BlogGetting Labels Right the First Time: AI for Label and Artwork Compliance

A single character is all it takes. A dropped zero turns 10 mg into 1 mg. A missing word weakens a warning. An allergen printed in regular type instead of bold breaks the rules. None of these errors is dramatic on screen, yet any one of them can trigger a recall once the product reaches the shelf.

Labeling is where GMP meets the outside world. It is the one part of the product every patient, pharmacist and consumer reads. This article explains why label errors are still so common, why careful human review struggles to catch them, and how AI label compliance gives packaging and quality teams a far stronger safety net.

The scale of the labeling problem

In food and consumer goods, the numbers are stark. A 2025 analysis of US recall data by the Public Interest Research Group found that undeclared allergens were the single biggest cause of food recalls in 2024, behind 101 recalls and about a third of all FDA and USDA recall announcements. The pattern is global. In Australia, undeclared allergens caused 38% of food recalls in 2025, with packaging errors and failures to communicate ingredient changes among the main contributing factors.

In pharma, the stakes are even higher. FDA enforcement reports regularly list recalls for wrong strengths, missing or incorrect expiry dates and outright label mix ups. Regulators treat labeling as a core GMP control for exactly this reason. In the US, 21 CFR Part 211 Subpart G sets strict rules for how labeling is examined, issued, used and reconciled. In Europe, EU GMP Chapter 5 covers packaging materials in the same way, and supplement makers follow labeling controls under 21 CFR Part 111.

The rules also keep moving. Sesame became the ninth major food allergen in the US on January 1, 2023, and every affected label had to change. The FDA's updated "healthy" claim has a compliance date of February 25, 2028. The EU Packaging and Packaging Waste Regulation applies from August 12, 2026, with labeling duties phasing in after that. For a brand with thousands of products across several markets, every change means checking labels all over again.

Why careful reviewers still miss errors

Label errors rarely come from carelessness. They come from the way artwork is produced. A single carton may pass through regulatory affairs, marketing, a design agency, a local affiliate and a printer, with changes at every stage. Late edits are common, and deadlines are tight. Picture a reviewer checking a carton for the third time that day: dense small print, a nutrition table and text in nine languages. A changed digit or a missing word can slip past even the most experienced eyes.

There is a deeper problem too. Most label checks compare the new file with the last approved file. That finds anything that changed, but it cannot find an error that was already in the approved version. If last year's carton carried an outdated allergen statement, a side by side comparison will pass it straight through to print.

Proofing versus compliance checking

This is the most important distinction in label quality today.

Traditional artwork proofing compares two files and highlights differences in text or pixels. It is valuable, but it only answers one question: did anything change?

Compliance checking answers a far more useful question: is this label correct? It checks the artwork against the approved label copy and against the rules that apply to that product in each market, including mandatory statements, required warnings, allergen emphasis, minimum text sizes and permitted claims. Because it checks against rules rather than only against another file, it catches errors even when both files match.

How AI label compliance works

AI makes compliance checking practical at scale. It reads the entire artwork file, including text, tables, symbols and barcodes, across every language on the pack. It matches the content against the approved source, such as the label copy or product information, and then applies the labeling rules for the product type and each target market.

Every issue is marked directly on the artwork with the reason it was flagged, so the reviewer sees both the problem and the rule behind it. The reviewer accepts or rejects each flag, and the complete check is saved as a record for audits. This keeps the process firmly within GMP: AI does the detailed reading, and qualified people approve every label.

The most effective tools are built around three inputs: the approved copy, a rulebook that captures regulatory, statutory and brand requirements, and the final artwork. Checking all three together is what separates true compliance validation from simple comparison. Each finding should carry a severity level, from critical issues such as a wrong dose or a missing warning to minor ones such as punctuation, along with a confidence score, so reviewers deal with the most serious problems first.

Good tools are also selective about what they flag. Text that has simply moved, wrapped onto a new line or changed font is not an error, and flagging it buries real issues in noise. Filtering out these cosmetic changes keeps reviewers focused on what genuinely affects compliance. And because errors can also enter at the printer, the same check should work on scanned or photographed labels, so printed stock can be verified against the approved artwork when it arrives.

What this looks like in pharma and FMCG

In pharma and life sciences, AI focuses on the details that matter most for patient safety: product name, strength, dosage form, warnings, storage conditions, batch and expiry fields, and consistency with the patient leaflet. For multi market packs, it also checks country specific rules and translations that reviewers may not be able to read themselves.

In FMCG, food and supplements, the emphasis shifts to ingredient lists, allergen declarations, nutrition panels, health and marketing claims and net quantity. Speed matters here as much as accuracy. With frequent launches, seasonal packs and changing regulations, AI lets teams check every label thoroughly without slowing time to market.

Choosing the right approach

The first question to ask of any label tool is whether it checks against rules, not just against another file. Beyond that, it should read the file types, languages and packaging formats your teams actually use, and explain every flag clearly with its exact location on the artwork.

Because labeling is a GMP activity, the tool should also keep a full audit trail with user sign off in line with 21 CFR Part 11, and the vendor should be able to support validation for your intended use. Most importantly, test it on your own artwork, including labels where you already know the errors, before you rely on it.

Label errors are preventable. With AI handling the detailed checking, packaging, regulatory and quality teams can focus on the decisions that matter, and get labels right the first time.

Benchmark IT Solutions helps pharma, FMCG, food and supplement brands bring AI into label and artwork review. Talk to us about checking your own artwork before your next print run.