Should We Still Create Power BI Reports and Dashboards in the Age of AI?

The ability to chat with data raises the question: Is it still worth investing in traditional BI and data visualization tools now that conversational Digital Data Analysts can extract data insights through chat? The short answer is yes, you should still be building Power BI reports and dashboards, and here's why.

Reason 1: The models that fuel your Power BI reports enable AI scenarios

At the heart of every insightful report or dashboard is a robust data model. In fact, every Power BI report developed comes with a semantic model baked in. Taken together, these semantic models form a powerful semantic layer. They define metrics, definitions, and relationships that are exactly the kind of context needed to answer your business's questions in natural language. These semantic models also feed directly into AI-driven tools like Copilot.

For example, consider the integration of Fabric data agents with Copilot in Power BI, as highlighted by Microsoft. These agents unify data from diverse resources, such as Snowflake, Databricks, Fabric, or your data warehouse, into a single queryable model. While this integration allows users to "chat with their data," the effectiveness of Copilot’s insights depends on well-designed underlying models.

Building reports will help your team develop and refine your semantic layer. Without semantic layer information, AI systems lack the structure needed to connect data points, analyze trends, or deliver actionable insights, and they struggle to consistently give accurate answers.

It's a twofer. By focusing on quality modeling efforts for dashboards, enterprises also set themselves up for AI success.

‍

‍

Reason 2: Building quality BI reports requires your organization to build same foundational skills and technologies that AI success requires

The skills for to build great BI models overlap greatly with those needed to build AI models. Here’s what goes into both:

1. Clear objectives – Whether you’re creating a dashboard to track sales KPIs or configuring an AI algorithm, success starts with clear goals.
2. Shared definitions – This turns out to be one of the most challenging and beneficial byproducts of building a dashboard: all the key stakeholders in the business must agree on what key words mean and how metrics are correctly calculated.
3. Data foundation – Reporting requirements spur improvements in underlying data platforms like data warehouses and data lakes.
4. Models required – BI reports force analysts and business SMEs to express how concepts relate to each other and drive clarity on business processes. In fact, the best data models really generate valuable conceptual models that people can use to understand the business's operations.
5. Tackling complexity – Building a useful dashboard often means untangling complex scenarios. The reduction of complexity and cognitive load is one of the greatest benefits AI can provide to an organization. If you have already tackled it to solve a problem with BI, that same work benefits your AI efforts.
6. Collaborative planning and testing – Building quality models is a team sport, requiring multiple disciplines. The teams that have worked together to build BI models are your natural first string to tackle your most valuable AI model scenarios.

Refining these skills while building reports means your workforce is better equipped to adopt innovative AI solutions confidently.

‍

‍

Reason 3: Reports and dashboards continue to be useful to people

AI-powered conversational tools, like natural language queries, are exciting advances—but they serve as complementary tools rather than replacements for traditional reports and dashboards. Visualization-focused reports still deliver immense value by providing repeatable, reliable, and visually engaging summaries of data.

Why Reports Still Matter:

• Static and reliable: While conversational tools generate insights on demand, dashboards provide a fixed reference framework. For instance, a weekly sales dashboard gives consistent updates for immediate decision-making. Just as we use trusted reports like financial statements to tie out our reports and dashboards, these BI data visualizations will become our sanity check for AI-generated results and insights. Static does not equal bad.
• Digestible by all: Not everyone in a business is comfortable relying on AI tools. Reports offer accessible information at a glance, reducing learning curves for non-technical users. Most everyone has some education or familiarity with how to read charts and graphs.
• Simpler actionable insights: People process visual summaries like graphs, charts, and heat maps faster than reading large datasets or query outputs. Start thinking of chat and text as just another option for cognitively processing data, suitable for answering specific questions, but not the best way to "visualize" data every time.

Simply put, reports retain their unique strength as a storytelling medium for transforming data into easy-to-consume narratives. They generate actionable insights with clarity, even as businesses drive toward AI-driven systems.

What should we do differently with BI reports and dashboards?

Even though we should continue to create reports and dashboards, not all are still relevant.

Build ad hoc models, not ad hoc reports

Many reports exist only for "ad hoc" purposes, meaning they don't exist to visualize data, but simply to answer one specific question. The models behind these reports still must exist, but their presentation should be rethought to ask the questions, "Why does the business need this answer, and what do they do with it?"

From our experience, you will often see results from these kinds of reports exported to be used in a downstream process. In these cases the data visualization is just an unnecessary transformation step in a data pipeline. Ask, could that export simply be generated automatically? Could the analyst just ask for the results they need in the format they actually want? These are the questions to help guide whether or not to actually build data visualizations to present your model.

Invest more in your models

Your data models should become the focal point of your BI efforts. A great data model, rich with metadata, ensures consistency across both traditional reports and AI outputs. Invest time in creating reusable models aligned with business goals.

Historically, data visualization requirements have driven the model development. While that's a natural progression, you need to start treating the model as a first class citizen, of equal (or greater) importance than the dashboard itself.

Here's what that actually looks like. When you plan to build a Power BI report, also plan the time needed to....

• Get every name exactly right. Check the names used in the model with the business to ensure they make sense to the right people.
• Add descriptions to every column and measure in your Power BI semantic model.
• Hide unnecessary and unused fields.
• Add documentation about your model that future model users know where to find (hint there are useful tools like Model Documenter from Data-Marc to make this easier.)
• Spend time enriching the linguistic metadata of your model to help the model better cope with future natural language queries
• Actually test out Q&A scenarios to see how your model performs against them. At a minimum, can it answer in language the same questions that it answers in data visualizations?

‍

Think hybrid AI and BI

Consider how dashboards and AI can work together. For instance, deploy reports to show high-level KPIs, while using AI assistants for nuanced queries. AI chat scenarios will be just another point of consumption of your high quality semantic models**.**

Streamline reports and dashboards

Focus on creating fewer, high-quality reports that align with core objectives. Replace “dashboard overload” with dashboards powered by semantic models that adapt based on user needs. Planning to go deeper on the development of each model means that we must be more selective on those we choose to invest in.

For example, should there be a semantic model to represent your company's core financials? Certainly. But do you need that Power BI report and model to track shipping costs? That should be evaluated on the strategic importance of that cost. Pro tip: you can just ask your Financial model, "What have we spent YTD on shipping costs? Break it down by vendor and also express it as a percentage of revenue." This may be something you only need for a one time initiative, and isn't worth building a full-blown report on.

Your business still needs BI

The great news for Microsoft shops is this: investments in Power BI pay dividends toward AI.

The rise of AI in enterprise doesn’t make traditional tools obsolete; it just urges us to use them smarter. Reports and dashboards remain integral for supporting business decision-making, but AI tools introduce rich, alternative ways for organizations to explore their data dynamically. The takeaway? AI and BI are not competing approaches but complementary ones.

For forward-thinking leaders, the focus should be on investing in strong data models that power a hybrid analytics ecosystem. Use reports for scheduled overviews and AI tools for exploratory deep dives. They set your business up for success today and in the uncertain future.

If you need help making an AI and BI strategy and building out semantic models the right way to set up AI success, we are here to help.

‍