Integrations · Azure AI Foundry
Azure AI Foundry natively connects to Dynamics 365 via Dataverse connectors and Azure API Management — enabling AI agents that read CRM context, trigger workflows, and update records autonomously.
What's Possible
Azure OpenAI reads Dynamics 365 contact history, firmographic data, and engagement signals to score and rank leads autonomously — updating CRM records and triggering SDR workflows without human intervention.
AI agents pull pipeline data from Dynamics 365 Sales, apply probabilistic scoring, and generate forecast commentary in natural language — giving sales leaders a narrative alongside the numbers.
Incoming cases in Dynamics 365 Customer Service are classified by urgency, category, and customer tier. The agent routes to the right team, populates resolution suggestions, and sets SLA timers automatically.
Azure AI Foundry agents connect to Dynamics 365 Finance & Operations via Dataverse and OData endpoints — automating purchase order creation, approval routing, and invoice matching against PO records.
AI dispatches field service jobs by reading technician availability, skill certifications, location, and job priority from Dynamics 365 Field Service — minimising travel time and SLA breaches.
Agents monitor contract renewal dates in Dynamics 365, draft renewal summaries, flag non-standard terms via Azure AI Document Intelligence, and alert account managers on a configurable lead-time.
How We Connect It
Azure API Management provides a unified gateway over Dynamics 365 REST and OData APIs. Authentication uses Entra ID managed identities — no service account passwords stored anywhere.
Semantic Kernel plugins wrap each Dynamics 365 API action. Azure AI Search indexes Dataverse tables for semantic retrieval — giving agents access to relational CRM data through natural language.
Agents are surfaced where work happens — as Copilot Studio bots inside Dynamics 365, Teams channels, or as background automation processes triggered by CRM events.
Use Cases
Scenario: Agent reads a new contact record in Dynamics 365, retrieves email engagement history from M365 Graph, and scores the lead against ICP criteria using Azure OpenAI.
Outcome: SDRs receive a pre-qualified lead with a recommended next action and email draft — reducing research time from 25 min to under 2 min per lead.
Scenario: A D365 Customer Service case arrives. The agent classifies the issue, retrieves similar resolved cases from Azure AI Search, and drafts a resolution for agent review or direct customer send.
Outcome: First-response time drops from hours to under 4 minutes. CSAT scores improve as customers receive specific, accurate replies rather than generic acknowledgements.
Scenario: Inventory sensor signal triggers a replenishment need. The AI agent checks approved vendor list in D365, validates budget availability in Finance & Operations, and raises a PO — routing for approval above threshold.
Outcome: PO cycle time drops from 3 days to 4 hours. Finance team reviews exception reports rather than processing every transaction manually.
Scenario: Agent runs nightly across all accounts approaching renewal. It reads product usage telemetry, support ticket history, and relationship health scores from D365 to produce a renewal risk rating.
Outcome: Account managers prioritise at-risk accounts 60 days before renewal rather than discovering churn risk after the fact. Renewal rates improve materially.
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