From back office to production floor: how AI and digital technologies are transforming food manufacturing

AI, Fi Europe 2026, 15 July 26/ in Featured Articles

Yannick Verry, Brand Director, Fi Events, Informa, examines how artificial intelligence and digital technologies are reshaping food manufacturing, from energy management and predictive maintenance to digital twins, fraud detection and workforce resilience. Drawing on examples from Coca-Cola, PepsiCo and Queen’s University Belfast, he assesses the operational gains on offer while cautioning that successful implementation demands robust governance, human oversight and strategic integration – not technology alone.

Yannick Verry, Brand Director, Fi Events, Informa

Not long ago, artificial intelligence (AI) felt like something we read about rather than used. Today, it’s embedded into our daily lives, powering everyday apps on our phones – from recommendations to translations, photo search, and navigation. The same shift is happening across several industries, including food and drink. For many producers, the first uses of AI were limited to back-office research and administration tasks. But today, forward-thinking businesses are integrating it across all manufacturing operations too.

As food companies face mounting pressure to reduce emissions, improve efficiency, accelerate innovation and strengthen supply chain resilience, technologies such as predictive analytics, digital twins and advanced automation are helping transform how facilities operate. Together, these tools are enabling them to analyse, predict and optimise processes in real time, shifting from reactive decision-making to a more proactive, data-driven approach.

In this article, I explore five key opportunities where AI and digital technologies are helping food manufacturers and processors solve critical operational challenges.

1. Managing energy consumption and reducing emissions

Energy remains one of the largest operational costs in the food and drink sector. According to the United Nations Food and Agriculture Organization (FAO), food systems account for around 30% of global energy consumption. At the same time, they make up almost a third (31%) of global greenhouse gas emissions,¹ highlighting the need for operators to balance environmental objectives with productivity, profitability and product quality, especially in light of tightening decarbonisation targets.

Specific technology tools are a powerful way of finding this balance. Digital energy management systems use real-time data from sensors, smart meters and connected equipment to monitor and optimise energy consumption across production facilities. This enables manufacturers to identify inefficiencies, optimise equipment usage and production schedules, and support the integration of renewable energy sources.

Increasingly, operational data is also informing product development and process design, helping to create more energy-efficient production systems. Real-time energy insights can and should influence decision-making beyond the factory floor, whereby producers are using data to create manufacturing systems that are more energy efficient from the outset. Let’s take Coca-Cola as an example. Coca‑Cola HBC Austria has implemented various efficiency measures at its Edelstal site to lower its CO₂ emissions, reducing them by 50% between 2010 and 2019.

2. Improving efficiency before problems occur

For decades, manufacturers have largely relied on historical data, manual inspections or operator experience to identify inefficiencies and equipment issues. While these approaches remain valuable, they most often detect problems after performance has been affected. Today, predictive analytics and advanced monitoring technologies are helping companies move from reactive maintenance towards a more predictive approach.

By continuously analysing operational data, predictive systems can identify bottlenecks, performance deviations and equipment issues before they escalate. Combined with technologies such as Internet of Things (IoT) sensors, machine vision and AI-driven analytics, food producers can reduce downtime, improve equipment effectiveness and maintain consistent product quality. What’s more, early alerts for potential errors enables them to perform immediate corrective actions, minimise risks and ensure compliance with hygiene standards.

This is especially important in cold-chain environments, where facilities operate continuously and hygiene can deteriorate between scheduled cleans. Each time doors open, warm air enters, condensation forms and air is recirculated – conditions that can allow mould and other contaminants to gradually establish, particularly in persistent moisture zones such as evaporator housings and ceiling joins. Continuous monitoring of temperature, humidity and airflow – paired with alerting and corrective action workflows – helps manufacturers maintain stable conditions during production, not just immediately after sanitation. That protects product integrity, without waiting for the next scheduled clean to spot a problem.

3. Accelerating innovation with digital twins

Physical production trials can be expensive, time-consuming and resource-intensive. Digital twins – virtual replicas of products, production lines or facilities – allow companies to test scenarios, forecast energy consumption and refine designs before making real-life changes. By simulating operations in a virtual environment, producers can ultimately accelerate innovation and improve investment decisions.

As an example, PepsiCo – in collaboration with Siemens and NVIDIA – is shifting to a digital-first planning strategy, leveraging physics-based digital twins and AI agents as co-designers to simulate, validate, and optimise facility layouts before any physical build. Leveraging digital twin technology, PepsiCo identifies up to 90% of potential issues before any physical modifications take place. This approach has delivered a 20% increase in throughput on initial deployment and is driving faster design cycles, nearly 100% design validation and 10-15% reductions in capital expenditure (Capex) by uncovering hidden capacity and validating investments in a virtual environment.³ What’s more, as digital twin technology continues to evolve, its potential applications are expanding beyond facility design to include product development, process optimisation and sustainability planning.

4. Combatting food fraud

Fraud remains a significant challenge for the food industry, particularly for commodities such as herbs, spices and highly processed ingredients that are difficult to verify through traditional inspection methods. To strengthen food integrity programmes, adopting technologies such as food fingerprinting, AI-powered analytics and digital traceability tools is becoming an attractive strategy.

Food fingerprinting creates unique profiles for ingredients and commodities, allowing organisations to detect anomalies that may indicate fraud risks. Researchers at Queen’s University Belfast, for instance, are using spectroscopic fingerprinting alongside AI and chemometric software to detect fraud risks across commodities including edible oils, herbs and spices, and soybeans.

Digital traceability and monitoring tools are helping organisations gain greater visibility across supply chains, enabling more informed risk assessments and stronger compliance programmes. However, as technology becomes more sophisticated, so too do the methods used by fraudsters. Industry experts have warned that AI may also be used to generate convincing fraudulent documentation and compliance records. This makes robust verification systems, strong supplier relationships and effective vulnerability assessment programmes more important than ever. While technology cannot eliminate food fraud alone, it is becoming a critical component of the industry’s defence strategy. In practice, this means pairing new detection tools with fundamentals such as supplier due diligence and a robust VACCP (Vulnerability Assessment and Critical Control Points) programme.

5. Building a more resilient workforce

Labour shortages continue to affect manufacturers globally, especially in countries like Japan where there is an ageing population. At the same time, increasing volumes place greater pressure on existing workforces. Yet again, technology is playing an important role in helping businesses respond. Automation can reduce reliance on manual labour for repetitive, labour-intensive tasks, allowing producers to maintain output despite workforce constraints.

Digital systems can also streamline workflows, improve operational efficiency and support more consistent production performance. In its recent State of Industry report, the Food and Drink Federation’s (FDF) notes that manufacturers of all sizes are investing in automation to address labour shortages, as well as upskilling their employees to operate in increasingly digital production environments. In fact, 24% plan to expand investment in workforce training and skills development, according to the FDF’s research. The most successful businesses will be those that combine automation with workforce development rather than viewing them as competing priorities.

The reality check: technology is only as effective as the strategy behind it

Despite the opportunities AI and digital technologies present, successful implementation is not guaranteed. Automation can improve efficiency, but it can also reduce flexibility. For example, locking lines into specific pack formats or changeover parameters that become costly (or impossible) to alter as customer requirements evolve would be highly undesirable. And while AI can reduce errors, it can also scale and exacerbate them: if systems are fed the wrong data, they can apply the wrong label, process step or pallet pattern at speed.

Manufacturers can therefore delegate tasks, but not responsibility. This is why human checks, escalation routes and data governance remain non-negotiable. As adoption increases, investing in skills and training is essential so teams can understand, manage and challenge system outputs when necessary.

Final thoughts

AI and digital technologies are no longer confined to research departments or administrative functions. Today, they are becoming embedded across the food manufacturing ecosystem, helping businesses improve efficiency, reduce emissions, accelerate innovation, strengthen food integrity and address workforce challenges. But the companies that will gain the greatest advantage will not necessarily be those using the most AI and digital tools. They will be the organisations that use them strategically to solve real operational challenges, build resilience and create smarter, more sustainable production systems.

As the industry continues its digital transformation, success will depend not simply on adopting new technologies, but on integrating them effectively into people, processes and long-term business strategy.

Fi Europe 2026

At this year’s Fi Europe Conference – to be held at Messe Frankfurt from 17-19 November – you’ll hear from industry experts on AI, digitalisation and ingredient innovation, with dedicated sessions across both days of the event.