IT wasn’t long ago when the commercialization of artificial intelligence (AI) was simply a digital assistant that one could talk to their internet-connected smart gadget to fix appointments or ask the title of a song that they had never heard of blaring from the car radio.
Today, AI and its evolutionary component, generative AI (GenAI), have been a major part of modern technology. Like the internet, it seems to be embedded in everything everyone does nowadays — from information gathering for educational purposes to scientific studies to include medical and quantum physics research, space exploration and even financial management, where the latter may also mean banks don’t want to interact with their customers anymore. Hence, no matter how good or terribly bad the customer experience, clients, from hereon, could only talk to the bank’s AI.
Although AI is actually not new, particularly in both worlds of science and fiction, it is only recently that the technology has gone mainstream. Thanks, in part, to OpenAI for bringing ChatGPT to the masses — well, at least to those who downloaded the app on their mobile phones and asked the AI to create a haiku. No longer confined to science fiction books or movies, AI is here. And the debate is on: all about its very existence and, of course, its impact on the environment.
The energy to operate AI machines is as big as man’s insatiable taste for knowledge. CONTRIBUTED PHOTO
In a recent post on TechUK.org by Preeti Garg, director of consulting at VE3, titled “Revolutionizing Green Data: The impact of generative AI on sustainability,” she describes AI as “framed within the context of robotics, automation or even as the backbone of futuristic sci-fi narratives.”
She adds that “generative AI, a branch characterized by its ability to create and simulate, is now stepping into the spotlight with a mission that resonates deeply in our current times, elevating the value of sustainability data.”
She further says: “Generative AI doesn’t just crunch numbers; it transforms them into a narrative, turning raw data into actionable insights that can drive more informed, sustainable decision-making. This technological marvel is reshaping how we approach environmental stewardship, proving that the right data in the right hands can, indeed, make a world of difference.”
AI as a solution
There is no question that AI has an impact on the environment. The energy to operate these powerful machines is as big as man’s insatiable taste for knowledge, so to speak. So, people often ask, how will AI impact sustainability in general?
“To truly appreciate the impact of generative AI on sustainability, it’s essential first to grasp what it entails,” says Garg in her TechUK post. “At its core, generative AI is a subset of artificial intelligence that goes beyond analysis and into the realm of creation and simulation. Unlike traditional AI, which interprets and learns from existing data, generative AI can generate new data, scenarios and models.”
“The backbone of generative AI lies in advanced machine learning techniques, particularly neural networks,” Garg says. “These are sophisticated algorithms modeled after the human brain, capable of learning from vast amounts of data. This continuous learning enables generative AI to interpret data and project future scenarios and outcomes, making it invaluable for sustainability.”
This is evident in many companies, which can benefit from interpreting and utilizing sustainability data. “Converting raw data into actionable insights enables a deeper understanding of environmental impacts and potential solutions.”
“In a broader context, AI and generative AI stand to significantly impact sustainability by bolstering sectors [such as] resource management, renewable energy utilization and supply chain optimization,” says Juan Luis Ng, operation head at Xinyx Design, a pioneering company in the field of integrated circuit design and consultancy services based in the Philippines.
Ng adds, “Their influence extends to forecasting and enhancing various sectoral functions, driving transformative progress in sustainability initiatives through strategic allocation of efforts for maximal efficacy.”
According to the Xinyx executive, this generation of AI not only improves tools but also offers direct benefits when implemented across the entire workflow. This holistic integration ensures clients and customers experience enhanced efficiency, productivity and innovation throughout their processes. In fact, industry-standard electronic design automation (EDA) tools have already begun adopting AI, providing tangible benefits to customers.
“As the industry increasingly integrates AI into EDA tools, our company is actively transitioning to AI-enabled solutions both internally and with our clients,” says Ng. “This migration enhances in delivering high-quality products to market swiftly, aligning closely with client needs and expectations.”
AI and GenAI are indeed next-generation advanced technologies that can bring advantages and opportunities to enterprises to help them address the challenges posed by sustainability toward a net-zero future.
“In the case of energy efficiency, AI can help optimize energy use in various systems from large-scale power grids to individual buildings,” says Ramesh Songukrishnasamy, senior vice president and chief technology officer at HID, an American manufacturer of secure identity products. “By predicting energy demand and adjusting supply accordingly, AI can reduce waste, improve the efficiency of renewable energy sources and help manage energy storage solutions.”
Songukrishnasamy adds that AI can also help in “more sustainable resource management by optimizing routing for transportation and logistics, hence reducing fuel consumption and emissions. In manufacturing, AI can streamline processes to minimize resource waste and improve product lifecycle management.”
Organizations can turn to AI to analyze and optimize business operations for improved sustainability outcomes, including reducing energy use and carbon footprint, enhancing worker safety, and ensuring compliance with environmental regulations.
Furthermore, AI can play a significant role in climate science by improving the accuracy of climate models and enhancing understanding of climate impacts. It can also optimize the deployment of renewable energy resources and enhance the efficiency of carbon capture and storage technologies.
Direct impact
On the direct environmental impact, “The considerable compute power used to train and utilize AI and GenAI models requires large amounts of energy that are likely to be drawn from nonrenewable sources,” says Eric Magdato, country head for Lingaro Philippines, an end-to-end data services provider.
“To illustrate, training a single model can result in 626,000 pounds (nearly 284 metric tons) of CO2 emissions, which is roughly equivalent to over 40 round-trip flights between Sydney, Australia and New York City. And as stronger AI models are trained, the more CO2 is emitted. Additionally, ChatGPT, one of the most popularly used chatbots in the world, purportedly emits 8,421 pounds (3.82 metric tons) of CO2 per day.”
Beyond its carbon footprint, GenAI affects the planet by utilizing and cycling through more hardware than most other types of computing. To run, the machines require rare metals, which must be mined and moved many miles to manufacturing plants. Moreover, the machines’ considerably shorter lifespan means they go to landfills at a faster rate than other types of electronic waste.
“Fortunately, AI developers [such as] OpenAI are aiming to run on 100 percent renewable energy, though the shift is not yet complete,” says Magdato. “So, while the training and utilization of AI contributes to CO2 emissions, the pollution can be offset by using AI and GenAI to dramatically reduce emissions in other areas of enterprise operations.”
Generally, GenAI can be used to optimize workflows and business processes while keeping profits healthy. Managers can come up with optimization strategies that are aligned with business goals. Once a strategy is chosen, AI can factor in all the levers for increasing both sustainability and profitability, work through countless permutations of how those levers are set and come up with options for decision-makers to consider.
“However, there’s more to sustainability than CO2 emissions,” Magdato says. “The scope of sustainability is expansive, but thanks to AI and GenAI, we can gain a better understanding and handle it all.”
In its current state, sustainability covers a range of issues that can be complex in and of themselves. For instance, new regulations require an extensive scope of data to be collected from multiple sources and analyzed in a structured way. This points to the urgent need for automation, which AI can address.
“The interconnectedness of sustainability issues adds an extra layer of complexity,” says Magdato. “Governments and enterprises must consider how their actions will help the environment, promote social justice and remain profitable. And if that doesn’t make achieving sustainability hard enough, sustainability challenges [such as] carbon emission reduction and water conservation often present unstructured data in the form of text documents, images and graphs, eluding the analytical grasp of conventional AI.”
It’s good to learn that GenAI excels in unstructured data analysis, modeling complex relationships, handling ambiguity and providing the possibility of human-AI collaboration, standing as a transformative solution to sustainability complexities. For example, GenAI can aid in climate modeling, prediction and analysis by processing vast amounts of data. The machine can help identify trends, simulate scenarios and support decision-making for effective climate change mitigation strategies.
In addition, AI can provide deeper data insights that drive faster and better decision-making. Not only that, it also offers clues about how to combine, integrate and take full advantage of various sustainability technologies and the company’s existing resources. This makes it a valuable tool that can augment, extend and redefine an organization’s sustainability efforts.
“We are leveraging AI and Gen AI to predict our demand and forecast and also to enhance the efficiency of building solutions faster,” says Magdato. “Apart from it as an organization, we are committed to sustainable initiatives, and we are also ensuring our offices are present in buildings that are ‘smart and sustainable buildings.’ Our infrastructure is also leveraging ‘green cloud’ providers, who have committed to building energy efficient computing systems. We have also adopted remote working to reduce the carbon footprint of our team through travel. We continue to identify ways to optimize the energy spent and make our ways of working more sustainable and greener.”
Ng says, “The ongoing advancement of AI and security measures presents opportunities to bolster sustainability initiatives. However, given the stringent security demands inherent in every business, further developments in data security are essential to fully harness AI’s potential across various applications.”
Utilizing AI and GenAI technologies to enhance enterprises’ sustainability efforts and align these efforts with corporate goals can increase efficiency, innovation and competitive advantage. Such tech solutions can help organizations across various functions, from business strategy, operations, sales and marketing to research and development, supply chain and sustainability.
As Garg says: “In the right hands, AI can indeed make a world of difference.”
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