Enterprise AI is a double-edged sword for sustainability.
On the positive side, AI can help organizations achieve sustainability goals such as managing and reporting carbon reductions, promoting sustainable design of buildings and products, developing climate modeling tools, and implementing waste and pollution control measures. Helpful.
However, the growth of enterprise AI also brings sustainability challenges, such as the enormous amounts of energy and water resources required to develop and operate AI applications, including large-scale language models (LLMs).
In this Q&A, Kumar Parakara, Founder and President of GHD Digital, a global consultancy providing advanced analytics, AI data management, and cybersecurity services, discusses the duality of AI in the enterprise.
Editor's note: This Q&A has been edited for clarity and brevity.
What is the role of AI in corporate sustainability efforts?
Kumar Parakala
Kumar Parakala: AI will play a huge role in the field of sustainability. AI investment in sustainability is growing, and companies such as hydro energy companies are already developing effective strategies to leverage AI to digitize and decarbonize their energy supply. Most of them have been rapidly advancing AI projects in the past 12-18 months. The benefits and use cases we are seeing in the sustainability space are around AI-based insights, such as providing recommendations on energy consumption patterns, usage and generation, and how to make facilities more efficient.
Do these use cases primarily use traditional AI, generative AI, or both?
Parakala: Traditional AI has been around for a long time. We're talking about how GenAI can be applied. With GenAI, you can input large amounts of data, such as documents and patterns, and develop models to gain insights. For example, sustainable design is one field that creates a sustainable and resilient built environment. Its build environment started with the design process, allowing GenAI to optimize designs during the concept stage. This not only keeps the look balanced, but also makes it more energy efficient, less wasteful and more purpose-built.
Are these sustainable built environments used in building structures and products?
Parakala: Energy facility design, public works design, automotive design, etc. For example, major car companies can design cars in locations with lower carbon footprints. Therefore, sustainable design principles can be applied to a variety of scenarios, and GenAI can help.
[With GenAI,] During the design process, you can visualize what will go into the structure, how it will be built and how it will operate, and simulate it to come up with a more sustainable design. Kumar ParakalaGHD Digital Founder and President
So how and what are some other concrete use cases for GenAI?
Parakala: Let's say you're designing a building. There's the structural design, there's the materials used in that building, and there are aspects of the design that make that structure more energy efficient. You can simulate all of this with on-the-fly modeling and then use GenAI. What happens with GenAI is that you have all the data and over time the machine learns from that data and makes improvements. The quality of that data.
[With GenAI,] During the design process, you can visualize what will go into the structure, how it will be built and how it will operate, and simulate it to come up with a more sustainable design.
GenAI for climate modeling is another example, predicting what will happen and how it will affect the climate based on climate scenarios such as greenhouse gas emissions and deforestation rates. Compared to the past, you can now make a real-time impact. When we had AI, it still took time, but now we can do this climate modeling in real time.
Water resource management and energy optimization are two other areas that we believe are directly relevant to the use of AI in sustainability. Another area is pollution prevention and waste management. This is a major problem in Asia and other parts of the world where the amount of waste generated from various industrial sources is increasing. So how can we translate this into greener manufacturing, smarter recycling, and more efficient waste separation? AI has a role to play in all of these and the entire ecosystem.
That's all on the positive side. What are the downsides to sustainability? What are the challenges with increasing AI adoption?
Parakala: Today's energy usage issues are purely speculative, but based on trends, they could become a reality within the next three to five years. What we're seeing is that AI today is not as energy-intensive as crypto mining, which is a very energy-intensive field. However, the large increase in high-performance data centers built by Google, Amazon, and Microsoft is likely to increase energy usage over the next three to five years.
But it's not just an energy issue, it's also a water issue. Data centers require pure water, but the moment they use that water, they are competing with farmers, agricultural workers, and others for that water. In other words, energy and water [sustainability] This may affect the results. Although not significant at this time, ESG-friendly advocates are encouraging the construction of green data centers because people believe it could be a risk.
Are there ways other data centers can address the issue of making AI more sustainable?
Parakala: There is a recommendation to use more AI-enabled energy efficiency models and tools. Optimists argue that the benefits of AI far outweigh the energy it consumes, and that efficient design and application of AI will result in savings. I think technology has advanced a lot and is becoming more and more efficient. Future chips are becoming more efficient.That innovation will continue, with GPUs [more powerful]This means it consumes less energy to produce and deliver the same type of results. Therefore, advances in chip design, advances in how LLMs operate, and increased use of the energy sources that power data centers – solar and other energy sources – will all lead to more ESG-friendly outcomes in the long run. You will make a contribution. .
Jim O'Donnell is a senior news writer for TechTarget Editorial, covering ERP and other enterprise applications.
Kumar Parakala
