A practical framework for evaluating data readiness, governance, architecture, and operational ownership before scaling AI.
Our approach begins with the business decision the technology must improve. We then make ownership, evidence, risk, and measurable outcomes explicit before selecting tools or committing to scale.
The research note covers data readiness, evaluation design, human fallback, and the operating model after launch. It is written for product and engineering leads, not for model leaderboard debates.
Teams can use the checklist as a go/no-go before an AI feature enters the quarterly roadmap.
