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Artificial intelligence is quietly revolutionising the food and beverage industry – from spotting defects faster than human inspectors to discovering novel ingredients at lightning speed. Yannick Verry, brand director at FI Europe, explains how companies are harnessing artificial intelligence to boost efficiency, reduce waste and innovate smarter, showing that the future of food isn’t just digital...it’s intelligent.

While consumers use artificial intelligence (AI) for recipes or meal plans, its real revolution in food unfolds behind the scenes, from satellite systems monitoring deforestation across cocoa supply chains to computer vision detecting food contamination faster than human inspectors.

Companies deploying AI across their operations are pulling ahead, while those relying on traditional methods risk being left behind in an industry where milliseconds can now mean the difference between profit and waste.

The AI revolution in ingredient discovery and formulation

One of the most transformative applications of AI lies in ingredient discovery, a process that traditionally takes decades and costs millions. Nuritas exemplifies this shift, using AI to identify novel peptides such as PeptiStrong, derived from fava beans, which required analysing 18 billion potential sequences. What once seemed impossible can now be achieved in years rather than the estimated 30 millennia Nuritas estimated it would have taken manually.

This capability isn’t limited to one company. Across the industry, AI is being deployed for numerous uses, from mapping functional ingredients to creating sensory experiences. Brightseed, for instance, links plant bioactives to specific health benefits by scanning molecular data at scale, eliminating the need for brute-force testing.

Shiru mines vast protein databases to design ingredients with tailored performance, like plant-based fats that mimic oil-holding capacity. While NotCo’s Giuseppe applies similar logic to taste and texture, reverse-engineering the molecular profiles of animal foods and rapidly finding plant-based equivalents.

Together, these approaches illustrate how AI collapses the traditional trial-and-error cycle: instead of testing countless combinations in the lab, it allows companies to design with intent, matching molecules to function, health or flavour with unprecedented speed. AI is creating a structural shift in how the food industry discovers and develops the building blocks of its future.

Manufacturing precision meets artificial intelligence

Inside food processing facilities, AI has become the invisible quality controller, scanning products with superhuman speed and accuracy. Computer vision systems can achieve 97% accuracy in detecting defective products, revolutionising quality control processes that traditionally relied on human inspectors.

The technology's capabilities extend far beyond simple defect detection. Modern systems analyse colour, texture, size and shape variations to grade products into specific categories, detect contamination and assess freshness levels. Advanced deep learning models can achieve false positive rates as low as 0.03% and false negative rates of 0.07%, ensuring that defective products are caught while minimising waste from false rejections.

Processing lines now integrate multiple AI technologies simultaneously. Computer vision systems inspect packaging seals, robotic systems adjust cutting processes based on product characteristics and predictive maintenance algorithms monitor equipment health to prevent unexpected breakdowns.

Advanced AI systems are revolutionising food safety by providing real-time detection capabilities or predictive models that identify potentially dangerous environmental factors, foodborne pathogens and chemical contaminants long before products reach consumers, significantly reducing public health risks compared to traditional testing methods. This orchestrated approach has transformed manufacturing from reactive troubleshooting to proactive optimisation.

Supply chain intelligence and waste reduction

The global challenge of food waste, estimated at $1 trillion annually, has found its most promising solution in AI-driven supply chain management. Real-time tracking systems now monitor products from processing facilities to retail shelves, using predictive analytics to optimise distribution routes and reduce spoilage.

In supply chains, real-time tracking supports managers to make informed decisions about sourcing while maintaining sustainability commitments. Businesses like Ofi deploy AI-powered carbon measurement tools created in partnership with geospatial specialists, using artificial intelligence analysis to provide high-quality data on forest cover and tree loss across different agricultural plots.

Kraft Heinz uses AI ‘watch towers’ to forecast demand and reroute deliveries in real time, while Kellanova and Danone are embedding process-control systems and machine-vision tools inside factories to catch defects and inefficiencies at their source.

The technology's impact extends beyond environmental monitoring. AI systems analyse historical sales data, weather patterns and market trends to predict demand fluctuations, enabling companies to adjust production schedules and inventory levels accordingly. This predictive capability helps prevent both shortages and surpluses that traditionally drove waste throughout the supply chain.

Marketing revolution through consumer intelligence

Beyond production infrastructure, AI has transformed how food companies understand and reach their customers. Consumer insight platforms like Puratos' Taste Tomorrow use semantic AI technology to track global consumer behaviours, attitudes and choices through continuous monitoring that combines interviews with analysis of millions of social media posts.

Kerry's Trendspotter, developed in collaboration with IBM, analyses social trends to predict nutrition trends, with both businesses demonstrating how established ingredient companies are integrating AI into their market intelligence capabilities.

This consumer intelligence enables companies to predict emerging food trends before they reach mainstream markets, informing product development decisions and marketing strategies. The platform monitors conversations in seven languages across more than 50 markets, providing unprecedented insight into global food preferences and emerging demand patterns.

Meanwhile, consumer marketing departments are using AI to create over 400,000 variations of personalised messages and product recommendations, as demonstrated by companies like Starbucks through its mobile application. These systems analyse purchase history, location data and even weather conditions to deliver targeted offers that drive both customer satisfaction and sales conversion.

The implementation reality

Despite AI's transformative potential, adoption remains uneven across the food industry. Potentially high implementation costs, fragmented data systems and skills shortages create significant barriers, particularly affecting smaller producers who lack the resources for comprehensive digital transformation.

The costs extend beyond initial technology investment. Companies require specialised personnel to manage AI systems, ongoing training for existing staff and robust data infrastructure to support real-time analytics. Many smaller food manufacturers find themselves caught between the necessity of AI adoption and the financial reality of implementation costs.

Where AI excels and where it struggles

The evidence reveals distinct patterns in AI's effectiveness across different food industry applications. Computer vision systems excel at repetitive inspection tasks, consistently outperforming human inspectors in speed and accuracy for defect detection. Predictive analytics proves most valuable in scenarios with large datasets and clear patterns, such as demand forecasting and equipment maintenance scheduling.

However, AI systems struggle with contextual decision-making that requires an understanding of complex, interrelated factors. Traditional expertise remains superior for handling unexpected situations, managing supplier relationships and making strategic decisions that involve subjective judgements about quality or market positioning.

Ethical implications, including data security, algorithmic bias and transparency concerns, present ongoing challenges that require careful management. Food companies deploying AI must navigate questions about data ownership, algorithmic fairness in pricing decisions and the transparency of automated quality assessments.

The competitive divide widens…or does it?

The data suggests AI adoption is accelerating the formation of distinct competitive tiers within the food industry. Companies with comprehensive AI integration report significant improvements in new ingredient and product development, efficiency, quality control and market responsiveness. Meanwhile, traditional producers face mounting pressure to modernise or risk losing ground.

This technological stratification has implications beyond individual company performance. At the moment, large corporations with resources for AI investment are expanding their market share while smaller manufacturers struggle to compete on efficiency and innovation.

Yet opportunities remain for strategic AI adoption that doesn't require massive upfront investment. Cloud-based AI services, modular implementation approaches and industry-specific platforms are lowering barriers to entry. Companies that identify high-impact, limited-scope AI applications can achieve meaningful improvements without comprehensive digital overhauls.

The question facing food industry leaders is no longer whether to adopt AI, but how quickly they can implement systems that deliver measurable returns while avoiding the pitfalls of rushed digitalisation.

Success requires understanding where artificial intelligence adds genuine value versus where a human approach remains superior.

Looking ahead: Fi Europe 2025

These developments in AI-driven food innovation will be explored in depth at Fi Europe 2025, where industry leaders will gather to discuss the latest technological advances. The conference features dedicated sessions on AI's role in food development, including ‘AI driven food: the next frontier’ and a panel discussion on ‘Responsible AI: how the food industry can accelerate innovation’ featuring experts from Big Idea Ventures, AKA Foods and Qina.

The Innovation Hub will showcase cutting-edge solutions from companies pioneering AI applications in ingredient discovery, manufacturing optimisation and consumer insights. As the industry continues its digital transformation, events like Fi Europe provide crucial platforms for sharing knowledge and fostering the collaborations that will define the future of food innovation