How AI Is Reshaping Product Development

In recent years, artificial intelligence (AI) and machine learning (ML) have rapidly evolved from futuristic concepts to practical tools driving innovation across industries. From health care to finance, these technologies are reshaping traditional approaches, making processes more efficient and data driven. The food industry, known for its blend of science and art, is no exception.

According to Grand View Research, the global AI in food and beverages market size was valued at nearly $8.5 billion in 2023 and is projected to grow at a compound annual growth rate of 39% from 2024 to 2030. AI is transforming the industry by improving efficiency and automating key processes such as processing, sorting, and packaging. Its role in ensuring strict quality control and regulatory compliance is becoming increasingly vital, driven by growing consumer awareness and stringent industry standards. Additionally, AI is revolutionizing personalized marketing and consumer engagement while optimizing supply chains to reduce waste and streamline inventory management. Perhaps the most understated aspect of AI’s impact in the industry is its role in product development, where it enables the creation of innovative food formulations, improves ingredient selection, and accelerates the testing and refinement of new products to meet evolving consumer preferences.

The use of AI is poised to become indispensable in the food industry. However, successful integration requires a balanced approach—leveraging technology while acknowledging the irreplaceable role of human expertise.

AI and ML: The Basics

AI is a broad field encompassing technologies that enable machines to perform tasks typically requiring human intelligence, such as problem-solving and decision-making. Machine learning, a subset of AI, involves training algorithms to recognize patterns and make predictions based on data. ML models excel in learning from past data to improve outcomes over time, reducing trial and error in product development.

The core principles behind AI and ML, such as predictive modeling, pattern recognition, and data-driven decision-making, have existed in the food industry for decades. Traditional food science and technology has always relied on statistical modeling, empirical data, and computational techniques to optimize formulations, ensure quality, and improve processing efficiency. What we are witnessing today is not an entirely new concept but rather a rebranding and reinforcement of these methods, thanks to advancements in computing power, data availability, and automation.

Traditional Predictive Modeling (Pre-AI Era). Long before AI became a buzzword, food scientists and engineers relied on mathematical models and statistical techniques to predict outcomes in food processing and formulation. Response surface methodology, for example, has long been used to optimize food formulations by analyzing how multiple variables—such as temperature, pH, and ingredient ratios—interact to influence product quality. Similarly, kinetic modeling in food preservation has helped predict microbial growth, shelf life, and the impact of heat treatment on food safety. Another traditional application is demand forecasting, where retailers and manufacturers have historically analyzed past sales data, seasonal trends, and economic factors to anticipate future demand and adjust production accordingly. While these methods have always been integral to the food industry, the rise of AI/ML has significantly enhanced their accuracy, speed, and ability to process vast amounts of data in real time.

AI/ML-Driven Predictive Modeling (Modern Era). Building on these traditional predictive modeling techniques, AI/ML are taking these capabilities to the next level by enabling deeper insights, real-time adaptation, and more precise optimization. One major advantage is greater computational power, allowing AI to analyze millions of variables in real time, whereas traditional models relied on simplified assumptions and smaller datasets. Additionally, AI enables big data integration, incorporating diverse data sources such as consumer preferences, social media trends, weather patterns, and even genomic data to refine product development. This expanded scope leads to more precise predictions and better-informed decision-making. Furthermore, AI-driven self-learning algorithms continuously improve over time, learning from new data and reducing the need for manual adjustments.

Top Applications in R&D

Accelerating Product Formulation and Ingredient Optimization. Developing new food products traditionally involves extensive trial and error, which can be time-consuming and costly. AI and ML streamline this process by analyzing vast datasets of ingredient interactions, nutritional profiles, and sensory attributes to predict optimal formulations. These technologies can rapidly evaluate thousands of ingredient combinations, ensuring that products meet desired taste, texture, and stability requirements.

Additionally, AI-driven simulations help fine-tune formulations by identifying alternative ingredients that enhance functionality, reduce costs, or align with clean label and sustainability trends. By minimizing guesswork and expediting R&D cycles, AI and ML enable food companies to bring innovative products to market faster and with greater precision.

Enhancing Sensory Analysis and Consumer Preference Prediction. Understanding how consumers perceive a product’s flavor, aroma, texture, and appearance is crucial for its success. Traditionally, sensory analysis relies on consumer panels and focus groups, which can be time-consuming and limited in scope. AI-powered tools revolutionize this process by analyzing vast amounts of data from consumer reviews, social media, market trends, and sensory panel feedback.

Machine learning models factor in variables such as regional taste preferences, dietary trends, and even emotional responses to different flavors and textures. Additionally, AI can integrate data from electronic noses and tongues—advanced sensors that mimic human sensory perception—to refine formulations further.

By leveraging AI-driven insights, food companies can fine-tune products to better align with consumer expectations, reducing the risk of failure and increasing market acceptance.

Optimizing Ingredient Sourcing. AI is playing a pivotal role in transforming how food companies source ingredients for product development. AI enhances supplier selection by evaluating a variety of factors, including quality, cost, reliability, and sustainability. It can assess historical data, market trends, and supplier performance to recommend the best sourcing options, ensuring that companies select partners who align with their product requirements and sustainability goals.

Moreover, AI can predict potential ingredient shortages and disruptions in the supply chain, allowing businesses to take proactive measures. AI tools can suggest alternative ingredients and suppliers based on availability or price fluctuations, reducing reliance on scarce resources and minimizing the risk of production delays.

Real-World Applications: AI in Action

McCormick & Company. In 2019, the global spices and seasonings maker partnered with IBM Research to integrate AI into its product development process. McCormick aimed to enhance efficiency, accelerate innovation, and explore unique ingredient combinations that might not have been considered through conventional methods.

To achieve this, McCormick and IBM developed an AI system capable of analyzing extensive historical data, including previous flavor formulas, raw material compositions, consumer feedback, and experimental results. Using ML, the system identified patterns and suggested novel ingredient combinations that aligned with specific taste profiles. This AI-driven approach provided product developers with data-backed insights, allowing them to make informed decisions more quickly. As a result, McCormick launched the first AI-assisted retail products under its “ONE” product family, featuring unique flavor combinations that would have taken significantly longer to develop using traditional methods.

The integration of AI into McCormick’s product development pipeline significantly reduced the time required to bring new flavors to market while also enhancing the creativity of product developers, who could explore AI-generated suggestions beyond their usual scope.

NotCo. The Chilean food-tech startup, founded in 2015, was among the first to integrate AI into product development in the plant-based food sector. NotCo’s proprietary AI platform named Giuseppe utilizes machine learning algorithms to analyze vast datasets, identifying optimal plant-based ingredient combinations that replicate the taste and texture of animal products. The name Giuseppe was based on the Italian artist Giuseppe Arcimboldo best known for creating human portraits made of objects like fruits and vegetables, which has parallels with the company’s original vision.

This approach led to the creation of products like NotMilk, NotBurger, and NotMayo. In 2022, NotCo entered a joint venture with global food giant Kraft Heinz, forming The Kraft Heinz Not Company to develop plant-based versions of products, leveraging NotCo’s AI-driven formulation expertise and Kraft Heinz’s extensive market presence.

NotCo has now taken its AI implementation further by expanding into business-to-business services beyond just plant-based meat product development by offering its AI technology to other companies seeking to innovate. This strategic shift allows NotCo to influence the broader food industry by enabling other manufacturers to shorten their product development timelines considerably. The company’s AI platform assists in identifying market gaps, generating product concepts, and formulating recipes, thereby streamlining the traditionally complex and time-consuming product development process.

Brightseed. The company uses its AI platform, Forager, to identify new products by analyzing plant-based compounds and uncovering hidden potential in plants that may not be immediately apparent. Forager leverages deep learning algorithms to scan vast amounts of biological data, enabling the identification of bioactive compounds with health benefits that can be used to develop innovative food products. By mapping plant-based ingredients to their potential health impacts, Brightseed is able to discover novel ingredients and formulations that meet growing consumer demand for functional, plant-based nutrition. This AI-driven approach not only accelerates product development but also helps create food products with scientifically backed health benefits.

Danone. The global food and beverage company integrated AI and robotics into its product development, testing, and marketing processes in the dairy sector. The company’s AI initiatives include analyzing large datasets to identify consumer preferences, market trends, and emerging flavors, enabling proactive responses to shifts in demand. Similar to electronic noses and tongues, Danone employs a proprietary “robot stomach” to simulate human digestion, providing detailed insights into the journey of probiotics through the digestive system, which contributes to developing products with validated health benefits.

Balancing AI and Human Expertise

AI is transforming food product development, but its effectiveness depends on addressing key challenges. Overreliance on AI without human expertise can lead to flawed products, as AI lacks the intuition, creativity, and sensory understanding of food scientists, chefs, and analysts. While AI can predict optimal ingredient combinations, human testing remains crucial for validating taste, texture, and consumer appeal. Another critical issue is the use of poor-quality or biased data.

The success of AI and ML models is heavily dependent on the quality of the data used to train them. Incomplete, outdated, or biased data can lead to inaccurate predictions and flawed product decisions. For example, if an ML model is trained primarily on Western consumer preferences, it may fail to produce products that appeal to other regions, such as Asian or African markets, limiting its global reach. Ensuring diverse, high-quality datasets is essential for meaningful AI-driven innovation.

Beyond technical limitations, regulatory and ethical considerations must be prioritized. The food industry is subject to strict safety laws, ingredient restrictions, and labeling requirements. Companies leveraging AI must ensure compliance with regulations like those from the U.S. Food and Drug Administration and the European Food Safety Authority to avoid legal and safety risks.

The most successful approach to AI adoption in food product development will be a hybrid model, where AI enhances efficiency, but human expertise remains central to decision-making. By integrating AI strategically and responsibly, companies can unlock its full potential, creating innovative, high-quality, and compliant food products that meet evolving consumer and industry demands.ft