AI is becoming part of the operational workforce

Customer service operations are entering a new phase of evolution, where AI is no longer just assisting work but actively performing it. In Dynamics 365 Customer Service and Contact Center, AI agents now drive core service workflows every single day. For example:

• Quality Evaluation Agent autonomously evaluates customer interactions against defined criteria, generating scores, summaries, and actionable insights to improve service quality at scale.
• Case Management Agent automates the case lifecycle by creating, updating, resolving, and closing cases using conversation context, reducing manual effort and improving consistency.
• Customer Intent Agent identifies customer intent from conversations, enabling intelligent self-service, guided interactions, and accurate routing to the right resources.

These agents are no longer peripheral tools; they are executing real workloads and contributing directly to business outcomes. This shift changes how organizations need to think about AI. If these agents are doing meaningful work, they are no longer optional enhancements; they are part of the workforce itself.

The Planning Gap: Demand Forecasting Without AI Cost Visibility

While AI adoption has accelerated, workforce planning practices have not kept pace.

Organizations today are well equipped to forecast demand, predicting case volumes, conversation trends, and channel distribution. However, when it comes to AI, a critical layer is missing. Teams often lack clear visibility into how forecasted demand translates into AI usage and, more importantly, into cost.

The challenge is not forecasting demand, but translating that demand into financial impact. Organizations can estimate how much work is coming, but they struggle to answer a simple question: if this is the expected demand, what will it cost in AI consumption?

Without a clear mapping between forecasted workload and AI credit consumption, planning becomes reactive. Finance teams discover AI costs only after they have occurred. Service leaders lack the data to allocate AI capacity confidently.  Even small variations in demand can lead to unexpected changes in consumption, especially as AI handles a growing share of interactions.

Addressing this challenge requires connecting operational forecasting with financial modeling, enabling organizations to plan with both demand and cost in mind.

Introducing AI Credit Estimation in Dynamics 365

AI Credit Estimation in Dynamics 365 Customer Service and Contact Center brings these domains together.

Built on top of the platform’s intelligent forecasting capabilities, it provides a direct and transparent way to translate forecasted demand into expected AI credit consumption. Rather than treating AI cost as something to be reconciled after the fact, AI Credit Estimation makes it a first-class planning input alongside case volumes, staffing ratios, and service level objectives.

This allows organizations to move from abstract assumptions to measurable planning insights.

How it works: A Step-by-Step Walkthrough

Here is how to get from volume demand forecast to credit estimate in three steps:

Step 1: Define and Generate a Forecast Scenario

Start by creating a forecast scenario within the Dynamics 365 Customer Service or Contact Center forecasting experience. Specify the planning horizon and include the channels and queues that are in scope.

The platform’s intelligent forecasting model analyzes historical traffic patterns, including seasonal variation, and trend lines  to generate predicted volumes for each queue and channel over your chosen time period. The result is a clear, data-driven view of expected demand.

Step 2: Select the AI Agents to Estimate

With your forecast in place, open the AI Credit Estimator and select the agent whose credit consumption you want to model. All out-of-the-box agents available in Dynamics 365 Customer Service and Contact Center are supported, including:

• Quality Evaluation Agent
• Case Management Agent
• Customer Intent Agent

Step 3: Review Your Credit Estimate

Once agent is selected, the estimation engine maps your forecasted workload to projected AI credit consumption. The output is a clear, readable credit estimate tied to the queues included in your forecast scenario.

This gives your team a single planning view that shows both expected demand and the associated AI credit cost required to service that demand. From here, teams can validate assumptions, adjust scenario parameters, and refine estimates before committing to capacity planning.

The screen below shows the estimator within the forecast scenario.

Shift Toward a unified workforce model

The deeper significance of AI Credit Estimation goes beyond a single feature. It represents a shift in how organizations can think about and manage AI in their service operations.

Until now, AI agents have often been treated as a separate category, evaluated for capability but not managed with the same financial and operational rigor as human staffing. AI Credit Estimation changes that. When you can forecast demand, estimate AI cost, and compare both against service objectives, AI agents become truly plannable workforce components.

This matters for several reasons:

• Finance teams can include AI credit projections in budget cycles and operational reviews, rather than discovering AI costs after they land on an invoice.
• Service operations leaders can balance human and AI capacity proactively, adjusting the mix based on cost, quality, and service level goals.
• IT administrators and solution architects can use credit estimates to validate deployment decisions and right-size AI agent usage before scaling.

As AI continues to take on a larger share of customer interactions the ability to plan human and AI agents within a unified workforce model becomes essential for both operational efficiency and financial transparency.

Learn more about AI credit Estimation To learn more about AI credit estimation in Dynamics 365 Customer Service and Contact Center, read the documentation on Microsoft Learn.

The post The next frontier of workforce planning: from forecasting to AI usage and Credit Estimation appeared first on Microsoft Dynamics 365 Blog.

Customers today expect more than a fast response — they expect to feel heard. Support emails often carry clear emotional signals like frustration, urgency, or appreciation, and when those are missed, even a correct reply can feel cold and increase escalation risk.

Email Sentiment in Dynamics 365 Customer Service brings that emotional context directly into the email workflow, helping service representatives respond with the right tone, empathy, and intent.

What is Email Sentiment?

An AI‑powered capability that automatically analyzes incoming customer emails and classifies their tone as Positive, Neutral, or Negative. The insight surfaces in the email and case workspace before the service representative drafts a reply — so they understand not just what the customer is asking, but how they feel.

Why sentiment awareness matters

Customers come from different backgrounds, geographies, and cultures, all of which shape how they express concern or frustration. Meanwhile, service teams handle high email volumes across regions, languages, and time zones, where emotional cues are easy to miss in long or complex messages.

With Email Sentiment in Dynamics 365 Customer Service, organizations can:

• Recognize dissatisfied or frustrated customers earlier
• Help service representatives proactively adjust tone and prioritize higher‑risk conversations
• Drive more empathetic communication, lower escalations, and stronger relationships

How it works

When a customer writes in after repeated service disruptions, Email Sentiment flags the message as Negative before service representatives reply. With that context, they can acknowledge frustration upfront, adopt a calmer tone, and focus on resolution — responding thoughtfully from the first reply instead of reacting after an escalation.

Key capabilities

• Automatic sentiment detection — no manual tagging or configuration
• In‑context insights — displayed directly in the email and case experience
• Consistent interpretation at scale — uniform classification across representatives and regions
• Language‑aware foundation — supports quality assessment and multilingual scenarios
• Actionable emotional signals — enables prioritization and empathy‑driven responses
• Email sentiment timeline — tracks sentiment evolution across a thread to spot inflection points
• Case sentiment — rolls email signals into a unified case sentiment for smarter prioritization

Designed for customer-obsessed organizations

Email Sentiment in Dynamics 365 Customer Service complements Email Assist and case management by adding emotional intelligence to everyday interactions, so empathy is built into the platform, not left to individual interpretation.

Learn more

• Configure sentiment analysis for emails | Microsoft Learn
• View, compose and respond to email — Power Apps | Microsoft Learn

The post Driving empathetic customer conversations with Email Sentiment appeared first on Microsoft Dynamics 365 Blog.

As organizations expand globally and adopt AI‑powered customer engagement, complying with call‑recording regulations while maintaining a seamless customer experience has become increasingly complex. To help address this challenge, we’re excited to announce Consent‑Based Recording for voice interactions in Dynamics 365 Contact Center with Microsoft Copilot Studio.

This new capability enables organizations to explicitly capture caller consent within a Voice Agent and automatically apply that consent decision across the entire interaction including when the call is handed over to a customer service representative.

Why consent‑based recording matters

In many countries and regions, laws require explicit customer consent before a call can be recorded or transcribed. Historically, enforcing these requirements across AI Agents, Customer Service Representative (CSR) handoffs, and recording controls have required custom logic, manual CSR checks, or complex integrations.

Consent‑Based Recording simplifies this by making consent a first‑class, system‑enforced signal ensuring that recording and transcription behavior always aligns with the caller’s choice.

How it works

With Consent‑Based Recording, Copilot Studio makers can configure a Voice Agent to request recording consent early in the conversation. Based on the caller’s response:

• If consent is granted
  • The call is recorded and transcribed.
  • Pause/Resume Recording control is available to CSRs in Contact Center Workspace – when the interaction is transferred to them.
• If consent is not granted
  • The call proceeds without recording or transcription.
  • Pause/Resume Recording control is disabled when the voice interaction is handed over to a CSR, preventing any accidental or unauthorized recording.

The consent decision is automatically preserved and enforced throughout the interaction lifecycle no additional configuration or agent intervention required.

Business value

Built‑in compliance for global operations

Consent‑Based Recording helps organizations meet call‑recording and privacy regulations, especially in countries where explicit consent is legally required. It ensures recording behavior is governed by the caller’s choice across both Voice Agent and human‑assisted interactions.

Reduced operational and agent risk

By automatically enforcing consent in the CSR experience, organizations eliminate reliance on manual checks or agent judgment. This significantly reduces the risk of non‑compliant recordings.

Seamless AI Agent‑to‑CSR experiences

Consent captured in the Voice Agent seamlessly carries over during CSR handoff, delivering a consistent and transparent customer experience without disruption or repetition.

Designed for trust and transparency

Consent‑Based Recording reflects Microsoft’s commitment to privacy‑by‑design principles. Customers are clearly informed, their preferences are respected, and agents are guided by UI‑level safeguards that prevent unintended policy violations.

Together, these capabilities allow organizations to confidently deploy Voice Agent and live agent experiences in Dynamics 365 Contact Center, while maintaining regulatory compliance, customer trust, and operational simplicity at scale and across borders.

Learn More

https://learn.microsoft.com/en-us/microsoft-copilot-studio/voice-consent-based-record?branch=pr-en-us-1774

The post Introducing Consent‑Based Recording for Voice Agents and CSR Interactions in Dynamics 365 Contact Center appeared first on Microsoft Dynamics 365 Blog.

A Dynamics 365 Customer Service admin has just configured the Case Management Agent to auto‑populate case fields from incoming emails and added field descriptions to help the AI make better predictions. The configuration looks right, but how will the admin ensure that the AI is able to predict case field values accurately? The new case enrichment simulation in Dynamics 365 Customer Service—now generally available—answers exactly that question.

Admins can measure AI‑powered field prediction accuracy against real organizational data before enabling case enrichment in production: test against historical records, review the results, refine field descriptions, and enable the feature when accuracy meets your bar. 

Empower admins to validate AI quality 

Case Management Agent simulations include: 

• Flexible test data — run predictions against historical case records or sample email and chat data uploaded via Excel 

• Field‑level match reports — see prediction accuracy across every field 

• Case‑level comparison view — review actual versus predicted values side by side 

• Excel export — share results offline with stakeholders 

• Re‑run capability — iterate after refining lookup field descriptions or prediction rules 

• Shadow runs — Validate predictions by activating case enrichment with live production data without writing to records. 

Run your first simulation today 

Don’t wait until production to find out how well the AI performs. Open the Copilot Service admin center, navigate to Case settings > Case Management Agent > Case creation and update > Simulation, and run your first test against historical data. Within minutes you will know exactly where to tune your field descriptions before turning on case enrichment. 

For step‑by‑step guidance, read the documentation: 

• Set up Case Management Agent | Microsoft Learn 

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