Copilot in Power BI Won’t Fix Bad Data. It Will Expose It. And That’s Just the Beginning

May 19, 2026

What will AI bring to your reporting? That’s not the right question. A better one is: what will AI reveal that we’ve been overlooking?

Power BI (Microsoft’s business intelligence and data visualization tool) is today a widely adopted reporting standard across many organizations.

But, it isn’t a magic button that automatically simplifies reporting on its own. What it does is expose everything that was previously hidden beneath the surface. And that is a huge opportunity for those who are ready.

Business intelligence has historically been built on reliable, precisely defined data. Artificial intelligence introduces something fundamentally different; probabilistic models and outputs without guarantees. That tension is both a paradox and a challenge: how do we capture the strengths of each?

What Does Copilot Need to Work at Full Capacity?

Copilot in Power BI can generate visuals, summarize data, and answer questions in natural language. But its performance depends directly on the quality of what it is built on. And that foundation is not the tool itself; it is the data and the layer that gives it meaning.

That layer is a semantic model. Simply put, it is the translation between the technical world of the data warehouse and the business world of the user. If the model is missing labels, metric definitions, or business context, Copilot is working without the information it needs. It returns answers, but their accuracy depends entirely on what is fed into the model.

Example:

Copilot receives a question about sales in the Czech Republic. It locates a relevant visual, but that visual also includes Slovakia. At the time this article was written, Copilot could not automatically filter that visual and return figures for both countries. This type of limitation is exactly why preparing the semantic model is critical: the better the data and visuals are prepared, the more reliably Copilot responds.

AI Needs Better Data, Not Worse

There is a common assumption that AI will compensate for poor-quality data, that it will somehow “fill in” the gaps. In the context of Power BI and Copilot, the opposite is true. The more complex and undocumented the semantic model, the less predictable the outputs.
A senior business analyst knows that the column “sales_ex_vat” means sales excluding VAT – they know whether the figures are in Czech crowns or dollars, whether it covers a calendar year or a fiscal year. They know this from memory, from experience, from dozens of complaints. The copilot cannot know this unless someone tells it in advance. And that is work that must happen before Copilot can deliver reliable results.

Paradox: AI demands that businesses know their own data better.

Start Small and in a Controlled Way

Companies that want to use Copilot without complications and with trustworthy outputs do not start by releasing it across their entire reporting landscape. They begin with a single dataset with clear, straightforward metrics. Sales is a good example of this: no hundreds of tables, no specific internal logic, no critical figures such as bonuses, remuneration, or controlling data.

They then proceed step by step:

1. Clean up labels in the semantic model.
2. Work with the business to define the typical questions that will be asked by Copilot.
3. Add instructions and context directly into the model.
4. Test and repeat.

An interesting side effect has emerged: AI can now generate documentation for semantic models automatically. Documentation that, historically, was rarely created is now produced by one AI so that another can consume it. It’s a paradox, but one that is highly effective in practice.

The Role of the Analyst Is Not Disappearing. It Is Shifting.

Data analysts will not be replaced by Copilot, but they will be doing different work. Less manually fixing labels, less waiting to rename a metric that may appear across dozens of places in a model. More time for business dialogue. More capacity for data education of users who are now working with data themselves and need to understand it.

An analogy that captures this emerging trend: AI helps scan X-rays and suggests diagnoses. But the doctor who reviews the result and stands behind it is not going anywhere.

Copilot is the reward for work well done. It is the cherry on top, not a shortcut to get there.

Listen to the Podcast on This Topic

Kristýna Merňáková is a Data Visualization Lead at Adastra. She works with companies to make their data meaningful before any tool is ever switched on. In the interview, she talks about how to get started with AI in Power BI and how to prepare data so that AI genuinely helps rather than causes problems.