Rapidly Training an AI Model

with Microsoft Power Automate AI Builder

1. Context & Objectives

• Client Need: Businesses need to automate document processing—extracting structured data (e.g. invoice fields) from unstructured inputs.

• Goal: Demonstrate how to train a custom AI model within Power Automate AI Builder in just minutes, requiring no coding skills.

2. Solution Overview

Microsoft’s AI Builder—part of the Power Platform—provides low-code capabilities for extracting text, images, and data from documents directly within Power Automate

3. Step-by-step Workflow
4. Data Selection
   Choose a set of sample documents (e.g. invoices, forms) containing key fields such as billing address, customer ID, or total amount.
5. Model Creation
   Within Power Automates AI Builder:

• Select “Form processing” or “Invoice text” as the model type.

• Define the fields you want the model to extract (e.g., Billing Addresss, customer Name).

3. Model Training

• Upload a small batch of labelled documents (~5–10)

• Assign labels to those fields

• Train the model—Power Automate processes the examples and learns to extract the fields automatically,

4. Testing & Validation

• Validate against test documents

• Mark missing or misread fields, retrain until accuracy is satisfactory

5. Publishing & Integration

• The trained model is published into AI Builder

• It can then be used as an action in automated flows within Power

•  Automate

4. Results & Benefits

• Speed & Ease: Models can be created, trained, and deployed within minutes—ideal for teams without formal AI expertise.

• Versatile Use Cases:

• Invoice data extraction

• Form processing (e.g., applications, surveys)

• Category classification, entity extraction, sentiment analysis—with minimal setup

• Seamless Automation: Once published, models can be integrated into existing Power Automate workflows to trigger downstream actions (e.g., store data, notify stakeholders).

5. Key Takeaways

6. Future Considerations

• Scaling: As data volume grows, you may retrain with more samples for better accuracy.

• Expand Use Cases: Leverage AI Builder’s other models, such as sentiment analysis or custom entity extraction, to further enrich automations

• Governance & Monitoring: Track model performance over time, add guardrails for error handling and continuous improvement.

🔍 Summary

This case study demonstrates how Power Automate AI Builder transforms document automation. From uploading a handful of labelled documents to deploying a live AI-infused automation pipeline, organisations can integrate intelligent capabilities swiftly empowering improved accuracy, efficiency, and scalability with no coding required.