The role of artificial intelligence (AI) in food has increased over the past decade, with most practical focuses on increasing the convenience and speed of MNCs for larger food and drinks.
But as algorithms mature and data sources grow, everyone is offering AI more to meet expectations, in addition to being much more accessible to large food companies as well as small and medium-sized businesses.
Below we present four of the most important food industry applications that AI aims to achieve in order to make it more important and make its existence more pronounced.
1) Shortening the product development cycle
The whole idea of integrating AI into corporate operations is to increase productivity, but in addition to supporting pre-research and meeting scheduling, experts believe that it has a much greater role to play when it comes to improving time to market (TTM) for new product development.
“There are many different food and beverage companies with a refinement of different portfolios that determine the level of AI tools they need, and in many cases this leads to maintenance use only in scheduling meetings and conversation translations, but there is much to discover here.
“One of the most obvious areas where AI has powerful performance potential is product development. It goes beyond algorithms that drive machines and drive production, but instead is the ability to speed up information gathering, analytics and overall innovation.”
While this may seem straightforward on the surface, TTM is one of the areas that many food and beverage companies face most challenges, and AI has many opportunities.
“Product development can go through many stages. In fact, it takes a very long time to complete, so the overall development can take months to years.
“AI can shorten this by screening all the ideas we have and being a finalist for concepts with the best potential outcomes. And the final product prototype can play a role in meeting key requirements such as regulatory aspects.”
Betablo is one of Thailand's leading protein companies, producing hundreds of proteins and other agricultural food related products exported worldwide.
“Traditional methods that rely on paper-by-paper analysis for either science or regulatory details can be long and laborious and can lead to a single product that takes 10-15 years to reach the market,” he added.
“Of course, we need to note that AI is not 100% accurate and is still learning using the data it feeds itself, but there are many ways to use it to speed up product development.”
This stands out as the key areas that AI can support is speed and accuracy, which is becoming increasingly important to today's food industry to maintain competitiveness.
“We used to be able to say, 'Slowly and steadily win the race,' but with today's food innovation, this is no longer the case. Speed and accuracy are the real winners,” Roy said.
2) Distinguishing things that are just “noisy” and those that are really trending
But in this age of social media, if not, almost every new event or idea is being promoted as “trends,” so how can we be sure that food manufacturers are really following the right trends in innovation? AI may also have an answer to this.
“The main challenge with trend-based innovation is that companies tend to only know about trends after they actually become trending. It's all about becoming a catch-up game.”
“There are additional challenges in the sense that something 'noisy' in terms of being pushed out by audiences all over social media today is not necessarily a real trend, and food companies need to be able to know the difference in not wasting time and money in innovation. ”
AI is where it can actually impact it, as it can use multiple data sources and billions of data points to remove biases that humans cannot detect. It helps you identify whether “trends” are truly trends.
“This provides more accurate and positive insight into trends. Every trend follows the Bell curve in terms of maturity. AI also knows better what the company should do, so AI can also identify maturity trends,” he said.
“This is a futuristic view of what comes next and can go a long way in making decisions to invest time and resources based on what's best for the food company at that point.”
3) Make sure the product makes sense to the consumer
Unfortunately, new products reach the market frequently, but they fail with consumers for a variety of reasons, and they have to create sad exits.
According to Roy, one of the biggest problems with such products is its lack of resonance with consumers and its failure to relate to them from the beginning.
“When you're looking for products to go to the market, the most important thing is to get insight in a really actionable and keen marketplace. This is what AI can offer,” he emphasized.
“This increases the quality of consumer engagement to ensure that the product is relevant to them, and is highly monetisable when combined with strong marketing stories and stories.”
This relevance becomes even more important as that initial contact or sale is not sufficient to ensure the survival of the product in the market.
“Relevance ensures that the product doesn't just give consumers a try, but that it makes sense to them,” he said.
“This makes a huge difference because the brand can have better conversations with their target audience.
4) Reduce food waste from above
Sustainable initiatives have become a major topic for many food companies, but at the same time many of these companies face major challenges in terms of related issues such as food waste and food loss.
To this end, Ganchoudury believes that AI holds the answers to reduce these problems from top-down.
“AI can understand what consumer demand for a product is in real time, as well as the specific demands and nuances of each market,” he said.
“This means that businesses have a better understanding of the amount of production they need. [everything from electricity to ingredients] Prevents the generation of food waste from excessive production. ”
Such insights can also be applied to the areas of investment and marketing. There, we described plant-based yogurt brands that have maintained a big investment plan for the Asian market based on AI insights and have saved thousands of investment dollars in doing so.
“It's AI technology that showed that the product doesn't show sustainable success here, and that's exactly proven because the plant-based industry hasn't taken off in Asia until now,” he said.
“On the contrary, we can also identify the best formats, flavors, ingredients, and more in each market, such as focusing on clean labels in Thailand or on better packaging in China to appeal to more audiences.”
