How can you tell if information is AI-generated or real?

Often you can't tell from the writing alone, because AI text can sound polished and confident. Look instead at the source. Can you trace the claim to a named person, original data or a document you can check? If nobody stands behind it and you can't verify it, treat it as unconfirmed until you can.

Why is AI slop a risk for executives?

Executives make decisions based on reports, market research, vendor claims and summaries. If more of that is generated quickly without real checking, wrong information can look just as credible as right information. The risk is a confident decision built on a number or claim that nobody actually confirmed.

How should a company verify AI-generated reports?

Require sources for every key number and claim, and have someone check the most important ones against the original. Label which parts of a report were AI-assisted. Keep a named person accountable for each report's accuracy. The bigger the decision it supports, the more of it should be checked by hand.

Should we ban AI-generated content internally?

Usually not. AI can save real time on drafts, summaries and research. The better approach is to set standards: what must be sourced, what must be checked by a person and who signs off. Banning it tends to push use underground, where you have even less visibility into what's real.

AI Slop Makes It Hard to Tell What's Real. How Executives Should Decide What to Trust

AI is flooding inboxes, reports and feeds with content that looks credible. Here's how executives can set standards for what information to trust.

AI slop makes it hard to tell what's real because AI can produce polished, confident content faster than anyone can check it. For executives, the bigger risk isn't your own team's output. It's the information coming in that drives your decisions. The answer is simple standards: know where a claim came from, who stands behind it and how much checking the decision deserves.

A technology leader I talked with recently described the feeling well. AI is flooding the zone with all of this slop, and you can't tell what's real and what's not.

I've felt that too. And I think it's going to get worse before it gets better.

The problem has moved upstream

Most of the conversation about AI slop is about output: the generic blog post, the email that sounds like a robot wrote it, the deck that says nothing.

That matters. But executives face a quieter version of the problem. The information they rely on is changing too:

Market research that reads well and cites nothing.
Vendor claims with impressive numbers no one can trace.
Internal summaries where nobody checked the source material.

When it all looks equally polished, the usual signals of quality stop working.

Polish is no longer proof

For most of my career, a well-written, well-formatted document was a reasonable sign that someone had done the work. Sloppy work usually looked sloppy.

That shortcut is gone. AI makes everything look finished. A report built on a misread number can look exactly as credible as one built on careful analysis.

So executives need a different test. Not "Does this look right?" but "Where did this come from, and who stands behind it?"

Three questions before you act on anything

Before a claim shapes a real decision, ask:

What's the source? Can it be traced to original data, a document or a named person?
Who checked it? Did a human with the right knowledge confirm the key points?
What happens if it's wrong? The higher the cost of being wrong, the more checking it deserves.

These aren't new questions. Good leaders have always asked them. The difference is that you can no longer skip them because something looks professional.

The same goes for what you send out. If your own team publishes content that can't be traced to a source, you're adding to the noise your buyers are trying to filter. The companies that stay trusted will be the ones whose claims hold up when someone checks.

Set standards, not bans

On the one hand, it's tempting to ban AI-generated content internally and be done with it. On the other hand, your people will use it anyway, and you'll lose sight of where it's being used.

Where I land: set clear standards. Every key number in a decision document needs a source. AI-assisted sections get labeled. Each report has a named owner who's accountable for its accuracy. And the bigger the decision, the more of it a human checks by hand.

A worked example

Say a team brings you a market-sizing summary to justify entering a new segment. It's well written and full of numbers.

Under the old habit, you'd skim it and approve. Under the new standard, you ask three things. Which numbers are sourced, and from where? Who on the team checked the top three figures against the originals? And if the market turns out half this size, what does that cost us?

Maybe the summary holds up. Maybe one key number traces back to nothing. Either way, you decide with your eyes open.

Signs the slop is already reaching your desk

Reports arrive faster than ever, but nobody can say where the key numbers came from.
Vendor pitches cite impressive figures with no source attached.
Two teams bring you summaries of the same thing that don't agree.

Who should own this

Information standards need an owner, or every team sets its own bar. Name one executive, often the COO or CFO, to own what "verified" means for decision documents. Give them the authority to send a report back when the sources aren't there, and a regular check-in with the leadership team. With one owner, the standard stays consistent. Without one, it erodes the first time someone's in a hurry.

Common mistakes

Trusting tone. Confidence is cheap now.
Checking everything equally. Focus the checking on what drives big decisions.
Leaving it to individuals. Without a shared standard, everyone sets their own bar.

What to do this week

Pick the last major decision your team made. Trace its three most important numbers back to their sources. If any can't be traced, you've found where your standards need to start.

Build a standard your team can follow

If you're not sure how much of what reaches your desk has been checked, a 15-minute AI roadmap is a quick way to see where your information standards need tightening.

Ready to close your AI readiness gap? AIReady Executive works directly with C-suite leaders on a structured advisory engagement that turns AI pressure into clear decisions, ownership and a plan the board can follow. Book an Assessment.