This presentation introduces a pragmatic, tool-agnostic approach to AI-assisted development in Python by focusing on the three stages that determine outcomes: plan, code, and verify. Rather than centering on specific products, it frames AI as a high-throughput collaborator that must be directed through explicit context and constraints, then held accountable through repeatable checks. Attendees will learn how to convert ambiguous requirements into actionable acceptance criteria and invariants, how to drive small, reviewable code changes that respect project boundaries, and how to validate results using layered verification (linting, typing, testing, integration checks, and security scanning). The session also distinguishes what should be standardized globally across an organization versus configured locally per repository, and it highlights practical pros, cons, and caveats of common AI tooling categories to support safe, scalable adoption.