Smarter Workflows in D365 with AI and Machine Learning

Smarter Workflows in D365 with AI and Machine Learning

Discover how to elevate your Dynamics 365 workflows to new heights with the latest advancements in AI and machine learning. In this insightful blog post, we delve into practical strategies and expert insights from CRM Knowledge to empower businesses in streamlining processes, reducing errors, and achieving unparalleled efficiency. From integrating AI models with Power Automate to harnessing predictive analytics for informed decision-making, learn how to unlock the full potential of Dynamics 365 and drive transformative results for your organization. Join us as we navigate the evolving landscape of CRM innovation, equipping you with the knowledge and tools to stay ahead in today's competitive business environment.

As businesses strive to stay competitive, the need for streamlined and efficient processes has never been greater. Dynamics 365 CRM is already a powerful tool for managing customer relationships and business operations. However, by integrating artificial intelligence and machine learning into your workflows, you can take your automation and efficiency to the next level. At CRM Knowledge, we specialise in helping businesses harness the full potential of Dynamics 365. In this blog post, we'll explore two practical ways to enhance your Dynamics 365 workflows with AI and machine learning: integrating AI models with Power Automate and using predictive analytics for smarter workflows.

Integrating AI Models with Power Automate 

Power Automate allows users to create automated workflows between various applications and services, simplifying business processes and improving efficiency. By integrating AI models into these workflows, you can significantly enhance their capabilities.

Using AI Builder 

AI Builder is a feature within the Power Platform that empowers users to add AI capabilities to their workflows without the need for coding. It offers a range of pre-built and customisable AI models, making it easy to integrate AI into your Dynamics 365 workflows.

Types of Models Available:

·       Form Processing: Automatically extract data from forms, such as invoices or purchase orders, and input it into Dynamics 365.

·       Object Detection: Identify and count objects within images, useful for inventory management or quality control.

·       Text Classification: Categorise and tag text data, such as customer feedback or support tickets, to streamline processing and response.


Step-by-Step Guide 

Step 1: Access AI Builder in Power Automate

·       Navigate to Power Automate:

·       Go to the Power Automate portal (

·       Sign in with your Microsoft account.

·       Open AI Builder:

·       In the left-hand menu, click on "AI Builder."

·       Select "Build" to view available AI models.


Step 2: Choose and Configure an AI Model

·       Choose the AI model that fits your needs. For this guide, we'll use "Form Processing."

·       Click on "Form Processing" and then "Create."

·       Upload a set of sample forms to train the model.

·       Follow the prompts to label the data fields you want to extract.

·       After labeling the fields, click "Train" to let AI Builder analyse the forms and learn to extract data.


Step 3: Integrate the AI Model into a Workflow

·       Create a New Flow:

·       Return to the Power Automate home page.

·       Click on "Create" and select "Automated Flow."

·       Choose a trigger that starts the workflow, such as "When a new email arrives" or "When a new record is created in Dynamics 365."

·       In the flow editor, click on "New Step."

·       Search for "AI Builder" and select the "Predict" action.

·       Choose the AI model you created (e.g., Form Processing).

·       Specify the input data for the AI model, such as the email attachment or a file uploaded to SharePoint.

·       Map the extracted data fields to Dynamics 365 entities or other workflow steps.


Step 4: Test and Deploy the Workflow

·       Test the Workflow:

·       Run a test to ensure the AI model correctly extracts and processes the data.

·       Review the results and make any necessary adjustments.

·       Once satisfied with the test results, save and activate the flow.

·       Monitor the flow's performance and make iterative improvements as needed.


Using Predictive Analytics for Smarter Workflows 

Predictive analytics leverages historical data, statistical algorithms, and machine learning techniques to forecast future outcomes. By incorporating predictive analytics into your Dynamics 365 workflows, you can make more proactive and informed decisions, ultimately driving better business outcomes.


Identifying Use Cases 

Predictive analytics can be applied across various aspects of business operations. Here are some key use cases where predictive analytics can be particularly beneficial:

·       Lead Scoring: 

Predict which leads are most likely to convert, allowing sales teams to prioritize their efforts on high-potential prospects.

·       Sales Forecasting: 

Forecast future sales based on historical data and current trends, helping businesses set realistic targets and plan resources effectively.

·       Customer Churn Prediction: 

Identify customers at risk of churning by analysing patterns in their behaviour, enabling proactive retention strategies.

·       Inventory Management: 

Predict inventory needs based on past sales data, seasonal trends, and other factors to optimize stock levels and reduce carrying costs.

·       Maintenance Scheduling:

Predict equipment failures or maintenance needs using historical performance data, ensuring timely maintenance and reducing downtime.


Implementing Predictive Models 

Implementing predictive models in Dynamics 365 involves several steps, from data preparation to model deployment. Here’s how to get started:

Step 1: Data Preparation

·       Collect Data:

Gather relevant historical data from Dynamics 365 and other sources. Ensure the data is clean and well-organised.

·       Preprocess Data:

Clean and preprocess the data to handle missing values, outliers, and inconsistencies. This step is crucial for accurate predictions.


Step 2: Train Predictive Models

·       Choose a Machine Learning Tool:

Utilise tools like Azure Machine Learning, Power BI, or other machine learning platforms to develop your predictive models.

·       Select an Algorithm:

Choose the appropriate machine learning algorithm based on your use case. For instance, use logistic regression for churn prediction or time series analysis for sales forecasting.

·       Train the Model:

Split your data into training and testing sets. Train the model on the training data and validate its performance on the testing data.

·       Evaluate the Model:

Assess the model's accuracy, precision, recall, and other relevant metrics. Fine-tune the model parameters as needed to improve performance.


Step 3: Deploy Predictive Models in Dynamics 365 Workflows

·       Export the Model:

Once satisfied with the model's performance, export it from the machine learning platform. Azure Machine Learning and other platforms offer options to deploy models as web services.

·       Integrate with Power Automate:

Use Power Automate to integrate the predictive model into your Dynamics 365 workflows. Create a new flow and add a step to call the predictive model’s web service.

·       Configure the Prediction Step:

Configure the flow to send the necessary input data to the predictive model and process the output. For example, update a lead's score or flag a customer at risk of churning based on the model's prediction.


Example Workflow: Sales Forecasting

Trigger: When a new sales opportunity is created in Dynamics 365.

Prediction Step: Call the predictive model to forecast the likelihood of closing the deal.

Action: Update the opportunity record with the predicted probability and notify the sales team for follow-up.

Integrating AI models with Power Automate and using predictive analytics in your Dynamics 365 workflows can transform your business processes, making them smarter and more efficient. By making the most of these advanced technologies, you can save time, reduce errors, and make more informed decisions. At CRM Knowledge, we are dedicated to helping you unlock the full potential of Dynamics 365. Contact us for expert guidance and support in implementing AI and machine learning solutions tailored to your business needs. Contact us at to find out more about we can support your business.

Peter Clements

Peter is the founder and chief consultant at CRM Knowledge. With an impressive background spanning over 20 years, Peter has established himself as a leading expert in CRM systems, particularly specialising in Microsoft Dynamics 365.Throughout his career, Peter has been dedicated to providing great support, training, and consulting services in the realm of CRM. His in-depth knowledge and hands-on experience with D365 have made him a trusted advisor for countless businesses seeking to optimise their customer relationship management processes.