Did you know that artificial intelligence (AI) can help food and beverage (F&B) brands get real-time feedback on ingredients that are likely to succeed in a market? This is the best thing about AI-powered research, according to Chik Liang Tan, Cargill’s Product Line, Innovation and Marketing Director. But that’s not all. AI is being increasingly adopted as a crucial tool for F&B innovators and marketers.
In a panel discussion on the disruptive power of AI, top food and beverage industry brands shared their hands-on experiences with AI solutions, particularly in market research. This article highlights the key insights from the conversation. For a deeper dive, watch the full webinar here.
AI use cases for the F&B industry: How exactly is AI being used?
1) AI is being used to identify flavours that are trending among consumers
The most widespread use of AI is in adding speed and efficiency to ingredient innovation or the process of identifying the right ingredients that customers are looking for in products, which could potentially make those products a bestseller. F&B brands use this data to not only identify what products are likely to witness demand in particular markets but also to derive insights into the estimated intensity of the demand. This type of insight stems from AI’s ability to hone in on whether demand trend is nascent, emerging, at its peak, or in decline.
Moreover, unlike traditional market research, which has a longer turnaround time, AI is able to churn out data in real-time. Leading F&B brands are making a beeline for AI products because of this ability to deliver insights quickly and its immense cost-effectiveness. Additionally, faster insights can translate to a lower time to market which in turn translates to potentially increased profitability.
This is how F&B brands make more informed decisions on ingredient innovation and activate quicker flavour innovation, using AI.
The quality of the data further supports smarter (or more potentially profitable) flavour development for F&B brands and food companies. For example, Givaudan recently launched the Aroma Kiosk, a mobile touch-screen-based interface that lets consumers smell various aroma profiles and rate them, after which AI steps to offer insights into linked flavour preferences. Marieke Otten, Head, Consumer Sensory Insights, Asia Pacific, says that it has helped them to evaluate how end-users respond to various flavours and scents. “It helps consumers articulate their preferences better,” she says.
2) AI promotes alignment of stakeholders because data is king
AI’s ability to make data more actionable emerges as a close second to flavour innovation using AI in the long list of AI use cases for the F&B industry. “At Mondelez, AI has been instrumental in driving the brand’s consumer-centric agenda. It not only helps us accumulate a lot of data but also helps make that data more actionable,” says Maria Galindo, Associate Principal Scientist – Consumer Science, Mondelez International, explaining that as an organization with a lot of scientists, data always has the last word when it comes to getting internal stakeholders on board about the potential of a product.
AI is gathering a strong reputation as the best way to market a product or ingredient with full confidence and all stakeholders completely aligned. Daniel Hitz, formerly Vice President, Business Development APAC at OFI offers a little industry background saying, “There tends to be quite a gap between the R&D and commercial teams.” However, in his experience, commercial, R&D, and application teams found it easier to agree when they began using AI-provided market research data to make decisions.
Examples of AI in action: The relationship between AI and the bottomline
Examples of flavour innovation using AI are becoming increasingly common. For instance, using AI-powered research, OFI launched a snack seasoning with the trending flavour-fusion that was quickly becoming a big hit among end-users. They developed the seasoning – lobster with “smoked flavours” – based on AI-powered research and the ingredient was picked up by a snack manufacturer. “Their CPG (consumer packaged goods) saw a very successful launch and the product is still on the market,” Hitz says.
OFI also similarly used AI to perfect the firmness of plant-based hamburgers. Brands with plans to launch plant-based products do seem to rely on the type of insights that AI makes possible to launch products that are more likely to meet taste preferences, and therefore more likely to succeed on the market. Givaudan too uses AI to amass insights on the desired juiciness of plant-based hamburgers.
Meanwhile, Cargill managed to spare one of its dairy customers in Indonesia from an unadvisable launch. AI insights showed that it was too early to launch the initially chosen dairy product in the Indonesian market, although demand for such products was on an uptrend in some other Asian markets.
Return on investment: How to get the most out of your AI market research
AI has tremendous capabilities, but that does not mean that is a ubiquitous solution to just about anything. It is therefore essential to enter with a thorough understanding of AI and reasonable clarity on its potential benefits and limitations specific to F&B brands. It is not uncommon for AI to be misinterpreted as a fix-all solution. However, AI is a tool that can provide a roadmap, and possibly uncover some uncomfortable truths that might enable brands to course-correct in terms of strategy. Moreover, it cannot be used in isolation. It is part of a larger toolkit, consisting of syndicated marketing, internal experience-based intelligence, and the digital piece, within which it rests. “You need to be critical of where AI is suitable and where a traditional method will do,” Galindo says.
With this firm grounding in terms of expectations, brands also need to chart out clear goals. For example, AI-driven research has a valuable social media listening component, but users also need to be very clear on what they are listening for.
Change management: How can F&B innovators find favour for AI in-house?
You might be strongly convinced that AI can help smarter and faster ingredient innovation and therefore appeal more strongly to your F&B customers; but how do you sell the idea to the rest of the team? More importantly, how do you sell the idea to those who might sign off on budgets? Equally significant: how do you drive acceptance and enthusiasm among the teams that will work hands-on with the chosen AI solution?
As an ambassador of change, one of the first things to deal with head-on is people’s fear of job loss. “People think that AI or machine learning will make their jobs redundant but in truth, it is the opposite. These tools will help us grow our businesses and focus our efforts on areas and issues that need manual thinking. It will help us do our jobs better,” Otten explains. In fact, just a few paragraphs ago, we addressed the fact that when it comes to flavour development for F&B brands, AI is part of a larger toolkit. The experience-based intelligence component of this toolkit emerges from the human who owns the project, and therefore the human component is essential; it cannot be eliminated from the process of flavour development with AI. AI is intended to be a tool in the hands of experienced marketeers and researchers, not their replacement – getting this message across is definitely a strategic primary move.
Otten learned by trial and error, how to deal with driving acceptance and enthusiasm for flavour development using AI when Givaudan built new [AI-driven] platforms to predict trends – in two countries, using two distinct approaches. In one market, the brand started out with a very small team and roped in the rest of the team gradually. “This did not work well. Instead, what we tried in the other market, roping in the entire team at once – builders and users – allowed us to have brand ambassadors within the organization,” she says.
At least in the initial phase of flavour development using AI, parallel use of traditional and AI-fuelled research can deliver dual benefits to teams: First, the culture change from traditional to AI will be phased and therefore easier to digest, and second, comparison becomes possible. This also ties in well with measuring to ensure that you are receiving a sufficient return on your investment, as we discussed earlier.
The biggest challenge for most AI enthusiasts is getting everyone on board after being upfront and realistic about how flavour development with AI works or explaining that failure is sometimes part of the process. “You need to be open to failing; and from that failure, you need to be able to learn and adapt,” Galindo underlines.
Moreover, as Tan puts it, while technology is expensive when it comes to flavour development for F&B brands, the bigger question is “What is the alternative?” Surely no brand would want to go into a market blind, or alternatively, accept a longer (and inherently more costly) time to market when there are better alternatives that also justify their cost.
Proving AI’s usefulness in ingredient innovation by starting small and displaying early wins is seen as one way of driving increased acceptance; another option is to point to the wins of competitors who adopt AI.
Can you afford to ignore flavour innovation using AI?
Experts seem to suggest that brands need to start leveraging AI-generated research immediately. The industry is already witnessing customers who are quickly and easily bored and therefore looking for something new all the time. By 2030, this trend will have compounded drastically. Consumers are likely to expect quicker product launches, which would call for higher agility in larger organizations on one hand and niche players or personalized products on the other hand. Flavour innovation using AI is therefore quickly becoming a requirement rather than an option.
Conclusion
Watch exactly what the experts say to develop your own stack of tricks? Get the whole picture when you watch the webinar.
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