As AI agents take on a larger role in customer service operations, Microsoft is introducing AI Credit Estimation in Dynamics 365 Customer Service and Contact Center. The new capability helps organizations translate forecasted service demand into projected AI usage and credit consumption, giving teams greater visibility into operational and financial planning. With AI becoming part of the workforce itself, Dynamics 365 is evolving workforce management to include both human and AI-driven capacity planning.
Here’s how AI Credit Estimation in Dynamics 365 Customer Service and Contact Center can help organisations plan smarter:
What’s creating the planning gap?
Traditional workforce planning models were built around people, not AI agents. While organisations can forecast case volumes and service demand, many still struggle to translate that into expected AI usage and cost.
Why use AI Credit Estimation?
AI Credit Estimation provides a transparent way to map forecasted workloads to projected AI credit consumption. It brings AI usage into the planning process alongside staffing ratios, service levels, and case forecasts — helping teams move from assumptions to measurable operational insights.
Step 1: Create a Forecast Scenario
Build a forecast scenario in Dynamics 365 Customer Service or Contact Center by defining your planning horizon, queues, and communication channels. Using historical trends, seasonality, and traffic patterns, the platform generates a data-driven prediction of future demand.
Step 2: Select AI Agents
Open the AI Credit Estimator and choose the agents you want to model. Supported out-of-the-box agents include: Quality Evaluation Agent, Case Management Agent, and Customer Intent Agent.
Step 3: Review AI Credit Estimates
The estimator maps forecasted workloads to projected AI credit usage, giving teams a clear view of both expected service demand and the AI capacity required to support it. This enables organizations to validate assumptions, refine planning scenarios, and make more informed operational decisions before scaling AI initiatives.
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