All Posts

photo by Eric Prouzet on Unsplash The financial services firm launched its new loan approval algorithm with every reason for confidence. Pre-deployment tests: passed. Model performance metrics: excellent. Bias audit: acceptable. Compliance review: clean. The executive team approved deployment satisfied they had built a responsible system. Eighteen months later, the system was denying loans to qualified applicants in ways the original testing never caught. The model had absorb

Photo by Fab Lentz on Unsplash The team had built something extraordinary. Their recommendation algorithm predicted with 94% accuracy what content would keep users engaged. Session time was up 40%. Daily active users up 28%. Every metric moving in the right direction. The CEO was proud. "We've created genuine value. People are spending more time because we're giving them what they want." Then an engineer raised her hand. "Can I ask a question about what we're optimizing for?"

Strategic planning practice over the past half century was built on an assumption so foundational we stopped noticing it: that the future is continuous enough with the present to chart a course forward. We now live in what Thomas Friedman calls the Polycene — an era shaped not by one disruptive force but by many converging at once. Of all those forces, AI is the most organizationally consequential, because it doesn't just change the environment around your organization. It ch

There is a question I hear from executives more than almost any other when the conversation turns to AI governance: Are we compliant? It is a reasonable question. It is also, increasingly, the wrong one. Compliance is necessary. No serious leader dismisses regulatory requirements or the legal frameworks that govern AI deployment. But compliance has become a ceiling when it needs to be a floor. Organizations that treat regulatory adherence as the destination for AI governance

When an AI system denies your loan, flags you as a flight risk, or filters your résumé before a human ever sees it, the instinct is to ask: Who is responsible for this? The honest answer is everyone — and therefore no one. That is not a scandal. It is a structure. And until we understand the structure, we will keep responding to AI failures the wrong way. The Question That Never Gets Answered On January 15, 2021, the entire Dutch cabinet resigned after an investigation reveal

Most executive conversations about AI risk start in the wrong place. They start with the model — its accuracy, its bias scores, its regulatory compliance status, its vendor validation documentation. They start with the technical question: Is the system performing within acceptable parameters? That question matters. But it is not the question that should be keeping executives up at night when it come to AI. The systems that have produced the most significant organizational and