Project Overview

As part of my graduate capstone at Boston University, I led the design of an AI-driven Enterprise Architecture (EA) for Ørsted, one of the world’s leading renewable energy companies. The strategic roadmap I developed leverages predictive AI, SCADA data, and ERP integration to reduce turbine downtime and boost operational efficiency.

Completed as part of CS 783: Enterprise Architecture at Boston University (Spring 2025)

The Challenge

Ørsted needed to shift from reactive maintenance to proactive, data-driven decision-making. I set out to deliver a system that improves real-time performance visibility, reduces costs, and maximizes output across their global wind portfolio.

The Solution

I proposed an enterprise-wide AI architecture featuring:
• Predictive maintenance modules
• Integrated SCADA and ERP systems (SAP)
• Cloud-based data infrastructure (AWS/Azure)
• Actionable insight dashboards
• A TOGAF-aligned framework for long-term scalability

Projected Results

• 15% reduction in turbine downtime
• 10% increase in energy output
• 20% improvement in maintenance efficiency

Tools & Technologies

Microsoft Visio, Lucidchart, ArchiMate, Python, Tableau, SAP ERP, AWS & Azure, SCADA data

Takeaways

This project combined strategic design with real-world impact in the clean energy sector, deepening my expertise in enterprise architecture, system optimization, and AI implementation — skills I now apply to support innovation in renewable energy.

Explore the Full Project

📘 Download Full Report (PDF)
📊 View Presentation Slides (PDF)

“Georgia has done excellent work. Her capstone is well-researched and highlights the kind of value IT can bring to renewable energy, particularly through emerging technologies like AI.”

— Joseph Couture, Ørsted US CIO & Regional Information Security Officer