The Three Questions Every Startup Should Ask Before Building AI

• Not all hard problems need ML—validate that adaptive learning actually solves your business problem before building. • Start with simple models (logistic regression, XGBoost) over cutting-edge architectures; prove concept viability first. • Data infrastructure matters as much as the model; invest in sourcing, labeling, and validation before training. • Build feedback loops and track real-world metrics from day one; plan for retraining and model iteration. • Calculate unit economics upfront—inference, training, and infrastructure costs must align with your business model to scale.