Artificial intelligence (AI) has transformed entire industries, promising faster workflows, sharper insights, and better decisions. Yet when it comes to AI in compliance, many food and beverage (F&B) companies remain hesitant—haunted by the myth that AI inherently introduces more risk than it removes.

It’s a fair concern—compliance isn’t an area where you can afford surprises. But modern AI isn’t some rogue robot making guesses in the dark. Today’s solutions are built to amplify human judgment, not replace it. Powered by years of contextual industry data, AI in compliance can bring precision, speed, and consistency to even the most complex tasks.

So let’s unpack the skepticism. Because the real risk isn’t in using AI—it’s in missing the opportunity to use it wisely.

The myth of AI-induced risk

Skeptics of AI in compliance often point to its capacity for errors. They argue that unpredictable or incorrect decisions generated by AI could expose organizations to regulatory and reputational risks. On the surface, this seems valid. After all, if compliance decisions are wrong, the fallout can be severe.

But this perspective overlooks the very features that purpose-built AI solutions now include to safeguard against such risks. The idea that AI operates autonomously without safeguards or human oversight is an outdated and overly simplistic view.

Can AI really handle COAs without constant human oversight? Yes—and then some

Certificates of analysis (COAs) illustrate both the promise of using AI in compliance, and the concerns surrounding it. The inbound COAs that manufacturing operations receive alongside inbound shipments of raw materials are critical to quality and compliance initiatives, containing key information that can dictate whether a given shipment of material is safe to use. Reviewing COAs and comparing them to relevant specifications can be crucial, but it’s also time consuming and error prone. Using AI-assisted technology to automate that process makes sense. But COAs can be tricky. They’re dense, unstructured, and wildly inconsistent across suppliers—so the idea that AI could reliably extract data without heavy human intervention sounds like science fiction to many compliance professionals.

But the reality is changing fast.

As we’ve outlined in a previous blog, legacy tools based on optical character recognition (OCR) have helped, but extensive setup has been required to “zone” COA formats and make them machine-readable. Even so, legacy solutions could stumble over formatting quirks and lacked the context to interpret meaning. However, OCR technologies paired with applied AI can leverage domain-specific data in the background to help the system see the full picture. It doesn’t just “read” a COA; it understands how the information it contains relates to your unique specs, workflows, and regulatory demands.

What smarter AI really looks like

Solutions like TraceGains’ Intelligent Document Processing (IDP) reflect how far the tech has come. Unlike traditional OCR, which simply scans and lifts characters from a page, IDP applies advanced AI built on years of F&B data management insight. That context matters. It enables the system to understand not just what it’s reading, but why it matters—and to act accordingly. COAs are no longer a wall of unpredictable formats and obscure terminology—they’re just data, ready to be put to work.

By pairing cutting-edge AI with proprietary taxonomies, rule sets, and strong contextual data structures, IDP is equipped to tackle the complexity of compliance workflows with clarity and precision. And it does so without compromising sensitive information.

For AI in compliance, context is king and security is the crown

Data security is a growing concern in the age of AI, and rightfully so. Feeding proprietary product or supplier information into a publicly accessible AI model could mean relinquishing control over how that data might be used in the future. For compliance teams, that’s a nonstarter. 

That’s why TraceGains built its IDP differently. At the core is a federated knowledge layer—a proprietary architecture that blends deep F&B expertise with the sophisticated natural language processing capabilities of modern AI in a secure, controlled environment. In this way, TraceGains’ approach bridges the gap between the raw capability of AI and the specialized, secure precision the F&B industry demands.

IDP: Built for F&B, designed for control

But what really sets IDP apart is its balance of automation and control. 

Need to override an extraction result? You can, and the system will remember your feedback and apply it to future data extractions, improving accuracy over time based on the input that your team provides.
Want to fine-tune how the system interprets certain values? Prompt management is built right in.
Prefer to transform data at the attribute level? IDP supports normalization scripts, routing workflows, and post-processing rules to match how your team works best. 

It’s not just AI that works for you—it’s AI you can work with.

More than compliance—it’s a business win 

Beyond just reducing risk, AI brings meaningful advantages to how companies manage compliance on a daily basis. 

• Cleaner Data, Fewer Errors: Manual data entry is a breeding ground for mistakes. AI improves data quality by automating the validation and standardization of incoming information. 

• Stronger Supplier Relationships: With better visibility and early alerts, teams can address potential issues proactively. That transparency builds trust and leads to stronger, more collaborative partnerships. 

• Fewer Surprises Downstream: When problems are caught early, they don’t snowball into bigger ones later. AI helps identify discrepancies at the source, saving time, money, and stress across teams. 

At the end of the day, AI in compliance isn’t just about ticking boxes—it’s about creating a foundation for better business decisions, tighter operations, and long-term success. To see how TraceGains Intelligent Supplier Compliance makes that vision a reality, explore the solution here.

Up Next: Still skeptical? The next blog in this series will tackle another persistent myth: “AI Can’t Learn or Adapt to Supplier-Specific Variations.” Spoiler alert—it absolutely can, and we’ll show you how.