0%
traceability in generic AIs
Annex 22
EMA guide on AI in pharma
100%
precision with validated systems
Why This Topic Matters Now
The adoption of generative AI has exploded in the last 18 months. Companies worldwide are experimenting with ChatGPT, Claude, and other LLM tools to optimize processes — including text and packaging review.
The pressure is real: reduce art approval time from 2 weeks to 2 days, automate manual review that consumes 30-40% of team time, manage hundreds of SKUs in multiple languages.
But there is a critical problem many companies are discovering too late: applying non-validated tools in regulated environments is a risk no audit will accept.
— EMA Annex 22, 2024The FDA, EMA, and ANVISA have already started questioning the use of generic AI in GMP. In 2024, the EMA published Annex 22 — the first official guide on AI in pharmaceuticals — making clear: "Generative AI and LLMs can only be used in non-critical applications."
What Generic AI Really Does Well
It's important to recognize where generic AI delivers real value before discussing its limitations.
Basic Spelling and Grammar Correction
ChatGPT is excellent at identifying typos, agreement issues, and sentence structure in non-regulated texts.
Fluency and Tone Suggestions
AI can improve paragraph readability, suggest synonyms, and adjust communication tone.
Initial Content Creation Support
For brainstorming, first draft generation, and ideation, ChatGPT is a creative and fast tool.
Where Generic AI Fails in Packaging and Compliance
Uncontrolled Changes to Approved Text
Problem: ChatGPT doesn't understand regulatory context. If you ask it to 'improve' a warning text, the AI may rewrite a sentence in a way that changes its legal meaning.
Example: An ANVISA-approved text read: 'May cause allergic reactions in sensitive individuals'. ChatGPT 'improved' it to: 'May cause allergies'. The first was approved because it specifies 'sensitive individuals'. The second is broader and may require new regulatory approval.
Lack of Explainability and Traceability
Problem: When ChatGPT makes a change, you don't know why. There's no audit trail, no decision documentation.
Example: In an FDA audit, when the inspector asks: 'Why was this text changed?' — you can't answer with 'the AI thought it was better'. You need documentation, technical justification, and evidence that the change doesn't affect compliance.
Modification of Claims and Mandatory Texts
Problem: Generic AI doesn't differentiate between marketing text, product claims, mandatory warnings, and ingredient information.
Example: The AI may 'optimize' ingredient text in a way that violates regulations, without warning that it's changing something critical.
Absence of Audit Trail
Problem: You can't trace who requested the change, when it was made, what the previous version was, why it was changed, or who approved it.
Example: In GMP, this is mandatory. You need documented Change Control. ChatGPT offers none of this.
Human Review: Strengths and Limitations
Human review brings irreplaceable strengths: contextual and regulatory judgment, intent and risk interpretation, aesthetic and layout evaluation, flexibility and adaptability.
However, it has real limitations: fatigue after multiple reviews, variability between reviewers, difficulty scaling, and inconsistency between versions.
— Precision ProofHuman review is reliable but doesn't scale. Generic AI scales but isn't reliable. The dilemma demands a third way.
Validated Systems: Where the Balance Lies
The solution isn't choosing between AI and humans. It's choosing "human + validated system".
Designed Specifically for GMP
Not a generic tool adapted for compliance. It was built with regulation in mind and passed validation protocol.
Precise Version Comparison
Shows exactly what changed, differentiates between significant and insignificant alterations, and allows selective approval.
Change Control and Traceability
Every change is documented: who made it, when, why. Complete audit trail.
Objective Evidence for Audits
Validation documentation, test protocol, and regulatory compliance — all ready for inspection.
How Precision Proof Addresses This Challenge
Precision Proof was designed specifically for this problem. It's not generic AI. It's a validated system that combines technology with regulatory control.
Quality Control System
Automated review with zero errors, detects inconsistencies, 24/7 standard with audit documentation.
- 100% precision in review
- Standardized and auditable process
- Available 24/7 without fatigue
Document Control
Traceable document comparison that identifies critical changes in 40+ languages with audit trail.
- Automatic inconsistency detection
- Compliance with FDA, EMA, ANVISA
- Complete change traceability
Artwork Workflow Management
Proofing and versioning with approval workflow, complete traceability, and centralized repository.
- Single source of truth for artwork
- Attributable approval workflow
- Complete version history
Audit current process
If you're using generic AI for packaging review, you're at regulatory risk.
Understand validation
Any tool you use needs validation documentation.
Consider GMP systems
Solutions exist that offer speed AND compliance.
Prepare for audit
When the FDA arrives, you need documentation ready.
What You Should Do Now
1. Audit your current process — if you're using generic AI for packaging review, you're at regulatory risk. 2. Understand validation — any tool you use needs validation documentation. 3. Consider GMP-designed systems — solutions exist that offer speed AND compliance. 4. Prepare for audit — when the FDA arrives, you need documentation ready.
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References:
- [1] EMA Annex 22 (2024)
European Medicines Agency. Annex 22: AI in pharmaceutical manufacturing. Available at: ema.europa.eu
- [2] FDA Reports (2024)
FDA. Recall Statistics and Data. Available at: fda.gov
- [3] Precision Proof
Precision Proof. Intelligent Packaging Review Solutions. Available at: precisionproof.com