Microsoft Dynamics 365 Blog
The future of agentic CRM and ERP
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Support Parallel Processing for Archive Jobs in Dynamics 365 Finance and Operations
We’re pleased to introduce a new capability for Dynamics 365 Finance and Operations archive with Dataverse long-term retention: parallel processing for archive jobs. -
Introducing Custom Voice for Dynamics 365 Contact Center
Custom Neural Voices enable organizations to move beyond generic text-to-speech and create voice experiences that reflect their brand, values, and customer promise—while still benefiting from the scalability and intelligence of AI-driven contact centers. -
Expanding global reach with Globalization Studio and new Türkiye localization in Dynamics 365 ERP
Global ERP environments are under increasing pressure.
Explore by Job Role
News and product updates
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All Transfer Capabilities in Dynamics 365 Contact Center
Dynamics 365 Contact Center enables seamless customer handoffs with flexible transfer options, including direct, warm, queue, Teams, and external transfers. -
Building Smarter Observability for Agentic ERP World using Dynamics 365
As enterprise workloads become more agentic, the expectations of ERP systems—and the teams that operate them—are shifting.
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Tips and guides
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Reimagining Secure Customer Interactions with Secure Consult & Transfer
Across industries such as financial services, healthcare, and public sector, organizations must enable customers to complete high‑trust actions (like payments or identity verification) without exposing sensitive information to agents or core contact center systems. -
Evaluating AI Agents in Contact Centers: Introducing the Multi-modal Agents Score
Introducing the Multimodal Agent Score (MAS)—a unified, absolute measure of end‑to‑end conversational quality designed for AI agents operating across modalities. -
Measuring What Matters: Redefining Excellence for AI Agents in the Contact Center
The contact center industry is at an inflection point. -
The Key Facets of AI Evaluation in the Contact Center
Organizations must think about when, how, by whom, and on what data AI systems are evaluated.