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In addition to being CFIN’s Regional Innovation Director for BC and Yukon, Lavina Gully is a food scientist and product developer with almost two decades of experience helping food and beverage companies innovate. In this series, Lavina answers questions from CFIN members on everything from product development, R&D, food industry careers, manufacturing best practices, and co-packing—just to name a few! 

If you have questions for Lavina, you can reach out to her directly and we can answer your question in next month’s mailbag. 

This month, Lavina answers your questions about communicating complex scientific concepts to non-technical audiences and where AI is actually being implemented in food development and manufacturing. 

Q: Our team deals with highly technical processes, and when talking to customers or investors, I often struggle to explain our work in a way that’s easy to understand. Any advice on communicating complex food science concepts clearly to a non-technical audience? 

This is a common challenge for many food science professionals, especially for researchers, entrepreneurs, and business leaders developing cutting-edge technologies or innovative processes looking to demonstrate the value of their work to critical stakeholders who do not share their subject matter expertise. It’s not an easy skill to learn, but here are a few principles I find helpful when communicating food science and technology concepts to a non-technical audience. 

Focus on the benefits: Foodtech founders and business leaders with technical backgrounds can often default to leading with the science — or the “how” — when communicating the impact or value of their team’s work. As a food scientist myself, I understand this impulse, because I find the science behind new products and technologies fascinating and compelling. But many of your audiences, including investors, cross functional colleagues, customers, and board members, will not share this admiration, at least to the same degree. Instead of explaining the science upfront, emphasize what benefits your innovation provides them. By making this connection, your “how” becomes more compelling and understandable. 

Stick to the essentials: When it comes to explaining the scientific concepts core to your work, keep the details as broad and digestible as possible. What this looks like in practice will vary depending on your specific audience. For a fantastic example of how it’s possible to simplify a technical concept for different audiences without misrepresenting it, check out this video series from WIRED, in which experts communicate their work at five levels of complexity: for a child, a teen, a college student, a grad student, and a fellow expert. Studying how experts in different fields refine their messaging for non-experts will help you do the same. 

Build a narrative: People remember stories, not facts and data. Indeed, storytelling has been shown to increase an audience’s engagement and willingness to act upon scientific information. Look for ways to present technical concepts as part of a larger narrative involving how your product or process solves a problem or improves and outcome. The more relatable the story is for your target audience, the better. Here’s some more great tips for finding and crafting effective stories. 

Ultimately, the goal is to leave your audience with a clear understanding of the value and impact of your work. Focusing on the benefits, striving for simplicity, and seeking out the larger narratives behind your work will help you do that effectively. 

Q: It seems like nearly every product and service now boasts about some AI feature, and it’s hard to tell what’s hype and what’s real. Where is AI actually being implemented in food manufacturing to streamline processes or enhance product innovation? 

Like every industry these days, AI is definitely creating a lot of excitement in the food sector, but it can be difficult to separate genuine breakthroughs from marketing buzz. However, there certainly are real-world examples where AI solutions are already being deployed and have the potential to make a genuine impact for food manufacturers, including: 

AI is likely following the “Gartner Hype Cycle,” a graph that illustrates the maturity and adoption phases of new innovations. Given the AI frenzy of the last few years, we’re probably solidly within the “Inflated Expectations” phase of the AI hype cycle, where excitement surrounding AI’s potential is soaring, but not always fully validated by results. That said, while most AI solutions are still in their early stages, the above areas of implementation have the potential to deliver tangible benefits for the food sector, and I’m excited to see how they continue to evolve.