Order Up: How AI is cooking up a storm in F&B

In 2007, we got Remy the rat under the hat with Pixar’s Ratatouille. Cut to 2025, and we’ve got a MasterChef in our pocket.

Gordon Ramsay’s AI On Call assistant serves up a digital twin of the British celebrity chef that listens to your kitchen commands, helps tweak recipes, manages timing, and even steps in to troubleshoot mid-dish mishaps. It’s a reminder of something we at Tiger Analytics work towards every day: a good model is important, but real value comes when its reasoning aligns with the way people actually work.

Step out of the kitchen and you’ll see a similar shift across the food and beverage industry. Machine learning and analytics systems are now embedded in the way leading F&B companies plan, produce, and move products. Forecasting systems recalibrate demand at a store, SKU, and channel level. RGM platforms make lift drivers transparent. Quality check systems use computer vision models to spot variation early and let manufacturers adjust in real time. Supply chain analytics surface specific opportunities, from smarter inventory positioning to network optimizations, before they become pressure points.  

We’ve been part of this shift in our work with global F&B leaders, collaborating on deep learning systems that keep manufacturing quality consistent without manual overhead; forecasting engines that adapt to hyper-local signals so labor and inventory stay in sync; and enterprise platforms that let business teams build workflows, run simulations, and trigger actions with minimal IT hand-offs. The secret to engineering systems that shape decisions? Ground them in real-world context: the operational nuances, data realities, and market dynamics. 

Our latest edition of AI of the Tiger explores how these technology shifts are redefining the world of food and beverage, where AI models may run the kitchen but human judgment still sets the table. 

What’s in a peel? Deep Learning handles the hot potato to keep chips crisp

Potato peeling rarely makes headlines, but peel too little and chip quality suffers, peel too much and you’re left with more edible waste. A global F&B manufacturer partnered with us to get more control over peel accuracy. We engineered a Deep Learning–powered CV system that learns optimal peel levels from thousands of annotated images, runs inference on the edge in real time, and adjusts machine parameters automatically through IoT integrations. A continuous retraining loop keeps the model tuned to changing crop conditions so the system improves with every batch. The results were a 75% drop in manual intervention, improved quality control, and a measurable reduction in waste. 

Fresh off the stove: Hyper-local predictions, real-time results on the café floor

The clock is about to strike 12pm, orders are set to surge, and kitchens need to respond. Managers need to know exactly how much fresh produce, proteins, and bread to prep, how many people are needed at the counter, on the floor and behind the stove… and fast. A model brings order. We partnered with a leading US restaurant chain to develop a hyper-localized ML forecasting engine that combines historical POS data, promotion schedules, and local event signals to produce café-specific predictions in near real time. These insights inform ingredient prep, labor allocation, and shift planning. The forecasting system is also agile enough to respond to demand fluctuations as they happen, helping reduce labor costs by 20% and inventory waste by 10%.

An agentic AI sous-chef for smoother warehouse operations

What’s the real origin story of great customer experience in F&B? Maybe it’s the personalized latte art or a perfectly timed promotion, or maybe the upstream choreography that keeps products flowing, orders fulfilled, and shelves stocked just right. Behind this reliability are thousands of operational decisions that need to stay in sync across plants, warehouses, and distributors. That’s exactly where agentic AI is stepping in. Take our work with a global F&B leader: We integrated a GenAI-powered Agentic Assistant with the ‘E2E Order to Fulfillment Control Tower’, unifying fragmented operational data into one real-time view and enabling proactive decision-making. The system surfaced fulfillment risks before they escalated, tracked shipment status, and automated routine interventions. It even adjusted labor priorities dynamically, leading to faster exception handling, smoother warehouse operations, and measurable reductions in operational cost.

Sugar, spice and scalable insights: Building a global semantic layer for faster decisions

How quickly does a global confectioner need to move today? A seasonal product launches in one region, a promotional spike hits another, leadership needs one coherent view of what’s selling where and why – and every market often speaks its own “data language”. We partnered with a global confectionery to close this gap. We pulled together sell-in, sell-out, and SISO data from multiple countries into a single lake, cleaned and harmonized it, and shaped it into a global semantic model that made cross-market comparisons simple. On top of that, unified Power BI dashboards gave teams shared KPIs, consistent filters, and one narrative for RGM and BPM. This led to faster insight cycles, smoother global-local alignment, quicker onboarding, and a scalable reporting foundation.

From getting each chip perfect to prepping smarter on the café floor to keeping shelves stocked right, F&B is moving at the speed of its own data. With agentic AI and unified data models starting to take hold, the next chapter is already unfolding. How do you see these shifts changing kitchens, cafés, and warehouses in the coming years?