The promise of sustainable AI may not overcome 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 in 2025, of which 75% will be connected to an IoT platform – a change that has posed a significant environmental challenge for organizations. The increased demand for data storage and computing power has raised many questions about their sustainability efforts and raises the question: How can businesses leverage and deploy artificial intelligence? (AI) and other smart technologies without increasing their carbon footprint?

There are two aspects to analyzing the intersection between digital transformation and sustainability. First, it is important to understand how AI can be used to address sustainability challenges. In addition, it is necessary to ensure that the subsequent use of such AI technology and machines does not expand the company’s carbon footprint.

Deep learning algorithms require a large amount of power when they analyze data. Left unchecked, this could be a vicious cycle in which, at the same time, AI techniques are being used to identify potential environmental hotspots while the machines themselves consume large amounts of electricity. huge – thus offsetting the positive impact.

The question is: How can organizations reap the benefits of sustainable AI while ensuring that the energy required to do so doesn’t do more harm than good?


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Realizing the promise of sustainable AI

Without the help of technology, mapping out sustainability goals would be a limited and difficult exercise. Businesses today struggle with quantifying the risks of climate change, especially when it comes to digital transformation. In reality, only 43% of global executives 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 those gaps exist and can therefore help companies combine more sustainable methods.

For example, organizations can build systems like insights dashboards, data centers to collate structured and unstructured climate data, and benchmarks to understand the big picture. technology and assessment of focus areas. This way, leaders can identify where they should narrow their climate efforts to achieve more impactful results.

There are a number of use cases where predictive analytics and AI are expanding sustainability initiatives, covering several industries, including:

  • Net zero banking, using a global ESG database to increase the frequency of ESG monitoring and reporting, integrating ESG as a core part of products and services.
  • Satellite-based systems for farming and agriculture that allow remote assessment of farm capabilities and use machine learning to provide insights – such as yield forecasting and soil quality analysis – can help farmers improve their crop cultivation.
  • A unified carbon dioxide data and analytics platform covering end-to-end supply chain and logistics for automakers to support data, analytics, automation, and AI through business capabilities needed, as well as support future reporting and analysis.
  • In the energy and utilities sector, this technology can estimate peak demand in advance, which will provide end customers with a reliable and uninterrupted power supply.

Prevent further emissions from using AI

From research 89% of organizations recycle less than 10% of their IT hardware. However, if a company is to truly achieve all the environmental benefits of sustainable AI, then IT must play an important role in using this technology as the organization’s biggest helper. position, not its opponent.

There are four broad areas of offset sustainable impact of machines and AI technology: reporting, cloud, circular economy, and encryption.

Accurate metrics and reports keep the AI ​​system intact and constantly improving, while the cloud promotes sustainability as users only pay for the infrastructure per use, discarding the need to run data centers at full threshold.

Additionally, investing in building a circular economy by reducing, recycling, and reusing product waste directly reduces carbon emissions and opens the door to better cryptographic methods. . By identifying code inefficiencies and identifying better coding methods – using DevSecOps with the ESG add-on – organizations can visualize the “before and after” impact of coding changes and how they directly impact carbon emissions.

Overcoming obstacles

While more and more businesses are realizing that they can use AI to achieve their sustainability goals, there is still a significant way to go until this becomes mainstream practice.

A significant obstacle organizations find it difficult to measure their IT carbon footprint, as many IT development teams still lack access to the necessary tools and standard measurements. Any digital interaction – such as email or data sharing – has a carbon cost, but many companies don’t track these touchpoints.

In addition, implementation remains a major challenge for sustainable IT, with more than 53% of organizations Report that they do not have the necessary expertise to set up green infrastructure. This led to concerns that a sustainable IT implementation could negatively impact the overall business and its security measures.

For businesses looking to grow and scale, the right kind of data is essential to derive meaningful insights and enable better decision-making. Advanced AI and data analytics can help bring together disparate data sources – both structured and unstructured – to connect the focus points of environmental and sustainability efforts. Organizations can use the data to assess gaps in their climate risk scenarios and build iteratively better models for calculating greenhouse gas emissions.

Strong business leaders will capitalize on the benefits AI offers while taking the necessary steps to mitigate its increased risks – but it’s a job that must be done. Building an eco-friendly business is the clear calling of our time, with sustainable IT serving as the backbone of a greener future.

Dharmesh Mistry is Vice President, Head of Technology Markets at Capgemini Americas.


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