Short, opinionated articles on the practice of building and running AI systems inside real organizations.
Most AI initiatives die in the gap between a working demo and a system that actually runs the business. Here's where they break.
Read: Why AI projects fail before they ever reach productionA chatbot answers questions. A copilot does work. The difference is grounding, tools and accountability — not the model underneath.
Read: AI copilots vs generic chatbotsMost operational time loss isn't on big decisions — it's on the dozens of small, repeatable steps in between. That's where automation pays.
Read: How to automate business workflows with n8n and APIsWhen a person keeps making the same judgment call, you don't need a dashboard. You need a system that issues a recommendation.
Read: Decision systems: BUY / WAIT / AVOIDAI runs on context. If the data isn't accessible, structured and trustworthy, no model will save the project. Fix the feed first.
Read: Data readiness for AI automationForget the demo. Ask three questions: which workflow, which decision, which owner. If any answer is fuzzy, the project will stall.
Read: How executives should evaluate AI opportunities