IWSR’s latest report looks at how artificial intelligence (AI) is being implemented throughout the alcohol industry, with brand owners globally using it as a research tool for new product development (NPD).
The use of AI within the alcohol industry is still a new commodity, currently being used in the areas of new product formulation, often linked to marketing activity; and driving increased efficiency across company operations.
The IWSR report states that the technology has the potential to deliver additional benefits across company operations, from revenue management to production efficiency.
AI and NPD
The use of AI to inform NPD and subsequent marketing campaigns currently is used for one-off projects, such as online drinks retailer The Whisky Exchange’s use of AI to design labels for a luxury collection of 12 bottles of The Glenlivet 50-year-old single malt.
Germany seems to be leading this charge, with the unveiling of NanoFizz, an ABV 7% ready to drink product from Germany’s Katlenburger Winery, that claims to be Europe’s first AI-generated RTD cocktail. Two new AI wines were also unveiled by Wine of Moldova at this year’s Prowein trade fair in Germany.
In both of these cases, product development involved a combination of machine learning and human skills – Katlenburger used AI in the naming, recipe and design of the RTD, and required the expertise of specialist fruit winemakers to finalise the recipe.
AI was involved in every aspect of the creation of the Moldovan wines – harvesting, winemaking, blending, labelling and communication – with the physical work and blending process performed by people.
This technique was used in the US to drive quality improvements, with Minnesota-based von Stiehl Winery using the technology to develop its AI White and AI Red wines. The company encouraged purchasers to give feedback and tasting notes – which will be used to generate AI recommendations improving the quality of the next batch.
Researchers at KU Leuven University in Belgium used machine learning to analyse 250 beers, alongside consumer reviews and tasting notes, to construct models predicting how beers of different compositions would taste, and how positively they would be received.
The results were then used to tweak the recipes of existing commercial beers, playing up components that the data suggested were predictors of improved appreciation, such as lactic acid and glycerol – with positive results in human trials for both full-strength and no-alcohol beers.
Sapporo Breweries has partnered with IBM Japan to establish N-Wing Star, an AI system to create new products. The system analysed 1200 product formulations, and 700 raw materials used in 170 existing products, to create Otoko Ume Sour Salty Plum, an ABV 5% cent chuhai launched in Japan in 2023.
The company claimed that AI saved time on various aspects of NPD by 50 to 75 per cent, and is now adopting the system as a key component of its future new product development.
AI: driving efficiencies
Beyond its use in new product development, AI has the potential to improve efficiencies in numerous aspects of company operations, from production processes to back-office functions.
As many of the world’s wine regions struggle to find affordable and adequately skilled vineyard workers, companies are investigating the ways in which autonomous machines can fill some of the gaps.
AI programmes can be used to show workers exactly where they should prune a vine without damaging it – meaning that less skilled or experienced people can take on the task.
AB InBev has integrated AI across functions including quality control, customer relationships, and supply chain efficiencies.
Dutch multinational company Heineken is also increasingly using AI across its operations, including revenue management, commercial mix optimisation and sales execution. In Mexico, AI has transformed the company’s distribution function by shaping sales strategies, with 80 per cent of orders now placed online.
IWSR’s COO research, Emily Neill, said that while it is still in early days, it’s clear that AI can be used across many areas of the business.
“It is important to experiment to see where the most gains will be, and then focus investment on the most successful use cases.”
