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The capability is designed for organizations using the Case Management Agent to automatically populate case fields from incoming emails, chats, or other customer interactions. It allows admins to validate how accurately AI predicts field values against real organizational data before those predictions are applied to live case records. 

The update addresses a common concern with AI-driven case automation. Even when field descriptions, prediction rules, and configuration settings appear correct, admins still need a reliable way to measure whether the AI can classify and populate case information accurately. Without that validation step, teams risk enabling automation that may create incorrect case data, require manual correction, or reduce trust in AI-assisted workflows. 

Case enrichment simulation gives admins a controlled testing environment for that validation. They can run predictions against historical case records or upload sample email and chat data through Excel. The system then compares predicted values with actual values, allowing teams to review accuracy at both the field level and case level. 

The feature includes field-level match reports, side-by-side comparisons of actual and predicted values, Excel export for offline review, and the ability to rerun simulations after refining field descriptions or lookup field guidance. It also supports shadow runs, allowing organizations to validate predictions using live production data without writing changes back to records. 

Microsoft said this helps admins tune case enrichment before rollout, rather than discovering prediction gaps after the feature is live. Teams can adjust descriptions, refine rules, rerun simulations, and enable case enrichment only when prediction quality meets their internal threshold. 

The capability is especially relevant for service organizations that want to use AI to reduce manual case handling while maintaining data quality. By validating predictions upfront, admins can improve confidence in the Case Management Agent and reduce the operational risk of inaccurate field population. 

Admins can access the feature through the Copilot Service admin center by navigating to Case settings, then Case Management Agent, Case creation and update, and Simulation.

The capability is designed to help contact centers manage call recording requirements more consistently across AI voice agents and human customer service representatives. It allows organizations to request consent through a Voice Agent and automatically carry that consent decision through the full interaction, including when the call is transferred to a live representative. 

The update addresses a growing compliance challenge for organizations operating across multiple regions. In many countries and jurisdictions, customer consent is required before a call can be recorded or transcribed. Until now, enforcing those requirements across AI agents, CSR handoffs, and recording controls often required custom logic, manual checks, or additional integrations. 

With Consent-Based Recording, Copilot Studio makers can configure a Voice Agent to ask for recording consent early in the conversation. If the caller grants consent, the call can be recorded and transcribed, and customer service representatives can use pause and resume recording controls after handoff. If consent is not granted, the call continues without recording or transcription, and the pause and resume controls are disabled for the representative. 

Microsoft said the consent decision is preserved and enforced throughout the interaction lifecycle, reducing the need for additional agent intervention or configuration during handoff. 

The feature is intended to reduce operational and compliance risk by making consent a system-enforced signal rather than something dependent on manual CSR judgment. This helps prevent accidental or unauthorized recording, especially in high-volume or globally distributed contact center environments. 

Consent-Based Recording also supports a smoother customer experience. Customers are not required to repeat consent decisions when moving from a Voice Agent to a live representative, and organizations can maintain a consistent recording policy across both AI-led and human-assisted interactions. 

The announcement reflects Microsoft’s broader focus on privacy-by-design in AI-powered customer engagement. As more organizations deploy voice agents and automate parts of the customer service journey, built-in consent controls are becoming increasingly important for regulatory compliance, customer trust, and operational simplicity.

The feature automatically analyzes customer emails and classifies sentiment as Positive, Neutral, or Negative. This insight appears directly within the email and case workspace, giving representatives added context about how a customer feels, not just what they are asking for. 

The update is aimed at helping customer service teams improve the quality of email-based support, especially in situations where customers express frustration, urgency, dissatisfaction, or appreciation. Microsoft said the capability is designed to help representatives respond with more empathy, adjust their tone earlier, and reduce the risk of escalation. 

Email remains one of the most common customer service channels, but emotional cues can be difficult to identify when teams are managing high volumes across regions, languages, and time zones. A customer may describe repeated service issues, delayed responses, or unresolved concerns in a long message, and the representative may focus only on the request itself. In those situations, even a technically correct response can feel cold if it does not acknowledge the customer’s frustration. 

Email Sentiment addresses this by surfacing emotional context before the response is drafted. For example, if a customer writes in after repeated service disruptions, the system can flag the email as Negative. The representative can then acknowledge the frustration upfront, use a calmer tone, and focus on resolution from the first reply rather than reacting only after the conversation escalates. 

The capability includes automatic sentiment detection, in-context insights within the email and case experience, consistent interpretation across representatives and regions, and support for multilingual service environments. It also includes an email sentiment timeline, which tracks how sentiment changes across a thread, and case sentiment, which rolls email signals into a broader case-level view for prioritization. 

Microsoft positions Email Sentiment as a complement to Email Assist and case management in Dynamics 365 Customer Service. Together, these capabilities are intended to help organizations bring emotional intelligence into everyday service workflows rather than relying only on individual judgment.

The update reflects Microsoft’s broader push to move Copilot from answering questions to executing work. Copilot Cowork, currently available through the Frontier program, is designed to let users delegate tasks and have AI complete them in the background across documents, inboxes, meetings, research workflows, and business systems. 

The new capabilities include support for iOS and Android, allowing users to assign work to Cowork from mobile devices. Because Cowork runs in the cloud, tasks can continue even when a user’s laptop is closed or they are away from their desk.  

Microsoft said this will help users delegate work during commutes, between meetings, or whenever a task comes up outside the traditional desktop environment. 

Microsoft is also introducing Cowork Skills, a feature that allows users and teams to turn repeatable work patterns into reusable instructions. A skill can define how a task should be completed, including structure, tone, process, and expected output. Built-in skills will support common Microsoft 365 workflows such as document creation, meeting coordination, and research. Users will also be able to create custom skills for team-specific or recurring processes. 

Copilot Cowork is also gaining deeper integration with enterprise tools through plugins. Microsoft is adding native integrations across its own ecosystem, including Fabric IQ with Power BI and Dynamics 365 applications across sales, customer service, and ERP. These integrations are expected to support workflows such as pipeline reviews, case resolution, order approvals, and data-driven reporting. 

Microsoft also plans to release connectors for third-party platforms including LSEG, Miro, monday.com, and S&P Global Energy. Organizations will be able to build custom plugins to extend Cowork into their own systems and business processes.

The announcement comes as AI agents are increasingly performing tasks that were traditionally handled by human employees. In Dynamics 365, agents such as the Quality Evaluation Agent, Case Management Agent, and Customer Intent Agent are evaluating interactions, managing cases, identifying customer intent, and contributing directly to customer service operations. 

As organizations expand their use of AI, many can accurately forecast customer demand but struggle to predict the AI resources required to support that demand. While workforce planning models can estimate call volumes, case volumes, staffing needs, and seasonal fluctuations, they often provide little visibility into future AI consumption. As a result, AI-related costs frequently become apparent only after usage has occurred. 

Microsoft said AI Credit Estimation connects operational forecasting with AI cost modeling, allowing organizations to estimate future AI credit consumption using forecasted service demand. The capability enables finance teams, service leaders, and IT departments to incorporate projected AI usage into budgeting, workforce planning, and capacity management decisions. 

The estimation process begins with the creation of a forecast scenario using Dynamics 365 forecasting capabilities. The platform analyzes historical trends, seasonality, and workload patterns to predict future demand across selected channels and queues. Organizations can then choose which AI agents they want to evaluate, including Microsoft's out-of-the-box agents such as the Quality Evaluation Agent, Case Management Agent, and Customer Intent Agent. The system then converts forecasted workloads into projected AI credit consumption estimates. 

By linking expected demand directly to AI usage, Microsoft aims to provide organizations with greater visibility into the operational and financial impact of AI deployments. The company positions the capability as a way to move AI planning from a reactive process to a more predictable and proactive one. 

The development also reflects a broader shift in how organizations manage customer service operations. Traditionally, workforce planning focused primarily on human employees, while AI was treated as a separate technology investment. As AI agents become more integrated into day-to-day operations, businesses increasingly need planning models that account for both human and AI contributions. 

Microsoft's AI Credit Estimation is intended to support that transition by helping organizations evaluate AI consumption alongside workforce requirements. As AI agents handle a growing share of customer interactions, companies are expected to place greater emphasis on planning for a blended workforce made up of both people and AI systems.