Customer Challenge
Moving from periodic analysis to intelligent recommendations
The customer had already invested in predictive analytics that generated sales recommendations from a limited set of business indicators. The process remained largely manual, requiring sales leaders to review insights, coach representatives, and prioritize customer engagement through periodic analysis and reporting.
The goal was to create a scalable AI-enabled analytics foundation that could prioritize opportunities, surface actionable insights, incorporate multiple business signals, reduce spreadsheet-driven decision-making, and support future AI-assisted sales coaching.
AI Edge Approach
Business questions first, technology second
01 | Business question engineering
Stakeholders formalized the decisions the solution needed to support, including which customers to prioritize, which opportunities had the highest conversion probability, and what action to take next.
02 | Data and semantic readiness
The team assessed source systems, Fabric readiness, semantic models, KPI definitions, business terminology, relationships, and security requirements.
03 | Fabric Data Agent design
The architecture used governed enterprise data and Fabric semantic models to support role-aware, grounded, explainable recommendations using existing business logic.
04 | Security and governance
The access strategy covered workspaces, semantic models, source connectivity, RBAC alignment, security-trimmed responses, and Microsoft 365 integration readiness.
Architecture Direction
A governed analytics layer with a path to orchestration
The design established a clear separation of responsibilities. Microsoft Fabric Data Agents provided the analytics layer for data access, semantic understanding, KPI interpretation, recommendation generation, and business grounding.
Azure AI Foundry was identified as the future orchestration layer for scheduling, multi-agent coordination, workflow management, and user-specific execution. This separation created a scalable foundation while maintaining clear ownership boundaries.
Expected Business Value
A path toward AI-assisted sales decision support
Improved prioritization
Help sales teams focus on the highest-value customer opportunities.
Faster opportunity discovery
Surface relevant signals and recommendations closer to the moment of action.
Less manual analysis
Reduce dependence on periodic reviews and spreadsheet-driven workflows.
More consistent coaching
Support sales leadership with a shared, governed view of customer signals.
Higher analytics adoption
Make enterprise analytics more accessible through natural language interaction.
Reusable AI foundation
Create a path toward future Copilot Studio and multi-agent experiences.
Benefits described here are expected outcomes from the designed capability and should not be interpreted as independently verified production results.