AI and Automation in Power BI: What’s New in 2025?

What is AI and Automation?

AI (Artificial Intelligence) refers to machines or software that can simulate human intelligence. This includes things like understanding language, recognizing patterns, making predictions, and learning from data. AI enables computers to perform tasks that typically require human thinking, such as chatbots, image recognition, and predictive analytics.

Automation on the other hand is the use of technology to perform tasks without human intervention. This can range from simple rule-based automation, like scheduling emails, to advanced robotic process automation (RPA), where AI-driven bots handle repetitive business tasks efficiently.

Together, AI and Automation are revolutionizing Power BI, making data analysis smarter, faster, and more accessible. The integration of AI to Power BI is helping companies eliminate repetitive tasks, reduce human errors, and unlock predictive intelligence, allowing users to focus on strategic insights rather than manual processes.

AI and automation are driving innovation - whether in healthcare, finance, transportation, or manufacturing. AI gives automation intelligence, while automation ensures tasks are done seamlessly.

This article explores some of the top AI features and automation capabilities shaping Power BI and data analytics.

Core AI Features Transforming Power BI

1. Natural Language Processing (NLP)

The Q&A feature in Power BI a powerful Natural Language Processing (NLP) tool that allows users to interact with their data using everyday language. Instead of writing complex queries or formulas, users can ask questions in plain text, and Power BI responds with relevant visuals.

For example, using the Sample – Superstore dataset, and by leveraging this feature I have created a simple map showing Profitability by State by simply asking a question in plain language.

The integration of NLP in Power BI encourages self-service analytics by lowering the barrier to entry, especially for non-technical users who want to explore data without needing to write complex queries.

2. Automated Insights

Automated insights is a feature that helps you easily explore and find insights such as anomalies and trends in your data as you interact and consume your reports. It notifies you if there are interesting insights and provides explanations for them. It works out of the box on any report, so you can automatically start getting insights from your reports without any setup.

Users can access automated insights through features like Smart Narratives and Explain the increase/decrease, which generate explanations and visualizations based on their data. It’s an efficient way to enhance data analysis without requiring deep technical expertise.

3. Machine Learning Integration

The integration of Power BI with Azure Machine Learning, enables users to incorporate custom ML models built and hosted in Azure directly into Power BI reports. Data scientists can share Azure ML models with BI analysts, who can invoke these models via Power Query functions in Power BI Desktop or service. This integration supports advanced, custom ML scenarios beyond pre-built models, facilitating enterprise-grade predictive analytics.

Power BI also supports running Python and R scripts, which allows users to create, train, and apply machine learning models using popular libraries like scikit-learn, TensorFlow, or caret. This integration enables advanced data scientists and analysts to embed custom ML workflows directly in Power BI reports and visualizations, expanding the scope of ML applications.

4. AI Powered Visuals

AI powered visuals utilize machine learning and AI algorithms to analyze data and create visualizations that go beyond standard charts and graphs, revealing hidden insights and patterns. These visuals can help users understand key drivers, make predictions, and discover relationships in data that might otherwise be missed.

Key aspects of AI powered visuals include.

• Key Influencers which help identify the factors that have the greatest impact on a specific outcome or metric. Key influencers perform a statistical and machine learning analysis to determine which variables are most influential.

• Decomposition Tree which allows users to drill down into data across multiple dimensions, revealing the underlying causes and factors contributing to a specific result. Decomposition tree helps explore data at different levels of granularity.

• Anomaly Detection which identifies unexpected changes or irregularities in data, enabling proactive response to issues before they escalate.

Automation Features Transforming Power BI

Power BI offers robust automation capabilities primarily through its integration with Microsoft Power Automate, enabling users to streamline data workflows, enhance real-time responsiveness, and improve collaboration.

Key automation features within Power BI include:

• Power Automate Visuals: This allows users to trigger workflows directly from reports. It integrates automation into dashboards, enabling users to take actions based on insights.

• Automated Data Alerts: This notifies users when key metrics exceed predefined thresholds, ensuring timely action. These alerts help businesses stay proactive by responding to data changes instantly.

• Scheduled Report Distribution: This allows users to automate the delivery of reports to stakeholders on a predefined schedule, ensuring timely access to up-to-date insights without manual effort.

• Workflow Automation: This enables users to streamline and automate repetitive data and reporting tasks without requiring complex coding. This integration enables users to create low-code workflows that connect Power BI with various applications and services, improving efficiency, accuracy, and collaboration.

Conclusion

The integration of AI and automation in Power BI marks a transformative shift in data analytics, making insights more accessible, actionable, and intelligent. Through AI-powered visuals, machine learning models, and automated data processing, businesses can uncover deeper trends, optimize decision-making, and streamline workflows.

AI-driven automation in Power BI empowers users to analyze vast amounts of data with minimal manual effort, enabling predictive analytics, anomaly detection, and intelligent reporting. By leveraging tools such as the Key Influencers Visual, Smart Narratives, and AutoML, organizations can extract meaningful insights without requiring extensive technical expertise.

As AI continues to evolve, its role in Power BI will expand, driving more sophisticated data modelling, enhanced natural language processing, and real-time analytics. The future holds immense potential for AI-powered automation to refine business intelligence, improve efficiency, and foster data-driven innovation across industries.

Ultimately, the fusion of AI and automation in Power BI is not just about enhancing productivity - it's about reshaping the landscape of data-driven decision-making and unlocking new possibilities for organizations worldwide.

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