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7 ways Amazon is using AI to build a more sustainable future

4 min
An illustration of planet earth in the middle with several images around it including solar panels, a woman planting a tree, mountains, and buildings.
From reducing packaging waste to democratizing data, Amazon’s unique approach to AI is helping advance and innovate on sustainability goals.

Climate change is one of the world’s greatest challenges—and at Amazon, we know we have to move fast, constantly innovate, invest, and stay nimble in order to continue to become a more sustainable company. Artificial intelligence (AI) and machine learning (ML) can help us meet our climate goals at the speed, scale, and urgency our planet requires. While there is a lot of talk generally about “AI and Sustainability,” we thought it would be helpful to get specific about some emerging use cases, as detailed below.

With Rufus, customers are now able to shop alongside a generative AI-powered expert that knows Amazon’s selection inside and out, and can bring it all together with information from across the web to help them make more informed purchase decisions.

From reducing packaging and food waste to powering fulfillment operations more efficiently, Amazon has been innovating with AI and ML for decades. Besides offering AI infrastructure and products to customers through Amazon Web Services (AWS), we also democratize AI so our customers and other companies can move faster to make their purchases, businesses, and lives more sustainable.

Here are seven of the latest ways Amazon is leveraging AI to reach our Climate Pledge commitment of net-zero carbon by 2040, and become a more sustainable business.

From reducing packaging waste to democratizing data, Amazon’s unique approach to AI is helping advance and innovate on sustainability goals.

Climate change is one of the world’s greatest challenges—and at Amazon, we know we have to move fast, constantly innovate, invest, and stay nimble in order to continue to become a more sustainable company. Artificial intelligence (AI) and machine learning (ML) can help us meet our climate goals at the speed, scale, and urgency our planet requires. While there is a lot of talk generally about “AI and Sustainability,” we thought it would be helpful to get specific about some emerging use cases, as detailed below.

With Rufus, customers are now able to shop alongside a generative AI-powered expert that knows Amazon’s selection inside and out, and can bring it all together with information from across the web to help them make more informed purchase decisions.

From reducing packaging and food waste to powering fulfillment operations more efficiently, Amazon has been innovating with AI and ML for decades. Besides offering AI infrastructure and products to customers through Amazon Web Services (AWS), we also democratize AI so our customers and other companies can move faster to make their purchases, businesses, and lives more sustainable.

Here are seven of the latest ways Amazon is leveraging AI to reach our Climate Pledge commitment of net-zero carbon by 2040, and become a more sustainable business.

  • 1.
    Reducing packaging use
    An image of Amazon's first automated US fulfillment center that uses paper packaging only in Ohio.

    The Packaging Decision Engine is an AI model helping determine the most efficient packaging options to ship millions of items available to Amazon customers. Data scientists have trained the model to understand a variety of product attributes, including an item’s shape and durability, and to analyze customer feedback on how different packaging options have performed. The model is constantly learning and has helped reduce the company’s use of packaging material since it launched in 2019. With this and other packaging innovations, Amazon has eliminated over two million tons of packaging material worldwide since 2015.

  • 2.
    Identifying damaged items to prevent waste

    AI-powered technology is being used across a growing number of fulfillment centers to detect damaged goods, with the goal of decreasing the number of damaged items that get sent to and returned by customers. The AI is three times more effective at identifying damaged goods than human beings and has been trained by analyzing millions of photos of undamaged and damaged items. If a product can’t be shipped directly to a customer due to imperfections, the item is flagged to an Amazon associate, who assesses the product and reroutes it to be resold at a reduced price, donated, or otherwise reused.

  • 3.
    Monitoring produce to reduce food waste
    Cherry tomatoes, mushrooms, dried peppers, garlic, and green beans on a plate.

    A growing number of Amazon Fresh grocery teams are using machine learning-based solutions to automate store shelf monitoring for fruits and vegetables. This AI-powered solution analyzes crate images to detect visual imperfections on the produce like cracks, cuts, and pressure damage. To ensure the defective produce is recycled whenever possible, Amazon Fresh resells the usable produce to local contractors who further resell the produce at reduced prices for use cases like feeding to livestock, ensuring less food goes to waste.

  • 4.
    Reducing returns by helping customers find the perfect fit
    Customers using AI to find the best fit when shopping for Amazon Fashion.

    Reducing returns leads to more sustainable shopping. Amazon introduced several AI-powered innovations to help customers shop for fashion with more confidence in Amazon’s store, while also helping reduce fit-related returns. They include personalized size recommendations using AI and ML to help customers find what size fits them best, personalized feedback from customers who wear the same size, and improved size charts. Amazon also developed a Fit Insights Tool to help brands and selling partners better understand customer fit issues and incorporate feedback into future designs and manufacturing, helping brands more accurately list their items for customers and reduce fit-related returns.

  • 5.
    Measuring the carbon footprint for products

    Estimating the carbon footprint for millions of Amazon products can be challenging—it can take a person hundreds of hours to research and calculate the carbon footprint for even a single product. To solve that challenge, Amazon developed Flamingo, an AI-based algorithm that leverages natural language processing to match text descriptions for Environmental Impact Factors (EIF)—a commonly accepted measurement for calculating the carbon impact of an item—with specific products.

    The algorithm is already helping Amazon’s team calculate the environmental impacts of everything from cotton t-shirts sold by Amazon Private Brands to carrots sold by Amazon Fresh. In one experiment, the algorithm reduced the time scientists spent mapping 15,000 Amazon products from a month down to several hours. Flamingo is also available for other companies to use in order to help accelerate their sustainability goals.

  • 6.
    Preventing deforestation in Brazil by democratizing data

    Amazon democratizes AI so other companies can use it to help meet their own sustainability goals. As one example, AWS worked with a Brazilian nonprofit to develop a large-scale AI model that monitors deforestation. This has enabled automatic monitoring of 20 million hectares of forest areas. With better monitoring, it’s estimated that 3.4 million hectares of forested areas will be restored within the state of Para.

  • 7.
    Using AWS chips to power AI more efficiently
    A phot of an trainium chips.

    Amazon is also improving the sustainability of AI by making our cloud infrastructure more energy efficient, including by investing in AWS chips. AWS Trainium is a high-performance machine learning chip designed to reduce the time and cost of training generative AI models—cutting training time for some models from months to hours. This means building new models requires less money and power, with potential cost savings of up to 50% and energy-consumption reductions of up to 29%, versus comparable instances.

    Our second-generation Trainium2 chips are designed to deliver up to four times faster training than first-generation Trainium chips while improving energy efficiency up to two times. AWS Inferentia is our most power-efficient AI inference chip. Our Inferentia2 AI accelerator delivers up to 50% higher performance per watt and can reduce costs by up to 40% against comparable instances.

These are just a few examples, and there are many others across nearly every part of our business. As the most transformational technology of our time, Amazon expects AI to be an increasingly important part of our work to build a more sustainable business, and we’re excited to share what’s coming next for our company and customers.

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