MasonIn 2018, Waymo became the first company to offer fully autonomous rides without a safety driver inside the vehicle. The technology behind it was not a single brilliant system. It was dozens of independent evaluation layers, each checking the others, each capable of overriding the others, all overseen by a team of engineers who understood what failure looked like. No single sensor was trusted. The redundancy was the point.
Plan review has more in common with that model than most people in the industry realize.
The permit process depends on professionals who can evaluate complex drawings against hundreds of pages of code, catch what other reviewers missed, and stand behind their conclusions with their license. That work has always required layered evaluation. You check your own work. You consult a colleague on a close call. You look at the code a second time when something does not feel right.
The question is not whether AI belongs in that process. It is whether the teams using it understand that the redundancy is still the point.
The teams that treat AI as a shortcut
Teams that treat AI plan review as a shortcut will find out quickly that it is not built for that. The tools that hold up are the ones designed around the same principle Waymo operates on: add an evaluation layer, show your reasoning, let the professional decide.
What that looks like in practice is a system that flags a potential code conflict and then shows the reviewer exactly which section of which code book it is drawing from. The reviewer reads the same section. If the reasoning holds, the flag goes in the letter. If it does not, the reviewer overrides it and the system learns from the correction. The decision belongs to the person with the license. The AI is the extra sensor.
Reviewers who have worked with this model describe a specific experience: catching issues they might have missed not because the AI told them what to conclude, but because the AI prompted them to look at something they had not looked at yet. That is not automation. That is augmentation.
Building better reviewers, not replacing them
The teams that are building this into their workflows are not replacing their best reviewers. They are making their best reviewers better and giving their junior reviewers a faster path to developing real judgment. A junior reviewer who has to verify every flag against the actual code is learning the code in a way that reading alone never produces.
Waymo did not get to driverless vehicles by removing human oversight from the development process. They got there by building systems where human oversight was so deeply embedded that it could eventually be reduced without increasing risk. The same logic applies here.
The plan review profession is not heading toward a world without professional judgment. It is heading toward a world where professional judgment is better informed, better supported, and harder to surprise. The firms that will thrive are the ones building those information layers now, while the professionals who use them still have time to calibrate what the AI gets right and what it gets wrong.
The calibration is the work
That calibration is the work. It is slower than just accepting every flag. It is worth it.
The profession is not changing who makes the call. It is changing how much information they have when they make it.
If you want to see how that plays out inside an actual review, the product page walks through the interface step by step.