SkySoft Insights

AI Builder with Dataverse

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Overview

This guide covers how to implement an AI Builder Prediction Model in Microsoft Dynamics 365 that automatically scores incoming leads as Likely to Qualify or Unlikely to Qualify helping sales teams focus on the highest-value prospects.

What this solution delivers:

92% accurate binary classification model trained on 1,100 historical lead records
Real-time predictions written back to Dynamics 365 Lead records via Power Automate
Custom fields storing prediction result, probability score, and key influencing factors
Sales teams see AI qualification scores directly inside the CRM interface

Technology Stack:

AI Builder : model training, publishing, and prediction
Dataverse : Lead entity as training data source and prediction output store
Power Automate : automated flow to run predictions on new/updated leads
Dynamics 365 Sales : CRM interface where results are visible to the sales team

Model Accuracy: 92% | Status: Implemented & Tested

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AI Builder with Dataverse

Lead Qualification Prediction

Microsoft Dynamics 365 | AI Builder | Power Automate | Dataverse

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Step 1 : Prepare Your Training Data

The model is trained on historical lead records from the Dynamics 365 Lead entity. Before training, the data must be filtered to include only leads with a definitive outcome.

Data source:

Table: Leads (Dataverse) | Total records: 1,100 historical leads
Exclude leads with status New or Contacted these have no final outcome yet
Include only: Qualified, Lost, Cancelled, Cannot Contact, No Longer Interested

Predictor fields to use (available at lead creation time):

Budget Amount, Annual Revenue, No. of Employees, Industry
Job Title, Lead Source, Rating, Purchase Timeframe, Purchase Process
Marketing Material accepted, Budget availability status

Outcome field:

Field: Lead Status (Status Reason)
Positive class: Qualified
Negative class: Lost, Cancelled, Cannot Contact, No Longer Interested

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filter_opacity="100" filter_blur="0" filter_hue_hover="0" filter_saturation_hover="100" filter_brightness_hover="100" filter_contrast_hover="100" filter_invert_hover="0" filter_sepia_hover="0" filter_opacity_hover="100" filter_blur_hover="0" background_color="var(–awb-color2)"][fusion_builder_row][fusion_builder_column type="1_1" type="1_1" layout="1_1" align_self="auto" content_layout="column" align_content="flex-start" valign_content="flex-start" content_wrap="wrap" center_content="no" column_tag="div" target="_self" hide_on_mobile="small-visibility,medium-visibility,large-visibility" sticky_display="normal,sticky" order_medium="0" order_small="0" hover_type="none" border_style="solid" box_shadow="no" box_shadow_blur="0" box_shadow_spread="0" background_type="single" gradient_start_position="0" gradient_end_position="100" gradient_type="linear" radial_direction="center center" linear_angle="180" lazy_load="none" background_position="left top" background_repeat="no-repeat" background_blend_mode="none" sticky="off" sticky_devices="small-visibility,medium-visibility,large-visibility" absolute="off" filter_type="regular" filter_hover_element="self" filter_hue="0" filter_saturation="100" filter_brightness="100" filter_contrast="100" filter_invert="0" filter_sepia="0" filter_opacity="100" filter_blur="0" filter_hue_hover="0" filter_saturation_hover="100" filter_brightness_hover="100" filter_contrast_hover="100" filter_invert_hover="0" filter_sepia_hover="0" filter_opacity_hover="100" filter_blur_hover="0" transform_type="regular" transform_hover_element="self" transform_scale_x="1" transform_scale_y="1" transform_translate_x="0" transform_translate_y="0" transform_rotate="0" transform_skew_x="0" transform_skew_y="0" transform_scale_x_hover="1" transform_scale_y_hover="1" transform_translate_x_hover="0" transform_translate_y_hover="0" transform_rotate_hover="0" transform_skew_x_hover="0" transform_skew_y_hover="0" transition_duration="300" transition_easing="ease" scroll_motion_devices="small-visibility,medium-visibility,large-visibility" animation_direction="left" animation_speed="0.3" animation_delay="0" spacing_left="0" spacing_right="0" last="true" border_position="all" element_content="" border_color="hsla(var(–awb-color2-h),var(–awb-color2-s),calc(var(–awb-color2-l) – 2%),var(–awb-color2-a))" first="true" min_height="" link=""][fusion_text animation_direction="left" animation_speed="0.3" animation_delay="0" disable_idd="no" hide_on_mobile="small-visibility,medium-visibility,large-visibility" sticky_display="normal,sticky"]

Step 2 : Create & Train the AI Builder Model

Navigate to Power Apps → AI Hub → AI Models to create the prediction model.

Steps to create the model:

Open Power Apps (make.powerapps.com) and select AI Hub from the left menu
Click AI Models → New → Prediction → ‘Predict future outcomes from historical data’
Select the Lead table from Dataverse as your data source
Set the outcome field to Status Reason, map Qualified as positive and all others as negative
Add the 11 predictor fields listed in Step 1
Apply data filters: exclude Status Reason = New and Status Reason = Contacted
Click Train training takes approximately 5–10 minutes

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Step 3 : Review Results & Publish the Model

Once training completes, AI Builder shows the model performance report before you publish.

Model performance results:

Overall Accuracy: 92% on the held-out test dataset
Most influential features: Annual Revenue (31%), No. of Employees (23%), Industry (16%)
Training split: ~880 records for training, ~220 records for testing (80/20 split)

Publishing:

Review the performance metrics and influential factors screen
Click Publish to make the model available in Power Automate
Note the model name you will select it in the next step

Step 4 : Build the Power Automate Integration Flow

Create an automated cloud flow that runs every time a lead is created or updated in Dynamics 365, calls the AI Builder model, and writes the prediction results back to the lead record.

Flow setup:

Go to Power Automate → Create → Automated Cloud Flow
Flow Name: Lead Qualification Prediction
Trigger: Dataverse — ‘When a row is added, modified or deleted’
Set Table: Leads | Change type: Added or Modified | Scope: Organization

Add AI Builder prediction:

Add step: AI Builder → Predict → select your published model
Map each predictor field to the matching dynamic content from the Lead trigger

Write results back to the lead:

Add step: Dataverse → Update a row → Table: Leads
Map AI Qualification Prediction → prediction result (Yes/No)
Map AI Qualification Probability → probability score (0.0 to 1.0)
Map AI Prediction Insights → feature influence JSON output

Step 5 : Create Custom Fields & Test

Before activating the flow, create three custom fields on the Lead entity in Dataverse to store the AI prediction outputs.

Custom fields to create (Power Apps → Tables → Lead → Columns → New Column):

AI Qualification Prediction Choice field: Likely to Qualify / Unlikely to Qualify
AI Qualification Probability (%) Decimal Number (0.0 to 1.0)
AI Prediction Insights Multiple Lines of Text (stores feature influence JSON)

Testing the full flow:

Go to Dynamics 365 Sales Hub → Leads → New
Fill in predictor fields (Budget, Revenue, Industry, Job Title, etc.) and click Save
The flow triggers automatically wait 2–4 seconds then refresh the lead record
Open the AI Prediction tab to see qualification result, probability score, and insights
Verify the flow run in Power Automate → My Flows → Run History — all steps should show green

Example prediction output:

AI Qualification Prediction: Likely to Qualify
AI Qualification Probability: 0.98 (98%)
Top factors: Annual Revenue, No. of Employees, Rating (Hot), Job Title

Implementation Complete Model trained, published, integrated, and validated successfully.

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