Earned value with AI: Are you on schedule and within budget?

Project Management Tips, Trends, and New Tools

Summary: - The episode introduces Earned Value Management (EVM) powered by AI, helping you see truth about progress, cost, and schedule instead of rosy stories. It ties what you planned (PV), what you’ve earned (EV), and what you’ve spent (AC to measure performance and variances. - Key metrics: - Planned Value (PV): what you intended to do/spend by today. - Earned Value (EV): value of what’s actually completed. - Actual Cost (AC): what you’ve spent. - Cost Variance (EV − AC): negative means cost overrun. - Schedule Variance (EV − PV): negative means behind. - Cost Performance Index (CPI = EV/AC) and Schedule Performance Index (SPI = EV/PV); values above 1 are good, below 1 are warnings. - AI adds real-time foresight: by analyzing progress patterns, hours, changes in scope, and approvals, AI can forecast finish date and likely total cost weeks in advance, and propose scenarios (replan, level resources, adjust sequences, prioritize critical tasks). - How to implement (step by step): 1) Define a clear WBS with measurable deliverables and a Definition of Done. 2) Set a realistic baseline (cost-distribution curve, planned % per period). 3) Establish a progress-measurement rule per package (all-or-nothing, milestones, or physical proportion). 4) Centralize data sources (tasks, timesheets, purchases) in one repository; start simple with a spreadsheet. 5) Activate an AI model to learn from history (or use initial weeks to train). 6) Start a five-minute weekly review: cost index, schedule index, variances, trend, and recommended actions. - Example: PV=100, EV=80, AC=90 → SPI=0.80, CPI=0.88. If testing tends to exceed by 20% after requirements change, AI might forecast a 15% cost overrun unless you act (stabilize requirements, improve inputs, reorder tests). - Quick context: EV originated in aerospace/defense; it endures because it answers how much value you built for the money spent. AI helps detect a mid-project “point of no return” and warn early. - Trends: hybrid agile-predictive management, automated reporting, near real-time dashboards, and predictive cost overruns analysis. EV can be measured per iteration to accommodate variable flow; if SPI drops below 0.90, replan the next iteration. - Mistakes to avoid: - Inflated progress (hours, not deliverables). - Late data feeding the AI. - Moving the baseline to look better. - Ignoring scope; EV is about delivered scope, not only time/cost. - No thresholds or action triggers. - How to start this week (5-day plan): Day 1: list deliverables and define “done.” Day 2: set a baseline with planned value by week. Day 3: build a dashboard showing PV, EV, and AC. Day 4: input progress and costs. Day 5: run a simple AI forecast to get trend and forecast. - Security/ethics: anonymize data when needed, control access to sensitive costs, validate AI recommendations with judgment. If AI suggests something inconsistent with known facts, update data and recalc. - Goal: use AI-assisted EVM to make decisions one week earlier, improving cost/schedule control and leadership conversations grounded in data. - Final prompt to drive action: decide which deliverable you’ll measure objectively today and what you’ll do if SPI drops below 1 this week; document, share with the team, and start the five-minute review. - Closing: subscribe, share thoughts, and tune your project to become a measurable story rather than a mystery. Remeber you can contact me at [email protected]