AI’s impact on sustainability

By 2029, IDC projects that the number of actively deployed AI agents will exceed 1 billion worldwide, which is 40 times more than in 2025. And these agents will perform 217 billion actions per day. To deliver on this demand, AI data centers are being built out at an unprecedented rate, with Gartner forecasting that global spending on data centers over the next three years will increase 31. 7% to surpass $650 billion, driven primarily by hyperscaler cloud providers building out AI foundations, and optimizing servers for heavy AI workloads. All this presents a significant strain on the energy grid as well as environmental sustainability, including: The power double-down: The International Energy Agency (IEA) projects that global data center electricity consumption will more than double from about 415 to 945 TWh by 2030, primarily fueled by energy-intensive accelerated computing for AI.

The inference premium: AI workloads are vastly more demanding than standard web activities. A gen AI query consumes roughly 10 times the electricity of a conventional keyword search, or roughly 2. 9 watt-hours as opposed to 0. 3 watt-hours. Water consumption: Cooling these dense clusters is highly resource intensive.

Global AI-related water demand is expected to reach 4. 2 to 6. 6 billion cubic meters by 2027. The good news, however, is it’s not all out of the control of end user organizations and CIOs. Just as in the client-server era, through careful planning and execution, CIOs have the potential to significantly improve the performance, costs, and sustainability impacts of their AI application portfolio.

Here are four recommendations to maximize value as you look across your AI applications and infrastructure estate.