The Asana AI-automations make tight organization possible with far less busywork. Now things are always in the right place, everything has an update, and nobody complains about having to fill out forms and check boxes.

Evan Hamilton

Director of Community, HubSpot

Winner: 2026 Global AI Breakthrough Award

This award recognized HubSpot for its innovative use of AI Studio to bridge the gap between unstructured requests and structured capacity planning. By decoupling the automation logic from the data via Google Sheets, HubSpot created a scalable model for enterprise-level AI orchestration.

Use case 1: Automated intake and task classification

Previously, every request submitted to the Community Team arrived as a "blank slate," requiring manual review to determine its importance and scope. HubSpot solved this by embedding an AI Studio rule directly at the point of intake. Now, the moment a task is created, AI Studio handles the classification — ensuring every request arrives with a priority, effort level, and time estimate before a human even opens it.

• Instant analysis: AI Studio reviews the task title and description for signals related to scope and complexity.

• Automated field population: AI Studio routes each task to the right priority and effort tier, setting three custom fields (Priority, Level of Effort, and Estimated Time) simultaneously with no manual input.

• External logic referencing: By pointing the AI toward a Google Sheet for its classification mapping, HubSpot allows any team member to update priority criteria without changing the rule configuration.

• Low-stakes automation: Starting with the boring, repetitive task of triage allowed the team to prove the AI's accuracy and build trust across the organization.

The best sign this was working was when nobody mentioned it anymore. Tasks come in, they get classified, and the team just sees accurate data. We stopped having assumptive conversations about capacity and started having real ones.

Kennedy Poirier

Community Program Manager, HubSpot

Use case 2: Real-time capacity and workload management

The primary failure of the team’s previous system was the lack of reliable data for Workload reports. By automating the "Estimated Time" field at the point of intake, HubSpot created a self-updating capacity plan. This transparency allows leadership to see exactly where project velocity is slowing down and where additional resources are required.

• Reliable time tracking: Automated estimates (ranging from 45 minutes to 40 hours) feed directly into team-wide Workload views.

• Human-in-the-loop accuracy: While AI provides the starting point, assignees can manually adjust estimates, ensuring that unique project nuances are still captured.

• Portfolio-wide consistency: Triage rules are applied via Bundles, ensuring that every community project—from Reddit campaigns to event planning—follows the same standardization.

• Data-backed resourcing: Leadership now has a quantitative view of team bandwidth, making it easier to justify hiring decisions or re-scope projects that are at risk.

The payoff shows up when your manager asks about capacity and instead of guessing, you can just pull up a report and point to exactly where things stand. It’s offset by 3 to 5 hours of coordination work every single week.

Kennedy Poirier

Community Program Manager, HubSpot

Conclusion

Before this transformation, HubSpot’s Community Team spent hours every month just trying to understand their own bandwidth. By building an AI-powered triage system that feeds directly into capacity planning, they have eliminated the administrative tax of "work about work." This model allows the team to stay focused on their core mission—supporting HubSpot’s global community—while maintaining an adaptive, data-driven operating rhythm that matches the speed of the AI era.