Is AI Plan Checking Accurate?

Vendors like to answer this with a single number: 90% accurate, 95% accurate. The problem is that "accuracy" isn't one thing. It's at least three separate questions, and most headline stats only answer one of them.

Three Questions Hiding Inside "Accuracy"

The first is recall: of the issues actually present in a drawing set, how many does the tool find? A tool with strong recall misses less. But recall alone doesn't tell you whether the tool is also burying you in noise.

The second is precision: of everything the tool flags, how much of it is real? A tool can have great recall and still be a pain to use if half its findings turn out to be nothing when you check them.

The third question gets talked about the least, and it might matter more than the other two: can a reviewer confirm a finding without redoing the work themselves? A tool that's technically precise but only gives you an opaque confidence score doesn't actually save time. Someone still has to go verify it, and now they're starting from scratch instead of from evidence.

Confidence Scores Tell You Less Than They Seem To

A lot of AI drawing-review tools attach a confidence percentage to each finding. "87% confidence this is a code violation" sounds precise, but that number is the model's estimate of its own certainty. It isn't a verification, and it doesn't tell a reviewer where to look to check it.

A more useful format is a finding tied to an exact page, an exact spot on that page, what's wrong, and how to fix it. That's the difference between asking someone to trust a probability and giving them what a colleague would hand over if they'd caught the issue by hand: enough to check it in seconds. Structured AI's QA/QC Compliance Checks work this way on purpose. A deterministic list is something your team can act on and sign off on. A confidence score is something they have to argue with.

A Better Way to Test Any AI Plan-Check Tool

If you're evaluating one of these tools, skip the marketing number and run a real test instead:

Take a project you've already reviewed manually, one where you know what the actual issues were. Run the tool on it. Then check three things: how many of your known issues did it catch, how many of its flagged issues turned out to be real, and how long it takes your team to confirm each finding using only what the tool gave them.

A tool that passes all three is doing the job. A tool that just hands you a confidence percentage is asking for trust it hasn't earned yet.

Independent benchmarks add another data point worth checking. AEC-Bench, a published benchmark covering 196 distinct tasks across complexity levels, is one example: it tests how well a document agent actually performs against a standardized task set rather than relying on a vendor's own claims. Structured AI's Document Agents scored 84.7% across all 196 tasks and held the highest score in every complexity category, a figure that's independently checkable rather than self-reported.

This Is a Pre-Check, Not a Replacement

Even a tool with strong numbers on all three fronts isn't a substitute for a licensed reviewer or a jurisdiction's plan check. It's a first pass, meant to catch likely issues early so the review that follows starts from a shorter, better-documented list instead of a blank drawing set.

FAQ

What accuracy rate should I expect from AI plan-check tools? It depends on the tool and on how "accuracy" is being defined. Ask specifically about recall, precision, and whether findings are verifiable rather than accepting a single headline percentage. Structured AI publishes two figures worth using as a reference point: 90% of flagged errors confirmed accurate in production, and an 84.7% score across all 196 tasks in AEC-Bench, an independent benchmark, holding the highest score in every complexity category tested.

Can AI plan checking replace a licensed reviewer? No. It surfaces likely issues before a licensed professional's review or a jurisdiction's plan check. It doesn't replace either.

Why do some tools show confidence scores and others don't? Confidence scores come naturally out of how certain AI models work, but they leave the verification work to the reviewer. Tools built around deterministic, source-linked findings are designed to make that verification fast instead of optional.