Did We Actually Approve This?

Your company is preparing for an external audit. As you start reviewing your key records that you expect the auditor to review, you find documents that are riddled with problems:

• A supplier risk assessment with inadequate justification for the quality risk score.

• A CAPA report with typos and inconsistent terminology that makes it unclear what was actually corrected.

• Training records that don’t align with the documented changes.

• Checkboxes in a complaint record that are incorrectly checked, incorrectly indicating that MDR reporting was required but not submitted.

• An NCMR approved without product disposition and that was extremely overdue.

• Audit findings copied forward from previous reports, but never resolved.

“How did this get through our approval process?” is a common phrase in a situation like this.

Unfortunately these issues aren’t rare.  They happen every day.  And worse, they can slip past multiple reviewers, only to be discovered by auditors or regulators when it’s too late.  Often companies combat this problem by adding more reviewers (inspect in quality) or creating checklists for reviewers to follow (or ignore).  But rarely do these additional manual checks provide a sustainable improvement.

That’s where AI-powered audit and review tools step in. They don’t get tired, skip steps, or let bias cloud their judgment. Instead, they scan every document against your own procedures and industry regulations, surfacing gaps, risks, and errors before they become findings. 

These tools are not just for auditors – they can be used by almost everyone across an organization to improve record quality and compliance.

Empowering Every Role in the Quality System

1. Document Authors: A Second Pair of Eyes

Authors are often under pressure to push records forward quickly. With an AI audit tool, they can self-check their work before submitting:

• Verify that all required fields are completed.

• Flag missing references to SOPs or related records.

• Identify incomplete problem statements or vague corrective actions.

• Catch spelling, grammar, and formatting errors.

  
  

This reduces rework and builds author confidence, ensuring cleaner documents enter the approval process.  An AI tool can also allow less experienced team members to tackle more complicated processes with confidence.  Think of it as a “Do it right the first time” enabling technology.

2. Approvers: Augmenting Reviews

Approvers carry the burden of ensuring compliance, but human review has limits, especially when reviewing dozens of pages. Often times reviewers may just focus on certain areas of a document or just check GDP.  Or maybe they see that another reviewer has already approved it, and they know that person is “very thorough”…..

AI can help by:

• Highlighting sections that deviate from procedural requirements.

• Comparing content against similar historical records for consistency.

• Pointing out missing signatures, attachments, or justification statements.

• Highlighting areas of weak justifications that require further review.

  
  

Instead of re-reading every word and focusing on minor GDP errors, approvers can focus their expertise where it matters most – digging into engineering details and problem solving.

3. CAPA Boards: Surfacing Weaknesses Early

Corrective and Preventive Action boards face one of the most challenging tasks - deciding if root causes and actions are sufficient. CAPA records are some of the most scrutinized in the industry, yet they still are the most common citation for FDA 483s. AI tools support them by:

• Checking whether identified root causes align with investigation evidence.

• Flagging “corrections” mislabeled as “corrective actions.”

• Suggesting deeper exploration when causes don’t fully explain the problem.

• Highlighting patterns across CAPAs that may point to systemic issues.

  
  

This turns CAPA review into a more proactive, data-driven discussion that truly pressure tests a CAPA team’s investigation.

4. Internal Auditors: Scaling Review Capacity

Internal auditors often don’t have time to review every record. This means an auditor might give a clean bill of health to a QMS area that is actually full of nonconformances, simply because they didn’t sample the right records.  AI can act as an upfront triage system:

• Scanning hundreds of documents to spot high-risk or potentially nonconforming records.

• Surfacing anomalies, inconsistencies, and missing approvals.

• Identifying clusters of records where deeper investigation is warranted.

  
  

With AI, auditors can actually perform strategic, risk-based auditing, giving organizations and leaders more confident assessments of a site’s true quality system health.

5. QMS Leaders: Preparing for External Audits

One of leadership’s biggest fears? Surprises during FDA or Notified Body inspections. AI audit tools act as a pre-audit safety net:

• Rapidly assess document readiness across the QMS.

• Generate summaries of common weaknesses or repeat findings.

• Provide evidence that records were systematically reviewed in advance.

  
  

Theoretically, QMS leaders could periodically run AI audits on every quality record in an organization, giving a QMS health score, helping to determine where resources should be focused.  Instead of scrambling at the last minute, QMS leaders can walk into external audits prepared and confident.

Beyond Error-Finding: Building a Culture of Quality

The reality is, mistakes in documentation are inevitable. But allowing them to reach approval - or worse, an auditor - is not. With AI as your partner, life science companies can move from reactive to proactive quality management.

And these tools are not science fiction – they exist today. 

Want to see how myQMS.ai can easily create audit reports for your quality system?  We offer a free proof of concept audit for your quality records.   Schedule a demo today