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Make your brand stand out with AI-driven eco-responsibility

Vincent de Montalivet

You’re likely already aware of AI’s strategic opportunities, but you may not realize AI’s infinite potential for transforming your environmental impact. AI offers massive computing power to help produce more sustainable products and services efficiently.

Take a closer look at carbon emissions

Large organizations tend to have a massive carbon footprint, so it’s 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.

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.

Build richer environmental impact models with AI

Awareness is increasingly essential (seven out of ten people in France consume organic foods at least once a month), but it’s 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 – it’s 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.

“Use AI to do good and to ensure the common good.”

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.

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?

Consider adopting the tips below and tailor them to fit your unique business needs:

  1. Invest in optimizing resource use and add global logistics operational excellence.

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’s 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.

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, Capgemini 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’s 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.

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 Capgemini 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.

Let’s 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:

  • Use image recognition to determine when the fruit is ripe.
  • Effectively match food supply and demand.
  • Augment the value of food by-products.

The good news? Large organizations now have the right tools and data to improve their economic scale and environmental impact. By implementing eco-responsible tactics, brands can progressively improve their carbon footprint starting with optimizing packaging. To illustrate, decreasing transport saves fuel while data-driven inventory management maximizes heating and cooling efficiencies. The optimizing stock also eliminates unnecessary production while enhancing resource utilization.

  1. Create fresh eco-responsible products to fortify a circular economy.

The development of sustainable products is a challenge for companies that pursue traditional business objectives focused entirely on cost optimization. The product design phase is a significant driver to prevent reuse, repair, refurbishment, and recycling materials. Considering tools to assess the environmental impact of eco-responsible products are complex and not user-friendly, designers and marketers often have to outsource critical analysis.

On the other hand, data mining makes it possible to recover and automate vital lifecycle data in the decision-making process. With AI, you can predict product and carbon costs right from the design phase to ensure optimized scenarios. For example, you can source local products and reduce the carbon footprint associated with transport or product substitution during the manufacturing phase.

 “Achieve your sustainability goals and eco-responsibility vision”

AI can also enhance and accelerate new product development, components, and suitable materials aligned with a circular economy. How? Well, by using machine-learning-assisted iterative design processes that enable rapid prototyping and continuous testing. Additionally, AI can facilitate the implementation of new economic models relevant to the circular economy. For example, AI makes it possible to simplify the resale process on the second-hand market.

Capgemini and its partners have developed a “circular” customer journey: Customers bring in used clothing and photograph the items with a store camera; the solution scans and analyzes the clothing and automatically generates a product page, listing defects, holes, stains, or scratches. Next, it estimates the value of the item based on brand, authenticity, description, size, condition, and materials. Decision-makers are increasingly relying on the circular economy and sustainable development. Recently, Ikea announced that it was creating a store 100% dedicated to second-hand sales. In northern Europe, they built a shopping mall to focus solely on second-hand resale.

  1. Sustainability as a financial scorecard.

It is increasingly vital to understand your company’s approach to eco-responsibility. The rapid clip of the innovative changes mentioned above has mushroomed to regulators who strive to increase transparency around consumer consumption’s ecological impact. For example, Goldman Sachs has made “sustainable finance” a foundation of its business. Consequently, we can no longer afford to evaluate companies based primarily on extra-financial criteria. Instead, it is essential to integrate the carbon standard into the entire value chain so that consumers can choose brands respectful to the planet. Similarly using a French example, nutritional qualities are evaluated with a Nutri-score, or a “carbon score.” The score informs consumers about the environmental effects of their products and helps drive environment-conscious purchasing behavior. Undoubtedly, resource-saving behavior is no longer a trend. It is the standard.

Final thought

As a society, we face tremendous challenges around ensuring ecological balance. AI will not save the planet alone. It is up to us to leverage its capabilities. Brands that want to improve customer retention over the next decade should take note. With the examples shared above, let’s continue to discuss the variety of ways AI implementation can positively impact your eco-responsibility initiatives.


1. Large organizations have a massive carbon footprint
Use innovative technologies like AI to drive a greener planet.

2. Consumers pay attention to brands’ sustainability actions
Many consumers make purchase decisions based on brands’ impact on societal issues. Improve profitability by pursuing sustainable development

3. Customers prefer eco-friendly brands.
Forward-thinking companies must follow suit.

4. AI makes data analysis more manageable, effective, and accessible
AI-powered platforms are intuitive and can democratize access to key insights organization-wide.

Interesting read?

Data-powered Innovation Review | Wave 2 features 21 such powerful stories from our leading technology partners and global top experts, covering fields like data for a better society, autonomous systems, data mesh architecture, creative AI, and data sharing ecosystems which will inspire you and activate your innovation muscles. Download your copy here!Is AI-driven sustainability sustainable?
Listen to this latest podcast by Vincent de Montalivet, Sustainable Data & AI Leader, Capgemini, and Kimberly Nevala, Strategic Advisor & Advisory Business Solution Manager, SAS.

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