The history of technology is full of examples of people mistaking a new tool for a new set of rules.
Take email.
When it arrived, the cost of sending a message effectively disappeared overnight. Marketers rejoiced. Printing, envelopes and postage became relics of a slower age. Suddenly every prospect was just a click away.
The opportunity was real. So was the problem.
Within a few years, inboxes were overflowing with unsolicited messages. The word “spam” no longer referred to canned beef, and legislation struggled to catch up. The organisations that survived weren’t necessarily the ones sending the most emails. They were the ones that remembered a simple principle: people’s attention has value.
Generative AI presents a remarkably similar moment.
The technology is extraordinary. Images can be created in seconds. Entire campaigns can be visualised before the first creative brief is approved. New models appear with dizzying frequency.
Yet amid the excitement, some fundamental content management principles risk being forgotten.
Provenance Matters
Much of the discussion around AI-generated content focuses on authenticity. But provenance is equally important.
Where did this content come from?
That question has always mattered, whether an asset was commissioned from a photographer, supplied by an influencer or generated by an AI model.
The legal environment surrounding generative AI remains unsettled and continues to evolve. Organisations need to know not only that content was AI-generated, but also which model generated it.
Could you identify every asset created by a specific model if future legislation required you to?
Could you isolate content generated by a particular version of that model?
Only if you’ve recorded where it came from.
Provenance isn’t administration. It’s risk management.
Rights Still exist.
Another misconception is that AI-generated content somehow exists outside traditional rights management.
It doesn’t.
Images have always contained layers of rights. Securing the creator’s rights does not automatically grant unrestricted usage rights.
Most marketers understand this when dealing with photography. A photograph containing a recognisable person, brand, product or building may require additional permissions before it can be used commercially.
The same considerations apply to AI-generated content.
A generated image may still depict recognisable intellectual property, branded elements or protected likenesses. The technology used to create the image does not remove the responsibility to understand how it can be used.
Slop Is the New Spam
Every technological revolution creates its own form of clutter.
Low-effort, mass-produced AI-generated content is rapidly filling libraries, archives and digital asset management systems around the world.
The challenge isn’t creation. It’s curation.
When content becomes effortless to generate, organisations often produce far more than they can realistically review, manage or use. Valuable assets become harder to find because they are buried beneath thousands of alternatives.
Storage may be cheap. Attention is not.
Without clear governance around how AI-generated content is reviewed, approved and archived, organisations risk overwhelming their own content ecosystems.
Looking Beyond the Technology
At Greatstock, we’ve spent over forty years helping organisations manage content through waves of change.
Every innovation changed the tools. None changed the principles.
Generative AI is no different.
The organisations that will benefit most won’t necessarily be the ones generating the most content. They’ll be the ones that understand where content comes from, recognise the rights attached to it, and maintain disciplined curation standards.
Because while technology never stops evolving, good content management has always been built on the same founding principles.
