Microsoft 365 - Prodigy Integration and Automation

Common Integration Use Cases Between Microsoft 365 and Prodigy

1. SharePoint-based data collection and annotation intake

Flow: Microsoft 365 ? Prodigy

Teams can store raw images, documents, audio transcripts, or text samples in SharePoint or OneDrive, then push selected files into Prodigy for labeling. This is useful when business users, analysts, or subject matter experts upload source data into a controlled Microsoft 365 repository, and the AI team needs a structured way to turn that content into training datasets.

• Centralizes source data in a governed Microsoft 365 workspace
• Reduces manual file transfer between business teams and data scientists
• Supports auditability by keeping the original content in SharePoint

2. Microsoft Teams task routing for labeling review and approvals

Flow: Prodigy ? Microsoft 365

When Prodigy identifies items that need human review, exception handling, or domain expert validation, notifications can be sent into Microsoft Teams channels. Reviewers can be assigned tasks, discuss edge cases, and confirm labeling decisions without leaving the collaboration environment used by the broader organization.

• Speeds up review cycles for ambiguous labels
• Brings business experts into the annotation process with minimal training
• Improves collaboration between data science and operational teams

3. Excel-driven label quality analysis and reporting

Flow: Prodigy ? Microsoft 365

Export annotation results, inter-annotator comparisons, and quality metrics from Prodigy into Excel for deeper analysis by data analysts or project managers. Teams can use Excel to track label consistency, measure progress by dataset, and identify categories with high disagreement or low confidence.

• Enables business-friendly reporting on annotation throughput and quality
• Supports ad hoc analysis without requiring direct access to Prodigy
• Helps managers monitor dataset readiness for model training

4. Power BI dashboards for annotation operations and model readiness

Flow: Prodigy ? Microsoft 365

Prodigy output can be loaded into Power BI to create dashboards showing labeling volume, completion rates, active learning progress, reviewer performance, and dataset coverage by class. This gives AI program leaders and business stakeholders a clear view of whether training data is on track for model development milestones.

• Provides executive visibility into AI data preparation work
• Tracks bottlenecks in labeling workflows
• Supports portfolio management across multiple AI initiatives

5. Secure document and media collaboration for labeling guidelines

Flow: Microsoft 365 ? Prodigy

Labeling instructions, taxonomy documents, and edge-case examples can be authored in Word or PowerPoint, stored in SharePoint, and shared with annotators working in Prodigy. As labeling rules evolve, updated guidance can be published back to Microsoft 365 so all stakeholders work from the latest approved version.

• Creates a single source of truth for annotation standards
• Improves consistency across distributed labeling teams
• Supports controlled document review and version management

6. Outlook and calendar coordination for annotation sprints and expert reviews

Flow: Microsoft 365 ? Prodigy

Project managers can use Outlook and Teams calendars to schedule annotation sprints, review sessions, and model feedback workshops tied to Prodigy workstreams. This is especially valuable when domain experts are needed for short, high-value review windows, such as medical, legal, or manufacturing labeling tasks.

• Aligns expert availability with labeling demand
• Reduces delays caused by ad hoc scheduling
• Improves throughput for time-sensitive AI projects

7. Compliance-controlled storage of labeled datasets and audit artifacts

Flow: Prodigy ? Microsoft 365

Completed annotation sets, reviewer comments, and approval records can be archived in SharePoint with Microsoft 365 retention and compliance controls. This is valuable for regulated industries that need traceability for how training data was created, reviewed, and approved before model deployment.

• Supports governance and audit requirements
• Keeps dataset versions and review evidence securely stored
• Helps organizations demonstrate responsible AI data practices

8. AI project collaboration workspace for cross-functional delivery

Flow: Bi-directional

Microsoft 365 can serve as the collaboration layer for the full AI labeling lifecycle, while Prodigy handles the annotation work itself. Teams can manage project plans in Teams, store source data and guidelines in SharePoint, analyze progress in Power BI, and use Prodigy for active learning and labeling execution. This creates a practical operating model for data science, operations, compliance, and business stakeholders working on the same AI initiative.

• Connects planning, execution, and reporting in one workflow
• Improves transparency across technical and non-technical teams
• Supports scalable delivery of custom AI models