Complete Is Not the Same as Reviewable: Why Most AI Pre-Plan Checks Solve the Wrong Problem

In our last post, Supercharging the Plan Reviewer, we made a simple argument: AI’s real job in plan review isn’t to replace human experts but to support them.

Plan review is judgment-heavy, safety-critical work. Reviewers are accountable for decisions that carry real consequences. Therefore, the most valuable role AI can play is removing friction so reviewers can apply their expertise more consistently and with less waste.

However, a concerning trend has emerged in the electronic plan review market. Many tools focus on making plans “complete,” but very few help make them truly reviewable.

That distinction sounds subtle, but it’s the difference between technology that feels helpful in theory and technology that actually improves a reviewer’s day.

Key Takeaways:

• A plan set can pass automated "completeness" checks while remaining fundamentally unreviewable, leading to invisible backlogs.
• Most AI tools solve for the applicant’s submission convenience rather than the reviewer’s professional capacity, shifting the burden of data validation onto the technical expert.
• High-value AI implementation should focus on reviewability, ensuring files are usable, versions are compared, and context is clear before a human expert ever opens the file.

The Mirage of “Complete” Plan Submissions

Completeness answers a narrow question: is everything attached?

Are all the required documents present?
Do the files exist?
Is there a plan number?
Are the expected sheets accounted for?

Those checks matter. Intake teams and permitting systems have been doing some version of them for years. Automating that layer can reduce obvious friction and avoid unnecessary back-and-forth.

But the problem is what happens next. A plan can be “complete” and still be fundamentally unreviewable.

Files can be in the wrong format. PDFs can be password-protected. Drawings can be unreadable at scale. Sheet sets can be inconsistent across submissions. Critical context can be missing even though every required box was technically checked.

From the system’s perspective, the plan passed. But from the reviewer’s perspective, the real work hasn’t even started — and now it’s already delayed.

This is where completeness quietly turns into a false sense of progress.

Why Plan Reviewers Are Frustrated by “Completeness”

If you spend time with plan reviewers, you hear the same frustrations over and over — and they rarely start with missing attachments.

They start with moments like these: A plan makes it through intake and lands in a review queue. Days later, a reviewer finally opens it days later and realizes it’s unusable. Now they have to stop real review work, diagnose the problem, and manually kick it back to the applicant.

That delay doesn’t show up as a system error. It shows up as lost time, frustrated applicants, and reviewers doing work they were never meant to do.

This is the invisible waste in plan review. Not missing documents, but unreviewable plan sets landing on a reviewer’s desk.And no amount of “AI completeness” fixes that unless it’s designed to care about what happens after submission.

Defining “Reviewability”: A Higher Bar for AI

Reviewability asks a different question: can meaningful review actually begin?

That means:

• Files are usable, viewable, and consistent
• Versions make sense relative to prior submissions
• Reviewers can see what changed and what didn’t
• The plan set supports judgment instead of blocking it

Reviewability isn’t about checking boxes. It’s about protecting reviewer attention.

This is why many teams feel underwhelmed after implementing surface-level AI checks. The system says progress was made, but the reviewer’s day hasn’t changed in any meaningful way.

Plans still arrive that can’t be reviewed, resubmittals still require manual comparison, and reviewers still burn time re-orienting themselves before real judgment begins. The technology did something, but not the thing that mattered most.

Moving AI Into the Review Workflow

Many AI conversations go off the rails because they focus entirely on pre-submission, which happens upstreams, before judgment begins. Although this can reduce some friction, it doesn’t touch the hardest part of plan review: applying human expertise reliably, consistently, and under real-world constraints.

That’s why so many AI claims in this category feel unsatisfying over time. They promise leverage but deliver shallow wins.

In our view, real AI leverage starts where reviewability begins — inside the reviewer’s workflow, not just at the front door.

AI should help answer questions like:

• What actually changed between submissions?
• Where might this plan violate known code relationships?
• Which comments still apply, and which no longer do?
• Where should a reviewer focus their attention first?
  

Those are reviewability problems, not completeness problems. And solving them doesn’t eliminate judgment. It protects it.

A More Responsible Way to Think About Pre-Plan AI

A responsible AI strategy in plan review recognizes that:

• Catching obvious issues early is helpful
• Preventing unusable plans from entering the queue is valuable
• But the real gains come from reducing rework and missed context downstream

If AI never changes what happens once a reviewer opens a plan, it’s not addressing the core constraint in the system.

That’s the bar we believe AI should be held to.

e-PlanSoft’s Strategic Approach to Building AI

Completeness feels comforting because it’s easy to measure. Reviewability is harder, but it’s where real progress lives.

At e-PlanSoft, this distinction shapes how we think about AI and how we prioritize where to focus.

We don’t start with what demos well or what sounds impressive in a pitch. We start by asking whether something meaningfully improves reviewability — whether it reduces rework, prevents misses, or helps reviewers apply judgment with less friction.

If it doesn’t make a reviewer’s day better, it doesn’t move up our priority list.

That’s not a roadmap promise. It’s a philosophy. And it’s how we avoid shipping technology that feels good in theory but disappoints in practice.

As AI becomes more common in plan review, the teams that get the most value won’t be the ones chasing the earliest checks. They’ll be the ones focusing on what happens after submission, when real work begins.

If you're looking to bridge the gap between “complete” and reviewable, let's start a conversation today.