AI is becoming increasingly integrated into day-to-day operations across industries - from customer service and logistics to finance and product development. Most discussions about AI focus on its power and potential. But the environmental cost is just as important. Training a single large-scale model can generate as much carbon as several cars over their entire lifespan.

In addition to emissions, data centres consume enormous amounts of water for cooling, and hardware is being replaced at an accelerating pace as systems become outdated. For instance, training large AI models alone can require lakhs of litres of water, adding to the environmental toll.

Much of this impact is not visible to the user, and is rarely explained by technology providers. Unlike airline bookings, where carbon ratings are now common, there is no equivalent 'CO₂ label' for AI queries. As a result, users are increasingly relying on AI for even the simplest tasks, unaware of its hidden environmental footprint. Generating a 'thank you' message using generative AI may consume as much energy as running several Google searches. The system still processes it like any complex query. These invisible costs reinforce the need to use AI judiciously, especially where simpler tools would suffice.

'Green AI' refers to ongoing efforts to reduce the environmental impact of AI systems. Research so far has demonstrated that efficiency improvements, particularly during the model training phase, can yield energy savings between 13% and 115%. But training is just one part of the equation. There remains considerable scope to improve efficiency during deployment and inference, as well as in the infrastructure that supports AI workloads.

Methods like pruning, knowledge distillation and low-precision computation are being explored as ways to lower energy use while maintaining performance. In addition to model-level improvements, practical steps like scheduling compute tasks during off-peak energy hours, or selecting more efficient hardware, can also contribute to lower consumption. Even individual decisions like choosing simpler AI queries when possible, or relying on local models instead of cloud-based ones, can make a difference.

The infrastructure powering AI - particularly data centres - is one of its most significant environmental touchpoints. These facilities require vast amounts of energy to run high-performance computing systems and maintain optimal temperatures, making them a key area for emissions reduction.

Improving data centre efficiency can yield immediate benefits. Organisations are increasingly adopting advanced cooling technologies, server virtualisation and dynamic power management to reduce energy consumption. The physical location of data centres also plays a role. Facilities situated in colder climates naturally require less energy for cooling, contributing to lower overall emissions. Also, real-time monitoring through data centre infrastructure management (DCIM) tools allows operators to track performance, detect inefficiencies and make data-driven adjustments. Migrating AI workloads to cloud platforms that are designed for energy efficiency and powered by renewable sources offers yet another impactful strategy to prioritise sustainability.

For those relying on AI-powered tools in daily life - from digital assistants to automated recommendations - there is value in recognising that every interaction travels through this vast physical infrastructure. Being mindful of frequency and necessity, just as we are with energy use at home, can complement broader sustainability efforts.

Infrastructure upgrades and more efficient algorithms are important, but are only part of the equation. Broader operational strategies, like structured energy management systems, defined reduction targets and regular audits, are essential. Tools like IoT-enabled monitoring and internal training programmes can help integrate these practices into daily workflows.

Some organisations are already aligning cloud infrastructure decisions with sustainability objectives, and embedding ESG considerations into how AI systems are developed and deployed. As adoption continues to scale, there is a growing need for consistent benchmarks. Including data points such as emissions from model training, infrastructure-related energy use and hardware lifecycle management in sustainability reporting can offer a more accurate picture of AI's environmental footprint.

Greater transparency around the environmental impact of everyday AI use can empower people to engage with the technology more thoughtfully, rather than relying on it by default.