The environmental impact of AI extends far beyond direct energy consumption. Critical factors such as water use, hard-to-track supply chain emissions, e-waste, and hidden lifecycle costs are routinely overlooked. Compounding the issue is a distinct lack of transparent, standardized reporting from technology vendors, which makes it difficult to calculate the carbon footprint of individual AI models. To achieve sustainable AI adoption, organizations must shift toward demanding comprehensive vendor transparency and adopting holistic measurement frameworks that integrate sustainability into their core business strategy.

Accurately measuring the environmental footprint depends heavily on the complexity of the AI model, including its size, parameters, volume of training data, and computational requirements. Organizations frequently use an aggregate approach to evaluate the carbon footprint as a subset of the overall IT footprint, benchmarking metrics like power usage effectiveness (PUE), water usage effectiveness (WUE), and IT equipment utilization. While this provides a high-level view, pinning down the footprint of individual models remains challenging due to the lack of detailed data from vendors.

To better capture this complexity, newly developed model-specific methodologies are emerging to quantify the lifecycle impact by breaking it down into component parts, including hardware, software, data lifecycle, water use, and energy consumption. These can be supplemented with software-based emission tracking tools and AI energy scores from organizations like Hugging Face and the Green Software Foundation. According to Autumn Stanish, director analyst at Gartner, these tracking solutions are rapidly improving. “Where possible, prioritize the use of component-based measurements, as that is the most accurate methodology for measurement,” Stanish noted.

Beyond technical metrics, social pushback has emerged as one of the largest barriers to effective AI deployment. Countries like the United States, England, the Netherlands, and Ireland have faced community boycotts over AI data center growth plans due to concerns regarding grid stability and local water availability. In response, operators are being urged to integrate social equity considerations. This includes implementing heat recovery systems to warm nearby buildings, investing in water recycling initiatives, and partnering with local recyclers to minimize electronic waste. Additionally, investing in new solar or wind farms helps local grids access cleaner power, ensuring that vulnerable populations are not left behind.

Embedding sustainability into an AI strategy requires leaders to focus heavily on model efficiency. Designing energy-efficient systems, such as sparse architectures, drastically cuts computation requirements. Furthermore, leveraging pre-trained, specialized models rather than massive general-purpose large language models (LLMs) can deliver identical functionality at a fraction of the environmental cost. Organizations must also evaluate cloud versus on-premise infrastructure deployment options on a case-by-case basis to align technological innovation with long-term environmental resilience.

The post Gartner Warns AI Could Account for 50% of IT Greenhouse Gas Emissions by 2028 appeared first on Brickinfo News Agency.