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    Prediction Models

    Rumman Ansari June 08, 2026 12 views Subject Details

    Prediction Models in AI Builder

    Prediction Models are an important feature of Microsoft AI Builder that help users predict future outcomes based on historical data. These models use past records, patterns, and business information to estimate what may happen in the future.

    In simple words, a Prediction Model learns from existing data and gives a possible result for new or future data. For example, it can predict whether a customer may buy a product, whether a customer may leave a service, whether an application may be approved, or whether a business opportunity may be won.

    What is a Prediction Model?

    A Prediction Model is an AI model that analyzes historical data and identifies patterns. After learning from those patterns, it can predict future outcomes for new records.

    For example, if a company has historical sales data, customer data, and purchase behavior data, a prediction model can analyze that information and predict which customers are more likely to purchase again.

    Prediction Model in AI Builder

    In AI Builder, users can create prediction models without writing complex machine learning code. AI Builder provides a low-code experience where users can select data, choose the outcome to predict, train the model, and use the model inside Power Apps or Power Automate.

    Prediction models are useful when an organization has historical data and wants to make better business decisions using AI-based predictions.

    Why Prediction Models are Important?

    Importance Explanation
    Supports better decision-making Prediction models help businesses make decisions based on data patterns instead of assumptions.
    Identifies future possibilities They estimate what may happen next by analyzing historical records.
    Improves business planning Organizations can plan sales, resources, inventory, and customer service more effectively.
    Reduces risk Predictions can help identify possible risks before they become major issues.
    Automates intelligent decisions Predicted outcomes can be used in Power Automate to trigger workflows automatically.

    How Prediction Models Work

    A Prediction Model works by studying historical data. Historical data means past records that already contain known results. The model uses this data to learn relationships between input columns and the final outcome.

    1. The user selects a data source such as a Dataverse table.
    2. The user selects the outcome column that needs to be predicted.
    3. The user chooses the columns that should be used for training the model.
    4. AI Builder trains the model using historical data.
    5. The model learns patterns from existing records.
    6. The trained model predicts outcomes for new records.
    7. The prediction result can be used in Power Apps or Power Automate.

    Simple Example of Prediction

    Suppose an online shopping company wants to predict whether a visitor will make a purchase. The company already has historical data about visitors, such as page visits, session duration, product views, bounce rate, and previous purchase behavior.

    AI Builder can use this historical data to build a prediction model. When a new visitor comes to the website, the model can predict whether that visitor is likely to purchase or not.

    Types of Outcomes Prediction Models Can Predict

    Prediction models can be used for different types of business outcomes. The outcome depends on the type of data available and the business question.

    Outcome Type Explanation Example
    Yes or No Outcome Predicts whether something will happen or not. Will the customer renew the subscription?
    Category Outcome Predicts which category or class a record belongs to. High risk, medium risk, or low risk customer.
    Numeric Outcome Predicts a number or value. Expected sales amount or estimated cost.
    Business Status Outcome Predicts the possible status of a business process. Opportunity won, lost, or pending.

    Key Terms Used in Prediction Models

    Term Meaning
    Historical Data Past data that contains known results and is used to train the model.
    Outcome Column The column that contains the result to be predicted.
    Training Data The selected data used by AI Builder to teach the prediction model.
    Prediction The estimated result generated by the model for new data.
    Confidence Score A value that indicates how confident the model is about its prediction.
    Model Training The process where the AI model learns from historical data.
    Model Publishing The process of making a trained model available for use in apps and flows.

    Steps to Create a Prediction Model in AI Builder

    Step 1: Identify the Business Question

    First, clearly define what you want to predict. A good prediction model starts with a clear business question.

    Examples:

    • Will this customer buy the product?
    • Will this employee request be approved?
    • Will this sales opportunity be successful?
    • What is the expected revenue amount?

    Step 2: Prepare Historical Data

    The model needs historical data to learn from. The data should contain previous records and their actual outcomes.

    For example, if you want to predict whether a customer will renew a subscription, your historical data should contain previous customer records and whether they renewed or did not renew.

    Step 3: Select the Data Table

    In AI Builder, select the table that contains the data. In many Power Platform solutions, this data is stored in Dataverse.

    Step 4: Choose the Outcome Column

    Select the column that contains the result you want the model to predict. This is called the outcome column.

    Example:

    • Renewed Subscription: Yes or No
    • Purchase Made: Yes or No
    • Opportunity Status: Won or Lost
    • Estimated Revenue: Numeric Value

    Step 5: Select Training Columns

    Select the columns that may help the model understand patterns. These columns are used as input information for prediction.

    Example training columns:

    • Customer Age
    • Purchase History
    • Location
    • Product Category
    • Number of Previous Orders
    • Support Ticket Count

    Step 6: Train the Model

    After selecting the data and outcome, train the model. During training, AI Builder analyzes historical patterns and learns how input data is related to the outcome.

    Step 7: Review Model Performance

    After training, review the model performance. This helps users understand whether the model is useful enough for business use.

    Step 8: Publish the Model

    Once the model is ready, publish it so that it can be used in Power Apps and Power Automate.

    Prediction Model with Power Apps

    Prediction Models can be used in Power Apps to create intelligent applications. For example, a sales application can show the probability of winning a sales opportunity.

    Example:

    • A sales executive opens a customer record in Power Apps.
    • The app sends customer information to the AI Builder prediction model.
    • The model predicts whether the customer is likely to purchase.
    • The prediction result is displayed inside the app.
    • The sales executive uses the result to prioritize follow-up actions.

    Prediction Model with Power Automate

    Prediction Models can also be used in Power Automate to create intelligent workflows. The predicted result can help decide what action should happen next.

    Example automation flow:

    1. A new sales lead is created in Dataverse.
    2. Power Automate sends the lead data to the AI Builder prediction model.
    3. The model predicts whether the lead is high priority or low priority.
    4. If the lead is high priority, the flow sends a notification to the sales team.
    5. If the lead is low priority, the flow adds it to a follow-up list.

    Real-Life Business Use Cases

    Business Area Prediction Use Case Example
    Sales Lead conversion prediction Predict whether a sales lead is likely to convert into a customer.
    Customer Service Customer churn prediction Predict whether a customer may stop using a service.
    Finance Payment risk prediction Predict whether a customer may delay payment.
    Human Resources Request approval prediction Predict whether a request may be approved based on historical patterns.
    Retail Purchase prediction Predict whether a customer is likely to buy a product.
    Operations Issue risk prediction Predict whether a process or task may face delay.

    Example Scenario: Customer Churn Prediction

    Suppose a company wants to identify customers who may stop using its service. The company has historical customer data such as subscription duration, support tickets, payment history, product usage, and renewal status.

    AI Builder can use this historical data to create a prediction model. The model can analyze current customers and predict which customers may be at risk of leaving.

    Based on the prediction, the company can:

    • Send retention offers to high-risk customers
    • Assign customer success managers for follow-up
    • Create automated reminders in Power Automate
    • Improve customer support for at-risk accounts
    • Track churn risk in a Power Apps dashboard

    Prediction Model Input and Output

    Element Description Example
    Input Data Data used by the model to make a prediction. Customer age, location, purchase history, ticket count.
    Historical Outcome Past result used to train the model. Purchased or Not Purchased.
    Prediction Output The predicted result for new data. Likely to Purchase: Yes.
    Confidence How confident the model is about the prediction. High, medium, low, or percentage-based confidence.

    Advantages of Prediction Models

    • They help organizations make data-driven decisions.
    • They reduce dependency on manual guesswork.
    • They can identify risks and opportunities early.
    • They improve productivity by supporting automated decisions.
    • They can be used inside Power Apps and Power Automate.
    • They help prioritize important records such as leads, customers, or requests.

    Limitations of Prediction Models

    • The model depends heavily on the quality of historical data.
    • If the data is incomplete or inaccurate, predictions may not be reliable.
    • Prediction results are estimates, not guaranteed outcomes.
    • The model may need retraining when business patterns change.
    • Human review is still important for critical business decisions.
    • Prediction models may not work well if there is not enough useful historical data.

    Best Practices for Prediction Models

    Best Practice Explanation
    Define a clear business question Know exactly what you want the model to predict before creating it.
    Use clean historical data Accurate and complete historical data improves prediction quality.
    Select relevant columns Use columns that are meaningfully related to the outcome.
    Avoid unnecessary columns Too many irrelevant columns may reduce model usefulness.
    Review model performance Check whether the model result is useful before using it in real business processes.
    Use human validation For important decisions, let users review prediction results before final action.
    Retrain when data changes If business behavior changes, update and retrain the model with new data.

    Prediction Model vs Other AI Builder Models

    Model Type Main Purpose Example
    Prediction Model Predicts future outcomes using historical data. Predict whether a customer will buy a product.
    Form Processing Model Extracts specific fields from forms and documents. Extract invoice number and amount from invoices.
    Object Detection Detects objects inside images. Detect products on a shelf.
    Text Recognition Extracts text from images and documents. Read text from a scanned receipt.

    Simple Practical Example

    A training institute wants to predict whether a student will complete a course successfully. The institute has historical data such as attendance percentage, assignment submission status, quiz scores, fee payment status, and previous course completion data.

    A Prediction Model can be created using this data. For every new student, the model can predict the possibility of course completion.

    1. Student data is stored in Dataverse.
    2. AI Builder trains the model using previous student records.
    3. The model predicts course completion probability for new students.
    4. Power Apps displays the prediction result to the admin team.
    5. Power Automate sends support reminders for students who may need help.

    Where Prediction Models Can Be Used

    • Sales forecasting
    • Lead scoring
    • Customer churn prediction
    • Inventory demand prediction
    • Risk identification
    • Approval probability prediction
    • Student success prediction
    • Service ticket priority prediction
    • Marketing campaign response prediction

    Important Points to Remember

    • Prediction Models use historical data to predict future outcomes.
    • They are useful for decision-making and business automation.
    • The quality of prediction depends on the quality of data.
    • The model needs a clear outcome column for training.
    • Prediction results should be reviewed in important business scenarios.
    • Prediction Models can be used with Power Apps and Power Automate.

    Conclusion

    Prediction Models in AI Builder help organizations use historical data to estimate future results. They are useful for sales, customer service, finance, operations, education, and many other business areas where decision-making depends on patterns in past data.

    By using Prediction Models with Power Apps, Power Automate, and Dataverse, businesses can create intelligent applications and workflows that support better planning, faster decisions, and smarter automation. Although predictions are not guaranteed results, they provide valuable guidance that can help users take proactive business actions.

    Microsoft Power Platform Mastery