Despite major advances in LLM capability and reported productivity gains of 5× in areas like software engineering, AI adoption in Finance remains strikingly low: fewer than 6% of finance professionals use AI-assisted workflows. The primary blocker is trust. In many knowledge domains, LLM outputs are inherently self-verifiable (for example, legal drafting, research synthesis, or slide creation). In finance analytics and modeling, however, LLMs often “solve” problems by producing code. Even when the answer is correct, the work is difficult to audit, stress-test, and explain, making it a non-starter for FP&A teams who are accountable for rigor and defensibility. The result is a practical dilemma: how do finance teams capture real productivity gains beyond trivial spreadsheet assistance without compromising the standards required in the profession? Consider a typical post-close variance analysis and diagnostic deep-dive that culminates in a CFO-ready deck: it commonly spans 10+ input files and takes days to complete. An LLM might reduce the cycle time dramatically, but not if the output is a 1,000-line Python script that most finance practitioners cannot confidently validate. In this talk, we’ll introduce a design paradigm and a practical framework for approaching different categories of analytic use cases so teams can achieve AI-level speed while preserving transparency, auditability, and verifiability, and ultimately produce analysis they can defend in a CFO review.