’t accidentally become someone else’s is to make sure it couldn’t have come from anyone but you in the first place.