![\"\"](\"https:\/\/www.inogic.com\/blog\/wp-content\/uploads\/2026\/04\/Automating-Training-Request-Approvals-Using-AI-in-Microsoft-Copilot-Studio-1.png\")
Organizations frequently receive employee requests for training programs, certifications, or skill-development courses. Traditionally, these requests go through manual review and approval processes which can delay decision making and create administrative overhead.<\/p>\n
With Advanced Approvals in Microsoft Copilot Studio, it is possible to automate such decisions using AI. Instead of relying on human approval stages, AI can evaluate the request details and decide whether the request should be approved or rejected based on predefined criteria.<\/p>\n
In this article, we will build a Training Request Approval System where:<\/p>\n
This implementation demonstrates how AI-driven approvals can automate business decisions without human intervention.<\/p>\n
Before starting, ensure the following are available:<\/p>\n
The workflow implemented in this article follows a simple structure.<\/p>\n
This removes the need for manual manager approvals and allows faster decision making.<\/p>\n
First, create a Dataverse table that will store the training requests.<\/strong><\/p>\n Example table: Training Requests<\/p>\n Suggested columns:<\/p>\n This table will be used by the AI flow to read and update request details.<\/p>\n Navigate to Copilot Studio \u2192 Agent Flows and create a new flow.<\/p>\n Agent flows allow you to automate processes using AI and actions connected to data sources like Dataverse.<\/p>\n In this implementation, the agent flow will:<\/p>\n Add the Run a multistage approval action in the flow.<\/p>\n This feature allows AI to evaluate requests based on specific instructions.<\/p>\n Since this implementation focuses on AI-only approval, no manual stages are added.<\/p>\n The flow will only contain the Evaluate Request AI stage.<\/p>\n Inside the Evaluate Request step, define clear instructions for the AI model so it knows how to evaluate the request.<\/p>\n Example instructions:<\/p>\n Evaluate the employee training request and decide whether it should be Approved or Rejected. <\/em><\/p>\n APPROVE the request if ALL of the following are true : <\/em><\/p>\n – The trainingCost <= 1000.00<\/em><\/p>\n – trainingStartDate is after the date the course is purchased (i.e. coursePurchaseDate)<\/em><\/p>\n – The request contains all required details including employeeName, courseName, trainingCost, and trainingStartDate.<\/em><\/p>\n REJECT the request if any of the above are false.<\/em><\/p>\n Note: Here <\/strong>trainingCost, trainingStartDate, employeeName, courseName, coursePurchaseDate are dynamic fields as shown below in the image<\/strong><\/p>\n These instructions guide the AI model to consistently evaluate each training request.<\/p>\n After the AI evaluates the request, configure the next step in the flow to update the Dataverse record.<\/p>\n Based on the AI decision:<\/p>\n This ensures the final decision is stored directly in Dataverse.<\/p>\n Once the flow is configured:<\/p>\n This demonstrates how AI can independently make approval decisions based on defined rules.<\/p>\n While implementing AI approvals in Copilot Studio, you may encounter some configuration challenges.<\/p>\n 1.Writing Effective AI Instructions<\/strong><\/p>\n The AI model relies heavily on the instructions provided. If instructions are vague, the decision may be inconsistent.<\/p>\n To avoid this:<\/p>\n 2.Mapping Dataverse Fields<\/strong><\/p>\n Incorrect field mapping between the agent flow and Dataverse may prevent the AI from reading request data correctly.<\/p>\n Always verify:<\/p>\n 3.<\/strong>Understanding AI Decision Outputs<\/strong><\/p>\n The AI stage returns structured output which must be correctly interpreted when updating Dataverse records. Improper condition checks may cause incorrect status updates.<\/p>\n 4.Preview Feature Limitations<\/strong><\/p>\n The Multistage Approval feature is currently in preview, so some UI elements or configurations may change over time.<\/p>\n Implementing AI-based approvals provides several advantages:<\/p>\n Advanced approvals in Microsoft Copilot Studio open new possibilities for automating decision-based workflows. In this example, we built a Training Request Approval System where AI evaluates each request and determines whether it should be approved or rejected.<\/p>\n Organizations frequently receive employee requests for training programs, certifications, or skill-development courses. Traditionally, these requests go through manual review and approval processes which can delay decision making and create administrative overhead. With Advanced Approvals in Microsoft Copilot Studio, it is possible to automate such decisions using AI. Instead of relying on human approval stages, AI\u2026 Read More »<\/a><\/span><\/p>\n","protected":false},"author":15,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[3313,2361],"tags":[3335],"class_list":["post-44244","post","type-post","status-publish","format-standard","hentry","category-microsoft-copilot-studio","category-technical","tag-training-request-approval-systerm"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.inogic.com\/blog\/wp-json\/wp\/v2\/posts\/44244","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.inogic.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.inogic.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.inogic.com\/blog\/wp-json\/wp\/v2\/users\/15"}],"replies":[{"embeddable":true,"href":"https:\/\/www.inogic.com\/blog\/wp-json\/wp\/v2\/comments?post=44244"}],"version-history":[{"count":0,"href":"https:\/\/www.inogic.com\/blog\/wp-json\/wp\/v2\/posts\/44244\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.inogic.com\/blog\/wp-json\/wp\/v2\/media?parent=44244"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.inogic.com\/blog\/wp-json\/wp\/v2\/categories?post=44244"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.inogic.com\/blog\/wp-json\/wp\/v2\/tags?post=44244"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n\n
\n Column Name<\/td>\n Type<\/td>\n<\/tr>\n \n Employee Name<\/td>\n Text<\/td>\n<\/tr>\n \n Course Name<\/td>\n Text<\/td>\n<\/tr>\n \n Training Provider<\/td>\n Text<\/td>\n<\/tr>\n \n Cost<\/td>\n Currency<\/td>\n<\/tr>\n \n Training Date<\/td>\n Date<\/td>\n<\/tr>\n \n Approval Status<\/td>\n Choice (Pending, Approved, Rejected)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Step 2: Create an Agent Flow in Copilot Studio<\/strong><\/h3>\n
\n
![\"Training](\"https:\/\/www.inogic.com\/blog\/wp-content\/uploads\/2026\/04\/Automating-Training-1-300x137.jpg\")
\nAgent Flow creation screen in Copilot Studio<\/p>\nStep 3: Configure the Multistage Approval Step<\/strong><\/h3>\n
![\"Training](\"https:\/\/www.inogic.com\/blog\/wp-content\/uploads\/2026\/04\/Automating-Training-2-300x136.jpg\")
Run a multistage approval (preview) configuration screen<\/p>\nStep 4: Define AI Evaluation Instructions<\/strong><\/h3>\n
![\"Automating](\"https:\/\/www.inogic.com\/blog\/wp-content\/uploads\/2026\/04\/Automating-Training-3-300x137.jpg\")
AI instruction configuration inside the Evaluate Request stage<\/p>\nStep 5: Update the Dataverse Record<\/strong><\/h3>\n
\n
![\"Training](\"https:\/\/www.inogic.com\/blog\/wp-content\/uploads\/2026\/04\/Automating-Training-4-300x137.jpg\")
\nDataverse Update Row action in the flow<\/p>\nTesting the AI Approval Process<\/h3>\n
\n
![\"Training](\"https:\/\/www.inogic.com\/blog\/wp-content\/uploads\/2026\/04\/Automating-Training-5-300x136.jpg\")
Dataverse Update Row action flow when all conditions are met \/ true<\/p>\n![\"Training](\"https:\/\/www.inogic.com\/blog\/wp-content\/uploads\/2026\/04\/Automating-Training-6-300x137.jpg\")
\nDataverse Update Row action flow when all conditions are not met \/ false<\/p>\n![\"Training](\"https:\/\/www.inogic.com\/blog\/wp-content\/uploads\/2026\/04\/Automating-Training-7-300x137.jpg\")
\nExample training request record before and after AI evaluation<\/p>\nChallenges You May Encounter<\/strong><\/h3>\n
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Benefits of AI-Driven Approvals<\/strong><\/h3>\n
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Conclusion<\/strong><\/h3>\n
FAQs: Automating Training Request Approvals with AI in Microsoft Copilot Studio<\/strong><\/h3>\n
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\nAI-driven approval in Microsoft Copilot Studio automates the decision-making process for requests, such as employee training requests. Instead of waiting for human manager approval, AI evaluates the request based on predefined rules and updates the approval status automatically in Dataverse.<\/li>\n
\nThe system works in a few steps:<\/li>\n<\/ul>\n\n
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\nTo implement AI-based training approvals, you need:<\/li>\n<\/ul>\n\n
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\nYes, AI can handle approval entirely if the evaluation logic is clearly defined. AI ensures faster, consistent, and scalable approvals, reducing administrative overhead. However, organizations can still add manual review stages if needed.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"