KEY TAKEAWAYS
Food and beverage brands need trusted, batch-level product data because laws like Texas’s MAHA Act demand accurate labeling of additives, sourcing, and health risks. Labels often pull from fragmented systems, so ingredient, allergen, and claim changes slip through. Centralizing data in a PIM and using AI to validate labels against product data helps protect trust and reduce compliance risk.
Laws like the MAHA Act raise the bar for additive, sourcing, and health-risk labeling accuracy.
Label errors often stem from fragmented sources such as supplier sheets, spreadsheets, and marketing decks.
Incidents like LMNT’s additive-free claim vs. detected maltodextrin show how data mismatches damage trust.
A modern PIM can serve as the version-controlled source of truth for ingredients, formulations, and label copy.
AI can flag label-to-data inconsistencies across many SKUs, helping compliance teams scale without replacing experts.
When Texas passed the MAHA Act in June 2025, it signaled a shift in how food and beverage companies need to think about compliance. This new law requires detailed labeling of additives, sourcing origins, and potential health risks, and it’s already being watched by other states looking to implement similar rules.
For brands, the impact is clear: labels can’t just be accurate in theory. The label has to be backed by data that reflects what is actually in the product, at the batch level.
That’s where most companies fall short.
Labeling often pulls from multiple systems: supplier sheets, spreadsheets, marketing decks, regulatory databases. Somewhere in that mix, changes slip through. An ingredient is updated; a supplier swaps, adds or removes allergen info; a claim lingers past its expiration. Potential data discrepancies that seem harmless until it’s too late.
It’s not hypothetical. Look at what just happened to LMNT. The electrolyte drink brand claimed to be additive-free, but independent tests flagged maltodextrin. Whether it was a mistake or a mismatch, the result was the same: public backlash, lost trust, and a scramble to explain.
That kind of incident doesn’t just hurt reputation. Under laws like MAHA, it could invite real penalties.
The complexity is real. Food products often change between batches, especially in today’s supply chain environment. Alternate ingredients are introduced. Regional rules vary. Even the same product might have multiple label versions depending on where it’s shipped or where it is sourced. Most internal systems aren’t built to handle that level of nuance, and certainly not in real time.
So how do you fix it?
It starts with centralizing product data. That’s where modern PIM systems come in. A good PIM becomes the source of truth for everything tied to the product — from ingredients and formulations to label copy and regulatory codes. It connects suppliers, compliance teams, packaging designers, and digital channels in one system that’s version-controlled and always up to date.
But that’s just the baseline.
The next layer is where things get interesting. AI is now being used to validate what’s in the PIM against what’s on the actual product. That might mean scanning a photo of a label and flagging inconsistencies. Or identifying claims that no longer align with the underlying ingredient data. AI can’t replace compliance experts, but it gives them tools to scale. It catches the things humans might miss, especially across hundreds or thousands of SKUs.
The goal isn’t just perfect labels. It’s trust.
In a market where consumers are more ingredient-aware and regulators are more aggressive, accuracy isn’t just a nice-to-have. It’s protection. And speed matters too. You can’t wait two weeks for an internal review when a supplier updates an allergen disclosure.
Your label is a public commitment. It tells your customer, “This is what’s inside.” If you can’t stand behind that with real, verified, up-to-date data, you’re taking on more risk than you think.
If you're unsure where to start, it may be time to evaluate how product data flows through your business. We offer a quick, no-pressure assessment to help food and beverage teams identify gaps, simplify their data strategy, and prepare for what’s coming next.
Because in this environment, getting your data right isn’t just smart. It’s essential.