Entity Extraction with AI Builder in Power Automate: A Comprehensive Guide

Entity extraction, powered by Artificial Intelligence (AI), is becoming increasingly vital for automating data processing and streamlining workflows. Microsoft’s Power Automate, coupled with AI Builder, offers a robust solution for extracting key information from unstructured text. This article details how to leverage entity extraction within Power Automate to unlock valuable insights from your data.

What is Entity Extraction?

Entity extraction is the process of automatically identifying and categorizing key pieces of information—like names, places, dates, organizations, and quantities—from text. This transforms unstructured text into structured data, enabling automated processes and improved data analysis. Google Cloud defines entity extraction as a crucial step in turning raw text into usable information.

Using the Prebuilt Entity Extraction Model in Power Automate

Power Automate simplifies entity extraction through its integration with AI Builder. Here’s a step-by-step guide to implementing this functionality:

1. Sign in to Power Automate: Start by logging into your Power Automate account.
2. Create a New Flow: Select “My flows” and then “New flow” > “Automated cloud flow.”
3. Choose a Trigger: Name your flow and select a trigger. A common trigger is “When a new email arrives V3 (Office 365 Outlook).”
4. Convert to Plain Text: Add a “Html to text” action (preview) to convert the email body (or other text source) to plain text. Select the body from the Dynamic Content list.
5. Add the Entity Extraction Action: Select “+ New step” and navigate to “AI Builder.” Choose “Extract entities from text with the standard model.” You can also opt to use a custom model if you have one. Microsoft’s AI Builder documentation provides detailed instructions.
6. Configure the Action:
   • Language: Select the appropriate language for the text.
   • Text: Select the plain text content from the Dynamic Content list (the output of the “Html to text” action).
7. Utilize Extracted Entities: In subsequent actions, you can use the columns extracted by the AI Builder model, such as “Entity type” and “Entity value.” For example, you can send an email with the extracted information.
8. Save and Test: Save your flow and select “Test” to verify its functionality.

Applications of Entity Extraction

Entity extraction, as highlighted by SPGuides, has diverse applications. One example is automating the processing of visitor requests for factory tours. By extracting details like visitor names, contact information, and preferred dates from emails, Power Automate can automatically create an Excel file with this information, streamlining the scheduling process.

Key Takeaways

• Entity extraction transforms unstructured text into structured data.
• Power Automate and AI Builder provide a user-friendly interface for implementing entity extraction.
• This technology automates data processing, saving time and improving accuracy.
• Entity extraction has broad applications across various industries, from manufacturing to customer service.