Foodborne illness has recently made headlines across the Unites States, as the effects of a particularly widespread outbreak of bird flu continue to be felt across the farming sector. In the United States, there have been over 740 food and beverage recalls in 2024, already more than doubling the total reported in 2023 and on pace to triple the total from 2022.1 This issue is also not limited to the United States. An estimated 600 million people worldwide are made sick by foodborne illnesses each year.2
Beyond the illnesses they cause, food safety incidents have significant negative effects on economies, farmers, the environment in the form of food waste, and governments. Returning to the example of the United States for a moment, the federal government each year budgets over $7 billion of its tax revenue to foodborne illness response programs.3 This is a reactive system, and to reduce the human, financial, and environmental effects of food safety incidents, we need to become more proactive.
The good news is that we have the tools at our fingertips to create much more predictable food systems. Removing the farming sector’s dependencies on paper record-keeping is a simple first step, as it increases the visibility and reliability of reports. With this groundwork, farmers can start digitizing the food system and using generative AI to analyze large datasets, identify trends, and present insights in easily digestible language and visualizations through tools like Copilot in Excel and Copilot in Power BI.
Farmers and food suppliers can detect important issues easily with generative AI solutions, like a disruption in the cold chain between the farm and the grocer, which can lead to spoilage. Generative AI can also be used to check for compliance issues and security breaches. It can suggest process improvements, track demand, and trigger alerts that automate real-time responses—all with the goal of responding to food safety incidents before they transform into public health incidents.
Paving the way for the advancement of AI
Microsoft Copilot and industry-specific AI agents built by partners with specific expertise in the food production industry represent a potential leap forward in preventative food safety, but they aren’t the only benefit digitalization represents. Other solutions, themselves part of the roadmap toward generative AI adoption, are already enabling meaningful change for food producers. Recent advancements in both Internet of Things (IoT) sensors and the AI technology behind them have enabled technology to mimic the human senses of sight, hearing, and smell to improve traditional food sorting, grading, and inspection processes. Azure Data Manager for Agriculture helps collect data on farms, aiding in the identification of conditions likely to introduce bacteria to crops.
For example, a food processing company can digitize its quality control process with the help of Microsoft Power Apps, Power BI, and Dataverse. Together, these technologies help the company better capture real-time data, generate more insightful reports and improve overall operational efficiency.
As companies build out capabilities like these, they gain the type of financial benefits and actionable insights and can simultaneously establish a deeper pool of information for future generative AI solutions to draw from. Microsoft Fabric also plays a crucial role in building an AI-ready data estate. By integrating data sources like IoT sensors, temperature monitors, and historical data, Fabric helps companies establish more comprehensive data platforms. With the advanced predictive analytics these platforms can generate, food suppliers can reduce product recalls, prevent the spread of counterfeit goods, minimize food waste, and increase consumer trust.
Bringing better farming data into the mix
By consolidating its data, increasing the number of advanced sensors it employs, and tracking broader types of data, the food production industry is making way for even greater advancement. Copilot and customized agents can rapidly analyze every stage of the food supply chain, from farm to table. Today’s visual recognition technology often identifies contaminants in food products faster and in smaller concentrations than its human counterparts. Generative AI models can use this data to aid in the detection of foreign objects and pathogens in either raw ingredients or finished food products. Analysis of historical and real-time data from temperature sensors in food production and warehousing facilities can help alert producers to conditions that contribute to excess food spoilage. When an agent recognizes farming or food processing irregularities, it can generate predictions based on historical data, check for compliance issues, and suggest operational improvements. By bringing together farm-specific data like local weather conditions, soil makeup, and pest populations, agents could help predict and mitigate seasonal risks to crops.
Looking ahead
The future of food safety will rely on the continued integration of technology and data into the world’s food production and distribution processes. Customized agents powered by AI can perform tasks and provide decision support to improve food safety. These agents can be built to analyze vast amounts of data from spreadsheets, handwritten documents, voice memos, and videos, uncovering previously undetected errors and missing information.
Companies in the farming sector can leverage Microsoft Copilot Studio to develop their own intelligent agents that assist with their most critical and risk-prone agricultural processes. Using the low-code interface of Copilot Studio, businesses can quickly create and deploy custom applications without extensive coding knowledge, enabling them to automate tasks such as crop monitoring, pest detection, and resource management. Companies can also choose to collaborate with Microsoft partners with industry-specific expertise, ensuring their solutions are tailored to their specific needs and comply with industry regulations. This partnership approach not only accelerates innovation but also ensures the deployment of robust and effective AI-powered solutions.
By maximizing the potential of generative AI in food safety, we can predict and prevent many of the sector’s most prevalent issues, improve food quality, and prevent many food safety incidents. There are tremendous opportunities ahead, and collaboration between food producers, the regulatory bodies that oversee them, and technology companies are key to the success of these initiatives. By working together, we can create a safer and more sustainable food system for everyone.
1 Food Logistics, Food Recalls in 2024 are Surging. What’s the Crisis Response?, September 2024.
2 World Health Organization, Foodborne Diseases Estimates.
Pepijn leads global engineering retail, consumer goods, and agriculture industry teams. He is passionate about empowering businesses through innovative technology. His expertise lies in driving business transformation by collaborating with organizations and re-imagine data and AI solutions. He has extensive experience in product-centric industries.
