With artificial intelligence disrupting traditional processes within the food industry, David Sack, Founder and CEO of foodtech company AKA Foods, shares his perspective on how the technology can reinvigorate innovation within R&D and the benefits to be had for food companies if AI is leveraged properly.
Food is a legacy industry, built on processes that have evolved over centuries. As the sector transitioned from the industrial age into the digital era, not all functions advanced at the same pace. Logistics and distribution were early adopters of computing and algorithms, enabling more cost- and energy-efficient systems. R&D, by contrast, remained largely unchanged. Many food scientists still organize critical information in spreadsheets, notebooks, or isolated databases.
This means that, when faced with regulatory or formulation changes, R&D teams are unable to quickly access this disparate company knowledge, leading to slow-moving innovation. This structural gap is where AI can have its most meaningful impact when applied as part of a dedicated R&D system, rather than as a standalone analytical tool. By integrating an AI platform within these R&D processes, companies can bring formulation data, analytical results, and subjective human sensory feedback into a single, unified framework, instead of having that information dispersed across departments and Excel spreadsheets.
Making Sense of the Ingredients: Turning Data into Compound Knowledge
The real value of AI in food R&D is not automation for its own sake, but context. AI turns a company’s existing data into usable context by giving scientists the information they need to make better and more informed decisions. By connecting food science syntax with analytical tools, AI can dramatically speed up product development by enabling teams to reuse prior formulations, decisions, and sensory outcomes rather than starting from scratch.
Teams can react quicker to trends, regulatory changes, or ingredient shortages, as it becomes easier to test ideas without wasting valuable resources or time. They can also quickly search historical formulations, understand the functional role of ingredients, and identify viable alternatives based on past experiments, sensory results, and constraints.
Rather than relying on guesswork or repeated trial-and-error, they can build on what the organization already knows, even when that knowledge was previously fragmented, helping teams refine formulations more quickly and with greater decision confidence under regulatory and cost pressure. In this way, AI modernizes the process by changing how data and insight are captured, analyzed, and reused, ultimately accelerating innovation.
From R&D Efficiency to Business Impact
Once R&D knowledge becomes structured and reusable, the business implications are significant. When implemented correctly, AI converts organizational knowledge into structured, measurable assets, which makes progress easier to track, decisions easier to justify, and the overall innovation pipeline more productive and profitable.
However, this only works when AI is applied deliberately. Companies should focus on practical applications where AI can reduce iteration cycles, improve decision quality, or reuse existing knowledge. When AI is embedded into a structured system and governed properly, it becomes a reliable and supportive tool rather than another experimental initiative.
Moreover, organizations are becoming more intentional about how AI is deployed, and we are seeing a shift towards clearer ownership within companies. In recent years there has been an increase in roles focused on digital transformation, even within manufacturing and product development teams. Without someone to drive this change, AI risks being relegated to a side project instead of becoming a strategic capability.
AI is also helping to bridge the gap between technologists and food scientists. Ensuring both groups understand each other’s needs is essential for successful adoption. This is where specialized platforms can offer real value, particularly when compared to general-purpose LLM models. AI works best as an assistant used alongside a food scientist’s expertise, rather than as a replacement. The real value comes when AI is applied to clearly defined R&D tasks, such as reformulation, optimization, or regulatory response, rather than being used in an open-ended way.
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Protecting the Secret Sauce: The Security Question
From a business perspective, security is often the first concern raised when AI enters the conversation. Formulations and process knowledge are highly sensitive intellectual property, so companies should always start by checking whether any AI platform they use is SOC 2 compliant and built to enterprise security standards.
Beyond that, it’s important to understand how data is isolated and where it lives. Platforms can be deployed in private cloud environments that offer strong baseline security, and for organizations with the highest security requirements, it is also possible to run fully private, on-premises deployments, where models operate entirely inside a company’s own infrastructure. The key takeaway is that AI can be adopted securely in food R&D, but only if privacy, isolation, and governance are designed into the architecture from the outset.
Changing Tastes, Changing Rules
Looking ahead, several converging trends will further increase pressure on food R&D. One of the most significant is the growing adoption of GLP-1 therapies, including oral formulations, which are already reshaping eating habits and portion sizes across large consumer segments.
This shift will force companies to rethink existing products and renovate portfolios at scale. In this context, success will depend not only on meeting nutritional targets, but on delivering products that still provide a satisfying sensory experience. Combining objective formulation data with subjective human sensory evaluation becomes critical.
By 2026, success in food innovation will not simply depend on making better food. It will depend on how quickly and confidently companies can adapt to fundamentally new consumption patterns. AI, when applied thoughtfully and securely, offers a way to meet that challenge not by replacing human expertise, but by enabling it at scale.
