In the current plan review market, most AI investment is clustering around the very front of the workflow: pre-submission checks. These tools promise to validate submittals before they ever reach a reviewer, focusing on completeness, formatting, and basic document hygiene.
That focus makes sense on the surface. Bad submittals create friction. Missing files, unusable formats, and incomplete plan sets waste time, frustrate reviewers, and introduce avoidable delays. Catching obvious problems earlier feels like progress.
But there’s a concerning trend embedded in this approach.
Pre-submission AI is increasingly being framed as the solution to plan review inefficiency — when in reality, it only addresses the easiest part of the problem.
At a glance, many pre-submission AI tools sound impressive. They confirm that required documents exist, enforce naming conventions, validate file formats, and check that fields are filled out. Some go a step further by applying rules to ensure a submittal looks “complete” before it enters the system.
That work is useful. It reduces noise. It prevents certain failures. It can even eliminate some avoidable back-and-forth.
But it doesn’t change the reality reviewers face once a plan actually lands on their desk.
A plan can be complete and still be unreviewable:
A submittal can pass automated checks and still violate requirements that only become obvious when someone applies judgment.
This distinction — completeness versus reviewability — is where many AI conversations quietly fall apart.
Buyers expect meaningful time savings or quality-of-life improvements. What they often get instead is the same review workload, just shifted slightly earlier in the process. Reviewers are still doing the hard work. Managers still see delays. Applicants still experience resubmittals.
The disappointment doesn’t come from unrealistic expectations. It comes from a mismatch between what’s being optimized and where the real work actually happens.
Once a plan clears intake, the real work starts.
Plan review isn’t a clerical exercise. It’s a judgment-heavy workflow with real consequences. Reviewers must:
This is also why “speed” is such a misleading promise in plan review.
When reviewers say they want to go faster, what they usually mean is that they want less waste. Fewer unnecessary cycles. Fewer resubmittals. Fewer situations where something gets missed and comes back later as a bigger problem.
Managers don’t want faster approvals if they create more downstream corrections. Applicants don’t benefit from a fast first pass if it leads to delays later. And reviewers don’t want to shave minutes off a review if it increases the risk of missing something important.
Pre-submission AI happens before judgment begins. It can reduce noise, but it doesn’t meaningfully reduce the cognitive load of review. It doesn’t assist interpretation. And it doesn’t address the work that actually determines outcomes.
That’s why so many AI claims in this category feel shaky- and many customers feel unsatisfied once their shiny new AI tool is deployed.
In the video below, we unpack this gap between the promise of AI and the reality of plan review, and explain why so much “early AI” focuses on what’s easiest to automate rather than what truly changes outcomes.
If this perspective resonates, you may also find it helpful to read, Supercharging the Plan Reviewer: Why AI’s real job in plan review is reviewer support, not replacement
It goes deeper on where AI creates real leverage inside the reviewer’s workflow — reducing rework, preventing misses, and supporting judgment without undermining accountability. It’s the mental model guiding how we’re building AI into the most critical parts of the plan review workflow.
If you’re sorting through AI claims in plan review and trying to separate what’s real from what’s performative, we’re always happy to share how we think about it — including where we see the most meaningful opportunities, what we believe matters first, and what we’re building into our product first.
Want to hear more? Get in touch. We’d love to share where we’re seeing success and what we’re prioritizing in 2026.