Analyze project data, create visualizations, build dashboards, and generate insights that drive better decision-making.
📚 Week 5🎯 Advanced
🎯 What You'll Master
Transform raw project data into actionable insights using AI
Generate executive dashboards that tell compelling data stories
Identify trends, patterns, and anomalies in project metrics
Create data-driven recommendations that influence decisions
The Data Analysis Blind Spot
Project Managers collect mountains of data: velocity metrics, budget burn rates, sprint performance, team capacity,
risk registers. But collecting data ≠ using data. Most PMs lack the analytical skills to turn
spreadsheets into insights that drive better decisions.
📉 The Data Problem:
Data Overload: Tracking 20+ metrics but only reporting 3-4 basic ones
Analysis Paralysis: Spending 5+ hours building reports manually in Excel
Surface-Level Insights: "We're 10% over budget" without understanding why
Missed Patterns: Not spotting trends until it's too late to course-correct
Executive Mismatch: Presenting data leadership doesn't care about
Result: Data exists but doesn't inform decisions.
ChatGPT acts as your data analyst—helping you identify meaningful patterns, generate visualizations,
and translate numbers into narratives that executives understand and act on.
📈 1. Analyzing Project Performance Metrics
The first step is understanding which metrics matter and what they're telling you. AI helps you
move beyond surface-level reporting to root cause analysis.
Essential PM Metrics by Category
⏱️ Schedule Performance
Sprint Velocity: Story points completed per sprint (trend over time)
Cycle Time: Average days from start to completion
Schedule Variance (SV): Planned vs actual timeline
Milestone Hit Rate: % of milestones delivered on time
💰 Budget & Cost
Burn Rate: Monthly spending vs budget
Cost Variance (CV): Budgeted vs actual cost
Cost Per Story Point: Efficiency metric
Forecast at Completion: Projected total cost
🐛 Quality Metrics
Defect Density: Bugs per 1000 lines of code
Escaped Defects: Bugs found in production
Test Coverage: % of code covered by tests
Mean Time to Resolve (MTTR): Average bug fix time
👥 Team Health
Team Capacity: Available hours vs allocated
Utilization Rate: % of time on project work
Turnover Risk: Team stability indicators
Satisfaction Scores: Team morale tracking
ChatGPT Metrics Analysis Prompt
Analyze these project metrics and provide insights:
Project: [E-commerce Platform Redesign]
Duration: [16 weeks, currently in Week 10]
SCHEDULE METRICS:
- Sprint Velocity: [Week 1-4: 32 pts, Week 5-7: 28 pts, Week 8-10: 22 pts]
- Planned vs Actual: [Behind by 12 story points cumulative]
- Milestone Hit Rate: [4/7 milestones delivered on time = 57%]
BUDGET METRICS:
- Total Budget: [$500K]
- Spent to Date: [$340K (68% of budget at 62.5% timeline)]
- Monthly Burn: [Month 1: $100K, Month 2: $120K, Month 3: $120K]
- Forecast at Completion: [$550K (10% over budget)]
QUALITY METRICS:
- Bugs Found: [Sprint 1-3: 12 bugs, Sprint 4-6: 28 bugs, Sprint 7-9: 45 bugs]
- Escaped Defects: [3 critical bugs found in staging]
- Test Coverage: [62% (target: 80%)]
TEAM HEALTH:
- Team Size: [12 people]
- Turnover: [1 developer left Week 7, replacement started Week 9]
- Utilization: [85% average]
Analyze:
1. What trends or patterns are concerning?
2. What's the root cause of declining performance?
3. What risks do these metrics indicate?
4. What actions should I take immediately?
5. What should I communicate to executives?
Be specific and actionable.
Onboarding Check: Ensure new developer is productive (pair programming, mentorship)
📢 Executive Communication (What to Say):
Subject: Project Status Update — Timeline & Budget Adjustment Needed
"We've encountered a quality and velocity challenge following our developer departure in Week 7.
Bug rates have tripled, and velocity has declined 31%. To deliver a quality product, I recommend:
2-week timeline extension (launch Week 18 instead of 16)
Reduce scope by 12 story points (removing lowest-priority features)
$50K budget increase OR identify features to cut to stay within $500K
This ensures we launch with acceptable quality vs rushing a buggy product. Happy to discuss trade-offs."
📉 2. Identifying Trends & Patterns
Historical data reveals patterns that predict future outcomes. AI helps you spot trends before they become crises.
Trend Analysis Prompt
Analyze these historical trends and make predictions:
Project Sprint Velocity (Last 12 Sprints):
Sprint 1: 28 pts
Sprint 2: 30 pts
Sprint 3: 32 pts
Sprint 4: 31 pts
Sprint 5: 29 pts
Sprint 6: 27 pts
Sprint 7: 25 pts
Sprint 8: 22 pts
Sprint 9: 21 pts
Sprint 10: 20 pts
Sprint 11: 19 pts
Sprint 12: 18 pts (current)
Team Changes:
- Sprint 3: Added 2 junior developers
- Sprint 7: Senior developer left
- Sprint 9: New senior developer started
Questions:
1. What trends do you see in velocity over time?
2. How did team changes impact performance?
3. What's the projected velocity for Sprints 13-16?
4. When will the team recover to Sprint 1-4 levels (28-32 pts)?
5. Should we be concerned about burnout or technical debt?
Provide data-driven predictions with confidence levels.
✨ AI Trend Analysis:
📊 Velocity Trend Breakdown:
Phase 1: Growth (Sprints 1-3)
Velocity: 28 → 30 → 32 pts (+14%)
Cause: Team ramping up, building momentum
Event: 2 junior devs added Sprint 3
Phase 2: Plateau (Sprints 4-6)
Velocity: 31 → 29 → 27 pts (-13%)
Cause: Junior dev onboarding overhead
Signal: Normal dip when adding team members
Phase 3: Decline (Sprints 7-9)
Velocity: 25 → 22 → 21 pts (-16%)
Cause: Senior dev departure Sprint 7
Impact: Lost domain knowledge + handoff delays
Phase 4: Crisis (Sprints 10-12)
Velocity: 20 → 19 → 18 pts (-10%)
Cause: Technical debt + burnout
Warning: Still declining despite new hire
🔮 Projections for Sprints 13-16:
Scenario 1: No Intervention (40% confidence)
Sprint 13: 17 pts
Sprint 14: 16 pts
Sprint 15: 15 pts
Sprint 16: 15 pts
Risk: Velocity continues declining to 15 pts (53% below peak)
Full Recovery: Back to Sprint 1 baseline (28 pts) by Sprint 16
⚠️ Burnout & Technical Debt Indicators:
YES, Multiple Red Flags Present:
Consistent Decline (6 Sprints): Velocity down 44% from peak (32 → 18 pts)
New Hire Not Helping: Senior dev joined Sprint 9, but velocity still dropping
Quality Issues: Likely accumulating technical debt (implied by continued decline)
Team Exhaustion: Working harder but producing less = burnout signal
Recommendation: Immediate intervention required. Without action, expect velocity to
bottom out at 15 pts (47% below sustainable levels).
📊 3. Building Executive Dashboards
Executives don't want 20 pages of data—they want one-page dashboards with key insights. AI helps
you design dashboards that tell a story and drive decisions.
Dashboard Design Prompt
Design an executive dashboard for this project:
Project: [Mobile App Launch]
Audience: [C-suite executives (CEO, CFO, CTO)]
Frequency: [Monthly update]
Key Metrics to Include:
- Schedule: [On track / X weeks behind]
- Budget: [X% spent, $Y remaining]
- Quality: [Defect count, test coverage]
- Team: [Headcount, utilization, turnover]
- Risks: [High-priority risks]
- Milestones: [Next 3 milestones with dates]
Executive Questions to Answer:
1. Are we going to launch on time?
2. Will we stay within budget?
3. What are the biggest risks?
4. What decisions do you need from me?
Design:
- Single-page visual layout
- Traffic light indicators (Red/Yellow/Green)
- 3-5 key insights at top
- Minimal text, maximum clarity
- Focus on trends, not raw numbers
Provide dashboard structure and content.
✨ AI-Generated Executive Dashboard:
Mobile App Launch — Executive Dashboard
Reporting Period: January 2024 | Project Week: 10 of 16
🎯 Executive Summary
Status: 🟡 YELLOW — Minor delays, on track for recovery
Budget Increase: Approve $50K additional budget OR identify $50K in scope cuts
Feature Prioritization: Sign off on 12 story points to cut from scope
🎯 Practice Exercise: Root Cause Analysis from Data
Scenario:
You're analyzing Q4 project data and notice customer satisfaction scores dropped from 8.5/10 to 6.2/10
over 3 months. Leadership wants to know why and what to do about it.
Data Provided:
Oct: NPS 8.5, 12 support tickets/week, 2 bugs reported
Nov: NPS 7.8, 28 support tickets/week, 8 bugs reported
Dec: NPS 6.2, 45 support tickets/week, 15 bugs reported
Team Changes: 2 developers left in November
Releases: Major feature launch mid-November (rushed)
Your Task:
Identify the correlation: Which metrics are connected?
Find the root cause: What triggered the decline?
Quantify the impact: How much revenue is at risk?
Recommend actions: What should leadership do immediately?
AI as Your Analyst: ChatGPT helps you identify patterns humans miss in complex datasets
🎉 Congratulations! You've Completed the Course!
You've mastered all 9 modules of AI for Project Managers. You can now use AI to plan projects, manage risks,
lead teams, run meetings, and analyze data—transforming how you work and multiplying your productivity.
What You've Learned:
✅ Generate project plans, WBS, and timelines in minutes
✅ Run Agile ceremonies with AI-powered prompts
✅ Communicate effectively with stakeholders at all levels
✅ Write professional emails that get results
✅ Identify and mitigate 50+ project risks proactively
✅ Navigate difficult conversations with confidence
✅ Run productive meetings with clear outcomes
✅ Transform data into actionable insights
Keep practicing, keep iterating, and remember: AI is your co-pilot, not your replacement.
You bring the judgment, experience, and human touch. AI amplifies your capabilities. 🚀
📝 Knowledge Check
Test your understanding of AI for data analysis and reporting!
1. How can AI help PMs with data analysis?
A) By replacing all analytical thinking
B) Data analysis doesn't need AI
C) By identifying patterns and generating insights from project data
D) By making data more complex
2. What can AI assist with in project reporting?
A) Making reports longer
B) Creating visualizations and executive summaries
C) Hiding important metrics
D) Reporting is manual only
3. How should PMs use AI for metrics analysis?
A) Identify trends and anomalies in performance data
B) Ignore metrics entirely
C) Accept all AI interpretations without validation
D) Metrics don't need analysis
4. What is important when using AI for forecasting?
A) AI forecasts are always perfect
B) No data validation needed
C) Ignore uncertainty ranges
D) Consider data quality and validate predictions with project context
5. How can AI improve dashboard creation?
A) By making dashboards confusing
B) By suggesting relevant KPIs and visualization types
C) Dashboards don't use AI
D) By showing all possible data
🎓
Get Your Completion Certificate
Showcase your AI-powered project management skills!