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, of which 75% will be connected to an IoT platform — a shift that has presented a significant environmental challenge for organizations. The increased demand for data storage and computing power has many questioning their sustainability efforts and raises the question: How can enterprises leverage and implement artificial intelligence (AI) and other smart technology without growing their carbon footprints?

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 solve sustainability challenges. Additionally, there is a need to ensure that the use of that AI technology and machinery is not subsequently expanding the company’s carbon footprint.

Deep learning algorithms require a colossal amount of power when they analyze data. If left untouched, this could be a vicious cycle where, simultaneously, AI techniques are being used to identify potential environmental hotspots while the machines themselves consume huge amounts of power — thereby offsetting the positive impact.

Therein lies the question: How can organizations reap the benefits of sustainable AI while ensuring that the energy needed to do so is not doing more harm than good?

Realizing the promise of sustainable AI

Without help from technology, outlining sustainability goals would be a limiting and difficult exercise. Enterprises today struggle with quantifying the risk of climate change, especially when it comes to digital transformation. In fact, 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 thus can help companies incorporate more sustainable practices.

For example, organizations can build systems such as insights dashboards, data hubs to collate structured and unstructured climate data, and benchmarks to understand the technology landscape holistically and assess areas of focus. This way, leaders can pinpoint where they should narrow their climate efforts to achieve more impactful results.

There are several use cases in which predictive analytics and AI are scaling sustainability initiatives, spanning several industries, including:

    Preventing further emissions from AI usage

    Research shows that 89% of organizations recycle less than 10% of their IT hardware. However, if a company is to truly reap all the environmental benefits of sustainable AI, IT must play a crucial role in using this technology as the organization’s biggest helper, not its adversary.

    There are four broad areas that offset the sustainability impact of AI machinery and technology: reporting, cloud, circular economy, and coding.

    Accurate metrics and reporting will keep the AI systems intact and constantly improving, while cloud promotes sustainability because users only pay for the infrastructure per use, eliminating the need to run data centers at full threshold.

    Additionally, investment in building circular economies by reducing, recycling and re-using product waste directly decreases the carbon footprint and opens the door for better coding practices. By identifying code inefficiencies and defining better coding practices — using DevSecOps with an ESG add-on — organizations can visualize the “before and after” effects of coding changes and how they directly impact carbon footprint.

    Overcoming the hurdles

    While there is a growing awareness among enterprises that they can use AI to achieve sustainability goals, there is still a significant way to go until this becomes mainstream practice. 

    One substantial hurdle is that organizations find it challenging to measure their IT’s 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 do not track these touchpoints. 

    Additionally, implementation is still a major challenge for sustainable IT, with over 53% of organizations reporting that they do not have the required expertise to set up green infrastructure. This results in concerns that deploying sustainable IT could negatively impact the overall business and its security measures.

    For enterprises 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 various data sources — both structured and unstructured — to connect the dots of where to focus sustainability and environmental protection efforts. Organizations can use data to assess gaps in their climate risk scenarios and iteratively build better models to compute greenhouse gas emissions.

    It will be up to strong business leaders to capitalize on the benefits AI has to offer while taking the necessary steps to mitigate its added risks — but it is a job that must be done. Building an environmentally friendly business is the clarion call of our times, with sustainable IT acting as the backbone of a greener future.

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



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