Smartsheet - Prodigy Integration and Automation

Common Integration Use Cases Between Smartsheet and Prodigy

1. AI Data Labeling Project Intake and Tracking

Data flow: Smartsheet ? Prodigy

Use Smartsheet as the operational front end for requesting and approving new annotation work, then push approved tasks into Prodigy for labeling. Business teams can submit dataset requests, define scope, assign owners, and track status in Smartsheet while AI teams receive structured annotation jobs in Prodigy.

• Standardizes intake for image, text, or document labeling requests
• Improves visibility into backlog, priorities, and due dates
• Reduces email and spreadsheet-based coordination across teams

2. Annotation Progress and Delivery Reporting

Data flow: Prodigy ? Smartsheet

Sync annotation progress, completion counts, and review status from Prodigy into Smartsheet dashboards for project and portfolio reporting. This gives program managers and business stakeholders a clear view of dataset readiness without needing direct access to the labeling environment.

• Tracks labeled records, review completion, and rework volume
• Supports executive reporting on AI project milestones
• Helps identify bottlenecks in labeling throughput

3. Domain Expert Review and Approval Workflow

Data flow: Bi-directional

Use Smartsheet to manage review assignments, approvals, and exception handling for labels created in Prodigy. When annotators flag uncertain cases or low-confidence items, those records can be routed to subject matter experts through Smartsheet for review, then the approved decisions are sent back to Prodigy to update the training set.

• Creates a controlled review process for sensitive or complex labels
• Improves label quality through business expert validation
• Maintains auditability of decisions and approvals

4. Active Learning Queue Prioritization Based on Business Priority

Data flow: Smartsheet ? Prodigy

Use Smartsheet to maintain business priority rules for datasets, products, regions, or customer segments, then feed those priorities into Prodigy to influence which samples are labeled next. This is useful when multiple AI initiatives compete for the same annotation resources.

• Aligns labeling effort with revenue, risk, or launch priorities
• Ensures high-value use cases receive faster model improvement
• Supports dynamic reprioritization as business needs change

5. AI Model Training Program Governance

Data flow: Bi-directional

Manage the full governance layer in Smartsheet for AI training programs, including milestones, owners, dependencies, and compliance checkpoints, while Prodigy handles the actual annotation work. Status updates from Prodigy can automatically update Smartsheet project plans, enabling PMO and AI leadership to monitor delivery against schedule.

• Provides a single operational view of AI training initiatives
• Improves accountability across data science, operations, and business teams
• Supports regulated environments with documented checkpoints

6. Quality Control and Rework Management

Data flow: Prodigy ? Smartsheet ? Prodigy

When quality checks identify inconsistent labels, edge cases, or failed validation rules in Prodigy, those items can be logged in Smartsheet as rework tasks with owners and deadlines. Once corrected, the updated records are returned to Prodigy for final dataset inclusion.

• Creates a structured loop for label correction and escalation
• Reduces training data defects before model training begins
• Improves collaboration between annotators and QA reviewers

7. Dataset Release and Model Readiness Coordination

Data flow: Prodigy ? Smartsheet

Use Prodigy to confirm when a labeled dataset reaches readiness thresholds, then publish release status into Smartsheet to coordinate downstream model training, testing, and deployment activities. This helps machine learning teams and business stakeholders align on when a dataset is ready for the next phase.

• Connects annotation completion to MLOps and delivery schedules
• Reduces delays caused by unclear dataset readiness
• Improves coordination between data labeling and model engineering

8. Cross Functional AI Initiative Portfolio Management

Data flow: Bi-directional

For organizations running multiple AI initiatives, Smartsheet can serve as the portfolio management layer for tracking all annotation projects, budgets, timelines, and resource allocation, while Prodigy serves as the execution layer for labeling work. Progress, risks, and completion metrics flow back into Smartsheet to support portfolio decisions.

• Enables centralized oversight of multiple AI workstreams
• Helps leaders allocate annotation capacity across competing projects
• Improves forecasting for AI delivery timelines and staffing needs