The Verification Bottleneck: Designing QC Processes for AI-Generated Regulatory Documents
What happens after an artificial intelligence (AI) tool generates your first regulatory document draft?
It sounds like a simple question, but for medical writing teams, it is where things get complicated. Joshua Kim, BA, presented a session at the 2026 Pacific Coast Conference that took on that question directly. Kim argued that this is the right question to ask because the post-draft review is when most of AI's efficiency gains are currently being lost.
To understand why these efficiencies are not being used, it helps to consider how the nature of document verification changes when a writer is no longer the one doing the drafting. For example, writing a regulatory document, such as a clinical study report (CSR), yourself is a lengthy process, but it relies on expertise and builds on familiarity. Writers end up knowing the data: which end points mattered, where the protocol had ambiguities, why certain numbers look the way they do.
Hand that task to an AI tool and the document comes back fast but with the embedded understanding from the writer lagging. The medical writer reviewing a long AI-generated CSR has to reconstruct the whole context from scratch, and traditional quality control (QC) processes are not built for that.
Additionally, large language models (LLMs) pose another challenge. They sound confident even when they are wrong. Human writers hedge when unsure, and reviewers have learned to read those signals. LLMs do not hedge the same way, so the usual cues that tell a reviewer where to look are missing. Together, these changes make verification a fundamentally different task for medical writing teams.
Recognizing that verification is now a completely different task highlights the need to define what an AI-generated document actually requires to be considered safe and ready for human review, such as source traceability. Source traceability is the ability to see which input informed a given statement. It helps reviewers move faster, but it only solves part of the problem. Someone must verify whether the correct source was used and the right conclusions were drawn.
Structured, consistent outputs go further. They make anomalies easier to spot and open the door to risk stratification. An ethics section in a CSR is mostly templated, low judgment, and low risk. By contrast, module 2.5 (Clinical Overview) and module 2.7.3 (Summary of Clinical Efficacy) narratives, both required sections of a regulatory submission, demand nuanced clinical reasoning about what the data actually mean. Useful tools should help teams segment documents by how closely each section needs to be read.
Audit trails matter too, although there is a real tension: a trail detailed enough to be useful can end up as time-consuming to review as the document itself.
Although establishing these baseline criteria for AI outputs is a basic first step, the greater challenge is organizational, requiring a complete restructuring of how teams handle the sudden influx of fast-generated content. AI generates content faster than most review workflows are designed to handle; teams that try to shoehorn AI-generated documents into existing QC processes will feel the strain.
Four practical shifts can help with organizing the large influx of content for regulatory documents:
Stratify documents by risk, as not every AI output needs the same level of scrutiny, and teams need clear criteria for what gets a full review versus a spot-check.
Build checklists specifically for AI, because the error types are different: fabricated confidence, misread source data, and incorrect end point definitions.
Separate the drafting step from the verification step rather than piling both onto the same person.
Define handoff responsibilities explicitly when AI and human contributions are mixed.
Even within a cross-functional team, people disagree on what counts as confident enough, and that problem does not disappear simply because an AI tool flags uncertainty. Overall, QC-ready AI output must make review interpretable, traceable, and scalable. Ultimately, organizations must redesign workflows around AI-assisted drafting rather than treating AI as an add-on.
To answer the initial question, addressing workflow gaps with AI is where the expected efficiency gains are either won or lost. Currently, the same manual review process that existed before AI is applied to documents that no one on the team actually wrote. That is the bottleneck, and it is quietly consuming the efficiency gains that AI was supposed to deliver.
Removing the bottleneck requires deliberate design: AI-specific QC checklists, risk-stratified review, separated workflow steps, and clear accountability. Teams who invest in that design will find the return on investment they were promised. Those who do not will keep wondering why AI feels slower than expected.
About the author: Maria Anwar is an aspiring medical writer. She is a PhD candidate in Physiology and Pharmacology at Wake Forest University, with a research focus on autonomic and cardiovascular responses to tobacco products. Drawing on her background as a clinician with experience in clinical trials and regulatory submissions, she brings a unique perspective to scientific research and clinical domains.
