🔍 Problem Statement:
A multinational telecom company receives over 5,000 service cases per day, including complaints, installation requests, network issues, and billing queries.
Despite an existing CRM setup, their key challenges were
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❌ Manual triage and routing of cases to different departments based on poorly structured customer input
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❌ Inefficiency in summarizing large case histories for quick resolution
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❌ High average handling time (AHT) and agent fatigue
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❌ Lack of real-time suggestions for agents handling similar issues in the past
They wanted a smarter system to improve the first-call resolution rate, assist agents, and reduce escalation volume — without rebuilding their D365 setup.
🎯 Solution Goals:
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Automatically understand and categorize inbound service requests
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Suggest previous case resolutions in real-time using AI
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Help agents summarize case history and communication using Copilot
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Use Power Automate and Dataverse for task routing, knowledge base tagging, and notifications
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Maintain compliance and auditability
💡 Solution Overview:
We built an AI-powered service case resolution engine using
| Technology | Purpose |
|---|---|
| Power Apps (Model-driven) | Case Management UI |
| Power Automate | Routing, tagging, notifications |
| Dataverse | Centralized data and relationships |
| Power Virtual Agents + Copilot | AI-enabled assistant for agents |
| AI Builder + Azure OpenAI | Case summarization, resolution suggestions |
Key Components:
1️⃣ AI-Powered Case Triage (Copilot in PVA)
When a case is submitted, Copilot uses natural language understanding (NLU) to extract:
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Issue category (e.g., Billing, Technical)
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Urgency based on keywords
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Sentiment analysis for escalation risk
Example Input: "My broadband is down since morning and I have a critical online interview"
Copilot Response: “Priority: High | Category: Network Outage | Sentiment: Negative”
This information is saved into the case record using Power Automate.
2️⃣ Copilot-Driven Resolution Suggestion
When an agent opens a case:
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Copilot in Dynamics 365 auto-summarizes past interactions and logs
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It suggests similar resolved cases with resolution notes and attached KB articles
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Agents can click "Apply Resolution Steps" and copy them to the current case
3️⃣ Case Summary Generator
Before closing a case, agents use Copilot to auto-generate the case summary and closure email, based on:
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Case timeline
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Activities
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Resolution steps
They can edit and send this directly from within Dynamics 365.
4️⃣ Power Automate Workflows
Power Automate handles:
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Escalating negative sentiment cases to supervisors
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Routing tasks to billing or technical departments
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Sending SMS/Email via connectors after resolution
✅ Results:
| Metric | Before | After |
|---|---|---|
| First Call Resolution | 48% | 79% |
| Agent Handling Time | Avg. 17 mins | Avg. 8 mins |
| Escalation Volume | High | Reduced by 60% |
| Customer Satisfaction (CSAT) | 3.2 / 5 | 4.5/5 |
🧠 Technical Insights:
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Copilot in D365 Customer Service is not just for chat—it becomes a real-time AI advisor.
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Use semantic matching in Copilot Studio to suggest knowledge base articles.
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AI Builder models can be trained for classification if OpenAI is not an option.
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Maintain transparency: Copilot-generated suggestions are always editable and auditable.
🛡️ Governance Tip:
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Use environment variables and data loss prevention (DLP) policies to safeguard Copilot and OpenAI API usage in Power Platform.
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Regularly audit case summaries to ensure Copilot's language remains professional and policy-compliant.
💬 Final Thoughts:
By combining Power Platform's low-code automation with Copilot’s intelligence, we didn't just automate—we augmented human decision-making. This hybrid approach is the future of enterprise service management.
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