Food Safety Trends Building Momentum into 2026

The three identified trends for 2026 will continue to develop and inform food safety and quality standards — standards that we at bioMérieux are proud to help pioneer and contribute to.

Today’s food safety systems have thousands of complex data points that can help fuel decisions. In this modern world, the translation of that data is constantly developing.

Risk modeling will continue to grow in popularity as an advanced method to optimize companies’ environmental monitoring data analysis and verify that quality controls are working. 

Key benefits can include increased operational efficiency, data-driven decision making, and the ability to answer questions like:

• Which sampling points or areas show high positivity rates?
• How am I testing, versus my test plan?
• Where are recurring risks?
• Can we reallocate sampling to reduce risk without budget increase?

Data points can be overwhelming without the right systems in place to understand what is being collected—an August 2025 FoodSafety.com article stated, “Wrestling with which data to gather, how to organize and store the data, and how to make decisions with the data are questions the food industry has been considering for some time now.”¹

Software and platforms for automated food safety monitoring will capture a large quantity of information, and the solutions that will succeed will be those that provide ease of use and actionable, fast insights.

Spoilage microorganisms in food are a longstanding and widespread problem for all tiers of the food supply chain, from farmers and producers to processors, logistics companies, retailers, and consumers. The negative impact of spoiled food reaching customers is potentially massive, encompassing brand reputation damage, financial loss with short- and long-term effects, and even legal ramifications. Beyond immediate customer dissatisfaction, these incidents can erode trust across supply chain relationships and leave companies exposed to significant long-term consequences.

Similar to comprehensive environmental models, smarter spoilage investigations can help shift problem identification from a reactive, singular data point to a proactive, automated regimen that collects numerous data points throughout production. Decisions become evidence-based and arise from a deeper understanding of process vulnerabilities. Information becomes more continuous and is collected from consistent, standardized points throughout the production process. Because this provides insight earlier in the process—not just after a problem is identified—risks can be reduced and issues can ultimately be prevented before they arise.

The opportunity for smarter spoilage investigations is significant,² and manufacturers stand to benefit from stronger investigation frameworks, clearer data for decision making, and greater confidence in their processes.

The formidable influence of artificial intelligence in food safety is evident in numerous ways, and it will continue to have major impacts over the next decade. AI is a game changer in food safety and quality control thanks to its ability to detect risks and defects faster and more accurately than traditional methods. AI not only prevents substandard or unsafe products from reaching consumers, it also reduces waste and the costly risk of recalls.³

The speed of data production and retrieval is significantly enhanced with AI. AI is helping the food safety industry move from reactive to proactive safety measures through predictive analytics, AI-powered inspections, pathogen detection, food product development and labeling, as well as operational efficiency and regulatory compliance.⁴

One groundbreaking example is the use of AI to scan and analyze restaurant reviews for text indicating foodborne illness. Early results showed that AI could help pinpoint the exact cause of illness from groupings of reviews, helping inform officials about outbreaks that may have escaped detection without this non-traditional data gathering. ⁴

To maintain strict compliance with food safety regulations, companies are also deploying AI for enhanced supply chain traceability. For example, combining AI with blockchain is emerging as a powerful approach for farm-to-fork traceability. Data is shared securely through blockchain, and the technology is complemented by AI algorithms that provide robust, verifiable traceability. This method has already proven successful in the textile industry, helping all parties trace back their supply network and create a transparent, sustainable supply chain.⁴

As the pace of food safety continues to accelerate, bioMérieux strives to innovate. These three food safety trends promise to be areas to watch as we continue to be a trusted partner to the ever-changing food safety industry.

1. Food Safety Magazine. Big Data, AI, and the Coming Philosophical Challenges with Food Safety. https://www.food-safety.com/articles/10637-big-data-ai-and-the-coming-philosophical-challenges-with-food-safety
2. Nature Reviews Microbiology. Microbial spoilage and food safety risk factors. https://www.nature.com/articles/s41579-024-01037-x
3. Food & Drink Network. AI Transforming Food & Beverage: 2025 Trends and 2026 Outlook. https://foodanddrinknetwork.co.uk/latest-news/news-appointments/ai-transforming-food-beverage-2025-trends-and-2026-outlook
4. Trends in Food Science & Technology. Applications of artificial intelligence in food safety and quality. https://www.sciencedirect.com/science/article/pii/S0924224425004145