Power Automate
Neil Haddley • February 17, 2026
Connect Power Automate to Azure AI Foundry
I added an Azure AI Foundry model call to a Power Automate Flow.
Create Foundry
Create New Resource Group
Review + create
Create
Go to resource
Go to Foundry portal
Copy KEY1 and API Endpoint (we will need to adjust API Endpoint later)
New Solution
Add Environment variable to Solution
Create lead qualification foundry endpoint environment variable
Create lead qualification foundry api key environment variable
+New|Table|Tables
Start with Copilot
Lead table is related to (standard) Dataverse Account and Contact tables
Save and exit
Solution has one table and two environment variables
+New|App|Model-driven app
Create
+ Add page
Dataverse table
Add (Lead)
View new page
App is added to solution
+ Deploy model
gpt-4o-mini
Standard
I copied model Endpoint
I updated lead qualification foundry endpoint environment variable
+New|Automation|Cloud flow|Automated
Cloud flow will be triggered when dataverse row is added, updated or deleted?
I updated the trigger to run only when a new Lead record was added or when the Description column of an existing row was updated
I used an Enabled variable to know if AI Enable AI processing was True (notice that Dataverse maps Yes to 0 and No to 1)
EXPRESSION
1not(triggerOutputs()?['body/hadd_enableaiprocessing'])
I created a Condition based on the Enabled variable value
I called the AI model. I used the environment variables to set the URL and API-Key values
PROMPT
1{ 2 "messages": [ 3 { 4 "role": "system", 5 "content": "You are an expert sales manager. Your task is to classify a lead based solely on the provided description and output a structured assessment.\n\nUse the following categories for the lead's potential:\n- **Very Promising**: High urgency, clear budget, decision-maker involved, strong fit with our offering.\n- **High Potential**: Good fit, expressed interest, but may lack immediate budget or timeline clarity.\n- **Good Chance**: Moderate interest, some qualification criteria met, but needs nurturing.\n- **Moderate Interest**: Vague interest, initial inquiry, still exploring options.\n- **Low Interest**: Unlikely to convert soon; may be research-only, no budget, or mismatched needs.\n- **Other**: If none of the above apply, provide a brief, accurate label.\n\nIn addition, provide:\n- **score**: A numeric lead score from 0 to 100, where higher values indicate stronger potential and fit.\n- **confidence**: Your certainty in this assessment, expressed as one of: \"Low\", \"Medium\", or \"High\".\n- **explanation**: A brief 1–2 sentence justification for the category, score, and confidence level.\n\nOutput your response **only as a JSON object** with the following keys:\n- \"category\": string\n- \"score\": integer (0–100)\n- \"confidence\": string (\"Low\", \"Medium\", or \"High\")\n- \"explanation\": string\n\nDo not include any other text, markdown, or formatting—just the raw JSON." 6 }, 7 { 8 "role": "user", 9 "content": "@{triggerOutputs()?['body/hadd_description']}" 10 } 11 ], 12 "max_tokens": 400, 13 "temperature": 0 14}
I used Componse to fetch the result (content) from the body of the HTTP response
EXPRESSION
1body('Call_LLM_(HTTP)')?['choices'][0]?['message']?['content']
I used Parse JSON to extract the category, score, confidence and explaination values
EXPRESSION
1outputs('Get_content_(Compose)')
SCHEMA
1{ 2 "type": "object", 3 "properties": { 4 "category": { 5 "type": "string" 6 }, 7 "score": { 8 "type": "integer" 9 }, 10 "confidence": { 11 "type": "string" 12 }, 13 "explanation": { 14 "type": "string" 15 } 16 } 17}
I used a Dataverse Update a row action to update the record (row)
EXPRESSION
1body('Parse_Result_(Parse_JSON)')?['explanation']
EXPRESSION
1body('Parse_Result_(Parse_JSON)')?['score']
I used the model driven app to manually update the Description column value
I checked the Update a row action
I viewed the updated row in the model driven app