The Internet Is Filling With AI Slop. Your Company Might Be Too.

AI Slop Is Everywhere. The Fix Is Not “Better AI”, It’s Better Provenance.

You know what’s changed in the last year or two? It’s not just that AI can make content, it’s that it can make plausible content at industrial scale, and the incentives online reward whatever gets clicks and shares.

That might be harmless entertainment, but it becomes a serious issue the moment it influences business decisions, customer conversations, internal comms, or even what teams treat as “true” when they are moving fast.

And it’s not just an internet problem. The bigger risk is what happens when this same dynamic hits the workplace: AI assistants confidently summarising, recommending, and drafting based on messy inputs. If the knowledge behind the answer is duplicated, outdated, contradictory, or ownerless, the output will look polished and still be wrong.

At that point, the real question is not “how smart is the model?”, it’s “can we trace where this came from, and should we trust it?”

And it’s not hypothetical. We are seeing a steady flood of low-quality, AI-generated material across the internet, and even the platforms admit it’s a growing concern. The incentives are obvious: more content, more engagement, more ad money. But the second-order effect is the bit that matters to businesses:

• attention gets diluted

• trust gets eroded, and

• verification becomes everyone’s unpaid job.

Now here’s the uncomfortable part: most organisations already have their own version of “AI slop”, and it has nothing to do with viral videos. It’s the internal knowledge estate. Duplicate SOPs. Conflicting policy PDFs. Old slide decks treated as gospel. “Final_v7” documents in three different folders. Content with no owner and no review date. If you point a copilot or RAG system at that, it will happily give you an answer that sounds right, and be completely wrong.

That’s not an isolated mess. That’s the system your business is making decisions on.

The real damage isn’t just people being fooled. It’s the business cost of uncertainty.

If you’re watching short videos for a laugh, your bar is basically “was it entertaining?” In a business context the bar is higher: “is this true, can I evidence it, and can I act on it?” The moment synthetic, low-quality content gets used to inform decisions, training, policies, customer responses, or brand comms, it stops being harmless noise and becomes operational risk.

And then comes the verification tax.

Every “is this real?” check takes time and attention. Do that ten times a day and people keep checking. Do it a hundred times a day and people start cutting corners, forwarding things “just in case”, or trusting whatever looks polished. Over time you end up with a shrugging culture: “who knows anymore”, which is exactly how errors quietly become normal.

Zoom out and it gets darker.

This is not only about silly viral posts. The same tooling can be used for harassment and abuse, including non-consensual sexualised image manipulation. X and Grok have faced intense regulatory and public scrutiny following reports around “nudification” and sexualised deepfake imagery, including concerns relating to minors.

So what’s the fix: detection, labels, or provenance?

Detection alone is turning into an arms race. The fakes keep getting better. That’s why a lot of serious people are shifting from “spot the fake” to “prove what’s real”.

There are real efforts here:

• C2PA (the Coalition for Content Provenance and Authenticity) publishes an open standard for attaching tamper-evident provenance information to media, so you can see origin and edit history.

• The Content Authenticity Initiative promotes adoption of those Content Credentials across the ecosystem.

• Companies like OpenOrigins are building “prove what’s real” infrastructure instead of playing whack-a-mole with detection.

• Platforms are also adding labels and disclosure requirements, like Meta’s AI labeling approach and YouTube’s altered/synthetic disclosure flow.

None of this is perfect. Labels can be missed, ignored, or misunderstood. Standards take time to become universal. But directionally, provenance is the only strategy that scales: you can’t rely on everyone becoming a trained forensic analyst.

Now the uncomfortable bit: your company has its own “AI slop” problem

Here’s where this stops being a social media rant and starts being a business problem.

Inside most organisations, the “slop” isn’t weird fake pictures. It’s:

• Outdated SOPs that sound official

• Contradictory policy docs in different folders

• Old slide decks being treated like current truth

• Knowledge bases with no owners, no review cycles, broken links, and duplicate “final_final_v7” documents

And then we roll out copilots, RAG, or internal assistants and act surprised when they confidently quote the wrong version. That’s not a model failure. That’s a governance failure.

This is exactly what index exists to fix.

We scan your knowledge estate, measure knowledge health, and surface the things that make both humans and AI stumble: duplicates, contradictions, ROT, broken links, missing ownership, stale content, and “source-of-truth” conflicts across repositories. Then we move from insight to action with governed remediation workflows.

And we’re very deliberate about how remediation happens: we bring together the best of tech and human expertise. Tech speeds up what used to take months. Knowledge Management specialists support the process. And we work closely with the people who actually use the knowledge day to day. We use AI and automation to recommend and execute the safe improvements, but we keep humans in the loop so every meaningful change is reviewed, accountable, and genuinely in users’ best interest.

Because the goal is not “let AI rewrite your business”. The goal is “make your business knowledge trustworthy enough that AI stops making expensive mistakes”.

The last word

AI slop is not going away. The incentive structure is too strong, and creation is too cheap.

So the winning move is boring but powerful:

• provenance

• governance, and

• human accountability

In public feeds, that means knowing where something came from before you believe it. In enterprise, it means cleaning and governing your knowledge before you let AI speak on your behalf.

If you want to see what your organisation’s “AI slop” looks like internally, and what it would take to turn it into a real source-of-truth, get in touch. That’s what index does: clean knowledge in, trusted answers out.