How to use AI in power bi

AI tools in Power BI simplify data analysis, speed up decision-making, and uncover insights without requiring technical expertise. Here's what you need to know:

• Why Use AI in Power BI?

  • Boost operational efficiency by up to 25%.

  • Improve forecasting accuracy by 20%.

  • Use natural language queries to cut analysis time by 25%.

• Key AI Features in Power BI:

  • Natural Language Queries: Ask questions like, "What are the top sales regions?" to get instant visualizations.

  • AI Visuals: Tools like Key Influencers and Decomposition Tree simplify complex data.

  • Azure Machine Learning Integration: Build predictive models for advanced analytics.

• Licensing Costs (as of April 2025):

  • Power BI Pro: $14/user/month (basic AI tools).

  • Power BI Premium Per User: $24/user/month (full AI suite).

  • Fabric F64: $8,409.60/month (enterprise-grade AI).

• Setup Tips:

  • Enable AI workload in settings.

  • Allocate 20% memory for Cognitive Services.

  • Ensure clean, high-quality data for reliable insights.

With tools like Querio, you can enhance Power BI's AI capabilities, making data exploration even easier. Whether you're analyzing trends, visualizing data, or predicting outcomes, AI in Power BI helps turn raw data into actionable insights.

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Setting Up AI in Power BI

To use AI features in Power BI, you’ll need the right licenses and proper configuration.

Required Licenses and Setup

Power BI's AI tools are linked to specific license levels. As of April 1, 2025, Microsoft introduced its first pricing update in nearly ten years [5]. Here’s an overview of the licenses and their AI access:

Once you have the appropriate license, you’ll need to configure your workspace settings:

• Enable the AI workload in the capacity settings.

• Set the memory limit for Cognitive Services to 20% [1].

• Adjust data privacy settings to either Public or Organizational.

After completing these steps, consider integrating Querio to unlock even more AI functionality in Power BI.

Setting Up Querio with Power BI

Querio brings advanced AI-powered analytics to Power BI. Here’s how to get started:

• Account Setup and Data Connection

  • Create a Querio account.

  • Connect your data sources.

  • Set up database connections with the required security protocols.

• AI Agent Activation

  • Enable Querio’s AI agents to enhance your Power BI visualizations.

  • Use natural language processing to turn complex queries into actionable insights.

Once Querio is integrated, you can fully leverage Power BI’s built-in AI tools.

Main AI Tools in Power BI

In Microsoft Fabric, AI features are enabled by default. However, administrators can disable them via the admin portal if their organization isn’t ready to adopt these tools.

Here are some of the standout AI capabilities:

• Text Analytics: Offers multilingual processing for in-depth text analysis.

• Vision Features: Available with Premium licenses for advanced image analysis.

• Azure ML Integration: Requires read access to your Azure subscription and Machine Learning workspace [1].

For Cognitive Services, you’ll need Premium capacity nodes (EM2, A2, P1, or higher) [1]. To make the most of Copilot, ensure your data is prepared to reflect your business context [4]. Keep in mind that Copilot requires a paid Fabric SKU (F64 or higher, or P1 or higher) and is not available on trial versions [6].

Using AI Tools in Power BI

Harnessing AI tools within Power BI, especially with enhancements like Querio, allows businesses to turn raw data into meaningful insights.

Text-Based Data Queries

Power BI's Q&A feature simplifies data exploration by enabling natural language queries that instantly generate visualizations.

• Ask Clear Questions
  

  Start with direct and specific queries to extract the insights you need. For example:

  • "Show me total sales by region for Q1 2025."

  • "What were our top 10 customers last month?"

  • "Compare revenue growth across departments."

• Utilize Text Analytics
  

  Power BI's text analytics features help you analyze unstructured data sources, such as customer feedback or social media. These tools can uncover key themes, trends, and even sentiment, offering a deeper understanding of your audience.

From here, you can dive into AI-powered visual tools to make the data even more accessible.

AI Visualization Tools

Power BI offers several AI-driven visualization tools that highlight trends and patterns in your data. These tools are designed to simplify complex datasets and make insights more actionable.

By focusing on the story behind the numbers, you can ensure that your data visualizations communicate effectively.

"Data storytelling is the concept of building a compelling narrative based on complex data and analytics that help tell your story and influence and inform a particular audience." [7]

Once you’ve identified key trends, the next step is to integrate predictive models for forward-looking insights.

Adding Predictions to Dashboards

Enhancing your dashboards with predictive analytics takes your data exploration to the next level, empowering proactive decision-making.

"Integrating predictive analytics into Power BI and other business intelligence tools can provide significant insights and competitive advantages in today's data-driven ecosystem." [9]

Here’s how to effectively integrate predictions into your Power BI dashboards:

• Prepare Your Data
  

  Start with clean, well-structured data to ensure accurate predictions.

• Select the Right Integration Method

  • Use Power BI’s built-in AI features for quick insights.

  • Leverage Azure Machine Learning for custom models.

  • Incorporate Python or R scripts for more advanced analytics.

• Monitor and Update Regularly
  

  Keep your predictive models up to date by feeding them new data and adapting to changing conditions.

For instance, a customer churn analysis can be implemented by using Python scripts to predict churn probabilities. These predictions can then be added to customer datasets, creating dynamic dashboards that help visualize retention strategies [8].

Tips for AI in Power BI

Data quality issues are a major hurdle for organizations, contributing to 60% of AI project failures and costing an average of $15 million annually [10].

Data Quality Guidelines

High-quality data is the backbone of dependable AI insights, boosting outcomes by as much as 20% [10]. To ensure your data meets the standards needed for effective AI, consider these strategies:

For example, implementing real-time monitoring for data health can significantly improve predictive accuracy and operational efficiency [10]. Once data quality is addressed, it’s essential to align AI initiatives with your broader business objectives.

Aligning AI with Business Needs

To maximize the impact of AI, aligning it with business objectives is critical. This alignment not only drives adoption but also ensures that AI initiatives contribute to achieving key goals. As highlighted:

"Effective business alignment helps to improve adoption. With effective business alignment, the data culture and data strategy enable business users to achieve their business objectives" [12].

Here’s how to keep AI efforts on track:

• Define Clear Objectives
  

  Begin with well-defined business goals. For example, if your aim is to improve customer retention, design AI models to analyze customer behavior and predict churn risks.

• Monitor and Adapt
  Regularly review AI model performance to ensure it continues to meet evolving business needs [11].

• Promote a Data-Driven Culture
  

  Use AI-generated insights to encourage a data-driven mindset across teams and support ongoing learning.

When objectives are clear and monitoring is consistent, collaboration becomes the final piece of the puzzle for achieving AI success.

Team Collaboration Methods

Collaboration plays a pivotal role in amplifying the value of AI within Power BI. Microsoft documentation emphasizes that integrating Power BI into Microsoft Teams enhances teamwork by allowing seamless access, sharing, and discussion of data insights directly within the platform [13].

To strengthen team coordination:

• Establish Common Ground
  Develop a shared vocabulary between data science and BI teams to eliminate communication gaps [14].

• Leverage Workspace Features
  Use Power BI workspaces for co-authoring reports and managing access permissions [13].

• Enable Direct Discussion
  Take advantage of Power BI’s commenting features for contextual and immediate feedback [15].

Conclusion

Key Benefits Review

AI integration in Power BI is changing the way organizations approach data analysis. Companies adopting AI-driven analytics report impressive results:

These numbers highlight why 80% of executives anticipate AI will play a major role in improving operations [3]. One standout example comes from the healthcare industry, where AI-powered Power BI dashboards analyze patient data to predict readmission risks. This not only enhances patient care but also reduces operational expenses [16].

Getting Started Steps

To tap into these benefits, here’s how to effectively incorporate AI into Power BI:

• Data Preparation: Ensure your data is clean and standardized to guarantee accurate AI-driven insights [2].

• Feature Activation: Enable AI tools like Quick Insights and AI Visuals to uncover actionable insights immediately. Starting with natural language processing tools can make data exploration easier for everyone on your team [17].

• Team Enablement: Build a data-first culture by securing leadership buy-in, offering targeted training, investing in reliable data infrastructure, and fostering collaboration across departments [2].

"AI in Power BI opens new frontiers in business intelligence, empowering organizations to glean deeper insights from their data." – Symphony Solutions [3]

With 48% of businesses already leveraging AI to boost their analytics capabilities [3], now is the time to act. By prioritizing data quality and teamwork, your organization can join the ranks of industry leaders using AI to revolutionize business intelligence. Embracing these steps will help you unlock the full potential of AI in Power BI.

FAQs

What are the main differences between Power BI Pro and Power BI Premium Per User licenses when it comes to AI features?

Power BI Pro comes with basic AI features, including AI-powered visuals and insights, making it a solid choice for everyday data analysis tasks. It’s ideal for users who need straightforward tools to explore and understand their data.

On the other hand, Power BI Premium Per User (PPU) steps things up with advanced AI capabilities. These include AutoML, enhanced dataflows, and seamless integration with Azure Machine Learning, making it better suited for more complex and demanding scenarios.

PPU also supports larger datasets and delivers better performance, which is crucial for handling resource-heavy AI tasks. If your projects require advanced modeling or large-scale data processing, PPU is built to handle those challenges with ease.

How does integrating Querio improve AI capabilities in Power BI for businesses?

Integrating Querio with Power BI brings a new level of ease and functionality to data analysis and visualization. By leveraging automation tools, Querio simplifies the process of preparing and analyzing data, helping businesses save time and work more efficiently.

One standout feature is Querio's natural language processing, which allows users to explore their data through conversational queries. This makes insights more accessible, even for team members without technical expertise. On top of that, the integration enhances predictive analytics, enabling smarter, data-driven decisions. Together, Querio and Power BI open the door to deeper insights and more confident decision-making.

What are the best ways to ensure high-quality data when using AI tools in Power BI?

When using AI tools in Power BI, maintaining high-quality data is a must. Start by focusing on standardizing your data during the preparation phase. Consistent formatting and careful validation during the ETL (Extract, Transform, Load) process can help you catch errors early, ensuring your data remains accurate from the start.

In addition, make data cleansing and profiling a regular part of your workflow. These steps help you spot and resolve anomalies effectively. Power BI offers built-in tools, such as automated validation and error-handling features, which can further improve data reliability. By sticking to these practices, you can trust that your AI-driven insights will be accurate and actionable.

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