The impact of AI on the consumer goods sector

The consumer goods, foodservice, and packaging sectors are undergoing digital transformations, accelerated by emerging technologies, reliance on digital platforms, and the constant change of consumer preferences.  

There are numerous potential uses for AI across all these sectors. Data science and machine learning (ML) are vital investments. AI can provide valuable insights into consumer behavior, enhance operational efficiency, and drive strategic decision-making. By integrating AI into their operational strategies, consumer goods companies can maintain a competitive edge in a changing marketplace.  

However, the sectors should not only focus on AI-powered data analytics. They must also explore computer vision (CV), smart robots, AI sensors that automate manufacturing and distribution logistics, and generative AI tools that increase efficiency across corporate departments, customer service operations, and innovations in product design.  

Generative AI has significant potential in many areas of the consumer goods sector to enhance processes in product design and development, improve product offerings, and optimize customer engagement. In marketing, generative AI can automate the creation of content, including text, images, and videos. This can lead to more effective and targeted market strategies, allowing companies to engage consumers more effectively based on customer data. 

The matrix below details the areas of advanced AI capabilities in which consumer and foodservice companies should focus their time and resources. The matrix is also relevant to packaging companies’ operations in R&D and innovation, manufacturing, and distribution and logistics. We suggest that companies invest in technologies shaded in green, explore the prospect of investing in technologies shaded in yellow, and ignore areas shaded in red. 

Every area of the consumer value chain can benefit from more than one AI technology. Human-AI interaction capabilities include computer vision (CV) and conversational platforms. CV technology is used to interpret images and videos that can be used for quality assurance in manufacturing and distribution processes.  

PepsiCo-brand Frito-Lay uses CV in its chip manufacturing to predict the weight of potatoes. This saved the company $300,000 per factory line in the US. CV can also be used in the food service industry to check product quality, like Domino’s DOM Pizza Checker, which checks for pizza type, correct toppings, and aesthetic appeal. CV for quality assurance should also be implemented across consumer factories and packaging plants to automate packaging processes. Conversational platforms have multiple use cases, such as improving customer service and online experience, including after-sales support.   

Decision-making capabilities include any AI technology, such as machine learning (ML) and data science, which help companies plan, identify, classify, and forecast by providing actionable recommendations for consumer companies based on identified outliers and potential wins and losses. Predictive analytic tools can curate new product offerings based on emerging customer needs and preferences by analyzing current and historical data and supporting forecasting trends.  

Behavioral analytics provides valuable insights into consumer attitudes, behaviors, and product performance. As a result, companies can determine consumers’ buying behavior using predictive analytics and analyzing data such as product searches, wishlists, time spent on items, and shopping cart activity.  

The motion category includes smart robots and motion control sensors common across manufacturing and distribution channels. Restaurant chains like Domino’s and Jollibee’s have tested smart waiter robots and autonomous delivery vehicles. However, these have not been widely adopted and are seen as a novelty rather than an effective cost-cutting measure.  

Generative AI systems are self-learning algorithms, often called large language models (LLMs), that use existing data, such as text, audio, or images to produce realistic new content. Generative AI will help simplify data analytics and improve marketing and customer service experiences. Nestlé, General Mills, and AB InBev have already adopted GPT-4 to aid with interpreting data in business intelligence. Coca-Cola is using ChatGPT and DALL-E 2 to create marketing campaigns.

Consumer preferences constantly change, making it challenging for the consumer industry to keep up with the latest trends. According to GlobalData, the difficult macroeconomic climate in 2024 means that cost savings have become a key factor for consumers when buying products. With Gen Z as a vital factor in environmental, social, and governance (ESG) practices, their financial influence and preferences are reshaping how companies approach sustainability. According to GlobalData’s Q2 2024 survey, 28% of consumers believe that it is essential for products to be sustainable/environmentally friendly when deciding to make a purchase. Therefore, companies and brands must align with their ethical beliefs around sustainability and social diversity to maintain relevancy as consumer tastes change.  

According to Shree Tiruvali, global director of consumer and shopper metrics at Coca-Cola, consumers are now less brand loyal and instead make purchasing decisions based on convenience and their ideologies. Shree explained that this shift has led Coca-Cola to adopt new strategies, such as sustainability, health, and including flavors to fit consumers’ new habits. Moreover, the trend towards personalization is changing the consumer goods industry. It is driven by factors, such as online shopping, digital technology, and consumer demand for tailored products. Brands need to understand consumer behavior and cater to changing consumer preferences to be successful.  

AI-driven analytics can use existing consumer data to predict future behavior and also provide personalized recommendations and experiences. Consequently, this allows companies to be flexible and keep up with new trends, maintaining relevancy among consumers. Since July 2024, Colgate-Palmolive has used predictive analysis with consumer insights, offering a range of options that satisfies consumer’s needs. By analyzing consumer motivations and the benefits they seek from products, Colgate-Palmolive can create more effective advertising and product offerings that are personalized to individual consumers.   

In research and development (R&D), large consumer goods companies use data lakes—a centralized repository designed to store, process, and secure substantial amounts of structured, semi-structured, and unstructured data—to test new formulations. In April 2023, Unilever began using AI-powered data analysis to formulate beauty and skincare products for Vaseline, Dove, and Ponds. This is because AI can help to better understand body composition by using imaging techniques through CV to support personalized skincare development. Similarly, in 2021, Nestlé began using AI analytics on customer data to understand preferences and behaviors, creating personalized marketing campaigns.   

Incorporating AI technologies will also offer more personalization in product development and advertising, and improve customer services, which can encourage consumers to return. In 2021, PepsiCo used consumer data analysis to optimize its marketing strategy for a new product by analyzing data on over 100 million US households and 500,000 US retail stores with AI. As a result, PepsiCo identified consumers that were likely to buy the drink, targeting them with marketing campaigns, and ensuring key retailers were adequately supplied. 

Consumer companies can also use customer data and social media analytics to curate new product offerings based on customer preferences and recommendations. As consumers become more health conscious, companies in the consumer industry need to adapt their products to meet consumer demand. Coca-Cola first introduced a zero-sugar version in 2005.  

Almost 20 years on, Coca-Cola Zero Sugar is acting as a growth driver for the brand, growing its volume sales by 5% in 2023, over the double rate of growth in the business’s total portfolio, which was 2%, according to Marketing Week. Similarly, Post Holdings acknowledged that shifts in consumer behavior, such as dietary trends and preferences for healthier options, require the company to adapt its product lines.  

Applications of AI, such as ML algorithms, will aid sugar reduction efforts by optimizing food formulations and identifying sugar substitutes. For example, Arzeda is a protein design company that uses AI to design proteins for natural sweeteners. Arzeda uses datasets of protein sequences and structures the natural sweeteners. There is a lot of competition among consumer companies offering healthier alternatives to existing products. AI will help companies choose other existing ingredients that align with consumers' preferences.

Consumers are now more connected than ever. The rise of smart devices and social media platforms has prompted consumers to spend more time online, influencing how they shop for consumer goods. Customers use these digital channels to research products, compare prices, and make purchases. They are looking for ways to save time and effort, through online shopping, same-day delivery, and other convenience-oriented services.   

Technologies, such as digital payments and AI-powered personalization of products and services, have become commonplace in the sector, meeting the growing consumer demand for convenience and improved communications. According to Salesforce, about 81% of consumers worldwide expect faster service as technology advances. Digitalization has made it easier for consumers to browse and purchase products online, leading to a shift toward online shopping.  

AI integration can support consumer goods companies to create distinctive products, services, and marketing strategies based on customer data and preferences, improving the overall customer experience. AI-driven tools like chatbots will collect data and respond to standard questions, supporting customer relationship management (CRM) processes.  

In October 2023, Domino’s Pizza collaborated with Microsoft to improve consumer experiences using generative AI and cloud computing. This partnership will use Microsoft Cloud and Azure OpenAI service to enhance the ordering process through personalization and simplification. Similarly, Estée Lauder deployed AI-based facial recognition to develop a chatbot that helps online customers choose and purchase their ideal lipstick. The chatbot is accessible on Meta’s Messenger and allows online and mobile users to interact with Estée Lauder’s select lipstick brand. As a result, Estée Lauder’s use of AI technology is vital for promoting its brand and solidifying its relevancy in the beauty market.   

Generative AI is emerging as transformative technology across the consumer goods, foodservice, and packaging sectors, with various applications that enhance efficiency, innovation, and customer engagement. In the consumer goods sector, generative AI simplifies data analytics, allowing companies to interpret vast amounts of data more efficiently. For example, companies like Nestlé and Coca-Cola are adopting advanced AI models like GPT-4 to enhance their business intelligence capabilities, enabling better decision-making based on data insights.

The rising cost of living means that many consumers have less disposable income, negatively impacting shopping habits and spending confidence. Consumer goods companies have increased prices to protect their gross margins, which have been affected by higher energy prices and supply chain disruption. According to GlobalData, Unilever raised its prices by 11.3% in 2022, but its gross margins declined, and volumes dropped 2.1% despite 9% sales growth. This case illustrates that higher prices tend to reduce consumption and profits, and this trend will continue as companies try to offset stagnant growth.  

To address these challenges, incorporating AI can help to keep costs down by optimizing processes and reducing waste. AI technologies can act as an administrator by analyzing thousands of data points at any given time, allowing companies to optimize operations and reduce costs on various fronts. For example, AI can automate stock inventory operations based on predicted demand to reduce waste and perform quality assurance with CV to minimize mistakes and optimize staffing (which refers to the requirement for an adequate number of qualified employees to operate various aspects of the business) by forecasting staffing needs.   

Introducing AI can be expensive for companies and requires a more skilled workforce. However, investing in AI can help companies save money in the long run through increased efficiency, enhanced customer experience, and better-quality products. For example, AI can reduce operational costs when sales are down as it can use data to identify areas for improvement, which can minimize costs. AI can also streamline operations by analyzing vast amounts of data to optimize various processes within the supply chain. This includes automating inventory management, enhancing quality assurance, and improving staffing forecasts. Additionally, predictive analytics can be used to forecast the supply needed based on consumer interest and sales figures, aiding in efficient inventory management and demand forecasting.

ESG is the most crucial theme of the decade. Consumer goods companies are under pressure from governments and consumers to reduce their environmental and social impact and ethically govern their operations and employees. As mentioned above, GlobalData’s Q2 2024 global consumer survey found that, for 28% of consumers, whether products are sustainable or environmentally friendly is essential when deciding on a new purchase.   

Applications of AI in product development will identify alternative ingredients that align with consumer interest in eco-friendly products, creating more sustainable and naturally sourced products. In 2021, Unilever-beauty brand Hourglass relaunched its Red 0 lipstick. Previously, the lipstick had been formulated with carmine—a pigment that requires over 1,000 crushed beetles per product. Using AI, the brand could analyze color combinations to formulate a cruelty-free identically colored alternative.   

Using AI can also help businesses reduce waste, a prominent issue across the consumer goods, food services, and packaging industries. AB InBev has used its AI-powered Smart platform since 2013. The platform provides farmers with access to seed varieties, technical training, insights and data, and the ability to invest in and grow their businesses. In the food service industry,  

Winnow, a waste management system that uses AI technology to reduce waste in commercial kitchens, develops AI-enabled tools that take photos and weigh discarded food, allowing commercial kitchens to measure, monitor, and reduce food waste. For example, Winnow’s data-driven insights help chefs pinpoint the areas generating the most waste, enabling targeted interventions, such as adjusting portions for high-waste items. With AI integration, AI will provide businesses with platforms that track, analyze, and reduce waste across the industry.  

Moreover, implementing AI can reduce carbon emissions and resource inputs. AI can monitor and analyze supply chains and operational processes to ensure they are as efficient as possible, reducing unnecessary emissions. For example, PepsiCo uses generative AI to enhance sustainable agricultural practices and helps the company track and reduce its carbon footprint across the value chain. Generative AI monitors energy consumption and emissions by identifying inefficiencies and recommending corrective actions at PepsiCo’s manufacturing facilities. As a result, AI helps reduce energy use and lower emissions.

Geopolitical tension is having a significant impact on the consumer goods sector. The ongoing Russia-Ukraine conflict has led to increases in the price of essential commodities, such as oil, gas, and crops. Supply chain disruptions have resulted in higher manufacturing and transportation costs. Consumer companies are shouldering the burden of high business costs, a cost which is then passed down to the consumer through higher prices. Conflicts can also lead to resource scarcity which negatively impacts consumer companies who face more uncertainty in being able to produce their goods.  

For example, Nomad Foods—a manufacturer and distributor of frozen foods—reported that in February 2024, the long-term conflict in Ukraine led to considerable reductions in the availability of commonly used raw materials, such as fish, wheat, and energy. Similarly, AmRest Holdings—a casual dining, fast-food restaurant, and coffee shop operator—highlighted that the conflict between Russia and Ukraine broadened the implications for global economies. The company closely monitors how the conflict affects its operations, particularly concerning inflation driven by increased prices of energy and non-energy commodities. As a result, the conflict in Ukraine created a ripple effect across various companies in the consumer goods sector, leading to resource scarcity, increased costs, and supply chain disruptions.  

AI applications can automate and optimize supply chain management, helping companies reduce costs and map out supply availability throughout the year. P&G used its in-house AI predictive analytics platform to manage supply chain disruption caused by the pandemic at a time when consumer spending was unpredictable. P&G claims that this and other AI applications, including monitoring stock inventory during the phasing in and out of products, have saved the company $60 million annually.   

With AI integration, predictive analytics will offer complete visibility of supply chain operations from start to finish by analyzing data to anticipate and respond to various challenges and opportunities. In collaboration with o9 Solutions in 2023, Estée Lauder launched an AI-backed supply and demanding planning tool that improved its forecasting accuracy by 30%, providing users with an engaging interaction for finding suitable beauty products using generative AI.  

Companies like General Mills have employed sensors and machine vision to track the movement of ingredients, ensuring that products, such as gluten-free items, maintain their integrity throughout the supply chain. Using sensors will help monitor the quality and safety of ingredients by providing real-time data on their status. As a result, incorporating AI will provide valuable insights and tools to navigate supply chain challenges during geopolitical tensions, allowing companies to make informed decisions and maintain operational efficiency.