Unveiling the carbon code: Reimagining AI integration for a sustainable future

Green coding practices enable researchers to craft more efficient algorithms

The interest in artificial intelligence (AI) has grown rapidly in recent years, not least with the development and launch of OpenAI's ChatGPT, investment from Microsoft and competitor tools such as Google Bard.

There has also been a marked increase in research, and development and adoption of AI by businesses has been rapid. If the IBM Global Adoption Index 2022 is anything to go by, then a staggering 35% of organisations have already adopted AI in their daily business operations, with an additional 42% of organisations reporting to be actively exploring various AI options that are right for them.

This drive to supercharge operations and fortify a competitive edge through leveraging AI is propelling organisations into a bold new era of work - so much so that a growing number of businesses are contemplating appointing Chief AI Officers, who will take on the role of leveraging AI and creating new opportunities for their organisation.

However, as AI drives a way forward for businesses, a pivotal question emerges: What is the environmental price tag of embracing AI, and do businesses' AI aspirations align with their environmental, social, and governance (ESG) commitments?

The ecological impact of AI

The interplay between AI and corporate social responsibility (CSR) is an intricate one. On the one hand, AI has the potential to help businesses to grow and innovate.

However, as highlighted in a recent Gartner Report, the energy consumption of AI could surpass that of the human workforce as soon as 2025, undermining net zero efforts.

But with the continued focus on the potential benefits of AI, and without clear metrics on the environmental impact, it is often perceived as being resource neutral, if it's considered at all. Transparency from researchers and data providers is crucial for quantifying and highlighting the environmental impact of AI.

Cloud companies are at least starting to pay attention to sustainability and trimming their carbon footprint. For instance, Google plans to operate its data centres entirely on carbon-free energy by 2030, whilst AWS is set to run on 100% renewable energy by 2025.

Microsoft indicates that businesses migrating to the cloud can double carbon efficiency.

Cloud computing is energy intensive but efficiencies can be found. Training AI models can also use a huge amount of energy, but again there are ways to reduce this.

Balancing ESG goals and keeping up with new innovations

The roadmap to seamless AI sustainability lies in harnessing lower carbon computing methods, including Green Coding.

Green Coding practices enable researchers to craft more efficient algorithms. With concise, robust code, the demand for datacentre power and storage is decreased. Many businesses have already adopted these principles - and with good reason. Green Coding reduces the pressure on datacentre resources, but also it elegantly aligns software development with a business's sustainability objectives, cutting energy consumption along the way.

Datacentres can employ other approaches to more energy-efficient AI, for example, switching to specialised processors fine-tuned for machine learning training, rather than generic devices. This improves both performance and energy efficiency by a substantial amount. Even the currently used GPUs can be optimised for more energy-efficient use.

Conclusion

Without a doubt, AI will help propel those businesses forward who are able to rapidly explore and embrace the technology, reshaping operations along the way. However, due consideration must also be paid to the impact this technology has on the environment.

Organisations adopting AI must also consider the emissions behind its development and subsequent usage. It is an encouraging start that certain avenues already exist, which help quantify and reduce the environmental impact of powering AI, such as Green Coding.

The objective here is not to hinder the progress of innovation, but to channel it towards a more positive direction across all aspects of the corporate world, including ESG obligations. Achieving this calls for collective action, whereby a trusted sustainable digital transformation partner can help utilise AI, but also amplify endeavours to combat the climate challenge.

Simon Thompson is head of AI, ML & data science at GFT

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