Evidence informs a prediction, a policy determines when to act, and evaluation helps us judge whether the process deserves our trust. The most useful AI system may be the one that can show us why it has enough information to make a responsible move confidently and backed by clear and easy to follow math.
In Moneyball to Machine Intelligence, I explored an idea that professional sports understood long before many enterprises did: Data matters
Professional sports might be one of the best ways to understand where enterprise AI is headed.
Long before we started talking about AI agents, machine learning, Gemini Enterprise, or predictive analytics, sports teams were already asking a very similar question:
How do we take all this information and turn it into a better decision than our competitor?
5-Day AI Agents: Intensive Vibe Coding Course With Google, June 2026 cohort, Creating the AfriSleep Da’Chi Obstructive Sleep Apnea and Wellness
“The enterprises that master these three areas will define the next generation of AI-enabled infrastructure.” I stepped away from my
Copilot Governance Lessons from a Multi-Account Access Incident During an enterprise Microsoft 365 Copilot rollout, I encountered an unusual identity
The data does not point in a single direction. It reveals a field that is scaling faster than the systems
I found out a bit late in the morning that Microsoft Copilot for Word and Excel has consolidated the previously
This month on my AI Tooling and Adoption journey, I explored the Amazon AWS AI Tool Chain and AI development
“As employees, it is crucial to consider which job functions in your current role can be easily automated. How are
Digital transformation is often framed as a technology problem. In my experience, it rarely is. It’s a coordination problem—between systems,
