According to bakingbusiness.com, AI adoption in food and beverage procurement remains largely experimental, with 48% of companies running AI pilots without commitment — the largest share among four adoption categories identified in a live poll at the June Sosland Purchasing Seminar in Kansas City.
Adoption Landscape and Industry Realities
The poll, electronically fielded by Nazetta, who has held roles at Rabobank and Bunge and brings experience across investment management, banking, and global agribusiness, drew over 100 attendees. Responses showed 24% not yet using AI, 18% with AI deployed in real workflows, and only 10% embedding it into daily work. Nazetta noted that physical infrastructure and fragmented digitization slow AI uptake in baking and agri-food supply chains relative to other sectors.
“Things are moving fast,” Nazetta said. “But again, in our industry, which is still very physically driven and (where) some pieces of the information and the processes are not digitized, it’s a little bit slower than in other industries, I would say, to be able to adopt AI.”
Research from Aptean, a supplier of vertical AI and industry-specific software, found that 23% of food and beverage organizations consider AI essential to their workflows and decision-making — underscoring its growing operational relevance despite uneven implementation.
Workflow-Specific AI Applications
Katherine Parr, senior food and beverage solutions consultant at Aptean, described how AI transforms routine tasks: daily commodity reporting — once a weekly manual process — now demands AI-driven updates every day. AI can track baking ingredients including flour, sugar, cocoa, and butter, modeling how a flour lot’s protein content or a swing in cocoa prices affects dough yield or formulation cost before production scheduling.
AI also optimizes allergen changeovers on bakery lines, minimizing downtime by sequencing production from least to most allergenic items — a logic extendable to color or flavor profiles. “You don’t want to spend all your shift time with your lines down for washouts,” Parr explained.
Next, AI is advancing into weather- and tariff-driven sourcing risk prediction. Parr noted that layering external weather and trade data onto operational data enables forecasts of how droughts affect wheat crops or how new tariffs impact specific imported ingredients — a capability poised to unlock the highest value for ingredient risk managers.
LLMs vs. Agentic AI: Functional Distinctions
Parr distinguished large language models (LLMs), which are reactive — responding to prompts with summaries or analyses — from agentic AI, which autonomously executes multi-step workflows, such as monitoring vendor on-time performance, flagging delays, and routing approvals while retaining human sign-off.
Aptean’s research found 63% of food and beverage organizations use general-purpose AI, but only 46% deploy AI built specifically for their industry. Those using industry-specific tools report stronger improvements in competitive positioning, workforce morale, and forecast accuracy.
Forecast accuracy rose by 19% after adopting generic AI, but increased by 29% when switching to industry-specific AI — a statistically significant difference confirming workflow-aware design drives measurable business outcomes.
Integrated Intelligence: FoodChain ID Scout
Marc Losito, vice president of regulatory solutions for FoodChain ID, described FoodChain ID Scout — an integrated platform combining LLM and agentic AI layers. The LLM layer reads and generates language from regulatory and commodity data; the agentic layer pulls data, applies proprietary rules, flags exceptions, and routes alerts — all without manual prompting.
Scout connects proprietary FoodChain ID data with partner and public streams — including commodity futures, weather, and geopolitical and trade feeds. When the Strait of Hormuz closed amid US–Iran tensions, Chicago Mercantile Exchange urea futures spiked within weeks — a signal Scout linked to downstream fertilizer scarcity, reduced wheat/corn/soy supplies, and historically documented fraud patterns like dilution and mislabeled origin.
This enabled bakery sourcing teams to act three to four months earlier — requalifying suppliers and initiating testing — instead of reacting to border rejections or failed audits. Scout also monitors weather anomalies against crop calendars, tariff announcements against harmonized tariff schedules, and currency moves against sourcing geography.
Data Foundation First
Both Parr and Losito emphasized data integrity as foundational. “You don’t get to good reporting without a good data backbone first,” Parr stressed, noting many clients still rely on QuickBooks or spreadsheets. “Garbage in, garbage out” remains central — meaning AI’s efficacy depends entirely on structured, centralized, and accurate source data.
“What is it that you’re trying to solve, and what’s the best tool to get the best outcome for that task?” Nazetta asked. “The winners aren’t necessarily going to have the best model. The winners are going to be able to combine their proprietary data with their commercial judgment and have the ability to act on it.”
Source: bakingbusiness.com
Compiled from international media by the SCI.AI editorial team.
