Artificial intelligence (AI) technologies are becoming widespread across all sectors and industries. While neural networks have been a staple in business intelligence (BI) for years, the rise of GPT technologies has significantly enhanced AI-driven BI solutions in 2025.
Foreword
Before we plunge into using artificial intelligence in BI, let’s face it: AI is one of the most heavily marketed fields in the realm of IT. It’s not a buzzword, though, as one can’t deny that machine learning optimizes many routine tasks. However, marketers often use AI to boost their product appeal, even when it offers no real benefits. In other words, it’s not unusual when AI technologies fail to live up to businesses’ expectations.
In this article, we will talk about focusing on practical applications of AI in BI. You will learn how to choose the right features and use them wisely, how to use them strategically for your business growth, and how to make the most of this powerful combination. But first, let’s incorporate AI into Power BI.
How to incorporate AI into Power BI
You can do it through two primary methods:
- AI insights. This feature utilizes pre-trained machine learning models within the Power Query Editor, enabling functionalities like text analytics and image recognition. Note that this requires a Power BI Premium subscription.
- Azure machine learning integration. This option allows you to incorporate your custom AI models developed in Azure Machine Learning directly into your Power BI reports.
For more detailed guidance on incorporating AI into Power BI, you can explore articles from Microsoft Docs.
The top 5 AI-driven features for Power BI
So, let’s cut out the marketing fluff, and dive into the top 5 AI-driven features for Power BI, the most popular business intelligence tool on the market.
1. Detecting data dependencies, trends, and deviations
AutoML, or Automated Machine Learning, is a technology that automates the entire machine learning process, making it more accessible and efficient. It encompasses several tasks, including data preprocessing, feature selection, model selection, and hyperparameter tuning.
This technology provides automatic analysis of dependencies within a loaded dataset. Users only need to select a target variable (column), and the system runs a variety of commonly used statistical models (e.g., different types of regression, random forest) on the data and generates results.
AutoML for dataflows empowers business analysts to easily train, validate, and deploy machine learning models directly within Power BI. Analysts can use their data pipelines to provide input data for training, while the service automatically extracts key features, selects the best algorithm, and fine-tunes the model. Once the model is trained, Power BI generates a detailed performance report with validation results. It can then be applied to any new or updated data within the data flow for continuous insights.
While AutoML can assist analysts, it has some limitations. First, it requires a very clean dataset with high-quality data. Second, the analyst still needs a solid understanding of statistical analysis to correctly interpret and validate the results. Moreover, the ability to manually adjust the model is crucial; relying solely on ‘black box’ outputs can limit accuracy and insight.
2. Microsoft Fabric
Okay, it’s not a feature per se, but rather an end-to-end analytics solution designed with a focus on user experience. The key features of Microsoft Fabric include:
- Supporting diverse users: The solution caters to business users with varying levels of data expertise.
- Visualizing comprehensive data: Microsoft Fabric offers a wide range of reporting styles, including charts, graphs, and scatter plots.
- Facilitating collaboration: The solution enables users to work together and share insights within the Microsoft ecosystem, including Microsoft Teams and Excel.
- Leveraging automated AI and machine learning: The tool offers capabilities like image recognition and text analytics to enhance insights.
- Processing data in real-time: Microsoft Fabric allows users to work with up-to-date data in their reports and dashboards for more timely decisions.
So why do we add it to this list? While Microsoft Fabric can be used independently, its integration with Power BI offers a much more comprehensive data analytics experience. The solution combines the strengths of Microsoft Power BI, Azure Synapse, and Azure Data Factory into one unified platform. With Microsoft Fabric, you can access and analyze all your data in one place, streamlining the entire analytics process.
3. NLQ: Natural Language Query in BI
One AI application that can reduce analysts’ workload and speed up BI system adoption within companies is the ability to process analytical queries in natural language. This feature, now implemented by all major BI platforms, allows users to easily formulate queries. Ideally, it enables managers to ask questions like, ‘Which products are top sellers?’ and receive the answer in a convenient format—such as a graph, report, or dashboard.
Among international BI vendors, Microsoft has made the most progress in this area. They’ve integrated MS Copilot functionality into Power BI, which, during demonstrations, has shown the ability to generate an entire dashboard from a single text query. While the initial output may contain some inaccuracies and require adjustments (either by refining the query or using standard dashboard tools) the process takes significantly less time than building a dashboard from scratch. Pre-configured templates and approximate DAX calculations will also be prepared in advance to further streamline the process.
4. Smart Narrative visuals
Smart Narrative generates natural language explanations for visuals, turning data insights into easy-to-understand language. Whether presenting to stakeholders or analyzing trends with your team, it offers quick, customizable, and relevant insights that bring your data to life. The solution is user-friendly, regardless if you’re new to data visualization or you are a seasoned Power BI expert. By adding Smart Narrative to your reports, it automatically explains trends, outliers, and patterns, providing clear insights.
For instance, Smart Narrative might explain why revenue decreased or increased in some Qs, highlighting the key contributing factors in plain language. It not only simplifies interpreting complex visuals but also amplifies the impact of your reports.
5. Anomaly detection
Automated Anomaly Detection helps identify outliers or irregularities in your data that may indicate potential issues or uncover valuable opportunities. By using proactive anomaly detection, BI enhances its ability to deliver real-time insights, allowing users to address concerns or capitalize on trends as they arise.
You can enable Anomaly Detection in Power BI in three steps:
- Download the latest version. Ensure you have installed the most recent release of Power BI Desktop.
- Enable anomaly detection. Go to Options & Settings > Options > Preview Features, then check the box to enable Anomaly Detection.
- Choose a visual. Select the visual you want to analyze. In the Visualization panel, go to the Analytics sub-panel to apply Anomaly Detection.
Key takeaway
AI data business analytics with BI is important because it enables organizations to make data-driven decisions, gain valuable insights, and stay competitive in today’s fast-changing business environment. Using advanced analytics tools like Power BI, companies can easily:
- Visualize and analyze their data
- Identify trends and patterns
- Discover key insights that can inform strategic decisions
This AI-driven approach allows companies to optimize operations, improve efficiency, and ultimately achieve better business outcomes.
Application areas of AI in business intelligence with Power BI
The application areas of AI-powered business intelligence using Power BI are diverse, spanning industries like finance, marketing, healthcare, retail, and more. For instance, Power BI enables financial reporting, forecasting, and risk analysis. In marketing, it helps analyze customer behavior, track campaign performance, and optimize strategies.
In healthcare, Power BI supports analyzing patient data, resource allocation, and improving operational efficiency. In retail, it assists with inventory management, sales tracking, and customer segmentation.
Overall, integrating AI-driven analytics with Power BI empowers organizations to make data-driven decisions, boost efficiency, and gain a competitive edge.
Tips for using Power BI AI tools
If you’re starting your journey with AI-based tools, you might experience a shaky start. However, with expert guidance, you can fully unlock the potential of Power BI’s AI tools. From enhancing data visualization to optimizing insights, these strategies will help you effectively leverage Power BI and AI in your analytics workflow.
- Understand your data. Before applying AI tools, thoroughly examine your data sources, quality, and structure to ensure you obtain accurate and meaningful insights.
- Start small. Begin with simple AI features like Key Influencers or Anomaly Detection to familiarize yourself with Power BI’s AI visual capabilities before progressing to more complex analyses.
- Iterate and refine. Continuously refine your AI models and visualizations based on user feedback and evolving business needs to improve their accuracy and relevance over time.
- Collaborate. Foster collaboration among data analysts, business users, and AI experts to leverage diverse perspectives and ensure that AI-driven insights align with organizational goals.
To sum up
Here at Mitrix, we’ve witnessed the shift to online environments and the adoption of advanced technologies that have made data analysis increasingly accessible and precise. Companies en masse are turning toward informed planning for their future business development strategies.
It’s safe to say that AI won’t replace analysts anytime soon. However, analysts who incorporate such technologies into their daily workflow can gain so much at every step.
As a Top Power BI and data company, we’ve been helping businesses fully leverage the AI features in Power BI. Reach out to discover how our team can support your BI initiatives and drive smarter insights.