I've watched organizations spend six months building what they think is agentic AI when they've given a chatbot access to a few APIs. TL;DR: Generative AI writes things. Agentic AI does things. The difference is operational. Generative systems produce outputs like itineraries and reports. Agentic
I've watched expensive AI initiatives collapse at the exact same point. Over and over. The model works. The demo impresses. The pilot succeeds. Then the organization tries to move from proof of concept to production, and everything breaks. The failure happens at a specific boundary: the moment an
I've been in enough boardrooms lately to recognize a pattern. Senior executives are asking for AI, but they're not always clear about what kind of AI they actually need. The confusion is understandable. The market treats "AI" like a single thing when it'
That experience debt compounds invisibly while organizations are measuring the wrong things. Most transformation programs start by asking: "What systems need upgrading?" "What platforms should we modernize?" "What technical debt is blocking us?" Those are reasonable questions. But they miss the more dangerous pattern.
