AUTOMATION AND AI IN BAKERY


enabling bakeries to enhance consistency while reducing labour dependency. STAQ represents a leap in how AMF embeds intelligence into its production infrastructure.


Rather than simply


attaching sensors or dashboards, the tool is designed to integrate vision systems, machine-learning models and connected control loops throughout the bake line. Cameras capture the shape, size and surface quality of each baked good; AI algorithms then evaluate deviations from target parameters and trigger corrective actions — such as adjusting conveyor speeds, oven zone


temperatures or


depositor volumes. This is not reactive monitoring, but an operational loop of sensing → analysis → actuation. The platform builds on AMF’s broader


Bakery Intelligence suite, where the company states that “our suite of Bakery Intelligence solutions is fully data-driven and engineered to reduce labour, energy and waste, while increasing food quality and consistency.” Under this umbrella, STAQ becomes the


eyes and decision-engine of the line. For example, in a pizza-line scenario the system might detect that topping distribution is drifting off target: it recalibrates the applicator speed,


flags an anomaly for


root-cause follow-up and logs the event for trend analysis. Earlier solutions such as the Smart Applicator showed the value of such AI-based topping control, reducing ingredient giveaway by at least 3%. The operational implications are


significant. With STAQ, AMF aims to minimise variation in product quality, shrink waste, reduce operator load and optimise energy use — all in real time. In environments where throughput is high and margins tight, these capabilities become differentiators. AMF’s own “lights-out” vision emphasises consistent quality, minimal downtime and improved sustainability. STAQ and AMF’s connected analytics platform enable deeper insights: the system archives data across shifts, lines and sites, identifying patterns (for example oven drift, changeover losses, supply-chain impact on dough behaviour) that feed improvements in scheduling, maintenance and recipe setup. It thereby turns machine data into operational intelligence.


Using AI to anticipate the next bakery trend Meanwhile, Puratos, another global leader in bakery, patisserie and chocolate ingredients, is leveraging AI from a different vantage point — market intelligence. As Nanno Palte, Group Marketing Intelligence Manager at Puratos, explains: “For us, one of the most valuable applications of AI we have found is as an accelerator for our market research efforts. Our proprietary trend platform, Taste Tomorrow, was established over ten years ago to give our customers real-time insight into evolving consumer preferences. In all our research, we combine two approaches: a ‘top-down’ view exploring opinions on known topics, and a ‘bottom-up’ method uncovering emerging interests before they enter the mainstream. Integrating AI tools such as social


listening, predictive forecasting


and trend aggregators has transformed our bottom-up research — delivering rich consumer insight in a fraction of the time once required.” This represents a more analytical, data-centric use of AI — not in production


or ingredients, but in anticipating what consumers will want next. Nanno adds a critical nuance to the discussion: “But even the most advanced AI tools are no replacement for human expertise. We find such technologies are most effective when guided by experienced analysts who bring depth and context to the data. When Taste Tomorrow recently detected a surge in culinary fusion trends for 2025 for instance, our researchers identified key differences by language market — revealing that growth will be strongest among Chinese, Spanish and German-speaking consumers.” “AI-powered trend forecasting has


transformed how we identify and act on emerging consumer behaviours. Its biggest advantage lies in revealing how global trends play out differently across regions — insight that’s almost impossible to gain through traditional research methods.” That capability has profound implications


for innovation and product strategy. “For example,” Nanno continues, “while the global boom in sourdough consumption might appear uniform at first glance, AI analysis shows clear variations: in Western markets,


36 • KENNEDY’S BAKERY PRODUCTION • OCTOBER/NOVEMBER 2025


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