The future of the consumer goods industry will be shaped by a range of disruptive themes, with artificial intelligence (AI) being an important theme that will have a significant impact across the value chain. A detailed analysis of the theme, insights into the leading companies, and their thematic and valuation scorecards are included in GlobalData’s thematic research report,Artificial Intelligence (AI) in Consumer Goods – Thematic Research. Buy the report here.
Artificial intelligence (AI) refers to software-based systems that use data inputs to make decisions on their own. Fast-moving consumer goods (FMCG) companies are slowly introducing AI initiatives. AI can be used across the consumer goods value chain. Machine learning and data science are strong tech investments in every area, and computer vision and context-aware computing represent investment opportunities for manufacturing.
The consumer goods industry is undergoing digital transformation, accelerated by Covid-19 and changing consumer demands. Digitalisation is a priority for FMCG companies in all subsectors and will pave the way for future AI investment. In this sector, industry leaders have predominantly used AI to enhance customer experience and improve value chain efficiency.
However, not all companies are equal when it comes to their capabilities and investments in the key themes that matter most to their industry. Understanding how companies are positioned and ranked in the most important themes can be a key leading indicator of their future earnings potential and relative competitive position.
Insights from top ranked companies
Unilever has a very competitive market position within the AI theme. The company has a comprehensive omnichannel AI strategy with multiple streams and initiatives. AI is used to improve the sourcing of raw materials, considering harvest times. Robots enable a single manufacturing line, handling products and packaging of different shapes, sizes, and forms. At the end of 2019, 31 of its sites were streaming live data using a digital twin, which tracks physical conditions and uses machine learning to analyse data and optimise processes. Unilever has an omnichannel AI marketing strategy. In 2018, it trialled in-store facial recognition technology to measure customer engagement, gathering insights on shoppers’ interaction with products. Facial recognition was also used in the TasteFace online marketing campaign for Marmite. A digital experience platform and app used computer vision to analyse consumers’ emotions when they tasted the product.
Unilever’s latest AI initiatives include building AI-based sensors for detecting the freshness of food. The company is working with Arm, PragmatIC, and the University of Manchester to design prototype sensor chips built on AI technology for detecting the freshness of fruits, vegetables, and packaged food. The sensor chips measure gaseous analytes that emanate odours associated with stale food.
Estée Lauder is an advocate of beauty tech and has piloted several initiatives involving AI. In 2019, the company piloted a voice recognition chatbot called Liv, in partnership with Google Home and technology agency Rehab. In 2020, this project developed further, and Liv was hosted on WhatsApp, a more accessible platform for users. The Liv chatbot helps customers build and follow a personalised beauty regime using machine learning. Estée Lauder is also utilising computer vision, natural language processing, and machine learning to create a chatbot, hosted on Facebook Messenger, that advises customers on personalised lipstick recommendations.
Coca-Cola is a market leader for AI adoption in the consumer goods sector. It predominantly uses AI for product innovation and market research. For instance, it created self-service drinks machines where customers could mix their own drinks, then used that data to develop the new Cherry Sprite flavour, as this proved most popular. The company also tracks their social media presence using computer vision to identify its drinks, or those of its competitors, in users’ pictures. Targeted ads are developed based on this data. In another project, the company introduced a pilot of intelligent vending machines in Japan. Customers can download the Coke On app to access a loyalty scheme, collecting points when visiting a vending machine by connecting their phones. Vending machines are also being launched in the US, Australia, and New Zealand, where customers can pre-order drinks using their phones.
Global Director of Digital Innovation Greg Chambers stated that “AI is the foundation for everything we do.” Coca-Cola has more than 500 brands in over 200 countries. This generates lots of data, and AI underpins many of its corporate strategies.
To further understand the key themes and technologies disrupting the consumer industry, access GlobalData’s latest thematic research report on AI in Consumer Goods.
Frequently asked questions
1. How are Consumer Goods companies using artificial intelligence (AI)?
Consumer Goods companies are using AI in various ways, such as improving supply chain operations, enhancing customer experience, and optimizing marketing strategies. They utilize AI-powered data analytics to gain insights into consumer behavior, forecast demand, and personalize product recommendations. AI is also employed in quality assurance processes, using computer vision technology to ensure product quality and detect defects. Additionally, AI is used in automating repetitive tasks, streamlining operations, and improving efficiency across the value chain.
2. How does artificial intelligence impact the Consumer Goods industry?
Artificial intelligence has a significant impact on the Consumer Goods industry. It enables companies to make data-driven decisions, enhance operational efficiency, and improve customer satisfaction. AI-powered technologies like computer vision and conversational platforms help in interpreting images, videos, and natural language, enabling better quality control, personalized customer interactions, and efficient supply chain management. AI also enables predictive analytics, allowing companies to forecast demand, optimize inventory, and improve production planning. Overall, AI empowers Consumer Goods companies to stay competitive, innovate, and meet evolving consumer demands.
3. Who are leading adopters of artificial intelligence (AI) in Consumer Goods?
Leading adopters of AI in the Consumer Goods sector include companies like AB InBev, Avery Dennison, Berry Global, Chipotle, Coca-Cola, Domino's, Heineken, P&G, PepsiCo, Nestlé, Tetra Laval, Unilever, and Yum! Brands. These companies have embraced AI across various aspects of their operations, leveraging its capabilities to enhance supply chain management, improve customer experience, and drive innovation.
4. Who are the leading vendors of artificial intelligence (AI) solutions to the Consumer Goods industry?
Some of the leading vendors of AI solutions to the Consumer Goods industry include Afresh Technologies, AI Palette, AMP Robotics, Basler, Black Dawn Data, EverestLabs, Eversight, Mastercard (Dynamic Yield), Nuro, o9 Solutions, Pluto7, Starship Technologies, Symbotic, and Tastewise. These vendors offer AI technologies and platforms that cater to the specific needs of Consumer Goods companies, enabling them to leverage AI capabilities effectively.
5. Which areas of artificial intelligence (AI) should Consumer Goods companies invest in?
Consumer Goods companies should consider investing in areas such as human-AI interaction, computer vision, and conversational platforms. These technologies can enhance customer experience, improve quality assurance processes, and optimize supply chain operations. Additionally, investing in data management and foundational AI capabilities can enable companies to leverage AI-powered data analytics for better decision-making and personalized marketing strategies.
6. What are the challenges with adoption of artificial intelligence in Consumer Goods?
The adoption of artificial intelligence in the Consumer Goods industry comes with certain challenges. One challenge is the cost associated with building AI models. Another challenge is the need for regulation and proper management of open-source models to ensure data privacy and security. Additionally, companies may face challenges in training natural language processing models with their own data. Overcoming these challenges requires a corporate strategy that addresses potential risks and focuses on effective utilization of AI technologies.
7. What is the projected market size of artificial intelligence in Consumer Goods?
The projected market size of artificial intelligence in the Consumer Goods industry is estimated to grow at a compound annual growth rate (CAGR) of 35.2% between 2022 and 2030. The increasing adoption of AI technologies across the Consumer Goods sector, driven by the need for operational efficiency and enhanced customer experience, is expected to contribute to the significant growth of the market.
8. Which Consumer Goods companies are laggards in artificial intelligence adoption?
Some of the Consumer Goods companies that are considered laggards in artificial intelligence adoption include BAT, Brinker, Darden Restaurants, Dine Brands Global, Doctor's Associates (Subway), Elopak, International Paper, Jollibee Foods, Owens-Illinois, Philip Morris International, Imperial Brands, Wendy's, and Whitbread. These companies have been slower in adopting AI technologies compared to their industry peers.
9. Who are the leading specialist artificial intelligence vendors in Consumer Goods?
Leading specialist artificial intelligence vendors in the Consumer Goods industry include companies like Afresh Technologies, AI Palette, AMP Robotics, Basler, Black Dawn Data, EverestLabs, Eversight, Mastercard (Dynamic Yield), Nuro, o9 Solutions, Pluto7, Starship Technologies, Symbotic, and Tastewise. These vendors specialize in providing AI solutions tailored to the specific needs of the Consumer Goods sector, offering technologies and platforms that enable companies to leverage AI capabilities effectively.
10. What are the components of the artificial intelligence value chain?
The artificial intelligence value chain consists of five main components: hardware, data management, foundational AI, advanced AI capabilities, and delivery. The hardware component includes semiconductors, cameras, sensors, and edge equipment that support AI systems. Data management involves collecting, storing, and processing data for AI applications. Foundational AI encompasses the fundamental algorithms and models that power AI systems. Advanced AI capabilities include technologies like computer vision, natural language processing, and machine learning. Delivery refers to the deployment and integration of AI solutions into business operations, including cloud-based platforms, APIs, and service providers.