AI Business Transformation, Consumer Goods Edition

While artificial intelligence (AI) and generative AI remain a hotly debated topic, consumer goods companies seem to adopt these capabilities at lower rates. In a 2024 McKinsey global survey on AI, 71 percent of consumer goods leaders reported adopting AI for at least one business function. However, to gain a competitive advantage and unlock AI’s full potential, consumer goods companies need to scale their efforts, using AI to transform their businesses across their value chain.

The aim of this blog is to offer guidance on three key areas of AI impact, based on the experience of Amazon Web Services (AWS) with top global consumer-goods players. This will help consumer goods leaders define strategic objectives and identify priority workstreams for transformation.

Streamline supply chain operations

AI-backed analytics have the power to identify patterns at remarkable speeds. With support from AI, organizations can be proactive and ensure transparency; both are crucial for sustainability and regulatory compliance. Hyper-automation at scale helps AI systems self-correct processes throughout the supply chain, including in manufacturing, warehousing, and transportation. And AI and AWS services, such as Amazon SageMaker Canvas and AWS Supply Chain, can generate accurate forecasts using historical data. They predict product demand and volume, improving demand forecasting and planning—while helping to optimize manufacturing.

For example, More Retail Ltd. (MRL), one of India’s top four grocery retailers, collaborated with Ganit as its AI analytics partner. Together, they forecasted demand with greater accuracy, building an automated ordering system to overcome the bottlenecks and deficiencies of manual judgment. MRL increased its forecasting accuracy from 24 to 76 percent, leading to a reduction in fresh-produce waste by up to 30 percent. This improved in-stock rates from 80 to 90 percent while increasing gross profit by 25 percent.

Consumer goods companies can also use AI to rapidly analyze large amounts of data. This analysis helps companies identify and mitigate risks such as weather delays, equipment downtime, and quality issues. Also, AI can assess the past performance and reliability of suppliers, then use the insights to make recommendations for optimization. By using scalable data and AI services, organizations can increase supply chain visibility, improve sustainability, ensure product availability, optimize inventory, and cut costs.

Manage channels more efficiently

AI can also help consumer goods companies optimize their promotional campaigns and product placements by analyzing not only historical sales data, but also additional data sources. These can include stock levels, weather forecasts, and consumer trend information. Companies analyze this broader range of data to receive nearly instant answers to business queries. AWS services such as Amazon Q in QuickSight help companies with in-store product positioning and future promotional efforts. For companies selling direct to consumer, AI offers AI assistants, virtual try-on opportunities, and personalized recommendations.

Rufus is a generative AI–powered shopping assistant trained on the extensive Amazon product catalog, customer reviews, community Q&As, and information from across the web. It uses this training to answer customer questions on various shopping needs and products. Rufus provides comparisons and offers recommendations based on conversational context. Customers use Rufus to make more informed purchasing decisions, as well as to navigate the vast Amazon Marketplace with greater ease and efficiency.

For companies with both an online and offline presence, it’s essential to collaborate with retailers. Collaboration can provide a unified commerce experience, where customers can seamlessly move between a company’s digital channels (such as its website or mobile app) and its physical retail stores. AI optimizes efforts, freeing staff to focus on enhancing the customer experience. There’s also more time to improve collaboration with retailers and to maximize revenue growth across all sales channels.

Innovate faster and hyper-personalize customer experiences

Consumer goods companies can use AI to enhance their innovation capabilities and deliver a more personalized customer experience. They can use sentiment analysis, social listening, real-time feedback loops, and predictive consumer trends to generate new product ideas. And with AI-generated packaging design, formulation improvements, and automated and optimized A/B testing, companies can significantly shorten new product development cycles.

Adidas has experienced such innovation. It trained a stable diffusion algorithm on 150,000 shoe images at different angles. Employees then generated a running shoe with certain criteria, such as a partner collaboration, or a mashup of two shoe types. Designers can choose from the generated ideas or use them as inspiration for a new shoe. When adidas China lacked product background and model images customized for the native market, it used Amazon EC2 and Amazon SageMaker to craft photorealistic models, cutting time to market and costs without needing photoshoots and graphic designs.

Companies can also maximize marketing effectiveness and grow customer loyalty through augmented real-time customer insights. Amazon Personalize and Amazon Q power these insights, helping with advertising optimization and AI-driven personalization. AI also embeds into customer service. Amazon Q in Connect supports contact centers with chatbots that assist web customers quickly. This reduces spend while improving the customer experience. By leaning on AI more, companies have the power to build more customized experiences, accelerate content creation and product development, and optimize marketing—delivering more innovation and value to consumers.

Of course, an effective AI strategy can’t exist without a robust, supportive data strategy. Access to high-quality, comprehensive data is essential for brands, which need to deliver personalized, timely, and relevant products and marketing campaigns. Without the right data foundation, AI applications are severely limited.

Historically, consumer goods companies have struggled to collect and analyze first-party customer data at scale. For this reason, they’ve relied on third-party data from partners. It’s nonetheless crucial to collaborate with network partners and exchange data with retailers, manufacturers, logistics providers, and media partners. To facilitate this and seamless data sharing, invest in the right technological infrastructure, such as cloud by AWS. By strengthening data-driven partnerships across the value chain, companies can unlock valuable insights and capabilities to better understand, engage, and serve their customers.

Want to discover how AWS can help you transform and modernize your business with AI? Connect with an AWS representative or AWS consumer goods Partner today.