While it’s possible to automate certain aspects of Packaging review, there is currently no technology capable of replacing the judgment calls that require both a deep understanding of the market and a thorough knowledge of the law. While the FDA has been actively involved in defining this landscape — the agency issued final updated guidance regarding food allergen labeling requirements on January 6, 2025, and hosted a public listening session on February 10, 2026, specifically on the topic of Allergen thresholds — there is significant evidence that suggests the FDA is increasing focus on ensuring label accuracy.
There is considerable regulatory pressure driving companies to test automated tools. As companies grow beyond a handful of SKUs across a few dozen markets, manual review does not scale well due to differences in font, Allergen wording and claim restrictions.
AI labeling: What it is and how it is relevant today?
AI labeling involves software that uses artificial intelligence to read product information off a product label (such as text, barcodes, layout panel(s), Braille) prior to printing. The concept of AI labeling is not new; Barcode scanners have performed rule based checks for decades. What is changing is the ability to evaluate the context, not simply individual characters.
Technology evaluating elements on a product label
A modern AI labeling platform will generally validate a number of elements simultaneously rather than evaluating one element at a time:
* Allergen statements – confirming ingredient text matches an approved master copy.
* Barcode integrity — symbology, contrast, quiet zones and encoded data.
* Nutrition and Supplement Facts panels – layout, font size and required fields.
* regulatory Claims – flagging wording that is not compliant with the legally-approved claim for that particular market.
* some platforms also support reading flattened or embedded image/text formats — thus a designer saving their artwork as a single graphical file will still have the software inspect the text line-by-line as opposed to allowing the graphics to slip past un-checked.
Time savings using AI labeling platforms
While speed is an obvious advantage of using AI labeling platforms, it’s important to clearly identify where the time savings come from. Camera-based systems on production lines can perform label verifications at speeds exceeding 200 units/minute. No human inspector would be able to verify labels at this rate for an 8 hour day.
The table below identifies areas where automation tends to out-perform manual inspection and areas where it doesn’t.
| Task | Manual review | AI labeling tool |
| Matching text to approved master copy | Slow, prone to fatigue errors | Fast, consistent |
| Barcode symbology and contrast checks | Difficult without specialized equipment | Near-instant |
| Detecting a misleading but legal claim | Strong – requires judgment | Weak |
| Cross-market cultural tone review | Strong – requires local knowledge | Weak |
| Checking flattened/image-embedded text | Easy to miss under time pressure | Reliable if supported |
Limitations of manual review
Even though AI labeling platforms are becoming increasingly popular, none of them eliminate the need for manual review. A label may pass every automated check (correct allergens, correct Barcode, correct font size) and still contain errors that the software cannot detect.
Judgments that require human review
In addition to eliminating the mechanical and rule-based portions of the task (e.g. Barcode format, Allergen text Matching, etc.), AI labeling platforms do not take away the necessity for a trained reviewer when evaluating judgments that require context the software is unable to determine. These include:
* Claims that are technically accurate but likely to confuse consumers scanning them quickly
* Packaging colors or designs that unintentionally mimic those of competitors
* Translations that are grammatically correct but land poorly culturally in a specific market
* a reviewer develops this type of judgment through experience — e.g., recalling a similar label that had previously been recalled, or a “healthy” claim that caused regulatory problems in one country but not in others. Institutional memory does not easily translate into a rule book.
Consequences of label errors
Label errors are not an insignificant administrative problem; they are among the most frequent and costly quality failure types for manufacturers. In its 2025 sampling report, the California Department of Food and Agriculture determined that 33.6% of commercial feed samples it collected were collected strictly for purposes related to label compliance vs. Potential food safety hazards — a reminder that regulators view labeling as a risk category unto itself separate from potential contamination risks.
Actual recall events illustrate how much money can be lost. In April 2025, the USDA’s Food Safety and Inspection Service announced a voluntary recall of approximately 64,008 lbs. Of pork buns because sesame (a recognized Allergen) was inadvertently omitted from the label. The product itself was safe to produce; however, the Packaging stage failed. That is exactly the type of error automated verification is designed to capture, since it relies upon verifying whether text appears on the product versus determining flavor/taste/formulation.
Choosing among competeting AI labeling vendors
Competing AI labeling vendors’ approaches vary significantly and can create meaningful gaps. When choosing among competitive options, consider reviewing:
* whether the platform supports reading flattened/embedded images as well as live editable copy.
* how frequently rule sets are updated when regulations evolve across geographies/jurisdictions.
* integration with existing product data. This means that checks occur against current specifications rather than against an uploaded static file.
* reporting style. Can you get a simple pass/fail result? Or can you receive additional detail identifying exactly where/how/why something failed?
Tip: a platform limited to reading live text only will fail to find errors embedded within an image/graphic file — a very common blind spot in legacy systems developed primarily for Barcode scanning.
Next steps over the next couple of years
The most likely course is not complete replacement — it’s reduction in scope of work for manual reviews. The mechanical/rule based elements of the review function are being absorbed by AI labeling platforms. Claims evaluation/cultural tone evaluation/legal judgment remain with humans for longer periods than some vendors might indicate. Regardless of how accurate software becomes, regulators continue to demand that someone be identified as responsible for releasing a label; therefore, there will always be a human check point in the process.
Last Updated: August 25, 2026
