Predicting business outcomes has always been critical for making informed decisions. However, traditional forecasting methods require complex statistical models, manual data analysis, and significant human effort.
With AI Builder in Power Platform, businesses can now leverage AI-driven predictions without needing advanced data science skills. By integrating AI Builder with Power Automate, Power Apps, and Dataverse, organizations can automate predictions, optimize processes, and make data-driven decisions in real-time.
1. Why Automate Business Predictions?
Manual business forecasting often leads to:
Delays in decision-making due to slow analysis.
Inaccurate predictions from human errors.
Missed opportunities because of outdated or incomplete data.
AI-powered predictions help businesses:
✔ Forecast sales trends and demand for better inventory management.
✔ Predict customer churn and retention rates.
✔ Identify fraudulent transactions and risks.
✔ Automate financial forecasting and cash flow predictions.
🔹 Example: A retail company uses AI Builder to predict seasonal sales trends, allowing them to stock the right amount of inventory.
2. How AI Builder Works for Predictions
AI Builder’s Prediction Model uses historical data to identify patterns and forecast future outcomes. The model:
- Learns from past data (e.g., customer behavior, sales figures, financial trends).
- Identifies key patterns influencing outcomes.
- Provides a probability-based prediction for future events.
Example: A subscription-based business uses AI Builder to predict which customers are at risk of canceling their memberships, helping them take proactive retention measures.
3. Step-by-Step: Automating Predictions with AI Builder
Step 1: Create a Prediction Model in AI Builder
1️⃣ Open Power Apps and go to AI Builder.
2️⃣ Select “Prediction” and click “Create a model”.
3️⃣ Choose the type of prediction:
- Yes/No Predictions (e.g., “Will a customer renew their subscription?”)
- Numerical Predictions (e.g., “What will next month’s sales revenue be?”)
4️⃣ Select a historical dataset (from Dataverse, SharePoint, or Excel).
5️⃣ Train the model by selecting input fields that influence the prediction (e.g., purchase history, engagement data, demographics).
6️⃣ Test the model and refine it for accuracy.
7️⃣ Publish the AI model for use in Power Automate or Power Apps.
Step 2: Integrate Predictions into Power Automate
1️⃣ Open Power Automate and create a New Flow.
2️⃣ Select a trigger, such as:
- “When a new lead is added to CRM”
- “When an order is placed”
- “When an invoice is overdue”
3️⃣ Add an AI Builder action: Use the Prediction model.
4️⃣ Use conditions to automate responses based on predictions: - If a customer has a high churn risk, send them a special discount offer.
- If sales predictions indicate low demand, adjust inventory orders automatically.
- If a payment is likely to be delayed, trigger a follow-up email.
Example: A financial services company predicts which customers may default on payments and Power Automate sends early reminders to reduce late payments.
Step 3: Use Predictions to Improve Business Workflows
1️⃣ Automate sales forecasting – AI Builder predicts future revenue trends, and Power Automate updates dashboards in Power BI.
2️⃣ Improve customer retention – AI Builder predicts which customers are likely to leave, and Power Automate sends personalized engagement emails.
3️⃣ Enhance fraud detection – AI Builder identifies suspicious transactions, and Power Automate alerts the compliance team.
Example: A telecom company predicts which customers are likely to switch to competitors, then Power Automate triggers loyalty offers via email.
4. Real-World Use Cases of AI-Powered Predictions
Sales & Lead Scoring
Challenge: Sales teams struggle to identify high-value leads.
Solution: AI Builder predicts which leads are most likely to convert, helping sales teams prioritize their efforts.
Customer Churn Prediction
Challenge: Businesses lose customers without warning.
Solution: AI Builder analyzes customer behavior to predict who might cancel services, and Power Automate triggers retention campaigns.
Demand Forecasting
Challenge: Retailers struggle with stock shortages or overstocking.
Solution: AI Builder predicts demand based on historical sales and market trends, helping businesses adjust inventory accordingly.
Financial Risk Analysis
Challenge: Late payments and financial risks impact cash flow.
Solution: AI Builder predicts which invoices are at risk of being unpaid, allowing finance teams to take proactive measures.
5. The Future of AI-Based Business Predictions
Upcoming Trends:
Real-Time Predictive Analytics – AI models continuously update with live data.
Industry-Specific AI Models – Custom prediction models tailored for healthcare, finance, and retail.
Deeper Integration with Power BI – AI-generated forecasts visualized in real-time dashboards.
Automated Decision-Making – AI models not only predict but also take automated actions.
Businesses that leverage AI-powered business predictions will gain a competitive advantage by making faster, smarter, and more data-driven decisions.