Scenarios

Real-world use cases with step-by-step technology recommendations.

Table of contents

  1. How to Use This Guide
  2. Scenario 1: HR Knowledge Base Bot
  3. Scenario 2: Invoice Processing Automation
  4. Scenario 3: Customer Support Agent with Enterprise Knowledge
  5. Scenario 6: Privacy-First / Offline Field Agent
  6. Scenario 7: Agentic DevOps (End-to-End Lifecycle)
  7. Scenario 8: Legacy App Modernization
  8. Scenario Comparison Matrix
  9. Next Steps

How to Use This Guide

Use scenarios as a guided learning path: they show how the same Microsoft portfolio components combine in different ways depending on constraints (data boundary, governance, skills, time-to-production). If you’re not sure where to start, use the Decision Framework to pick a build style and trust boundary, then use Evaluation Criteria to sanity-check complexity and readiness.

Each scenario follows this structure:

  • Business Context: The problem to solve
  • Key Requirements: Must-have capabilities
  • Recommended Technologies: Best-fit Microsoft AI technologies
  • Implementation Steps: High-level approach
  • Alternative Approaches: Other valid options

Scenario 1: HR Knowledge Base Bot

Business Context

Employees need instant answers to HR questions (policies, benefits, PTO) without waiting for HR staff responses. HR team maintains documentation in SharePoint but also depends on Workday and ServiceNow to view status or create tickets.

Key Requirements

  • Access company HR policies and procedures
  • Available in Microsoft Teams (where employees work)
  • Stay within M365 trust boundary (sensitive HR data)
  • Connect HR knowledge with Workday/ServiceNow/SAP workflows
  • No coding resources available (HR team manages)
  • Quick time to production (weeks not months)

Primary Solution: Employee Self-Service agent for Microsoft 365 Copilot

Component Technology Purpose
Agent Platform Employee Self-Service managed solution in Copilot Studio Prebuilt HR + IT agent with guardrails, instructions, telemetry
Knowledge Sources SharePoint knowledge connectors with filtering Serve curated HR policies and official answers
HR/IT Transactions Workday, ServiceNow, SAP SuccessFactors extension packs Read/write employee data and tickets with packaged Power Platform flows
Deployment Microsoft 365 Copilot (Teams, Outlook) Keeps experiences inside M365 tenant and Entra ID
Governance Power Platform ALM (dev/test/prod) Enforces DLP, approvals, and monitoring

Why This Stack:

Implementation Steps

  1. Prepare Environment & Prerequisites (1-2 days)
    • Confirm Microsoft 365 Copilot licensing or Copilot Studio prepaid/Pay-as-you-go coverage
    • Provision dev/test/prod Power Platform environments and allow required connectors per DLP policies
    • Assign Global Admin, Power Platform Admin, security, and HR owners for the rollout (Prerequisites to deploy the Employee Self-Service agent, Nov 5 2025)
  2. Install Employee Self-Service Solution (1 day)
  3. Configure Knowledge & Systems (3-4 days)
  4. Test & Govern (3-5 days)
    • Build golden prompts, regression suites, and telemetry dashboards
    • Pilot with HR stewards, validate ticket creation, and review safety rails
    • Document handoff procedures for sensitive topics or low confidence responses (Customize the Employee Self-Service agent, Nov 5 2025)
  5. Deploy & Monitor (1 day)

Time to Production: 2-3 weeks (depends on connector entitlement)
Ongoing Maintenance: HR team updates SharePoint content, monitors connectors, and evolves prompts via ESS telemetry

Alternative Approaches

If you cannot adopt ESS yet:

  • Stay with Copilot Studio + SharePoint knowledge connectors for a knowledge-only bot, then graduate to ESS once Power Platform environments and connector allowlists are ready.

If you need bespoke orchestration:

  • Use M365 Agents SDK or Azure AI Foundry Agent Service for custom routing, non-supported HRIS integrations, or multi-channel experiences beyond Microsoft 365 Copilot. Incorporate Azure AI Search BYOK when data spans on-premises or third-party stores (Agentic retrieval overview, May 2025).

Scenario 2: Invoice Processing Automation

Business Context

Accounts Payable team manually processes 500+ supplier invoices per month. Need to extract data (vendor, amount, line items) and update ERP system automatically.

Key Requirements

  • Extract structured data from PDF/image invoices
  • Validate against purchase orders
  • Route for approval if amount > $10K
  • Update ERP system (SAP, Dynamics, etc.)
  • Low-code solution for finance team to manage

Primary Solution: AI Builder + Power Automate + Dataverse

Component Technology Purpose
Document Processing AI Builder (Invoice Model) Prebuilt OCR + extraction
Workflow Power Automate Orchestration and approvals
Data Storage Dataverse Temporary invoice data
Integration Power Platform Connectors Connect to ERP

Why This Stack:

  • AI Builder invoice model: Prebuilt, high accuracy for invoices (Streamline document processing with AI Builder)
  • Power Automate: Visual workflow designer for finance team
  • Dataverse: Stores extracted data before ERP update
  • 1,400+ connectors: Likely has ERP connector

Implementation Steps

  1. Set Up AI Builder Model (1 day)
    • Activate AI Builder in Power Platform environment
    • Test prebuilt invoice model with sample invoices
    • Verify extraction accuracy (vendor, date, amount, line items)
  2. Create Dataverse Tables (1 day)
    • Create “Invoice” table (vendor, amount, status, etc.)
    • Create “Invoice Line Item” table (description, quantity, price)
    • Set up relationships
  3. Build Power Automate Flow (3-5 days)
    • Trigger: Email arrives with invoice attachment or SharePoint folder
    • Action 1: AI Builder processes invoice
    • Action 2: Store extracted data in Dataverse
    • Action 3: Validate against PO (if applicable)
    • Action 4: If amount > $10K → approval workflow
    • Action 5: Update ERP via connector
    • Action 6: Notify AP team via Teams
  4. Handle Exceptions (2-3 days)
    • Build fallback for low-confidence extractions
    • Manual review queue in Power Apps
    • Error handling and retries
  5. Test & Deploy (1 week)
    • Test with historical invoices
    • Pilot with one supplier
    • Gradually expand to all invoices

Time to Production: 3-4 weeks
Cost Savings: 80-90% reduction in manual data entry

Alternative Approaches

If invoices are non-standard:

If you need multimodal processing:

Scenario 3: Customer Support Agent with Enterprise Knowledge

Business Context

Support team handles 1,000+ tickets per month with repetitive questions. Company knowledge spread across SharePoint, Confluence, Salesforce, and internal databases. Need AI agent to deflect simple questions and assist support agents.

Key Requirements

  • Answer questions from multiple knowledge sources
  • Available on company website and Teams
  • Escalate complex issues to human agents
  • Track customer satisfaction
  • Developers available for custom integrations

Primary Solution: Copilot Studio + Azure AI Search (BYOK)

Component Technology Purpose
Agent Platform Copilot Studio Multi-channel agent deployment
Knowledge Retrieval Azure AI Search Unified index across data sources
Deployment Web widget + Teams Customer-facing and internal
Integrations Custom actions (Azure Functions) Ticket creation, CRM lookups

Why This Stack:

  • Azure AI Search: Indexes multiple disparate sources (SharePoint, Confluence, databases, etc.) with agentic retrieval preview (What’s new in Azure AI Search, May 2025)
  • Copilot Studio BYOK: Connects to Azure AI Search for advanced RAG
  • Multi-channel: Same agent on website and Teams
  • Custom actions: Extend with ticket creation, order lookups

Implementation Steps

  1. Build Azure AI Search Index (1-2 weeks)
    • Deploy Azure AI Search service
    • Create indexers for each data source (SharePoint, Confluence, SQL)
    • Configure knowledge agent objects and semantic ranking (Agentic retrieval overview)
    • Test retrieval quality
  2. Build Copilot Studio Agent (1-2 weeks)
    • Create agent with generative answers
    • Configure BYOK to connect Azure AI Search
    • Test answer quality and citations
    • Add topics for specific workflows (create ticket, track order)
  3. Build Custom Actions (1-2 weeks)
    • Azure Function: Create ticket in Zendesk/ServiceNow
    • Azure Function: Look up customer in CRM
    • Register as custom actions in Copilot Studio
  4. Deploy Multi-Channel (1 week)
    • Publish to website (JavaScript widget)
    • Publish to Teams for internal support agents
    • Configure authentication (SSO for Teams, anonymous for web)
  5. Monitor & Improve (Ongoing)
    • Track resolution rate and customer satisfaction
    • Review unresolved questions
    • Add new topics and refine prompts

Time to Production: 6-8 weeks
Expected Impact: 50-70% ticket deflection on common questions

Alternative Approaches

If you need full control:

If budget is limited:

  • Start with Copilot Studio + SharePoint only (no Azure AI Search)
  • Expand data sources gradually

Scenario 6: Privacy-First / Offline Field Agent

Business Context

Field technicians need to analyze schematics and summarize repair logs while working in remote locations with intermittent connectivity. The data is highly sensitive (IP) and cannot leave the device for cloud inference due to strict regulatory compliance.

Key Requirements

  • Offline Capability: Must function without internet access.
  • Zero Data Exfiltration: Inference must happen locally on the device.
  • Low Latency: Instant analysis of text/images.
  • Cost Efficiency: High volume of queries shouldn’t incur cloud token costs.

Primary Solution: Windows AI Foundry (Phi-4-mini + Windows ML)

Component Technology Purpose
Local Model Phi-4-mini (via Foundry Local) High-quality reasoning on-device (3.8B params)
Runtime Windows ML (ONNX) Hardware-accelerated inference (NPU/GPU)
App Framework Windows App SDK Native Windows application
Data Access Local File System Secure access to schematics/logs

Why This Stack:

  • Privacy: Data never leaves the device; meets strict compliance.
  • Offline: Full functionality without connectivity.
  • Cost: Zero cloud inference costs; leverages client hardware.
  • Performance: NPU acceleration ensures low latency.

Implementation Steps

  1. Model Selection: Use foundry model list to select Phi-4-mini.
  2. App Integration: Use Windows App SDK to integrate the model via Windows ML.
  3. Optimization: Use AI Toolkit for VS Code to quantize/optimize the model for the target device.
  4. Deployment: Package the app with the model (or download on first run).

Scenario 7: Agentic DevOps (End-to-End Lifecycle)

Business Context

A software development team is bogged down by routine maintenance, technical debt, and production firefighting. They spend more time fixing bugs and writing boilerplate tests than innovating.

Key Requirements

  • Autonomous Coding: Delegate complex tasks (refactoring, testing) to an AI agent.
  • Asynchronous Work: Agent works in the background without blocking the developer.
  • Production Monitoring: AI proactively detects and diagnoses production issues.
  • Self-Healing: Automated root cause analysis and fix suggestions.

Primary Solution: GitHub Copilot Coding Agent + Azure SRE Agent

Component Technology Purpose
Developer Agent GitHub Copilot Coding Agent Autonomous coding, testing, and refactoring
Operations Agent Azure SRE Agent Production monitoring, alert response, RCA
Platform GitHub Unified workflow (Issues, PRs, CI/CD)
IDE VS Code (Agent Mode) “Peer programmer” for complex local tasks

Why This Stack:

  • Coding Agent: Moves beyond “autocomplete” to “autonomy”. It can take a GitHub Issue (“Fix the login bug”) and independently write code, run tests, and open a PR.
  • SRE Agent: Connects production reality back to development. It detects an outage, analyzes logs, identifies the root cause, and can even assign a fix task to the Coding Agent.
  • Unified Flow: The entire lifecycle (Code -> Deploy -> Monitor -> Fix) is orchestrated by agents working together.

Implementation Steps

  1. Enable Agents: Activate GitHub Copilot Coding Agent and Azure SRE Agent (Preview).
  2. Configure SRE Agent: Connect to Azure Monitor and Kubernetes clusters.
  3. Delegate Tasks: Developers assign routine refactoring and test coverage tasks to the Coding Agent via GitHub Issues.
  4. Monitor & Respond: SRE Agent watches production. Upon alert, it performs RCA and logs findings.
  5. Review & Merge: Developers review the PRs created by the Coding Agent (either for features or SRE fixes) and merge.

Scenario 8: Legacy App Modernization

Business Context

An enterprise has hundreds of legacy Java and .NET applications running on outdated frameworks. Manual upgrades are cost-prohibitive and risky. Security vulnerabilities in old dependencies are a major risk.

Key Requirements

  • Scale: Upgrade hundreds of apps/repos.
  • Accuracy: Handle complex dependency chains and breaking changes.
  • Safety: Ensure the upgraded app still compiles and passes tests.
  • Speed: Reduce timeline from years to months.

Primary Solution: GitHub Copilot App Modernization

Component Technology Purpose
Modernization Agent GitHub Copilot App Modernization Specialized agent for Java/.NET upgrades
Platform GitHub Repository hosting and CI/CD
Validation GitHub Actions Automated testing of upgraded code

Why This Stack:

  • Specialized Knowledge: The agent is specifically trained on migration patterns (e.g., .NET Framework to .NET 8, Java 8 to 17).
  • Plan & Execute: It doesn’t just write code; it generates a comprehensive upgrade plan, executes it across thousands of files, and summarizes the changes.
  • Risk Reduction: Automated remediation of security vulnerabilities during the upgrade process.

Implementation Steps

  1. Assessment: Point the App Modernization agent at the legacy repository.
  2. Plan Generation: Review the AI-generated upgrade plan (identified dependencies, breaking changes).
  3. Execution: Authorize the agent to execute the plan. It modifies project files, code, and configurations.
  4. Validation: Agent runs local builds/tests.
  5. Review: Developer reviews the massive PR (often touching hundreds of files) and merges.

Scenario Comparison Matrix

Scenario Complexity Time to Prod Skill Level Key Technology
HR Knowledge Base Low 2-3 weeks Maker Employee Self-Service + Workday/ServiceNow packs
Invoice Processing Low-Medium 3-4 weeks Maker AI Builder + Power Automate
Customer Support Medium 6-8 weeks Professional dev Copilot Studio + Azure AI Search (agentic retrieval)
Privacy-First Field Agent Medium 4-8 weeks Professional dev Windows AI Foundry (Local Phi-4-mini)
Agentic DevOps High 4-6 weeks Professional dev GitHub Copilot Coding Agent + Azure SRE Agent
Legacy App Modernization High 3-6 months Professional dev GitHub Copilot App Modernization
Copilot-to-Copilot Mesh Medium 3-4 weeks Maker + light dev Copilot Studio A2A + MCP tools
Financial Reconciliation (Multi-Agent) High 4-6 weeks Pro dev Foundry Agent Service + Cosmos DB threads
Multi-Channel Corporate Assistant Medium 3-5 weeks Pro dev M365 Agents SDK (Teams/Outlook/M365 Chat)

Next Steps

Need to evaluate your situation?
→ Evaluation Criteria to assess requirements

Need detailed technology comparison?
→ Feature Comparison for side-by-side analysis

Need visual decision guidance?
→ Visual Framework for decision tree diagrams

Need implementation patterns?
→ Implementation Patterns for architecture guidance

Last Updated: December 2025
Next: Visual Framework - Walk the decision trees to choose the right path

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Copyright © 2025. This documentation is based on official Microsoft sources and best practices.