Anyone who works with data within companies is very familiar with this scenario.
Much of the time is spent building solutions: data modeling, creating metrics, developing dashboards, and structuring analyses that help turn numbers into decisions.
This is the daily reality for many professionals working in the fields of Business Intelligence, Analytics, and Data Engineering.
But there is one important question that rarely comes up during this process: who verifies whether the story the data is telling actually makes sense?
In most data projects, the focus is on building the solution. The main concerns are typically the quality of the modeling, the performance of queries, the organization of metrics, and the clarity of visualizations.
All of this is essential.
A dashboard is not just a set of charts arranged on a screen.
It is a framework that organizes information so that someone can quickly understand what is happening in the business and make decisions with greater confidence.
When that narrative isn't clear, an increasingly common phenomenon arises in companies: a wealth of information is available, but there is little real understanding of what it means.
In this context, an interesting point arises as artificial intelligence tools continue to advance.
Most people use AI to create solutions.
It is used to generate text, write code, structure analyses, or even help develop ideas.
But there is an equally powerful application that remains largely untapped: using artificial intelligence as a critical reviewer of what has been created.
In other words, using AI to analyze a solution from an external perspective and identify its strengths, weaknesses, or opportunities for improvement.
This approach can be especially useful in data projects.
Anyone who develops dashboards or analyses knows that we are often so close to the solution that it becomes difficult to evaluate it with a critical eye.
The sequence of charts may seem logical to the person who created the analysis, but not necessarily to those who will use it.
Indicators may make sense to an analyst, but they can raise questions for a manager who needs to make decisions quickly.
When a dashboard is analyzed from an external perspective, some important questions begin to arise:
- Does the sequence of information make sense to those who will be using the solution?
- Is the data narrative clear, or does it require an excessive amount of effort to interpret?
- Is there a logical progression between executive vision, analysis, and detail?
- Do metrics really help support business decisions?
These questions aren't just technical.
These are issues related to communication, interpretation, and data storytelling.
Ultimately, well-designed dashboards aren't just those that feature lots of charts or metrics.
They are the ones who can clearly explain what is happening in the business.
And that is precisely where Artificial Intelligence can make a valuable contribution.
When you ask an AI to evaluate a solution, you can gain an outside perspective that often helps you see aspects that go unnoticed during development.
This analysis can highlight everything from issues with visual clarity to opportunities for improvement in how the information is presented.
More than just automating tasks, AI can help data professionals develop something equally important: critical reflection on the solutions they are building.
Ultimately, successful data projects don't depend solely on technology, tools, or efficient modeling.
They depend on something even more essential: clarity.
Clarity to turn data into insight.
Clarity to turn understanding into decisions.
And sometimes, to achieve that clarity, a second perspective can make all the difference—whether it comes from a coworker, an end user, or even artificial intelligence.