Building AI products right starts with measurement, not architecture

AI-generated writing.

Hamel Husain’s field guide distills a common failure pattern: AI teams invest weeks in architecture and tooling while neglecting measurement. The single highest-ROI investment is a custom data viewer — a simple interface letting anyone examine what the AI is actually doing in context. Teams with these purpose-built viewers iterate 10x faster, and they can be built in hours with AI-assisted development.

Evaluation criteria cannot be fully defined upfront. Reviewing AI outputs actively shapes what “good” looks like — Husain calls this “criteria drift” — so evaluation rubrics should be treated as living documents, not fixed specs. Binary pass/fail judgments paired with written critiques work better than numeric scales: the binary forces actionable clarity, while the critique captures nuance and surfaces implicit domain knowledge.

AI roadmaps should count experiments, not features. Committing to specific capabilities by specific dates fails because AI development involves long plateaus followed by sudden breakthroughs. Better to commit to a cadence of timeboxed experiments with clear pivot points, measuring progress through a capability funnel rather than feature shipping velocity.