AI is no longer just a tool for progress, it’s fast becoming a test of responsibility. As South African companies race to harness artificial intelligence for innovation and growth, few are asking the most critical question − the environmental cost. Behind every breakthrough model lies a surge in electricity demand, water use and carbon emissions, realities that can no longer be dismissed as side effects.
This is not a future problem, it’s a now problem. AI is expanding rapidly and without urgent intervention, its environmental footprint could outpace the very benefits it promises. In a country already grappling with grid instability and water scarcity, South African boardrooms can’t afford to treat AI sustainability as an afterthought. If we’re serious about building a digital future, we must ensure it’s one the planet can sustain.
Across the globe, generative AI is expanding at a blistering pace, bringing with it massive computing demands, energy surges and water-intensive data centres. Our latest research estimates that by 2030, AI workloads could consume more than 600 terawatt-hours of electricity annually. That’s equivalent to the energy used by hundreds of millions of homes. More troubling still, the water required to cool data centres could reach crisis levels, especially in regions that already face water scarcity. In South Africa, we are already living with the twin pressures of unreliable electricity and severe water stress. Meanwhile, local enterprises are racing to adopt AI without asking how sustainable this growth is.
The uncomfortable reality is that if we don’t change course now, AI will push us closer to climate instability even as it helps us solve other problems. It’s the ultimate contradiction, using a future tool with a 20th-century energy model. This contradiction must be resolved, and it starts with accountability. Every South African organisation that embraces AI must do so with full awareness of the environmental cost and a commitment to minimising it.
At Accenture, we’ve developed a pragmatic solution to this challenge, the Sustainable AI Quotient (SAIQ). It’s not just another ESG checklist. It’s a performance framework that allows companies to evaluate AI investments against four critical thresholds: financial return, energy usage, water dependency and carbon emissions. In other words, it’s a 360-degree view of AI’s impact, designed to ensure that growth doesn’t come at the planet’s expense. But more than that, it’s a governance tool. It allows CIOs, sustainability heads and even regulators to track and manage the environmental efficiency of AI across its lifecycle from design to deployment.
If you’re leading an organisation in South Africa today, you should be asking where your AI models are being trained, what energy sources power your data centres, whether you are overtraining models for marginal gains and whether you understand the carbon footprint of your digital infrastructure. If those questions aren’t on your radar yet, they need to be urgently, because the hidden cost of AI is fast becoming the next frontier in corporate accountability.
Fortunately, the solutions are within reach. Start with smarter silicon. New computer architectures like Processing-In-Memory (PIM) and Compute-In-Memory (CIM) can dramatically reduce the energy intensity of AI operations, instead of constantly shuttling data between memory and processing units, an energy-hungry exercise. These chips perform computations directly within memory, cutting power consumption significantly. That’s not a technical detail, it’s a sustainability breakthrough.
Then consider the geography of your data. AI workloads must be located where clean, affordable energy is available. That might mean shifting some operations to regions with high solar penetration or hydro capacity. In South Africa, we can’t afford to run advanced AI models on coal-fired power. It’s inefficient, expensive and reputationally risky. The next generation of competitive advantage will come from clean computing.
There’s also the matter of design discipline. Too many organisations fall into the trap of experimentation for its own sake, running endless AI model iterations that consume resources without yielding proportional value. It’s time to apply restraint. Use AI where it matters. Train models with purpose. Avoid redundant data cycles. This is about thoughtful innovation, not performative digitalism.
Finally there is governance. Sustainability in AI cannot be a bolt-on consideration. It must be written into the code. That means deploying governance-as-code frameworks that automate sustainability guardrails, monitor energy thresholds in real time and flag violations before they spiral. It means giving your IT and sustainability teams a common language and the tools to enforce it.
South African companies have an opportunity and a responsibility to lead in the design of responsible AI. Our energy grid is fragile. Our climate is under strain. Our water resources are finite. But we also have some of the world’s most creative technologists, a growing green finance movement and an emerging generation of sustainability-savvy consumers. We can be the continent that shows how to scale AI responsibly, equitably and profitably.
The AI decisions we make in the next 24 months will determine whether we lock in a high-carbon future or build the foundation for sustainable digital transformation. South African businesses must partner with experts who understand both sides of the equation innovation and impact. Only those who balance the promise of AI with the principles of sustainability will be truly future-ready.
For more information contact Jonathan Mahapa, Accenture South Africa,
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