Piloting AI QA/QC on a Live Project

Adopting a new review tool firm-wide off a demo alone is a hard sell, and it should be. The more reliable way to evaluate whether AI-assisted QA/QC actually works for a firm is to run it on a real project and see what it does with drawings the team already understands well.

Why a Known Project Is the Right Starting Point

The most useful pilot isn't a brand new project where nobody knows yet what issues exist. It's a project the team has already reviewed manually, ideally one where the known issues list is documented. Running the AI check against that project lets the team directly compare what it found against what they already know is there, which answers the accuracy question with evidence instead of a vendor's stated percentage.

What to Actually Look For

Three things matter most in a pilot. First, recall: of the issues your team already knows about, how many did the AI check catch? Second, precision: of everything it flagged, how much was real versus noise? Third, and often underweighted, verifiability: for each finding, could a reviewer confirm it quickly using what the tool provided, or did they have to go re-derive it themselves?

A tool that does well on all three in a pilot is a reasonable candidate for wider rollout. A tool that does well on the first two but forces manual re-verification on every finding hasn't actually solved the time problem, even if its accuracy numbers look good.

Starting With Baseline Checks Before Custom Ones

It's usually easier to evaluate baseline QA/QC Compliance Checks, run against standard code and compliance libraries, before layering in Custom Checks built around firm-specific standards. Baseline checks give a clean comparison point since they're not dependent on how well a custom check was written. Once the baseline performance is understood, Custom Checks can be introduced and refined using tools like Prompt Lab and Agent Playground to test and observe how firm-specific logic performs.

Getting the Right People Involved Early

A pilot works best when it includes the people who'll actually use the tool day to day, not just decision-makers evaluating it from a distance. A QA lead or BIM/VDC manager running the pilot directly will surface practical friction, like how findings integrate into existing workflows or how easy it is to assign issues by trade, that a demo alone won't reveal.

What a Reasonable Pilot Timeline Looks Like

Because AI-assisted review runs fast, a pilot doesn't need to stretch over months to produce a meaningful result. Running the check against one or two known projects, comparing results, and getting hands-on feedback from the team using it can realistically happen within a week, which is part of why it's a lower-risk evaluation step than it might initially seem.

FAQ

Should a pilot run on a full project or just a sample of drawings? A full project set gives a more complete picture, especially for testing cross-discipline coordination checks, but a representative sample can work as an initial gut check before committing to a full pilot.

What if the AI check finds issues the team missed during the original manual review? That's a valuable outcome worth investigating carefully, not dismissing. It might indicate the tool caught something real that slipped through manual review, which is itself useful evidence, though it's also worth double-checking those findings specifically since they weren't part of the original known-issues baseline.

Does a pilot require IT or security review before starting? For firms with data security requirements, it's worth reviewing deployment options, including private cloud or on-premise deployment, before running a pilot on sensitive project data.