%20(1).png?width=725&height=725&name=Preparing%20Your%20Data%20for%20AI_%20A%20Strategic%20Guide%20for%20Future-Ready%20Enterprises%20%2011-(Compressify.io)%20(1).png)
%20(1).jpg?width=750&height=240&name=Preparing%20Your%20Data%20for%20AI_%20A%20Strategic%20Guide%20for%20Future-Ready%20Enterprises%20%2013-(Compressify.io)%20(1).jpg)
Sustainability covers a broad range of interconnected issues ranging from deforestation to farmers’ livelihoods to making a profit. To tackle the challenges posed by sustainability, technology companies have developed tools that are sophisticated enough to help us become more aware of our impact on the planet and the people affected by our business practices. To help us process all of the data those tools make and turn that data into actionable insights, we use AI.
Gen AI is particularly useful, given how it can learn from vast amounts of data and become able to create projections of the future. For example, imagine having to manage a fleet of trucks and determining their best routes in terms of fuel efficiency and customer satisfaction. Given high-quality training, a Gen AI model optimize supply chains, maximize resource utilization, and enhance particular functions that propel transformative change.
However, training and implementing Gen AI requires a lot of energy. According to a study by researchers at the University of Amherst in Massachusetts, training a single model could produce over 280 metric tons of CO2. That was way back in 2019 — and it didn’t cover the carbon footprint of using the model. Given how AI models are much more powerful and popular now than back then, we can safely presume that AI is emitting way more carbon dioxide than it did before. Side note: We don’t have new figures because tech companies don’t disclose the carbon footprint of their AI solutions and there are currently no standardized methods for attributing emissions to AI training and utilization.
Other environmental issues, namely the consumption of large quantities of water to cool down servers, the impact of extracting the precious metals needed for hardware manufacturing, and the disposal of burgeoning hardware waste must also be addressed.
AI as a technology could be made much cleaner. When it comes to reducing carbon emissions, the main direct method identified so far is to shift away from power generators that use fossil fuels towards generators that draw energy from renewable sources. This is the general shift that entire nations are undertaking, so emissions reductions become a matter of where AI draws power from.
Another effective emissions reduction method is an indirect one: to use AI for increasing energy efficiency. For example, AI could be used to discern electricity consumption patterns to identify how energy should be distributed and pinpoint areas where electricity is transmitted with too much loss or is needlessly consumed. AI could also factor in how the cost of electricity changes throughout the day so that it could generate cost savings, too.
As a tool for solving complex problems, AI can be used to drive many sustainability initiatives, such as:
- Optimizing delivery routes to minimize fossil fuel consumption.
- Developing more eco-friendly materials.
- Minimizing packaging.
- Optimizing crop production while minimizing the use of pesticides and herbicides.
Generally, increasing efficiencies and reducing waste could lead to significant cost savings, greater competitiveness, and a healthier bottom line. Learn more by reading On AI’s impact on sustainability by The Manila Times (gated) and by visiting our Sustainability page.
