The promise of sustainable AI may not outweigh the organizational challenges

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An organizational movement towards mass digitization is underway, and no industry is exempt. The number of connected devices is expected to reach 55.7 billion by 2025, 75% of which will be connected to an IoT platform – a shift that has presented a significant environmental challenge for organizations. The growing demand for data storage and computing power is causing many to question their sustainability efforts and begs the question: how can companies harness and implement artificial intelligence (AI)? and other smart technologies without increasing their carbon footprint?

Analyzing the intersection between digital transformation and sustainability has two aspects. First, it is important to understand how AI can be used to address sustainability issues. Moreover, it is necessary to ensure that the use of this technology and these AI machines does not subsequently increase the carbon footprint of the company.

Deep learning algorithms require colossal power when analyzing data. If left unchecked, this could become a vicious cycle in which, simultaneously, AI techniques are used to identify potential environmental hotspots while the machines themselves consume massive amounts of energy, which offsets the positive impact.

Therein lies the question: how can organizations reap the benefits of sustainable AI while ensuring that the energy required to do so does not do more harm than good?

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register here Realizing the promise of sustainable AI

Without the help of technology, defining sustainable development goals would be a limited and difficult exercise. Businesses today struggle to quantify the risk of climate change, especially when it comes to digital transformation. In fact, only 43% of global leaders say they are aware of their organization's IT footprint. Data analytics and AI offer a solution to this challenge, as they provide meaningful insights across industries to understand where these gaps exist and thus can help businesses adopt more sustainable practices.

For example, organizations can create systems such as insight dashboards, data hubs to bring together structured and unstructured climate data, and benchmarks to comprehensively understand the technology landscape and assess areas of interest. This way, leaders can determine where they need to reduce their climate efforts to achieve more impactful results.

There are several use cases where predictive analytics and AI are advancing sustainability initiatives, spanning multiple sectors, including:

Net zero banking, which uses a global ESG data store to increase the frequency of ESG monitoring and reporting, embedding ESG at the heart of products and services. A satellite-based system for agriculture and agriculture enables remote assessment of farm capacity and uses machine learning to provide information - such as yield forecasting and soil quality analysis - that can help farmers improve their crop. A unified carbon dioxide data and analytics platform spans end-to-end supply chain and logistics for automakers to support data, analytics, automation, and logistics. 'AI through required business capabilities, as well as support for future reporting and analytics. In the energy and utilities sector, this technology can estimate...