Most companies don’t have a technology problem. They have a decision-making problem.
I’ve spent 30 years helping leadership teams evaluate technology decisions before they get expensive, and diagnosing the ones that already have.
D.Sc. Information Systems · CTO · Fortune 500 to Series A
This week's executive brief covers AI liability rulings, digital sovereignty concerns, solar energy milestones, and location data privacy.
Read this issue →Half the technology projects I evaluate shouldn’t be built — at least not the way they’re being pitched.
The vendor demo is designed to sell you. The question is whether it works for your data, your team, and your timeline.
Nobody wants to be the one who says ‘we should kill this project.’ That’s what I’m for.
How to measure AI initiatives when the old metrics don’t work.
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I take on a small number of consulting engagements — typically for leadership teams facing a technology decision they can’t afford to get wrong.
Founded a fintech startup based on my doctorate research that used NLP and machine learning to analyze market sentiment. Built the core AI models. Got acquired in 2015.
Served as CTO for a financial research platform where I built the AI-powered products, data pipelines, and analytics tools that became the company’s primary revenue drivers.
Created an IT strategic plan for a nonprofit that transitioned them to the cloud and cut their technology spend by 50%.
Built ML-driven revenue forecasting models for manufacturers that saved 20% in raw materials costs. No AI hype; just a good model solving a real problem.
Led development of RAG-based AI systems for an education platform. We built AI agents for tutoring, NLP for analyzing student interactions, measurable improvements in learning outcomes.
Helped Fortune 500 clients optimize their data analytics and reporting, reducing the people-hours needed for data processing by 75%.
Practical guides for technology leaders making AI and infrastructure decisions.
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