Economic Determinants of AI Enterprise Integration
Doctorate of Engineering · Artificial Intelligence & Machine Learning
George Washington University
My doctoral thesis investigates the economic conditions under which firms translate AI capability into measurable enterprise value. Most adoption research frames AI in narrow technical or productivity-curve terms; my work goes deeper — into the capital structures, organizational design choices, governance constraints, and incentive systems that separate firms that capture economic return from those that don't.
The research bridges three threads of my prior training: economics (causal identification of returns), financial engineering (modeling investment under uncertainty), and applied AI engineering (what enterprise-grade deployment actually costs and yields). The output is a framework for AI investment that treats capability as a function of operational and economic constraints, not just model quality.