Online roundtable · [ROUNDTABLE DATE]
AI in regulatory document audits: why “just prompt it” isn’t enough.
AI tools that check regulatory documents are everywhere. Many follow the same pattern: a language model, a prompt, and a result that looks like an assessment. Whether it is professionally robust is a different question. Regulatory experts from G&L Scientific and Entourage open five discussion points, and a small group of peers works through them. Peer exchange, not a sales pitch.
What we will discuss
01 Is the AI’s regulatory knowledge current, and can it tell “new” from “never checked before”?
02 Compliance is interpretation, not lookup. Where does expert judgement come in?
03 Why checking requirements one by one misses inconsistencies across documents.
04 How do you validate a system that doesn’t always give the same answer?
05 Should the AI decide or escalate, and does it know when it can’t judge?
Agenda
60 minutes, five discussion points.
Each block opens with a short impulse of two to three minutes from a G&L Scientific and Entourage expert. Then one question goes to the room. There is no product presentation; the AriaFlow team answers “how do you handle this?” only when asked.
- 0–5 min
Welcome
Why AI audit tools are booming, and why “AI plus regulatory text plus prompt” produces output that only looks like an assessment.
Question to the room What has your team already tried?
- 5–15 min
01 · Currency of regulatory knowledge
Training cut-offs, and guidelines that are revised, superseded or withdrawn. How to tell a genuinely new requirement from one that was always there but rarely checked.
Question to the room How do you keep the regulatory baseline behind an AI tool current?
- 15–25 min
02 · Interpretation, not lookup
Compliance is subsumption, not text matching. Wording such as “adequate” or “justified” leaves room for judgement, and seasoned reviewers rely on experience no guideline writes down.
Question to the room Where in your documents does expert judgement matter most?
- 25–35 min
03 · Systemic checks and applicability
Checking requirements one by one misses inconsistencies across documents, such as an intended use or population that shifts between labeling, evidence and promotion. Applicability rules belong in deterministic logic, not in the model.
Question to the room Which cross-document inconsistencies have hurt you in an inspection or review?
- 35–45 min
04 · Multi-model and validation
Model ensembles only help against independent errors; shared knowledge gaps produce false consensus. Validate results for rule-based steps, process fidelity for interpretive steps, and monitor reproducibility continuously.
Question to the room What would your QA need to see before relying on an AI finding?
- 45–55 min
05 · Decide or escalate?
A reasoned finding versus “ask an expert”. The real test is whether a system honestly says “cannot be assessed”, and whether its uncertainty separates missing evidence, poor document quality, unclear product facts and contested interpretation.
Question to the room Should AI give a clear finding, or escalate when in doubt?
- 55–60 min
Close
Six questions to ask any AI audit tool before you rely on it, and what happens next.
Question to the room Who wants to test their documents against these questions?
Pre-read
The AI Hype in Regulatory Document Audits
The discussion builds on this article. It does not argue against AI. It names the structural weak points where naive approaches to AI-assisted audits fail, because the task is more demanding than “AI plus regulatory text plus prompt” suggests. You receive the full article with your registration confirmation. [author, role]
Currency
Models answer from a training cut-off. Outdated interpretations sound just as confident as current ones.
Interpretation
Whether evidence is “sufficient” has more and less defensible answers, not a binary one.
Tacit knowledge
The patterns experienced reviewers recognise are not written in any guideline.
Systemic gaps
Each document can be correct on its own while the set contradicts itself.
Applicability
Whether a requirement applies is rule logic and should be decided in code, not by the model.
False consensus
Several models with the same knowledge gap agree, and agreement looks like certainty.
Validation
Results can be validated for rule-based steps. For interpretive steps, the method is what you validate.
Decide or escalate?
Whether a tool answers boldly or cautiously matters less than whether it recognises and discloses the limits of its own judgement.
Speakers
Regulatory practice, with the AriaFlow team in the room.
G&L Scientific and Entourage
[Name]
[Title] · Impulses for blocks [01–02]
G&L Scientific and Entourage
[Name]
[Title] · Impulses for blocks [03–05]
AriaFlow
[Name]
[Title] · Moderation [confirm]
Who it is for
Leaders who are asked to trust AI findings
- Regulatory affairs and CMC
- Drug safety and pharmacovigilance
- Quality assurance and MedTech regulatory affairs
- Consultants and auditors who assess AI-supported work
What you take away
Better questions for any AI audit tool
- A one-page checklist: six questions to ask before you trust an AI audit tool
- How peers keep the regulatory baseline current and validate AI findings
- The recording, and anonymised answers from the room
Registration
Save your seat. 15 places.
You'll receive the dial-in details, a calendar invitation and the pre-read by email. The session is recorded for registrants.
- [date], [time], 60 minutes, online
- Free of charge, no sales pitch
- Can't make it? Book a 20-minute call instead
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