Introduction

Food safety is a global priority, impacting public health, brand reputation, and economic stability. In 2025, the World Health Organization (WHO) estimates that unsafe food still causes over 600 million cases of foodborne illnesses annually, with 420,000 deaths worldwide. As food supply chains grow more complex, traditional safety methods often fall short. That’s where artificial intelligence in food safety is making a transformative impact.

According to a 2025 report by The Business Research Company, the AI market in the food and beverages sector has experienced exponential growth in recent years. It is projected to increase from $9.4 billion in 2024 to $13.61 billion in 2025, at a compound annual growth rate (CAGR) of 44.8%, driven largely by the demand for automated quality control and predictive risk management in food safety systems.

From detecting contaminants in real time to automating compliance with global regulations, the use of AI in food safety enables faster, smarter decisions that protect both consumers and brands. This article will explore how AI in food safety is reshaping the industry and what the future holds for this critical application of technology.

Use of AI in Food Safety

The use of AI in food safety is rapidly advancing from theory to practice across global food supply chains. In 2025, over 70% of food businesses reported the implementation or active planning of AI technologies to enhance safety standards and operational efficiency. This reflects a significant shift toward smarter, data-driven safety solutions.

Real-Time Contaminant Detection

The use of AI in food safety is significantly enhancing the ability of food manufacturers to detect contaminants in real-time, thereby improving product quality and consumer safety. 

Traditional methods often involve manual inspections or laboratory testing, which can be time-consuming and may delay the identification of potential hazards. In contrast, AI-powered systems enable continuous monitoring and immediate detection of contaminants, allowing for swift corrective actions.

Case Study: Nestlé's AI-Powered Quality Control

Nestlé has successfully implemented AI-driven technologies to bolster its food safety protocols. In one of its chocolate production facilities, Nestlé integrated AI-powered vision systems to inspect wrapper integrity and fill levels. This implementation led to an 80% reduction in manual checks, thereby increasing production efficiency and consistency. The AI system's ability to identify defects and inconsistencies in real-time minimized the risk of contamination due to human error.

The surplus food, which included unsellable but edible items like broken KitKat bars or products with short expiry dates, was redirected to charities, potentially saving up to 700 tonnes of surplus edible food, equivalent to 1.5 million meals, and preventing 1,400 tonnes of CO₂ emissions. This pilot, supported by a £1.9 million match-funded grant from Innovate UK’s BridgeAI scheme, is set to expand across the food supply chain by March of the following year via subscription-based access.

Regulatory Compliance Automation

Another example of using AI in food safety is regulatory compliance automation. Traditional compliance methods often involve manual documentation and periodic audits, which can be time-consuming and prone to human error. In contrast, AI-powered systems offer real-time monitoring and automated reporting capabilities, ensuring continuous adherence to food safety regulations.

Artificial intelligence proves useful in such compliance-related tasks as:

• Automated Regulatory Monitoring - AI systems continuously scan and analyze regulatory databases, ensuring that food businesses are promptly informed of any changes or updates in food safety laws and standards.
• Real-Time Risk Assessment - By evaluating various data points, AI can identify potential compliance risks in real time, enabling businesses to take immediate corrective actions.
• Document Drafting and Management - AI tools assist in preparing compliance-related documents such as reports and certifications, ensuring accuracy and adherence to regulatory requirements.
• Gap Analysis - AI conducts thorough assessments to identify discrepancies between current practices and regulatory standards, offering actionable insights for improvement.
• Predictive Compliance Analytics - By leveraging historical data, AI predicts potential compliance issues, allowing for proactive risk mitigation.

Case Study: IONI

IONI is an AI-powered platform that can help food industry businesses manage regulatory compliance more efficiently.

• Real-time regulatory updates: IONI automatically flags changes in laws or standards and provides source-linked citations, keeping food businesses up to date at all times.
• AI document analysis: The platform analyzes complex regulatory texts to support accurate interpretation and better decision-making.
• System integration: IONI connects with existing tools and workflows, improving operational efficiency without requiring major process changes.

Smart Quality Control

Quality control is one of the most critical elements in maintaining food safety, and the use of AI in food safety is helping manufacturers manage this process with greater accuracy and consistency. 

By combining computer vision with machine learning, AI apps can automatically check each item on the production line for defects, irregularities, or compliance issues, helping teams catch problems early without slowing down operations.

Case Study: PepsiCo’s AI-Based Visual Inspection

PepsiCo has adopted artificial intelligence in food safety through the deployment of computer vision systems in its snack production facilities. Using AI-powered cameras, the company can now inspect every chip on the line for size, color, and shape. This system automatically rejects undercooked or overcooked products and ensures consistency in every batch.

According to internal results, this use of AI in food safety has improved defect detection accuracy by up to 95%, significantly reducing the reliance on human inspectors and enhancing throughput without sacrificing safety or quality. Moreover, these AI systems have allowed PepsiCo to fine-tune cooking times and temperatures, further minimizing waste and boosting energy efficiency. 

AI-powered quality control also facilitates automated record-keeping for regulatory compliance. These systems store detailed inspection data that helps food manufacturers demonstrate adherence to safety standards during audits, reducing risk and administrative overhead.

Predictive Maintenance of Equipment

The use of AI in food safety extends beyond contamination detection to include predictive maintenance of equipment, a critical aspect of ensuring continuous, safe, and efficient food production. Traditional maintenance practices often rely on fixed schedules or reactive measures, which can lead to unexpected downtimes and potential safety risks. In contrast, artificial intelligence in food safety enables manufacturers to anticipate equipment failures before they occur, thereby minimizing disruptions and maintaining high safety standards.

Case Study: ThroughPut AI Implementation

A prominent global food and beverage manufacturer, operating across nine countries and exporting to over 50 markets, faced significant challenges with unplanned machine outages, leading to substantial productivity losses and inefficiencies. To address these issues, the company implemented ThroughPut’s AI-driven platform, which provides real-time data analytics and root cause analysis for equipment performance.

The integration of this AI system resulted in:

• $0.5 million in weekly productivity savings: By identifying and addressing bottlenecks promptly, the company was able to recover significant lost productivity.
• 5% increase in output: Optimized machine utilization led to enhanced production efficiency.
• Reduction in unplanned downtime: Proactive maintenance scheduling based on AI insights minimized unexpected equipment failures.
• Improved capital expenditure (CAPEX) planning: Real-time validation of machine performance informed more accurate investment decisions.

Supply Chain Traceability and Risk Management

Building upon improvements in real-time contaminant detection, the use of AI in food safety also extends deeply into supply chain traceability and risk management. Food safety involves tracking every step food takes from farm to consumer to quickly identify and respond to potential risks.

AI-powered traceability systems analyze vast amounts of data collected at each stage of the supply chain, including sourcing, transportation, storage conditions, and processing. By integrating this data, companies can pinpoint where contamination or safety breaches may have occurred, enabling faster recalls, reducing risk exposure, and improving compliance with food safety regulations.

Case Study: IBM Food Trust and Walmart

A prime example of artificial intelligence in food safety for supply chain traceability is the collaboration between Walmart and IBM Food Trust. Walmart adopted the blockchain-based Food Trust platform, boosted with AI analytics to improve transparency and traceability in its fresh produce supply chain.

Using AI, Walmart was able to reduce the time required to trace the origin of contaminated lettuce from seven days to just 2.2 seconds. This rapid traceability enabled Walmart to remove only affected products quickly, minimizing food waste and protecting consumers from potential foodborne illnesses. The system continuously monitors suppliers and shipment data to flag irregularities and potential risks before they escalate.

The AI-enhanced supply chain also supports risk management by predicting potential disruptions, identifying weak links in the chain, and optimizing sourcing decisions for safer and more reliable food delivery.

The Future of Artificial Intelligence in Food Safety

As the food industry continues to navigate challenges like global supply chain complexity, climate variability, and evolving regulatory requirements, the role of artificial intelligence in food safety is poised to grow significantly. What began as automated quality checks and contaminant detection is evolving into fully integrated, predictive safety ecosystems.

1. Predictive Prevention Over Reactive Response

In the future, AI will shift the paradigm from responding to food safety incidents to preventing them altogether. Machine learning models will increasingly leverage historical data, climate patterns, and supply chain trends to forecast contamination risks before they occur. For instance, AI could predict the likelihood of a salmonella outbreak based on weather patterns and farm conditions, allowing suppliers to intervene early and reduce public health risks.

2. Hyper-Personalized Compliance

As global food safety regulations become more nuanced, AI systems will be able to tailor compliance strategies in real time. Instead of one-size-fits-all rules, companies will adopt intelligent compliance engines that adapt protocols based on the product type, region, or supplier history, reducing audit failures and streamlining certification.

3. Digital Twins for Food Production Facilities

AI-driven “digital twins” of production environments - virtual replicas that simulate operations in real time - will allow food manufacturers to model risks before making physical changes. Want to tweak a sterilization process or switch suppliers? The digital twin can simulate the outcome in terms of food safety and compliance, helping teams make smarter decisions.

4. Consumer-Facing Transparency

Consumers are demanding more visibility into how their food is sourced, processed, and transported. In the future, AI will enable dynamic, consumer-facing transparency—scannable QR codes on packaging will reveal an item’s entire journey, including safety checks, storage conditions, and even AI-generated safety scores. This will build trust while promoting accountability across the industry.

5. Collaboration With Regulatory Agencies

Forward-thinking food companies are already engaging with AI not just for internal operations, but as a bridge to regulators. In the coming years, real-time data-sharing platforms powered by AI will allow regulators and manufacturers to collaborate proactively, detecting trends, preventing fraud, and responding swiftly to emergencies. 

The path forward will require collaboration between technologists, regulators, and food industry leaders - but the direction is clear. With data-driven insights at the core, the use of AI in food safety is becoming essential for companies aiming to stay ahead of risks, comply with regulations, and earn consumer trust.

FAQ

What types of AI technologies are most commonly used in food safety today?

Common AI technologies in food safety include:

• Computer Vision – Used for automated visual inspections to detect contamination or quality issues.
• Machine Learning – Analyzes data to predict risks like spoilage or equipment failure.
• Natural Language Processing – Helps interpret regulatory documents and reports for compliance.
• Predictive Analytics – Identifies patterns to prevent food safety incidents before they occur.
• IoT + AI – Analyzes sensor data (e.g., temperature, humidity) in real time to maintain safe conditions
• Robotic Process Automation  – Automates tasks like documentation and audit prep.

Are there any risks or downsides to using AI in food safety?

Yes, while AI offers many benefits, there are potential risks:

• Data Quality Issues – AI is only as good as the data it’s trained on. Inaccurate or incomplete data can lead to faulty decisions.
• Over-Reliance on Automation – Human oversight is still essential. Fully depending on AI could cause critical issues to be missed.
• High Implementation Costs – Especially for small businesses, initial setup and integration can be expensive.
• Privacy and Security Concerns – Collecting and storing large volumes of data raises concerns about compliance with data protection laws.
• Regulatory Uncertainty – AI in food safety is still evolving, and some applications may not yet be clearly addressed by existing regulations.

Is AI in food safety regulated by government agencies like the FDA or EFSA?

AI itself isn’t directly regulated yet, but its applications in food safety fall under existing food safety and data protection regulations. Agencies like the FDA (U.S.) and EFSA (EU) are increasingly monitoring the use of AI and may issue future guidelines. For now, companies must ensure that AI tools used for food safety comply with standards for traceability, hygiene, labeling, and data privacy.

How does IONI help companies implement AI in food safety processes?

IONI supports companies by integrating AI technologies that enhance food safety management, especially within HACCP (Hazard Analysis and Critical Control Points) frameworks. By using AI-driven data analysis, real-time monitoring, and predictive insights, IONI helps identify potential hazards and control points more efficiently. This improves risk assessment, automates compliance checks, and ensures continuous quality control, making HACCP implementation more precise and proactive.