Task Routing Algorithm: How AI Assigns Work Fairly
Inside TaskOrbit's smart assignment system: how it balances workload, skills, and timezone to suggest optimal task ownership.
One of the hardest problems in team management: who owns this work? Assign to the expert, and they're overloaded. Assign to the junior dev for growth, and they're blocked.
TaskOrbit's smart routing solves this by weighing multiple signals.
The Algorithm (Simplified)
When a task is created, TaskOrbit scores each team member:
1. Current Workload (40%)
Who has capacity? If person A has 8 open tasks due this week and person B has 2, B scores higher. We prevent the common trap: work always flows to the fastest person until they drown.
2. Skill Match (30%)
You tag tasks with required skills (frontend, Python, design review, etc.). The system scores team members based on their profile skills and historical task data ("You've completed 47 frontend tasks").
3. Timezone Alignment (20%)
Who can start this immediately without waiting for their timezone to arrive? For async handoff tasks, who's in the optimal timezone to hand it off next?
4. Growth Opportunity (10%)
Someone trying to learn backend development? If they've marked that as a growth area and have capacity, they get a slight boost on backend tasks.
The Human Override
The algorithm suggests. You decide. Every recommended assignee shows the reasoning: "Suggested because: 60% capacity, strong design background, same timezone."
Disagree? Reassign instantly. TaskOrbit learns from overrides—if you consistently pick different people, the weights adjust.
Preventing Bottlenecks
The system flags when critical work is concentrated: "3 of 5 remaining Q1 goals depend on one person." Forces you to either build redundancy or parallelize.
Gaming the System
Can people artificially inflate their availability? Technically yes. But TaskOrbit shows managers actual completion times and cycle times per person, surfacing who's really capacity vs. who's overcommitting.