A Reddit post described a case that is relevant for many companies - especially in e-commerce:
A team used an AI agent to answer leadership questions about key metrics. The answers came quickly, were explained in detail, and were widely used inside the company. Only months later did it become clear that the AI had invented or misassigned analytics numbers.
The post has since been deleted, but the scenario is still highly relevant. It shows not only that LLMs can produce wrong numbers. The real risk is how those numbers are presented: structured, well argued, and close to a decision. That is exactly how they can enter steering processes before anyone has checked the calculation logic.
LLM analytics lowers the barrier to fast analysis. You ask a question, and a few seconds later you get an answer. For business teams, that is attractive because it reduces waiting time and makes complex analysis more accessible.
But this creates a new risk: speed gets confused with reliability.
There is also the language quality of the output. An LLM can formulate uncertain or wrong results in clear business language. It provides tables, summaries, bullet points, and recommendations.
As a result, the output often looks like a finished analysis - even when the analytical foundation has not been properly checked.
This becomes especially critical when definitions are missing:
For users, this difference is hard to spot because the presentation looks clean and convincing.
The core issue: An obviously wrong number is usually questioned. A well explained number, on the other hand, is often accepted as a basis for action.
In e-commerce, metrics are translated directly into operational and strategic decisions - from performance marketing to assortment planning.
Example:
A team wants to decide which channel should receive more marketing budget in the next cycle. The question asked to the LLM is which channel drives profitable growth. The answer recommends channel X because its profit contribution appears to be the highest.
Later, it turns out that profit was not calculated according to the company’s internal business logic. Returns, discounts, fees, or marketing costs were handled differently than required for steering decisions.
The example does not need to be dramatic to become economically relevant. Even small differences in KPI definitions can be enough to:
The problem is not that LLMs are generally unsuited for analysis tasks. The problem is that they work with probabilities, assumptions, and linguistic plausibility when they do not have a clear working base.
Reliable LLM analytics therefore needs more than a good prompt.
An LLM does not automatically know company-specific metrics. It does not know what "profit", "contribution margin", or "revenue" means in your setup:
Without this semantics, the model can use a definition that sounds plausible but is factually wrong - and present it very convincingly.
Even a powerful model needs clear rules for analytical work. It must be defined:
Without such guardrails, the LLM decides on the calculation path itself - and that remains invisible to business users.
Analytics results must be auditable. If a number is used as a basis for decision-making, it must be traceable:
If this validation is missing, you are left with trust in the way the answer is presented. In day-to-day decision-making, that is not enough.
The answer to this risk is not just a better prompt.
Reliable LLM analytics needs a setup that brings data, meaning, and method together - ideally on top of your existing BI or e-commerce data model.
Clean data is the foundation. It must be:
so that the model does not have to react to unclear or conflicting inputs.
Semantics make data interpretable. They explain:
This clarity is especially important in e-commerce, where many channels, campaigns, and touchpoints are involved.
Guardrails control the process. They define:
and also when the model must not output a number.
Traceability ensures that results do not just sound plausible, but are understandable and reviewable. That is especially important for metrics that steer budget, profit, or purchasing decisions.
The Reddit story may look like an extreme case at first. But the underlying mechanism is everyday business reality:
That makes it easy to accept. For LLM analytics, this is a central challenge.
When AI results influence decisions, plausibility is not enough. Companies need a foundation that combines:
Only then does a fast answer become a reliable analysis.
Without semantics and guardrails, correct numbers are a matter of luck. The risk is confident false numbers.
If you want to learn how minubo creates this foundation for reliable LLM analytics in e-commerce, take a look at our solutions for AI-supported data analysis or get in touch with us directly.
That is how "fast answers" stop being a source of "fake insights" and become a real lever for better decisions in your e-commerce business.