{"id":658465,"date":"2021-12-01T06:42:00","date_gmt":"2021-12-01T06:42:00","guid":{"rendered":"https:\/\/www.capgemini.com\/no-no\/?p=658465"},"modified":"2025-03-20T03:49:35","modified_gmt":"2025-03-20T03:49:35","slug":"make-your-brand-stand-out-with-ai-driven-eco-responsibility","status":"publish","type":"post","link":"https:\/\/www.capgemini.com\/no-no\/insights\/expert-perspectives\/make-your-brand-stand-out-with-ai-driven-eco-responsibility\/","title":{"rendered":"Make your brand stand out with AI-driven eco-responsibility"},"content":{"rendered":"\n

Make your brand stand out with AI-driven eco-responsibility<\/h1><\/div><\/div><\/div><\/div>
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2021-12-01<\/h5><\/div><\/div>
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You\u2019re likely already aware of AI\u2019s strategic opportunities, but you may not realize AI\u2019s infinite potential for transforming your environmental impact. AI offers massive computing power to help produce more sustainable products and services efficiently.<\/p>\n\n\n\n

Take a closer look at carbon emissions<\/strong><\/h3>\n\n\n\n

Large organizations tend to have a massive carbon footprint, so it\u2019s essential to use innovative technologies to drive a greener planet. One of the primary reasons for higher carbon footprints is high energy consumption in industrial applications. Unfortunately, many companies cite challenges around absorbing the costs associated with replacing their current infrastructures with a low-carbon emitting infrastructure. AI addresses this key issue by using data analytics to optimize operational efficiencies while delivering deep insights into carbon emissions, reducing expenses, and facilitating sustainable transformation. With this in mind, AI is a key driver for an eco-friendly brand evolution.<\/p>\n\n\n\n

Many consumers are also paying attention to the impact of their purchasing and consumption habits. In fact, 57% of consumers prefer brands that make a positive impact on societal issues. When consumers demand brands prove their value and commitment to social, economic, and environmental issues, this figure also conveys that consumers would be indifferent if 77% of brands disappeared altogether.<\/p>\n\n\n\n

Build richer environmental impact models with AI<\/strong><\/h3>\n\n\n\n

Awareness is increasingly essential (seven out of ten people in France consume organic foods at least once a month), but it\u2019s still tricky to benchmark market demand concerning sustainability. Whether the topic is organic, carbon-neutral, recycling, recycled, zero-plastic, circular economy, green, vegan, natural products, and something else \u2013 it\u2019s not always easy to deploy an eco-responsible strategy. However, there are several proven strategies to ensure an eco-responsible approach to organizational operations and product design.<\/p>\n\n\n\n

\u201cUse AI to do good and to ensure the common good.\u201d<\/em><\/strong><\/p>\n\n\n\n

More importantly, you can pursue sustainable development while improving profitability. Invariably, nearly 80% of brands say that doing so increases customer loyalty, and 63% report that it directly contributes to revenue increases. Not to mention, artificial intelligence has already reduced greenhouse gases by about 13% among manufacturers and retailers. It can also help brands reach 45% of their carbon reduction target by 2030.<\/p>\n\n\n\n

AI makes data analysis more manageable and more effective; it democratizes access. Visual platforms, with drag-and-drop functionality, helps to close the giant carbon data loophole. You can certainly use an AI-driven solution to monitor, predict, and reduce emissions. Therefore, the question remains: How can AI solidify and accelerate your eco-responsible initiatives?<\/p>\n\n\n\n

Consider adopting the tips below and tailor them to fit your unique business needs:<\/p>\n\n\n\n

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  1. Invest in optimizing resource use and add global logistics operational excellence.<\/strong><\/li>\n<\/ol>\n\n\n\n

    At the most basic level, manufacturing firms aim to produce only the exact number of products they sell without any excess. How can AI help? With the correct data, you can decrease the number of unsold items and reduce your carbon footprint. From supplying materials to shipping products, it\u2019s important to incorporate AI across multiple stages of the value chain. Once you produce each unit, you can send the right products to the right stores while mitigating errors and optimizing supply chain logistics.<\/p>\n\n\n\n

    For example, AI solutions have helped Amazon reduce packaging requirements by 33%, saving more than 915,000 tons of packaging materials, equivalent to eliminating 1.6 billion shipping cartons. In addition, ÎÚÑ»´«Ã½ offers a tool that generates a complete cost-benefit analysis on the shipping side, which details estimated fuel consumption, fuel costs, and CO2 emissions while depicting a variety of potential scenarios to optimize delivery strategies. Forecasting is a vital component of the process. Yet, most sales forecasts still rely on legacy or historical projections. In contrast, new algorithms can achieve unprecedented accuracy translating into solid gains in economic and environmental performance. Let\u2019s take a look at an example from H&M. In early 2018, the Swedish multinational clothing company announced their stock of unsold clothing exceeding $4 billion in costs.<\/p>\n\n\n\n

    The conundrum highlights the increasingly complex sales forecasting with tight-flow logistics. Continuous investment in AI makes it possible to predict better in-store demands based not only on a post sales perspective but also by using external data such as weather forecasts, scheduled sports, or cultural events. In essence, your organization can make consistently reliable data-driven decisions down to the item and store level. For instance, Carrefour partnered with ÎÚÑ»´«Ã½ to integrate an AI-driven SAS solution for supply chain management. As a result, Carrefour was able to optimize inventory management and reduce waste. By collecting and processing data from stores, warehouses, and e-commerce sites, Carrefour can now consolidate the right data to anticipate demand and refine incoming supplier orders. As a result, Carrefour reduced the number of breaks and overstock in their stores and warehouses.<\/p>\n\n\n\n

    Let\u2019s chew over the agri-food industry: stakes in this sector are vast. Food waste is a prominent issue. Moreover, reducing food waste using AI algorithms can save companies around $127 billion. Regarding agri-specific AI implementations, consider the following benefits:<\/p>\n\n\n\n