Is AI the answer for the food industry’s woes?
With artificial intelligence disrupting many traditional processes as it strengthens its foothold in the food industry, David Sack, Founder and CEO of food tech company, AKA Foods, gives his insight into how technology can reinvigorate innovation within R&D and the benefits to be had for food companies if AI is leveraged properly.
1. Before we talk about how and why AI is key for the food industry’s future growth, can you give us a brief overview of your company and what you do?
AKA Foods is a pioneering food tech company, aspiring to transform new product development via artificial intelligence. In 2019, we started designing AKA Studio, a food-specific, secure AI-powered system. This platform enables food companies to create, optimize and launch products much faster, smarter and affordably by unifying a company’s own knowledge, R&D data and sensory analytics, into one structured and accessible location.
Within the platform, AI is a tool to be used by food scientists, helping to suggest formulations and optimizations based on the requested requirements, such as, reduced sugar content or high in fiber.
2. What challenges exist for food companies insofar as new product development or optimization of existing products?
Food is a legacy industry with long-standing processes and systems that have evolved over centuries. As we’ve taken the jump from the industrial age to the digital era, like any major shift, different elements of the food sector have developed at different rates. Logistics, for example, has already implemented computing and algorithms to distribute products more cost- and energy-efficiently.
The process and tools involved in R&D, however, haven’t changed much. Many food scientists still rely on organizing information in a tabular format and creating mathematical functions – whether that’s on an Excel spreadsheet or in a notebook. This means that, when faced with regulatory or formulation changes, R&D teams are unable to quickly access this disparate company knowledge, leading to a slow-moving innovation.
3. So, how does leveraging AI address and overcome the challenges you mention?
By integrating an AI platform into the R&D process, companies can bring formulation data, analytical results, and human sensory feedback into a single, unified framework, instead of having that information dispersed across departments and Excel spreadsheets.
Each iteration within our own system builds on previous experiments and sensory outcomes, helping teams refine formulations more quickly and with greater confidence. AI modernizes the process by changing how data and insight are captured, analyzed, and reused, ultimately accelerating innovation.
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. Not only does this help speed up the core process, and save on time and costs, it encourages more innovation within R&D. Teams can react quicker to trends or ingredient shortages, as it becomes easier to test ideas without wasting valuable resources or time.
For business leaders, AI also converts organizational knowledge into structured, measurable innovation assets. That makes progress easier to track, decisions easier to justify, and the overall innovation pipeline more productive and profitable.
4. As you noted, ingredient shortages are a consistent concern in the face of political and environmental issues. How can AI strengthen supply chain resilience in an increasingly unpredictable global food system?
Alongside ingredient shortages and price volatility, regulatory pressure is now driving large-scale reformulation across many categories. For example, restrictions on artificial colorings in the US, including red dyes, or sodium reduction targets and taxes in markets such as Thailand, can trigger the need to renovate entire product ranges in a short time frame.
In these situations, the value of a system like AKA Studio is speed and confidence. Instead of starting from scratch, R&D teams can quickly search historical formulations, understand the functional role of ingredients, and identify viable alternatives based on past experiments, sensory results, and constraints. This allows teams to respond faster to regulatory and supply-chain shocks while reducing risk. 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.
5. Security is a common concern when it comes to using AI LLMs like ChatGPT – how does one tackle this when the food R&D is incredibly IP-sensitive?
Formulations and process knowledge are highly sensitive IP, 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. For example, platforms can be deployed in private cloud environments, such as tenant-isolated deployments on Google Cloud Platform, offering bank-level security even at an entry level.
For organizations with the highest security requirements, it’s also possible to run fully private or on-premise deployments, including air-gapped systems where models operate entirely inside a company’s own infrastructure. While those approaches are more complex and costly, they give organizations maximum control over their data.
The key point is that AI can be adopted securely in food R&D, but only if the underlying architecture is designed for privacy, isolation, and governance from the outset.
6. Now that we’ve established the benefits and hurdles of AI, are there any barriers that exist before wider AI integration is a reality within food companies?
The major barrier we see is people’s openness and comfort level with using AI tools in a way that is productive for the company. People are growing more comfortable with AI, and we are seeing a shift within organizations to leverage it. For example, we’ve seen an increase in job roles on digital transformation, even within manufacturing and product development teams. Without someone to drive the change, AI will just be relegated as just another side project, instead of a strategic tool.
Another key challenge is bridging the gap between AI developers and the food scientists who will use these tools daily. Ensuring both groups understand each other’s needs is essential for successful adoption. This is where specialized platforms, including our own, can offer real value, especially when compared to general LLM models.
7. As you mentioned, AI needs someone to pioneer it within the company to see the full benefits, and in fact there have been growing concerns around companies “AI washing”. How should food companies look to adopt AI technology mindfully, rather than just hopping on a trend?
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.
At the same time, the AI landscape is evolving extremely quickly. That means organizations need clear ownership, with someone responsible for tracking developments and understanding when new capabilities meaningfully change what’s possible. As more advanced forms of AI emerge, they will further expand what these systems can support, including within food R&D.
Companies should still focus on practical, measurable 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 support mechanism rather than a source of risk or noise.
8. Finally, what do you think 2026 has in store for the food industry?
I think that one of the biggest shifts will come from the growing penetration of GLP-1 therapies, including oral formulations, which are already changing eating habits and portion sizes for large segments of the population.
That shift will force food companies to rethink existing products and develop new ones for consumers who simply can’t eat the way they used to. In this context, reformulation isn’t just about nutrition on paper; it’s about creating foods that work physiologically while still delivering a satisfying sensory experience. This is where combining objective data with human sensory feedback becomes especially important.
At the same time, we’re seeing rapid growth in functional categories such as electrolyte products, energy and caffeine-based drinks, and nutrition designed around metabolic health, microbiome support, or medical-adjacent use cases. These products often sit at higher price points and margins, but they also demand a much more precise R&D approach.
From our perspective, 2026 won’t just be about making better food. It will be about adapting to fundamentally new consumption patterns, where speed, confidence, and sensory understanding all matter more than ever.
David Sack
AKA Foods is a pioneering food tech company transforming how new food products are created. AKA Studio, its proprietary platform, combines advanced artificial intelligence with real human sensory data to accelerate research and development across taste, texture and aroma. Unlike generic AI tools, AKA integrates each company’s tacit knowledge and disparate data sources into a private and secure system. This unique approach enables food companies to shorten development cycles, cut costs and deliver better products that consumers love.