We brought together operators, technologists, and advisors to cut through the hype and talk about where AI is already delivering value in food safety, QA, and supplier compliance, and where it isn’t.
Meet the Panelists
Jeremy Schneider (Moderator), Schneider Food Safety Services
Will Turnage, Co-founder & CTO, BruceAI
Martín Ramírez, Co-founder & CEO, Signify
Donya Litowitz, Co-founder & CEO, imPASTA! Foods; Board President, Upcycled Food Association (UFA)
1) Compliance pain shows up as soon as you get traction
Early on, founders chase product-market fit, not paperwork. The wake-up call happens when a distributor or retailer says, “Send me your FDA reg, HACCP plan, insurance, organic certs.” At scale, systems replace goodwill. Treat compliance as part of your brand story and buyer trust, not a side chore.
Most common recall cause? Undeclared allergens. One missed line can shutter a launch.
2) Think “augment,” not “replace”
AI is best at the dull, repetitive, error-prone tasks humans hate:
Scanning supplier packets (COAs, allergen statements, audits)
Extracting key fields and expirations
Flagging gaps and exceptions
Translating and normalizing foreign-language documents/dates
Keeping you audit-ready continuously, not in a once-a-year scramble
Manage by exception: let AI clear the 90% that’s fine, so people focus on the 10% that’s risky.
3) Start earlier than you think
Every early decision (ingredient, co-packer, packaging, claims) has downstream compliance implications. If you “wait until Whole Foods,” you’re late. Build light-weight processes now so scale is a series of small steps, not a panic pivot later.
4) Specialized beats generic for regulated work
General models (ChatGPT/Gemini/Copilot) help, but domain-specific tools reduce risk because they encode food-industry context and workflows. Example wins we discussed:
Artwork/label review: dropping from 60–120 minutes to ~5 minutes with high accuracy
Ingredient watchouts: mapping regulatory changes to affected SKUs and remediation paths
Supplier onboarding: standardizing custom questions, auto-validating expirations, and tracking renewals
5) ROI you can actually measure
Three buckets to watch:
Accuracy / Risk – fewer errors and omissions; fewer label/allergen misses
Time – hours to minutes on reviews; continuous audit-readiness
Capital efficiency – delay hires, reduce reliance on costly third-party services
A practical benchmark: one QA manager handling 200 suppliers instead of 60 with exception-based workflows.
6) “Ready for AI” is a mindset, not a company size
Pick one repetitive, rules-driven process. Instrument it. Ship an MVP. Measure time saved and error reduction. Scale from there. Scrap what doesn’t work and double down on what does.
7) Prediction vs preparation
AI won’t “see the future,” but it runs scenarios well:
“What if X ingredient is restricted in the EU?” → impact analysis + SOP updates
Outlier detection across process and quality data → faster investigations
Policy alignment checks → “Does this onboarding packet meet current FDA expectations?”
8) Culture lift: from chore to trust signal
Fast, precise responses to buyer requests signal you’re ready to scale beyond a single door. AI-enabled workflows also improve training - teams understand the why behind steps, not just the what.
Practical next steps
List your top 3 repetitive QA/compliance tasks.
Automate one end-to-end with an exception queue.
Define success metrics (turnaround time, exceptions caught, expired docs avoided).
Review monthly. Keep what works; kill what doesn’t.
