Why 'Just Add AI' Fails Food Safety Quality Managers
Your quality manager isn't leaving the office Friday night because she lacks tools. She's still there reconciling Plant 112 and Plant 218 naming conventions in a spreadsheet before Monday's supplier audit. The AI you're evaluating won't fix that. It'll just process the inconsistency faster.
That was the core argument from a recent FSMA Friday panel hosted by SafetyChain, featuring SafetyChain CEO Dean Brown, regulatory strategist Dr. Benjamin Miller of The Acheson Group, and Chris Brandsey, founder of Frame & Flight and a veteran of food safety programs at Starbucks and Nestlé. The conversation was one of the more honest discussions of AI in food safety compliance you'll find right now, because the panelists spent more time on what AI can't do than what it can.
Here's what they got right, and what it means for how you approach this in your operation.
The 70/30 problem
Dean Brown described a real customer initiative: a manufacturer wanted to use machine learning to identify root cause patterns across hundreds of facilities. Cross-facility correlation. Systemic CAPA insight. Exactly the kind of analysis that can change how you manage nonconformances at scale.
It didn't go as planned.
"You were looking like seventy, thirty," Dean said. "Seventy percent of effort just to clean up the inconsistencies and fragmentation, and then thirty percent to actually get into some really interesting causal data."
Seventy percent. Not building models. Not interpreting results. Cleaning up the mess left behind when different plants named the same thing differently, formatted records inconsistently, and captured data in ways that made sense locally but not across the network.
The data existed. It had been collected, stored, and was technically accessible. It just wasn't usable in any analytically consistent way. That's the data readiness problem. And it's not a technology problem. It's a governance and process problem that no AI vendor can solve for you.
In our live audience poll during this session, food safety leaders identified fragmented data across systems as the leading barrier to AI adoption, followed closely by inconsistent processes across facilities. professionals who responded...'] Those aren't AI problems. They're the foundational work that has to happen before AI delivers anything real.
Why "just add AI" doesn't work in food safety
The market right now is full of AI tools promising HACCP plans in minutes, automated root cause analysis, compliance documentation on demand. For quality and food safety teams that are chronically understaffed and buried in paperwork, that promise is appealing. It's also dangerous if your data isn't ready to support it.
Chris Brandsey put it plainly: "AI is being applied without first defining how decisions should be supported across the business through AI. Quality and compliance decision-making is largely implicit. It lives in people, and it's not fully in structured systems. AI doesn't typically fix that problem."
His prescription: "Start looking at your data, and structure your data around knowledge and decisions."
Data readiness isn't a phase you skip on the way to AI. It determines whether AI delivers anything real or simply processes garbage at scale. Before your operation can benefit from AI-assisted root cause analysis or supplier risk scoring, the records feeding those analyses need to be structured, consistent, and connected.
Teams are already using AI tools to accelerate CAPA drafts and flag supplier risk signals across the industry. The question isn't whether those applications work in principle. It's whether your data is clean enough to make the outputs trustworthy.
What FSMA actually requires, and where accountability lands
Dr. Miller grounded the conversation in regulatory reality. FSMA's preventive controls framework under 21 CFR Part 117 requires validation, verification, and recordkeeping. That framework is actually well-suited to AI augmentation. But it also defines exactly where the boundaries are.
In adjacent regulated industries, the FDA has raised explicit concerns about AI-generated compliance documentation that wasn't adequately reviewed by a human. The agency has signaled clearly, in guidance and public statements, that firms cannot outsource validation to an algorithm, whether in pharmaceutical or food manufacturing contexts. The FDA's underlying concern applies broadly: if you can't answer "how do we know that worked, and what's the ongoing verification of that workflow," you have a compliance problem, not just a technology one.
For food manufacturers operating under FSMA, that question isn't rhetorical. If AI is supporting compliance decisions in your operation, you're responsible for validating what it produces, and for what happens when it's wrong. The panel's accountability question produced a near-unanimous answer: when an AI tool generates an incorrect compliance recommendation, the entity making the food is responsible. Not the software vendor.
That accountability doesn't argue against using AI. It argues for using it with traceability built in. As Dean put it: "The decisions in our AI cannot be black box or people simply will not trust them."
Research on automation bias, the documented tendency to over-trust confident AI outputs, consistently shows that users accept wrong answers at alarming rates when systems present conclusions without surfacing their reasoning. (Parasuraman & Manzey's work on automation bias is probably the most-cited treatment of this.) In a compliance environment where a wrong recommendation has real consequences, food safety, regulatory, and commercial, that's a design requirement, not an academic footnote.
The traceability deadline that's closer than it looks
The FDA Food Traceability Final Rule, implementing Section 204(d) of FSMA under 21 CFR Part 1, Subpart S, establishes requirements for Key Data Elements (KDEs) and Critical Tracking Events (CTEs) for foods on the Food Traceability List. The compliance date has been extended to July 20, 2028, per H.R. 5371 from the 119th Congress.
That extension is real. But organizations waiting until 2027 to start are already behind.
The rule doesn't just require that you track lot-level information. It requires that the information be accurate, standardized, and retrievable. FDA expects firms to provide required records within 24 hours or an agreed-upon timeframe. If your lot codes aren't consistent across facilities, if your CTE data doesn't link cleanly from receiving through production through shipping, if your records require manual reconciliation before they're interpretable, you have a data standardization project on your hands. Getting FSMA 204-ready is a data standardization project first. The software comes after.
Add the pressure of GFSI audit schemes, major retailer food safety program requirements, and increasingly frequent third-party audits, and the urgency compounds. SQF, BRC, and FSSC 22000 all expect documented, traceable records. Your retail customers are asking for the same thing in their supplier programs. FSMA 204 isn't a new category of problem. It's a higher bar on a problem your auditors have been flagging for years.
For a deeper look at FSMA 204's specific requirements, see our food traceability guide.
What data readiness actually produces
This is where theory becomes operations. Here's a concrete example of what connected, structured records actually make possible.
A frozen foods manufacturer was fielding recurring customer complaints that their internal investigation couldn't explain. The complaints were real. The root cause wasn't visible from production records alone. Because their records were digital and linked at the lot level, connecting complaint data to production records became a tractable problem. That analysis surfaced a non-obvious temperature relationship at a specific process stage, something that would never have appeared in manual records or a system where complaint data and production data lived in separate, unlinked silos. The lot-level traceability didn't just satisfy a regulatory requirement. It made the root cause findable.
That's what "connected records across workflows" actually means in practice. Not a compliance checkbox. An investigation capability.
Sokol & Company took a similar approach to audit readiness. By automating electronic documentation, their team shifted from manually assembling evidence for FSMA compliance audits to having it structured and accessible on demand. When your audit trail is built into how records are captured, not assembled after the fact, the conversation with your auditor changes.
These outcomes point toward a few concrete areas to assess in your own operation:
- Standardized data capture at the source. If forms are filled out differently across shifts, facilities, or product lines, no downstream system fixes the upstream inconsistency. That includes naming conventions, field formats, and lot code structures.
- Connected records across workflows. Supplier documentation, in-process checks, CCP monitoring, CAPA records, and pre-shipment verification need to link. When they don't, every cross-functional analysis becomes the 70/30 cleanup problem Dean described.
- Consistent processes across facilities. Before AI can identify patterns across plants, the processes generating the data need to be consistent enough that comparisons are valid. Different forms, different frequencies, different escalation paths all break cross-facility analysis.
- Audit trails that support validation. Because 21 CFR Part 117 requires verification, and because you're accountable for AI-assisted decisions, the ability to trace how a recommendation was generated and what data it was based on isn't optional.
SafetyChain's digital forms and records capabilities, lot-level traceability, CAPA workflow management, and audit trail documentation are built around exactly these requirements. Not as AI features. As the data foundation that makes AI viable when you're ready for it, and that supports FSMA compliance and quality assurance in food industry programs regardless of whether AI is ever layered on top.
Where AI in food safety is actually heading
The panel wasn't pessimistic about AI. They were precise about it.
Dr. Miller pointed to cross-facility pathogen investigation as a compelling future direction: connecting disparate data sources to identify root cause patterns that wouldn't be visible from within a single facility's records. When a positive environmental finding can't be explained by internal investigation, the ability to correlate it against supplier data, production parameters, or environmental monitoring results from other facilities would be genuinely valuable. That capability doesn't exist at scale in most operations today. It's the right direction for where AI investment in food safety is heading, and the prerequisite for getting there is structured, accessible data.
Dean identified supplier management and CAPA writing as near-term areas where AI is gaining traction across the industry. Writing CAPAs faster is efficiency. Cross-facility CAPA pattern analysis, understanding what's actually driving nonconformances across your operation, changes how you manage food safety compliance at scale. The distinction matters when you're evaluating where to invest.
Chris reinforced the decision-support framing as the highest-impact starting point. AI doesn't need to be autonomous to be useful. Systems that help quality teams interpret standards, evaluate evidence, and surface patterns they couldn't see manually are already delivering value in the field. Without requiring autonomous action and without the compliance exposure that comes with unvalidated outputs.
The resistance data from the panel is worth sitting with. In that same audience poll, plant floor operators ranked as the leading source of resistance to AI adoption, with quality teams and leadership close behind. Dean's read on that is right: "If you put good and explainable data in the hands of an operator, they'll be all in. But if you give them the algorithm and say 'trust this', they won't." AI in this environment isn't an IT project. It's a change management initiative that requires the same principles any successful technology rollout does: involve the people who will use it, make the traceability visible, and don't skip the foundational work in pursuit of speed.
What to do Monday morning
If you're trying to figure out where to start, or how to have an honest conversation with leadership about what AI readiness actually requires, here's the practical framing.
Start with data. Not with AI.
Check whether your records are digital and structured. Paper-based forms that aren't digitized aren't just an efficiency problem. They're invisible to any analysis, AI-powered or otherwise.
Audit your naming conventions across facilities. If Plant 112 and Plant 218 describe the same nonconformance differently, you can't do cross-facility analysis. This is fixable, and fixing it has immediate value for supplier quality management and CAPA trending even without AI.
Map whether your records link. Can you connect a customer complaint to the production lot, the CCP records from that run, and the supplier COA for the incoming ingredient? If that connection requires manual effort, you've found your starting point.
Close the loop on your CAPA process. Not just documentation. Verified resolution, with records that show the corrective action worked. That's what root cause analysis in food industry settings and FSMA verification both require.
Operations that invest in this foundational work typically reduce audit prep time from weeks to days, not because they've automated the audit but because the evidence is already organized. That's a concrete return before AI enters the picture.
The companies that will see real returns from AI in compliance aren't the ones who deployed it fastest. They're the ones who did the prerequisite work first.
Watch the full session
The panel goes deeper than what's covered here. Dr. Miller walks through FDA's current posture on AI in regulatory oversight. Dean and Chris discuss the practical mechanics of building organizational trust in AI-assisted decisions. There's live Q&A and perspectives on where this is all heading.
