The Apple of Your AI: Solving Food and Beverage Pain Points

The appeal of artificial intelligence (AI) lies in its ability to generate useful insights from data, which Food & Beverage Service Providers (FBSPs) can leverage to create value. For example, 43 percent of Instagram's 1.16 billion users list "Food & Drink" among their interests. FBSPs (and Instagram itself) use AI tools to analyze user data, resulting in insights that enable better marketing and, by extension, more revenue.

This blog post will explore AI use cases in the Food & Beverage industry. We’ll focus on three pain points faced by FBSPs: shifting consumer demographics, accelerating trend cycles, and evolving regulatory expectations.

1. Shifting Consumer Demographics

The global population is growing. Fifty years ago, it was less than four billion; today, it’s closer to eight. By 2050, FBSPs will serve more than 10 billion people worldwide.

To prepare for this shift, FBSPs must scale rapidly. That means automating processes, reducing waste, and increasing efficiency — tasks at which AI excels. For example,  the start-up Wasteless uses machine learning (an AI application that enables tools, systems, and other software products to “learn” from data) to help supermarkets reduce waste by “by dynamically pricing items” based on expiration dates. [1]  

Synaptiq used an AI application called “machine vision” to help an American foodservice distributor scale to meet the needs of more than 10 thousand restaurants. Our solution automated the process of extracting and analyzing restaurant menu data, which allowed our client to expand its reach without increasing its labor costs. 

2. Accelerating Trend Cycles

Algorithmic social media has accelerated trend cycles across industries, including Food & Beverage. Products can “go viral” at any moment, creating a meteoric spike in demand that poses logistical challenges, only to fade into obscurity a short time later. Predictive analytics: an AI application that predicts future events based on past data can help FBSPs anticipate and respond to these sudden changes in consumer demand.

Starbucks, for instance, uses predictive analytics to inform its product development. The multinational chain analyzed customer data and found that 43 percent of tea drinkers do not add sugar to their tea. This insight led Starbucks to develop two unsweetened iced tea K-cups: Mango Green Iced Tea and Peachy Black Tea. [2]

SLOW BRANDS DON’T REACH CONSUMERS.

Predictive analytics can also help FBSPs respond to accelerating trend cycles by preventing equipment breakdown, which leads to supply-chain congestion. Manufacturing software provider L2L used AI-enabled “predictive maintenance” tools to help specialty food maker Lakeview Farms reduce line downtime (34 percent), equipment repair costs (15 percent), and worker overtime ratio (17 percent). [3]

3. Evolving Regulatory Expectations

FBSPs face mounting pressure from climate-conscious regulators to track and improve their environmental, social, and governance (ESG) scores. The United States requires public FBSPs disclose ESG-related information to investors [4]. The European Union, the United Kingdom, and many other world powers enforce similar requirements. Many more (including Canada and China) are strongly considering them.

A POORLY TRACKED ESG PROGRAM IS UNHELPFUL AT BEST, UNLAWFUL AT WORST

Tracking ESG requires large-scale data-collection and analysis: a time-consuming and costly process for human workers, but not for AI. Consequently, many FBSPs are turning to AI-powered tools to ensure compliance with ESG disclosure regulations. Singaporean digital solutions company Magellan X, for instance, offers an AI-powered tool called “ecoMax” to track greenhouse gas emissions and fuel consumption. [5]

AI-powered tracking tools also help FBSPs improve their ESG metrics by generating actionable insights.  For example, the startup Winnow has helped commercial kitchens reduce food waste by up to 50 percent with a “smart” scale that enables sustainable inventory management by tracking what ingredients are wasted. [6]

Looking to the Future

AI applications such as predictive analytics, machine learning, machine vision, and predictive maintenance can help FBSPs automate processes, reduce waste, increase efficiency, respond to sudden changes in consumer demand, and improve ESG scores. The future is bright for those who embrace this transformative technology.

Photo by Karla Hernandez on Unsplash

About Synaptiq

Synaptiq is an AI and data science consultancy based in Portland, Oregon. We collaborate with our clients to develop human-centered products and solutions. We uphold a strong commitment to ethics and innovation. 

Contact us if you have a problem to solve, a process to refine, or a question to ask.

You can learn more about our story through our past projects, blog, or podcast.