GPT Feels Like It Knows Your Data—But It Might Be Lying

By Brad Peters, CEO of Scoop

There’s a growing belief in the industry that if you stick a chatbot on top of your data, you’ve solved analytics. Just plug GPT into your warehouse, ask it a question, and it spits out a summary. It sounds helpful. But it’s a shortcut that doesn’t actually get you where you need to go.

GPT and other large language models weren’t designed to do analytics. They don’t calculate. They don’t apply logic. They don’t understand your data structures. What they do is generate plausible-sounding answers based on probabilities—not precision. And in analytics, that distinction matters.

That’s not a knock on LLMs. They’re impressive tools for plenty of tasks. But analytics isn’t one of them—not if you care about structure, repeatability, or trust.

Probable ≠ Correct

GPT is a language model. It generates text that sounds likely—not answers that are right. When you ask it about your data, it doesn’t apply filters, run aggregations, or reference your metrics in any structured way. It’s just producing words that seem like a reasonable response.

That might be fine if you’re drafting an email. But if you’re making business decisions based on those responses, you’re taking a real risk.

I've seen teams take GPT outputs at face value—numbers that weren’t calculated, trends that weren’t actually present in the data, conclusions that couldn’t be replicated. It “felt” right, until someone actually checked.

Good Analysis Has Structure

What GPT lacks is the one thing analytics depends on: structure. Real analysis isn’t just about giving you a number—it’s about showing how that number was calculated, what filters were applied, and what rules were followed.

In a proper analytics workflow, you can trace every result back to the logic behind it. That’s what makes it repeatable. That’s what makes it trustworthy. GPT can’t give you that. It doesn’t follow a process—it generates an output.

And even when it’s connected to your data, it’s still approximating what a summary should sound like—not actually producing a deterministic result.

Ask it the same question twice, you might get two different answers. And you won’t be able to tell which one—if either—is correct.

What We Actually Need Is Agentic Analytics

There’s a better way to use AI in analytics, and it’s not about asking GPT to guess at your numbers. It’s about letting AI drive the analytical process itself—through systems that were designed to produce accurate, structured results.

This is what I call Agentic Analytics™.

In this model, AI agents don’t generate answers—they operate the tools that do. The agent handles the logic, the data prep, the visualizations, the analysis. But underneath that, every calculation is still being run through a structured, deterministic system you can inspect and verify.

The agent isn’t replacing the stack—it’s orchestrating it. And that’s a critical distinction.

You get speed and automation, without giving up accuracy or control. You still get a chart with real filters, real calculations, and real data behind it. You just didn’t have to build it yourself.

Why I Built Scoop This Way

I’ve spent years working in data and analytics, and I’ve seen the same pattern play out over and over again: either teams rely on traditional BI tools that are slow and fragile, or they get caught chasing new AI features that sound promising but don’t hold up under pressure.

Neither approach really works.

Scoop is my response to that problem. It’s built on the idea that AI should drive analytics—not imitate it. We use agents to run the full process—from cleaning the data to producing the final narrative. But every step is done in a structured, verifiable way.

You don’t get an approximation. You get analysis that’s actually grounded in your data—and built on logic you can see, test, and trust.

Final Thought

Analytics is too important to hand over to a system that just sounds confident. You need real answers, not best guesses. That’s why we built Scoop around the concept of Agentic Analytics™—because business decisions deserve more than a convincing paragraph.

They deserve real analysis.

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