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  • Writer's pictureMohamed Soltan

The Five Ways Generative AI Will Impact Consumer Packaged Goods

A recent McKinsey report indicates generative AI could trigger productivity worth trillions to the global economy. The study of 63 use cases across multiple industries identified the most significant gain arising from its deployment in retail and consumer-packaged goods (CPG): up to $4.4 trillion annually.

Generative AI is a branch of artificial intelligence that can create new content or data from scratch, such as images, text, music, or code. The technology will affect most business functions. However, marketing and sales, customer operations, software engineering, and R&D will likely account for 75% of the total annual value realized.

The following are five ways we could potentially see generative AI impacting the consumer goods sector:

1. Product innovation and personalization

Generative AI can help consumer goods companies create new products or variants that meet the needs and preferences of different customers. It can generate new flavors, ingredients, packaging designs, or logos using customer feedback or market trends. For example, Mattel uses OpenAI’s DALL-E to generate ideas for new Hot Wheels toy cars. And Nestlé, PepsiCo, and Starbucks use generative AI to create new recipes and flavors.

It will even be possible for companies to personalize products for individual customers by using their data and preferences to generate customized recommendations, offers, or experiences.

2. Content creation and optimization

Producing engaging and relevant content for marketing campaigns, websites, social media platforms, or e-commerce channels previously required a lot of time from skilled workers. But now, generative AI can generate product descriptions, stories, slogans, headlines, images, videos, or audio based on keywords or prompts. It can also help optimize content for different audiences, channels, or formats by using natural language processing and computer vision to analyze and adapt the content.

Coca-Cola is one of the first major consumer goods companies to announce its generative AI use publicly. It has partnered with OpenAI and Bain & Company to develop an AI platform called "Create Real Magic," which combines GPT-4 and DALL-E to generate text and images, respectively. Coca-Cola will use it to generate personalized ad copy, images, and messaging for campaigns and experiences. And it has invited digital artists to use the platform to generate original artwork for billboards in New York’s Time Square.

3. Customer service and satisfaction

Consumer goods companies will provide better customer service and satisfaction by using generative AI chatbots, voice assistants, or conversational agents that can interact with customers in natural language. For example, generative AI can help answer customer queries, provide product information, offer suggestions, handle complaints, and even process orders. Cosmetic brands like L'Oréal, Estée Lauder, and Sephora use generative AI to create virtual makeup try-ons, beauty tutorials, and skin care advice.

Generative AI is even capable of sentiment analysis and emotion recognition and thus can gauge customers’ moods and tailor the responses accordingly.

4. Supply chain optimization and sustainability

Generative AI can help consumer goods companies optimize their supply chain and improve sustainability using predictive analytics, simulation, and optimization techniques. For example, it can help forecast demand and inventory levels, plan production and distribution schedules, optimize logistics and transportation routes, reduce waste and emissions, or improve resource efficiency using machine learning and deep learning models. Coca-Cola uses generative AI to assess supplier risk, forecast demand, and optimize its supply chain. L’Oréal is using it to assess the environmental impact of the organization.

5. Business intelligence and analytics

Consumer goods companies are using generative AI to gain business insights and make better decisions through data visualization and analysis. For example, they can create interactive dashboards, reports, or presentations that display key metrics and trends using data visualization techniques. Using natural language generation techniques, generative AI can also help generate summaries or explanations of complex data or analyses. And it can even generate synthetic data that can be used for scenario planning or testing.

Risks Associated with Generative AI

As powerful as generative AI is, it’s important to remember that it’s still in its infancy. And being reliant on data inputs implies certain risks.

  • Output is potentially subject to bias that could be offensive, and insensitive content creation risks damaging brand reputation

  • It could also potentially infringe copyright

So consumer goods companies need to work with providers who aim for balanced input, are transparent about their data sources, and have clear usage policies.

The uptake of generative AI applications has rocketed since November 2022, when Chat GPT was introduced, and is expected to continue expanding rapidly. The market offers significant value-creation opportunities to both incumbents and new entrants in the CPG space. Leaders should prepare to capture the benefits and mitigate the risks of generative AI by familiarizing themselves with the technology and by building it into their strategy.

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