Artificial intelligence built into Power BI
Microsoft is consistently developing AI features available directly in Power BI, so they do not require analysts to have knowledge of coding or machine learning. The most important built-in features include:
- Q&A – the ability to ask questions in natural language (e.g., “What were the sales in the southern region in the last quarter?”) and then receive a dynamic visualization of the answer.
- Decomposition Tree – cause-and-effect analysis that automatically discovers the factors influencing a given business outcome.
- AI Insights – built-in algorithms for anomaly detection, forecasting, and text analysis (e.g., sentiment analysis in customer reviews).
- Smart Narratives – automatic description of data and charts, which significantly speeds up reporting for management.
These tools make Power BI accessible not only to IT professionals but also to managers and non-technical users, enabling them to obtain strategic insights quickly.
Integrating Power BI with Azure Machine Learning
For more advanced scenarios, Microsoft enables the integration of Power BI with Azure Machine Learning (AML). This allows you to:
- analysts can train their own predictive models (e.g., predicting employee turnover or product demand),
- data processed in AML can be directly linked to Power BI reports,
- the forecasting process becomes cyclical and automatic – each new data set is immediately analyzed by the model and presented in reports.
Forecasting and predictive analytics in Power BI
Power BI supports forecasting features based on machine learning algorithms:
- Time series forecasting – e.g., predicting monthly sales, demand for raw materials, or the number of new customers.
- Anomaly Detection – automatic detection of deviations in data, which is crucial in finance and e-commerce.
- Customer segmentation – clustering makes it possible to group customers with similar behaviors and personalize offers.
Gartner research indicates that companies using predictive analytics increase their revenues by an average of 21% over three years compared to organizations relying solely on historical analysis. This clearly shows that forecasting is not just a curiosity, but a real competitive advantage.
Automating reporting with Copilot
In 2024, Microsoft introduced the Copilot feature to Power BI, which leverages generative artificial intelligence and revolutionises the way we work with data. This tool acts as an intelligent assistant that not only automates the technical aspects of reporting but also supports users in interpreting data and making decisions.
With Copilot, users can:
- automatically create reports based on a simple description in natural language – just enter a query such as “Show sales by region for the last six months” and Power BI will generate the appropriate visualization,
- generate DAX formulas and M queries, eliminating the need for manual coding, which is particularly appreciated by beginners and managers who do not have technical knowledge,
- prepare narratives and business recommendations – Copilot not only describes data, but also suggests potential courses of action, e.g., by identifying regions with the greatest growth potential.
Microsoft research shows that implementing Copilot in the reporting process can reduce dashboard preparation time by up to 40% and, in many cases, reduce human error. This feature significantly lowers the barrier to entry into the world of analytics, allowing non-technical employees to quickly leverage the full capabilities of Power BI.
In addition, Copilot facilitates the standardization of reports in large organizations—teams can create consistent visualizations and narratives based on the same commands, which improves the quality of data communication across the entire company. In the long term, Copilot becomes not only a reporting tool but also a partner in the strategic decision-making process.
Practical examples of AI use in Power BI
Artificial intelligence in Power BI is utilised across various industries, yielding measurable operational and strategic benefits.
Retail – by analyzing historical sales data, weather factors, and seasonal trends, retail companies can accurately predict demand for specific products. This allows them to optimize inventory, reduce costs associated with overproduction, and minimize stock shortages. For example, a clothing store chain can forecast which collections will be most popular during the holiday season, which facilitates the planning of promotions and marketing campaigns.
Finance – banks and financial institutions use AI in Power BI to create dynamic credit risk assessment models. Analysis of thousands of transactions, repayment histories, and demographic data enables them to determine a customer’s creditworthiness accurately. Algorithms can also detect unusual patterns that indicate fraudulent attempts. The result is shorter decision-making processes and a better customer experience while reducing risk.
Production – predictive maintenance, i.e., predicting machine failures, is based on the analysis of data from IoT sensors and ML models. Power BI integrates data on temperature, vibrations, and energy consumption to predict the moment of failure. Companies can plan maintenance at the optimal time, minimizing downtime and repair costs. According to McKinsey, implementing predictive maintenance reduces infrastructure maintenance costs by as much as 25–30%.
E-commerce – personalizing product recommendations based on customer behavior analysis and purchase history is becoming standard practice. Power BI reports integrated with AI enable managers to identify customer segments more quickly and design effective cross-selling and up-selling campaigns. This directly translates into increased basket value and improved customer retention.
HR – employee data analysis enables the prediction of turnover and the assessment of factors affecting employee satisfaction. Thanks to Power BI reports combined with AI, HR departments can implement preventive programs, offer more tailored benefits, and facilitate targeted development activities. As a result, organizations reduce recruitment costs and increase team stability.
Challenges and best practices
Although the capabilities of artificial intelligence in Power BI are impressive, implementing them in an organization requires a conscious approach.
Data quality – Access to high-quality data is the foundation of effective AI models. Inconsistencies, gaps, or duplicates can significantly distort forecasts. That is why it is crucial to implement data governance processes and regularly clean up data. Forrester research indicates that as many as 60% of AI project failures are due to poor-quality input data.
Interpretability of results – the so-called “black box effect” makes it difficult to understand the model’s decisions. In business reports, it is necessary to supplement forecasts with additional explanations and visualizations so that managers can make informed decisions and trust the system’s recommendations.
Security and compliance – sensitive data must be processed in accordance with regulations such as GDPR or sector-specific regulations (e.g., in banking and medicine). Microsoft offers encryption, data masking, and access control features in Power BI, which must be configured appropriately.
Continuous optimization – predictive models must be regularly updated to remain accurate in a rapidly changing business environment. Companies should build processes for monitoring forecast quality and adapt algorithms to new realities.
Following these rules allows you to fully leverage the potential of AI in Power BI while minimizing the risks associated with implementation.