I work at the intersection of enterprise technology, artificial intelligence, and decision systems connecting business needs, technology execution, adoption, and measurable outcomes.
A practitioner-led perspective on how organizations adopt, measure, and scale technology.
Frameworks for measuring enterprise AI adoption, project decisions, and business value.
Introduces the Decision Impact Score and Project Success Score to evaluate AI-driven projects through sequential decisions, failure propagation, and project-level outcomes. The framework has subsequently been cited in IEEE research on explainable and risk-aware AI systems.
Read on SSRN →Examines enterprise AI adoption as an iterative system and introduces a framework built around adoption, implementation, and change readiness.
Read on SSRN →EAVOF, the Enterprise AI Value Optimization Framework, connects adoption readiness, decision intelligence, and ROI optimization in a closed-loop model for enterprise AI.
How can organizations move beyond isolated AI experimentation and measure whether their AI initiatives are creating durable operational and business value?
A practical reflection on how management is evolving through the AI lens.
The book brings together practical observations on how project and product management are evolving in the age of AI, while keeping the focus on decision making, execution, and the human side of management.
View on AmazonManagement is not disappearing in the age of AI. It is becoming more intentional.
A concise book for practitioners navigating the changing relationship between people, decisions, systems, and intelligent tools.
Sharing applied research on enterprise AI, decision systems, and management.
Accepted for presentation at the INFORMS Annual Meeting 2026. The talk focuses on evaluating AI-driven projects through sequential decisions, Decision Impact Score, Project Success Score, and failure propagation.