Dynamics 365 Copilot: AI for Business Applications

Dynamics 365 Copilot brings AI assistance to sales, customer service, marketing, and supply chain. Generate emails, summarize records, predict outcomes, and automate routine tasks.

Sales Copilot

Help sellers close deals faster:

class SalesCopilot:
    """AI assistance for Dynamics 365 Sales."""

    async def draft_customer_email(
        self,
        customer_context: dict,
        email_purpose: str,
        tone: str = "professional"
    ) -> str:
        """Draft personalized customer email."""

        prompt = f"""Draft a sales email.

Customer: {customer_context.get('name')}
Company: {customer_context.get('company')}
Previous interactions: {customer_context.get('history', 'None')}
Open opportunities: {customer_context.get('opportunities', 'None')}

Purpose: {email_purpose}
Tone: {tone}

Generate a personalized email that builds on the relationship."""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )
        return response.content

    async def summarize_opportunity(
        self,
        opportunity_data: dict
    ) -> str:
        """Summarize opportunity with next steps."""

        import json
        opp_str = json.dumps(opportunity_data, indent=2)

        prompt = f"""Summarize this sales opportunity.

Opportunity Data:
{opp_str}

Provide:
1. Executive summary (2-3 sentences)
2. Key stakeholders and their concerns
3. Competition status
4. Risk factors
5. Recommended next actions"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )
        return response.content

    async def predict_deal_outcome(
        self,
        opportunity_data: dict,
        historical_patterns: dict
    ) -> dict:
        """Predict deal outcome with reasoning."""

        prompt = f"""Predict the outcome of this deal.

Opportunity: {opportunity_data}
Historical patterns: {historical_patterns}

Provide:
1. Win probability estimate
2. Key factors influencing prediction
3. Risks to close
4. Actions to improve chances"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )
        return {"prediction": response.content}

Customer Service Copilot

Resolve issues faster:

class CustomerServiceCopilot:
    """AI assistance for Dynamics 365 Customer Service."""

    async def suggest_resolution(
        self,
        case_details: dict,
        knowledge_base: list[dict]
    ) -> dict:
        """Suggest case resolution from knowledge base."""

        import json
        kb_str = json.dumps(knowledge_base[:5], indent=2)

        prompt = f"""Suggest resolution for this support case.

Case Details:
- Issue: {case_details.get('description')}
- Product: {case_details.get('product')}
- Priority: {case_details.get('priority')}
- Customer tier: {case_details.get('tier')}

Relevant Knowledge Articles:
{kb_str}

Provide:
1. Recommended resolution steps
2. Relevant KB articles to share
3. Estimated resolution time
4. Escalation criteria if needed"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )
        return {"resolution_suggestion": response.content}

    async def draft_response(
        self,
        customer_message: str,
        case_context: dict,
        resolution: str
    ) -> str:
        """Draft response to customer."""

        prompt = f"""Draft a response to this customer inquiry.

Customer message: {customer_message}
Case history: {case_context.get('history', 'New case')}
Resolution: {resolution}

Requirements:
- Empathetic and helpful tone
- Clear explanation of resolution
- Next steps if any
- Professional closing"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )
        return response.content

    async def summarize_case(self, case_data: dict) -> str:
        """Generate case summary for handoff."""

        import json
        case_str = json.dumps(case_data, indent=2)

        prompt = f"""Summarize this support case for handoff.

Case Data:
{case_str}

Include:
1. Issue summary
2. Actions taken
3. Current status
4. Customer sentiment
5. Recommended next steps"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )
        return response.content

Marketing Copilot

Create campaigns with AI:

class MarketingCopilot:
    """AI assistance for Dynamics 365 Marketing."""

    async def generate_campaign_content(
        self,
        campaign_brief: dict
    ) -> dict:
        """Generate marketing campaign content."""

        prompt = f"""Create marketing content for this campaign.

Campaign Brief:
- Objective: {campaign_brief.get('objective')}
- Target audience: {campaign_brief.get('audience')}
- Product/Service: {campaign_brief.get('product')}
- Channels: {campaign_brief.get('channels', ['email'])}
- Tone: {campaign_brief.get('tone', 'professional')}

Generate:
1. Campaign tagline options (3)
2. Email subject lines (5)
3. Email body template
4. Social media posts (3 per channel)
5. Call-to-action suggestions"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )
        return {"content": response.content}

    async def segment_description(
        self,
        segment_criteria: dict
    ) -> str:
        """Describe segment in natural language."""

        import json
        criteria_str = json.dumps(segment_criteria, indent=2)

        prompt = f"""Describe this marketing segment in plain language.

Segment Criteria:
{criteria_str}

Provide:
1. Segment description
2. Typical customer persona
3. Recommended engagement strategies
4. Content preferences"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )
        return response.content

Supply Chain Copilot

Optimize operations:

class SupplyChainCopilot:
    """AI assistance for Dynamics 365 Supply Chain."""

    async def analyze_disruption(
        self,
        disruption_data: dict,
        supply_chain_context: dict
    ) -> dict:
        """Analyze supply chain disruption impact."""

        import json

        prompt = f"""Analyze this supply chain disruption.

Disruption:
{json.dumps(disruption_data, indent=2)}

Supply Chain Context:
{json.dumps(supply_chain_context, indent=2)}

Provide:
1. Impact assessment
2. Affected products/orders
3. Alternative sourcing options
4. Recommended actions
5. Communication plan"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )
        return {"analysis": response.content}

    async def optimize_inventory(
        self,
        inventory_data: dict,
        demand_forecast: dict
    ) -> dict:
        """Suggest inventory optimization."""

        prompt = f"""Optimize inventory based on this data.

Current Inventory: {inventory_data}
Demand Forecast: {demand_forecast}

Recommend:
1. Items to reorder
2. Items with excess stock
3. Safety stock adjustments
4. Seasonal considerations"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )
        return {"recommendations": response.content}

Dynamics 365 Copilot transforms business applications from data systems into intelligent assistants that augment human decision-making.