AI is set to grow from a $153B market in 2023 to a projected $3.6T by 2033. But speed without stewardship is a risk. Training and running models are resource intensive processes, and poorly governed systems can amplify bias with models and erode trust among users. The next decade will be defined by whether sustainable principles are built into AI’s design, infrastructure, and oversight.

Executive Summary 

In just one decade, the global AI market is set to surge from USD 153 billion in 2023 to USD 3.6 trillion by 2033, at a remarkable 37% CAGR. Behind this meteoric growth lies a broader imperative. AI’s rapid rise must align with ESG imperatives. As AI shapes sectors from healthcare to energy, enterprises face critical questions about energy consumption, carbon emissions, social equity, and corporate governance. Embedding responsible design, sustainable AI infrastructure, and ethical oversight from the earliest stages will determine whether AI becomes a force for genuine transition or a technology impeded by unsustainable paths.

A Crossroads of Innovation and Responsibility

For the first time in history, we are building machines that not only calculate, but also understand. They parse language, interpret images, and draw inferences from oceans of data. 

While breathtaking, this breakthrough comes with a hidden price. In 2023, the AI market stood at USD 153 billion. By 2033, it is projected to exceed USD 3.6 trillion. Yet behind this exponential growth lies an insatiable appetite for resources. Training and running these systems consumes colossal amounts of electricity, strains freshwater reserves for cooling, and creates new vulnerabilities for society. 

The International Energy Agency warns that the electricity demand of data centres could more than double by 2030, with AI-specific servers alone expanding at an annual rate of 30%. The story of AI, then, is not just about intelligence. It is about responsibility and whether we can learn to govern this synthetic intelligence in a manner that safeguards our planet’s future through sustainable AI practices. 

The Environmental Weight of AI

Data centres today already account for 1.5% of global electricity consumption. While some projections exaggerate the long-term energy consumption of AI systems, data centres could still account for nearly 3% of global energy demand by 2030. Moreover, training large language models (LLMs) requires significant volumes of water for cooling and rare materials for hardware. Sustainable AI practices thus become imperative in the face of these ESG challenges. This means rethinking models, infrastructure, and energy sourcing. The approach requires optimising hardware and software design, adopting green energy, and reshaping compute to reduce water and carbon footprints.  

Social and Governance Imperatives in AI’s Rise

AI is not just a black box. It is now ingrained into our everyday life. This has profound implications. Irresponsible AI deployment can reinforce bias, erode privacy, and deepen inequality. Governance, therefore, becomes a non-negotiable fulcrum. Yet, while 70 of every 100 companies today leverage AI in at least one function, only 20 have formal policies for sustainable AI deployment aligned with ESG principles. This is a curious statistic, given AI is playing a transformative role in ESG initiatives. It powers tools that can monitor air quality, manage emissions, optimise logistics, measure labour conditions in supply chains, and support real-time sustainability reporting.

Market Dynamics Within an ESG Frame

Machine learning has become the beating heart of artificial intelligence. It is the invisible force driving sales algorithms that score leads, set prices, and predict customer churn. It is also the same force that is now poised to revolutionise healthcare. (See: AI in Energy – From Hospitals to Power Plants)  

Yet, if these systems value bottom-line optimisations at the neglect of environmental considerations, their long-term value will corrode. 

That is why the most consequential sectors, primarily energy, healthcare, and finance, must weave ESG principles into their AI strategies from the outset. This is not about corporate checklists, but about safeguarding our future generations. Will AI be deployed to deepen inequality and accelerate emissions, or will it become a tool for sustainability and resilience? Already, generative AI and machine learning are helping energy firms model carbon pathways and redesign operations. In oil and gas, for instance, sustainable AI is no longer just a profit tool; it is becoming part of the race to decarbonise. (See: How AI is Powering the Path to Net Zero in Oil & Gas) 

Can AI Deliver on ESG?

Over the next decade, AI will define a new way of life. Whether it does so sustainably depends on the choices today. The forecasted $3.6 trillion expansion could fuel breakthroughs in healthcare, climate modelling, smart cities, energy, and more. Yet unchecked, it risks swamping grids, consuming scarce water, amplifying inequality, and undermining trust. The convergence of AI with ESG is not an afterthought. It is the only path to ensure AI becomes a constructive, sustainable force.

The story of AI’s next decade must be written not just with dollars and data, but with an abiding commitment to environmental and social stewardship.