Fact-checking saved the company up to $100,000 in ad spend

A convincing answer from a neural network can easily be mistaken for analysis—especially when it confirms an employee’s theory. That nearly led to an expensive marketing decision in the Kumar & Solo case: the increase in customer acquisition cost was attributed to seasonal inflation in the ad auction, and Claude endorsed the hypothesis. The team was already discussing an increase in its Google Ads budget, but a separate review found no reliable evidence of a seasonal spike.

The authors claim that pausing the decision preserved up to $100,000 in ad spend and potentially around $400,000 in revenue. These figures cannot be considered independently verified: the publication includes no exports from advertising dashboards, CAC trends, attribution structure, or calculation of foregone revenue. Still, the failure mechanism itself will be familiar to any product team: a person proposes a plausible cause, the model develops it into a coherent explanation, and the confident wording is treated as evidence.

Where the reasoning broke down

A prompt such as “act as an experienced marketer” defines the style of the answer; it does not give the model access to company facts. If the context contains no data on bids, competitors, conversion, ad frequency, and creative quality, the neural network fills the gaps with the most plausible scenario. Seasonality may indeed have affected the auction, but without supporting figures it remains only a hypothesis.

This creates a dangerous feedback loop. A marketer formulates a preliminary conclusion, the LLM makes it sound more persuasive, and the employee interprets the model’s agreement as an independent check. In reality, both conclusions grew from the same initial assumption. The more authoritative the assigned role and the more confident the answer’s tone, the harder it becomes to notice the absence of evidence.

How to validate decisions with an LLM

It is more useful to ask the model to examine a specific decision than to play the role of an expert. Give it the baseline metrics, comparison period, constraints, and the threshold beyond which an action is considered justified. Then require it to separate facts from hypotheses, present arguments for and against, find primary sources, and identify the data that could change the conclusion.

For example: “We want to move budget from Meta to Google. Here are weekly spend, CAC, conversion, and revenue figures. Which explanations are supported by the data? Which remain hypotheses? What three checks should we run before moving the money?” Such a prompt does not eliminate model errors, but it makes them easier to spot.

Links in the answer must also be opened manually. The page must exist, contain the stated claim, and apply to the relevant period. If the source cannot be found or discusses a different market, the claim returns to the hypothesis category.

What to add to the workflow

For decisions with a meaningful price tag, introduce a short control gate. First, record the initial hypothesis and metrics. Then request alternative explanations and the strongest objections. After that, verify the sources and run a limited experiment instead of sharply reallocating the budget.

In the case described, the team focused on producing creative assets and reported record October revenue of more than $430,000. The publication does not prove a causal link between that result and the chosen tactic. The practical lesson is more modest but more useful: an LLM can broaden the decision space, but the authority to confirm a cause and put money behind it should remain with the data, the experiment, and an accountable person.