AI Auto-Assignment of Tasks: Team Capacity Used to Its Full Potential
The PM opens a new sprint. There are 23 tasks, 5 team members, varying skills and varying workloads. Manual assignment: half an hour of intuition. Result: one person overloaded, two underutilised, and one given a task they’re not skilled for.
How AI Assigns Tasks
Team Member Profiles
Every team member has a Modulario profile with skills, availability, and performance history:
CRM Kovospol Project Team:
Jana N. (consultant) — CRM configuration ⭐⭐⭐⭐⭐, SQL ⭐⭐⭐
Current workload: 60% | Available: 16h this week
History: average CRM task time: 95% of plan
Peter N. (developer) — API integrations ⭐⭐⭐⭐⭐, SQL ⭐⭐⭐⭐
Current workload: 85% | Available: 6h this week
History: integrations at 110% of plan (take longer)
AI Assignment
New task: "Import and cleanse 3,000 contacts from Excel → CRM"
AI analysis:
Required skills: Excel transformation, CRM data model, SQL
Jana N.: Match 87%, available 16h ✅ → RECOMMENDED
Peter N.: Match 71%, available only 6h (task: 3 days) ❌
Consultant B: Match 65%, available 20h ⚠️ (less experience)
Suggestion: Jana Nováková
Rationale: Highest skill match, sufficient capacity.
Estimated duration: 2.5 days (based on similar import history)
Load Balancing
When assigning tasks, AI monitors overall team load — if one member is consistently overloaded, AI escalates this to the PM with a redistribution recommendation.