AI vs Manual Code Review

Manual code review depends on the reviewer's knowledge, experience, and available time. An experienced plan examiner with deep code knowledge produces a thorough review — but even the best reviewer cannot check every requirement on every sheet within the time constraints of a typical review cycle. AI code review applies every applicable requirement to every element on every sheet, every time.

The comparison is not about which approach is better — it is about how they complement each other. AI excels at the systematic, measurement-intensive checks: corridor widths, fixture counts, travel distances, fire-rating verification, accessible route dimensions. Human reviewers excel at the judgment-intensive evaluations: design alternatives, performance-based approaches, unusual conditions, and the holistic assessment of whether a building design achieves its life-safety objectives.

The most effective approach combines both. AI performs the comprehensive systematic check and produces a findings report. The human reviewer evaluates the AI findings (confirming, dismissing, or modifying each one), then applies their expertise to the areas where AI cannot contribute — evaluating the adequacy of a code alternative, assessing the constructability of a proposed fire protection system, or determining whether an unusual building configuration satisfies the intent of the code even if it does not meet the prescriptive requirements.