1. Overview
Managing multi-team operations across design, ads, content, and account management often leads to one consistent issue:
everyone defines “priority” differently.
Tasks pile up, deadlines shift, and without a clear prioritization method, teams struggle with misalignment and inefficiency.
To fix this, I built an AI-powered prioritization engine inside ClickUp that evaluates each task
using urgency, deadlines, workload, blockers, complexity, and impact—then automatically assigns a score from 0 to 100.
This removed hours of manual triaging and created a consistent, scalable prioritization system across all teams.
2. Background & Context
The agency handled 90–120 active tasks per week across departments, including:
- Creative and Design
- Paid Media
- Email Marketing
- Client Success
- Content Production
- Project Management
Before automation, the project manager manually reviewed every task to determine urgency, adjust workloads, track deadlines, and surface blockers. This consumed 6–8 hours per week and was prone to human error.
3. Problem Statement
Key operational issues included:
- 1. No standardized prioritization system
- 2. Weekly triaging took 6–8 hours
- 3. Hidden dependencies caused delivery delays
- 4. Uneven workload distribution
- 5. No real-time visibility into changing priorities
The system needed an automated, unbiased way to score tasks and reorganize work continuously.
4. Tools & Automation Stack
- ClickUp (task management and automations)
- OpenAI API (AI scoring logic)
- Make.com / Zapier (automation workflow)
- Slack (priority notifications)
5. Automation Flow
The system followed this structure:
- 1. A task is created or updated in ClickUp
- 2. Automation sends task details to the AI model
- 3. AI returns a numeric priority score (0–100)
- 4. ClickUp updates the task’s priority and position
- 5. Slack sends alerts for high-priority or critical tasks
This created a self-correcting workflow that adapts automatically to task changes.
Fig. 1: System Architecture for AI-Based Task Scoring and Priority Alerts
6. Implementation Details
6.1 AI Prompt (The Core Logic)
The following prompt powered the scoring system:
“Based on the following task details, assign a priority score (0–100).
Consider urgency, proximity to deadlines, task complexity, business impact, workload, and dependencies.
Output a single number only:
Task Title: {{title}}
Task Description: {{description}}
Due in: {{days_left}} days
Dependencies: {{dependency_count}}
Workload of Assignee: {{assignee_current_hours}} hours
Complexity: {{complexity_tag}}
Impact Tag: {{impact}}
Score meaning:
0–30 = Low
31–60 = Medium
61–100 = High"
The AI returns a single numeric score.
6.2 Score Mapping
| Score Range | Priority | Behavior |
|---|---|---|
| 0–30 | Low | Moved to Backlog / Later |
| 31–60 | Medium | Stays in the normal workflow |
| 61–80 | High | Moved to High Focus |
| 81–100 | Critical | PM alerted; added to urgent lane |
6.3 ClickUp Automations
Rules used inside ClickUp:
If Priority = High → Move to "High Focus"
If Priority = Critical → Add to "Urgent Queue" + Tag PM
If Priority drops from High → Notify Team Lead
If Priority increases → Ping assignee
6.4 Data Extracted for AI Scoring
The system evaluated:
- Task name & description
- Deadline proximity
- Assignee workload hours
- Number of blockers or dependencies
- Time estimate/complexity
- Impact on the project or the client
This ensured the model scored tasks holistically and accurately.
7. Code-to-Business Breakdown
| Logic / Code | Business Impact |
|---|---|
| AI scoring (0–100) | Consistent and objective prioritization |
| Due-date weighting | Prevents last-minute bottlenecks |
| Workload checking | Balances workload across team members |
| Impact scoring | Ensures high-impact tasks rise automatically |
| Dependency detection | Surfaces blocked tasks early |
| Slack alerts | Improves response time to urgent tasks |
| Auto-moving tasks | Removes PM’s repetitive sorting work |
8. Results & Performance Impact
1. Time Saved
- PM saved 6–8 hours weekly previously spent on manual prioritization
- Team members saved 15–20 minutes daily, identifying what to work on next
2. Delivery Improved
- Missed deadlines dropped by 42%
- High-value tasks completed 30% faster
- Blocked tasks surfaced 60% earlier
3. Team Alignment
- Every team member followed the same logic for priority
- No confusion regarding what to execute next
- Daily standups became 50% shorter
4. System Scalability
The system worked across more than 12 team members, 4 departments, and multiple client accounts without modification.
9. Challenges & How They Were Solved
Challenge: Too many AI triggers
Solution: Added filters to only re-score on meaningful updates.
Challenge: Vague task descriptions
Solution: Implemented a description template and minimum required fields.
Challenge: Score volatility
Solution: Used weighted logic and smoothing for more stable outputs.
10. Lessons for Project Managers
- Automation drastically reduces repetitive PM work
- AI removes emotional bias and enforces objective prioritization
- PMs can scale their management capacity by designing systems rather than micromanaging workflows
- Continuous re-evaluation ensures priorities stay accurate even as situations change
- Well-designed automation compounds in value over time
11. Conclusion
By integrating AI scoring with ClickUp automations, I transformed a tedious and inconsistent prioritization process into a reliable, data-driven system. The AI engine now evaluates urgency, workload, blockers, and deadlines to automatically reorganize tasks — providing the team with clarity, improving delivery speed, and significantly reducing the PM’s workload.
This case study highlights how AI and workflow automation can fundamentally improve operational efficiency and shift a project manager’s role from reactive to strategic.
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