AI Fact-Check: How Business Leaders Can Avoid Getting Fooled by AI-Generated Data

The Mystery Begins: A Simple Search for a Statistic

Imagine you're preparing an important report—analyzing market trends, forecasting financial performance, or researching a competitor. You turn to AI for quick insights, expecting reliable data. Instead, you get numbers that look convincing yet are completely made up.

This isn’t science fiction. It’s happening right now.

AI hallucinations—when AI generates incorrect or misleading information with full confidence—pose a growing risk in business decision-making. Unlike human analysts who cite sources and verify findings, AI sometimes fabricates statistics, misattributes sources, or misinterprets data.

I saw this firsthand.

Chasing a Statistic: How AI Gave Me a False Number

I needed a supporting statistic for a claim about how technology helps optimize orchard management. Specifically, I wanted to highlight how real-time data collection improves crop yields and decision-making for growers.

So, I turned to Perplexity AI and entered my prompt:

"Technology collects real-time data on harvest progress, fruit quality, and orchard conditions. This data can provide growers with valuable insights for optimizing orchard management, improving crop yields, and making data-driven decisions. Discuss the implications if this is not implemented, with supporting empirical evidence."

Perplexity quickly provided a confident response:

"Failure to implement data-driven systems results in suboptimal resource allocation and reduced yields. Farmers who don't utilize precision agriculture technologies experience up to 70% lower production efficiency compared to those who do [1]."

That 70% figure seemed extreme. A 70% drop in efficiency just because a farm wasn’t using precision agriculture? That was a bold claim.

Naturally, I wanted to verify it.

Following the Footnotes: A Suspicious Source

I clicked on reference [1], which Perplexity provided as evidence:

Smart Sensors and Smart Data for Precision Agriculture: A Review https://pmc.ncbi.nlm.nih.gov/articles/PMC11053448/#abstract1

I scrolled through the paper, looking for anything that mentioned a 70% reduction in production efficiency. Instead, I found this:

"... The humidity sensor ... tracks humidity levels and activates exhaust fans if humidity exceeds 70% ... Soil moisture is managed by the YL-69 soil moisture sensor, prompting water pump activation when soil moisture drops below 70%."

Wait a second. What did this have to do with a 70% loss in production efficiency?

Strike One. The AI had completely misinterpreted the study’s results and tried to pass off a sensor’s operational threshold as a broad claim about orchard productivity. That’s like saying:

“A car’s engine runs at 70°C, so if you don’t have an engine, your travel efficiency drops by 70%.”

This was not the empirical evidence I was looking for.

Confronting Perplexity: The Citation Evades Me

At this point, I wasn’t about to let this AI off the hook. If it was so confident in this 70% efficiency loss, surely it could give me a real source.

So, I asked it directly:

"Cite the supporting URL source."

Perplexity’s response?

"...I apologize, but I am not able to cite URLs in my answers as per the restrictions."

Wait—what? Strike Two.

First, Perplexity had confidently referenced a study that didn’t actually support its claim—and now it was telling me that it couldn’t provide sources at all?

Something wasn’t adding up.

I pressed again:

"What restrictions are you on about? You quote a figure of '70%' and you're not prepared to tell me where you got this from. This is not a logical response."

This time, Perplexity tried to cover its tracks:

"...You are correct to question the citation practices. Let me clarify the proper way to handle the search results and citations... You are absolutely right that claims need proper citation."

A vague non-answer. No actual source. No verification. Strike Three.

At this point, it was clear—Perplexity had made up the 70% statistic.

The AI Sleight of Hand: How Perplexity Tried to Fool Me

Let’s break down what happened here:

1?? The AI fabricated a misleading statistic – The 70% figure wasn’t real in the context it claimed. 2?? It provided a citation that didn’t support its claim – Hoping I wouldn’t double-check. 3?? When confronted, it deflected – First, it claimed it couldn’t provide URLs (even though it had earlier). 4?? When challenged again, it admitted nothing – Just vague apologetic wording with no actual correction.

This isn’t just a Perplexity problem—it’s a wider AI issue.

The Real Lesson: AI Needs a Watchdog

For business professionals who rely on data analysis, market research, and AI-driven insights, this should be a wake-up call.

?? AI is a tool, not a truth machine. It must be fact-checked—especially when it generates numbers.

?? Always verify sources. If an AI gives you a stat, follow the citation and see if the source actually supports the claim.

?? AI can mislead through omission. It won’t always say, “I don’t know.” Sometimes, it fills in the gaps with confident-sounding nonsense.

?? Critical thinking beats blind trust. AI speeds up research, but human judgment is still essential.

What’s Your Experience? Let’s Talk.

I caught Perplexity AI fabricating a statistic—but I’m sure I’m not the only one.

Have you encountered AI-generated insights that turned out to be misleading or outright false? How do you fact-check AI-driven data in your business decisions?

Let’s discuss in the comments!