Step-by-Step Breakdown:

1. Introduction to the use of Power Automate and OpenAI for automating tasks.
2. Demonstration of a common case classification scenario in engineering.
3. Identification of key fields to automate: case type, subject, and priority.
4. Explanation of how case data (calls, emails, comments) is collected and processed.
5. Integration of OpenAI via Azure to classify cases using system and user chat messages.
6. Details on setting context for classification including priority standards and subject IDs.
7. Execution of the flow in Power Automate to retrieve and process case data.
8. Feeding processed data into OpenAI and receiving classification results.
9. Updating the case in Dynamics with the returned classification values.
10. Adding a note to the case to inform engineers of the automated classification.
11. Discussion on security: using a private model within Azure to ensure data privacy.

12. Cost analysis: estimating token usage and cost per case classification.
13. Scalability and efficiency benefits of automation over manual classification.
14. Broader applications including email processing, lead classification, and knowledge base integration.

Key Takeaways:

• Power Automate and OpenAI can significantly reduce manual effort in case classification.
• Using a private model within Azure ensures data security and compliance.
• Automated classification is cost-effective, scalable, and improves operational efficiency.
• The approach can be extended to various business processes beyond case management.