Case Study 3: AI-Enhanced Project Forecasting & EAC Management (Dynamics 365)

Project managers were stuck in spreadsheet mode.
Forecasts lagged behind real costs.
And financial surprises kept popping up.

In a mid-sized engineering and construction firm, project managers spent 8–12 hours each month manually compiling actual costs and predicting the future. With no real-time link to Dynamics 365, forecasts were often outdated before they were finished, leading to missed variances and revenue recognition headaches.

Why This Was Really a Data Problem

This wasn’t just a forecasting issue — it was a data visibility gap.

They had all the data: labor, materials, subcontractor costs, and more. But it lived in disconnected systems and static spreadsheets. When forecasts were built by hand, the numbers couldn’t keep pace with reality.

What they needed was a live feed of actual costs and an AI-driven engine to turn that data into proactive, scenario-based insights.

Quantitative Impact

They were spending 8–12 hours per project each month on manual updates. That’s over 110 hours a year per project, with forecasts that still missed the mark and increased the risk of budget overruns.

Solution Overview

We built a custom EAC management platform integrated directly with Dynamics 365. Now project managers could plug in their forecasts for labor, materials, and other costs, while the system automatically pulled real-time actuals from the ERP.

The result? A dynamic, real-time project financial dashboard with:

• Interactive charts for cost trends and variances
• Side-by-side comparisons of forecasts vs. actuals
• Early alerts for potential overruns
• Mobile-friendly dashboards for updates on the go

Implementation Approach

Weeks 1–2: ERP integration and initial prototyping
Weeks 3–4: Core dashboard and EAC calculation development
Weeks 5–6: Advanced features like alerts, commentary, and mobile optimizations
Weeks 7–8: User training, parallel testing, and go-live

Results

The impact was clear:

• Monthly forecasting time dropped from 8–12 hours to under 2 hours per project
• Over 110 hours saved annually per project
• Forecast accuracy improved, reducing overruns by 15–20%
• Estimated savings: $11,000+ per project each year

In short, we turned project forecasting into a real-time, proactive process — giving project managers more confidence and leadership fewer surprises.