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Artificial Intelligence at the service of ESG



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ESG, acronym for "Environmental, Social, and Governance", represents three key factors for measuring the environmental and ethical impact of a company. In recent years, many entities have been adapting to these factors, which are increasingly consulted by investors.


With the consolidation of ESG, in order to have an increasingly comprehensive view of the impact of companies, the variables that compose it have also increased: environmental impact, greenhouse gas emissions, energy consumption, waste management, and supply chain-related impact are all variables that could once be discretionary but are now analyzed.


For companies integrating sustainability into their business model, Artificial Intelligence becomes a valuable tool. Thanks to it, many issues related to the environment and governance can be managed and automated.

First and foremost, AI allows the management of large amounts of data from different sources and with varying degrees of reliability. This management becomes crucial for designing corporate reports.


Another interesting use is understanding and controlling areas within companies where there is room for improvement, bringing the company closer to ESG goals. For example, a company could use AI to analyze data on waste production and find ways to reduce the quantity. Additionally, AI can help companies identify materials that can be recycled or reused, thus reducing the amount of waste ending up in landfills


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AI can be used to analyze data on the energy consumed by the company. For instance, it could be employed to identify areas within the company that consume more energy and find ways to reduce consumption.


In summary, AI can be utilized to help companies improve their sustainability in various ways. Thanks to AI, companies can monitor many variables crucial for ESG certifications, such as reducing greenhouse gas emissions, using sustainable materials, and managing waste. Additionally, AI can assist companies in integrating data from external sources with varying levels of reliability, such as supplier or customer data. Lastly, AI can be employed to enhance the sustainability of products themselves by analyzing data on the materials used in production and proactively designing the product life cycle


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