As AI becomes progressively sophisticated, industries are employing machine learning, natural language programming (NLP), and generative AI to reduce human input in data analytics. Aside from displaying precise, actionable data insights and predicting outcomes, AI can advance sustainability initiatives by mitigating the environmental impacts of material production and business operations, among additional capabilities. However, this technology can make mistakes and has its limitations, so it’s crucial to assess use cases before implementing a strategy.
So how do AI models impact sustainability, and how can you capitalize on their unique potential?
AI’s initial emergence introduced ethical implications surrounding usage, prompting two distinct stages of morality. AI models’ subsequent evolution and widespread adoption have led to a third phase of ethics focused on sustainable development. This coincides with a greater movement that pioneers AI technology for practical applications.
How are corporations implementing AI to advance global sustainability in various sectors?
To comply with ESG (environmental, social, and governance) regulations, companies must reduce their impact on the ecosystem by adapting their products to the circular economy. In response, businesses are leveraging machine learning to recycle raw materials damaged in the supply chain process and regenerate them into alternative products. Similarly, the agriculture industry has implemented AI to transform production and manage environmental waste to reduce the use of harmful fertilizers and pesticides and to enhance crop growth and vigor.
Energy consumption is a primary focus of sustainability, and renewable sources are becoming a coveted commodity to mitigate utilization. AI can decrease energy transmission by recommending alternative sources and facilitating the shift from geographical to micro power grids.
Despite AI’s significant strides in reducing corporations’ environmental impacts, training these models emits considerable carbon due to extensive data computation. For instance, the healthcare industry utilizes NLP to assist in consumer research development. Training this model to interpret a medical term with multiple meanings and contexts uses countless data and storage, depleting power and other resources in the process.
So how can you alleviate the environmental ramifications of training AI models?
Sustainability is a global issue that necessitates companies taking calculated actions to promote comprehensive change. True global transformation happens when AI advances beyond its environmental applications to incorporate sustainable practices into its lifecycle. This involves building and training AI models with sustainability at the forefront by intentionally structuring and powering the hardware, developing ecological training methods for information gathering, and generating functional ideas for implementation.
ETT World’s Chief Technology Officer and Interim COO, David Smith, provides a pragmatic example of how AI-powered edge computing can minimize power consumption from data processing, “What we're seeing as we look at processing at the edge of the cloud, that's where many microgrids have come in. Things like sustainable power generation from taking the leftover crops on a field or cow manure and putting those into a bio plant to generate the power to feed that computation is a green process.”
Businesses must address AI’s social, ethical, and ergonomic implications to meet ESG requirements and facilitate sustainable operations.
ESG investments flood the stock market as the criteria demand increases in business processes. 78% of investors believe companies should invest in ESG, but only 55% of businesses do, planning for short-term profitability instead. Nevertheless, ESG is a necessity and must be sufficiently supported and funded. AI models can be trained to display partiality to ESG investment allocations.
Historically, AI used for sustainability investments has addressed general and superficial forecasts. However, given investors’ sophistication, AI models must be transparent and consistent when allocating investments. Yet ESG data is difficult to assess, with multiple variables impacting profitability, so AI is not always equipped to handle the demand. Therefore, implementing AI in ESG investments requires reducing inherent errors to maximize efficiency.
AI has variable potential for addressing sustainability and ESG concerns, so it’s imperative to consider its biases and limitations. A sound course of action is to develop a structured AI data strategy that can be implemented into next-generation models to predict obstacles and achieve favorable results.