Why the First Two Weeks of an AI Deployment Decide Its Outcome

An engineer embedded with a customer during the opening stretch of a deployment works to replace assumptions with facts before building anything. The brief written in a meeting room often describes a proxy rather than the real problem, which becomes clear only after observing daily operations. The article outlines five principles: focus on the actual problem rather than a stated tool, request data and system access immediately, examine raw unfiltered data before designing, select a small but meaningful first workflow, and identify every stakeholder who could block the work. Skipping this phase, it argues, results in building the wrong solution efficiently. Early discovery turns potential delays into useful signals about how the deployment will run.