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You can edit it, crop it, rewrite it and make it your own—but what invisible AI information came along for the ride?
You generate an AI image. You edit it, crop it, add your own work, rename it and perhaps convert it to another format. Or you use AI to help create an article, audio clip or video and then substantially transform the result.
You see the finished content.
But you may not see everything that’s travelling with it.
AI companies are developing and deploying provenance technologies – including metadata, content credentials, digital signatures and invisible watermarking – designed to provide clues about where content originated and whether AI was involved.
There’s a strong argument for this. As synthetic images, voices, text and video become increasingly convincing, knowing where something came from can provide valuable transparency.
But things get considerably more complicated once humans start editing AI output.
Should you be able to control what’s invisibly embedded in content you own? What happens when AI created 10% and a human created the other 90%? And does calling something “AI-generated” tell us anything meaningful when AI might have done everything—or merely corrected three typos?
Those aren’t abstract questions anymore.
Let’s look at this strange new layer of digital content from three angles:
- What’s hiding inside your files,
- Do you control it,
- Is, “AI-generated” even relevant anymore?
Your AI Content May Be Carrying a Secret Passport
Images, audio, video—and eventually more text—can carry invisible clues about where they came from. Marketers should know what’s traveling with their content.
When you download an AI-generated image, audio clip or other asset, what you see isn’t necessarily everything you get.
A growing number of AI companies and technology organizations are developing provenance systems designed to provide information about where digital content originated and, in some cases, what happened to it afterward. These can include metadata, cryptographically signed credentials and invisible watermarking technologies.
Think of it as a digital passport for content.
Google, for example, uses SynthID to watermark and identify AI-generated content across multiple formats. The broader industry is also adopting standards such as C2PA, which can attach signed information about an asset’s origin and editing history.
Why bother?
Because we’re entering a world where a realistic photograph, voice recording or video might never have happened. Provenance could help platforms, businesses and consumers distinguish authentic material from synthetic or manipulated media.
That’s the good part.
But marketers should understand what this means for everyday content creation, too.
A little story: I bought a product off Facebook because it was ‘seemingly’ endorsed by a celebrity I trust. She later appeared on the same platform to declare that the vendor had used an AI image of her, and she certainly did not endorse it. More fool me, and I was only hurt in the pocket (and pride), but there have been financial AI identity scams with more serious consequences.
Your File Is More Than What You Can See
A JPEG, PDF, audio file or document can contain information invisible when you simply open it. Depending on the format and tools involved, that might include ordinary metadata such as software information, timestamps and editing details, or newer provenance credentials.
And not every type of provenance works the same way. Some information lives in file metadata. Some uses cryptographic credentials. Some watermarking is embedded into the content itself.
That distinction matters because editing, resizing, screenshotting, converting or reposting a file may affect different signals differently.
So add a new question to your publishing checklist:
What’s traveling with this file?
You don’t need to become a digital-forensics expert.
But if AI is becoming part of your marketing production process, understanding the invisible layer of your content may soon become as normal as checking image rights or proofreading your copy.
Because increasingly, your content doesn’t just contain a message.
It may contain a history.
You Wrote It. You Edited It. You Own It. But Do You Control What’s Hidden Inside It?
AI provenance can help us understand where digital content came from. But it also raises a surprisingly tricky question: Who gets to control the invisible information attached to your work?
Let’s say you ask AI to create an image.
Then you crop it, add your own photography, change the background, replace several elements, add typography and spend another two hours turning it into an advertisement.
At that point, who created the finished image?
That’s complicated enough. But here’s an even more interesting question:
Who gets to decide what invisible information travels with it?
There are good reasons for AI provenance. We’re entering an era of remarkably convincing AI-generated images, cloned voices and synthetic video. Being able to trace where digital content came from could help distinguish genuine material from something that was generated or manipulated.
That’s valuable.
But there’s another side to the argument that marketers and creators should be thinking about:
If it’s your finished content, how much control should you have over what’s hidden inside it?
Transparency and Control Can Collide
Businesses have dealt with invisible file information for years. Photos and documents can contain metadata about things such as the software used to create them, when they were created and, in some cases, where they were created.
There can be perfectly legitimate reasons to remove some of that information before publishing a file, including privacy, security and simply cleaning up unnecessary data.
AI provenance adds a new wrinkle.
Suppose AI created 10% of an image and you created or substantially changed the other 90%.
Should the finished image forever be identified simply as AI-generated?
What if AI supplied the original image, but after hours of editing almost nothing from that version remains?
Or what if you want to preserve useful information about where an asset originated while removing unrelated metadata you don’t want publicly attached?
Suddenly, a seemingly simple idea—“Let’s identify AI content”—isn’t quite so simple.
And there may not always be one correct answer.
Marketers Need to Decide Their Own Rules
This is why businesses using generative AI should start thinking about their policies before a problem forces them to.
When will you disclose that AI was used? What provenance information will you preserve? What ordinary metadata will you remove for privacy? How will you handle AI-assisted work created by employees or freelancers? Will you keep original files so you have your own record of how important content was created?
And here’s perhaps the most important distinction:
Provenance isn’t necessarily proof of authorship.
Knowing that AI was involved somewhere along the way doesn’t tell you who came up with the idea, conducted the research, made the important creative decisions, spent hours editing the result or ultimately created the finished work.
That’s why the usual question—
“Can we tell whether AI was involved?”
—may eventually give way to a much more interesting one:
“If I created the finished work, how much control should I have over the invisible information that travels with it?”
And that leads directly to the next question in this series:
When we say something is “AI-generated,” what do we actually mean?
Stop Asking “Was AI Used?” Ask “What Did the Human Contribute?”
The label “AI-generated” is becoming increasingly useless because it can describe completely different kinds of creative work.
Imagine two marketers.
Marketer A types:
“Write me a 1,000-word article about email marketing.”
AI produces it. She copies it, pastes it into WordPress and hits Publish.
Marketer B spends three days researching a subject, interviews two experts, develops an original argument and writes the article. Then she asks AI to suggest five alternative headlines and proofread the finished draft.
Both marketers used AI.
Did they create equivalent AI content?
Obviously not.
And that’s the problem with our increasingly binary conversation about AI-generated versus human-generated work.
We Need a Better Question
AI can participate at almost every point in creation: brainstorming, research, outlining, transcription, drafting, image generation, editing, fact-checking, formatting, translation and proofreading.
The mere presence of AI tells you remarkably little about how much human thinking went into the finished product.
So instead of asking only “Was AI used?”, ask:
- Where was AI used?
- What did it contribute?
- What did the human contribute?
- Who made the important creative decisions?
- Was the finished work independently checked?
Those questions tell you considerably more.
Here’s the Bigger Opportunity for Marketers
As AI-generated material becomes plentiful, human contribution itself can become part of your positioning.
Conduct the experiment yourself. Interview the customer. Collect original data. Tell the story only you experienced. Develop an opinion. Test the advice before publishing it.
Then use AI wherever it genuinely makes you faster or better.
The goal doesn’t need to be 100% human or 100% AI.
It should be 100% worth consuming.
That’s why I suspect the phrase “AI-generated content” will eventually become inadequate. The future is likely to contain a huge spectrum—from one-click machine output to deeply original human work that happened to use AI somewhere along the way.
And when everybody has access to essentially the same AI tools, the competitive advantage won’t simply be knowing how to use them.
It will be having something worth contributing before you turn the AI on.
Metadata vs. Watermark vs. Content Credential: They’re Not the Same Thing
It’s easy to lump all of these technologies together, but they do different jobs.
Metadata: Information stored with a digital file, such as creation details, software information, descriptions or other file data.
Content credential: Cryptographically signed provenance information that can record details about an asset’s origin and editing history.
Watermark: A signal deliberately incorporated into content that may be visible or invisible and, depending on the technology, may be designed to survive some editing or transformation.
AI detector: Software that analyzes content and infers whether it may have been AI-generated.
The important distinction: A detector makes an assessment. A provenance system provides evidence or signals about origin or history.
Those are very different things.
“The 10% Problem”
AI generates an image. You replace the background, add your own photography, change the colors, redraw portions and add original typography.
At what point does it stop being an “AI-generated image”?
90% AI / 10% human? Easy enough.
50% / 50%? Hmm.
10% AI / 90% human? Now the label starts feeling inadequate.
There is no universal percentage at which authorship magically changes. That’s why simply labeling something “AI-generated” can conceal more than it explains.
My Own Stance on Using AI
I freely admit that I use a mix of my own written content, plus AI generated and non-AI PLR content, because I don’t have the expertise capacity to keep my readers provided with up to date information about the fast changing online business industry.
You can read my full description of why and how I use AI in my business here.
99% of my social media images are AI-generated, because I don’t have an ounce of design talent in my bones. Even assisted by the excellent platform Canva.
But as I conscientously flip on the AI-generated button for each social media platform, I’m a little resentful about the time I spent on the text that accompanies the posts.
