How industries use AI to ensure sustainability

How can e-commerce companies optimize shipping routes to reduce emissions? How can data centers lower energy use? The answer is a sustainability strategy driven by AI technologies.

The perception that AI development and training has a negative impact on the environment is reversible when industries apply AI systems in ways that support sustainability.

The public sector and private enterprises can address climate change and related environmental challenges with concerted action. More than 130 countries and 800 large organizations have pledged to become net-zero, where atmospheric emissions they make are canceled out by those they remove. When companies set net-zero targets, they focus on both direct emissions from their operations and indirect emissions. Many net-zero companies reduce and offset their greenhouse gas emissions and mitigate negative impacts on the environment with the help of AI.

The terms net-zero; environmental, social and governance (ESG) and sustainability overlap, but are not the same. Sustainability is a broad term that encompasses ESG while net-zero is a component of a company's sustainability strategy. A company's net-zero plan typically consists of three objectives: measure and report emissions, reduce said emissions by optimizing operational and business processes, and offset residual emissions through mechanisms like carbon credits.

The potential for sustainable AI is twofold. First, a plethora of potential AI applications exist for industries at large, which can collectively make up their sustainability strategies. Second, many industry-specific use cases for AI are hitting the mainstream.

Sustainability strategy applications for AI

An enterprise's sustainability strategy can apply AI in the following ways:

List of ways AI helps corporate sustainability strategies
  • Emissions monitoring and reporting. Monitor and analyze a company's emissions data to identify areas for improvement.
  • Supply chain tracking. Analyze and monitor the sustainability practices of suppliers.
  • Predictive maintenance. Predict equipment failures and schedule maintenance to minimize unscheduled downtime and reduce energy waste.
  • Smart transportation. Optimize transportation networks and logistics to minimize emissions from transportation.
  • Energy efficiency. Optimize building energy systems, lighting, HVAC systems and other equipment to reduce energy consumption and carbon emissions.
  • Renewable energy integration. Integrate renewable energy sources, such as wind and solar, at the grid level.
  • Carbon capture and storage. Optimize the design and operation of carbon capture and storage systems to reduce emissions from industrial processes.

Industry-specific uses of AI for sustainability

AI can reduce the carbon footprint of industries ranging from manufacturing and mining to travel and transportation, to name a few. Even if it were to increase some costs for those industries in the short term, there are likely to be several long-term reductions as well as other benefits, including increased brand reputation and customer loyalty.

Real-world use cases toward sustainability depend on the industry.

  • Retailers use AI to optimize energy use in stores and reduce transport emissions for product delivery.
  • E-commerce companies use AI to optimize shipping routes and reduce emissions from delivery trucks.
  • Telecom companies look to AI-based data analysis for predictive maintenance. The same approach can increase network efficiency and reduce downtime and energy consumption.
  • Tech companies use AI to optimize data center energy consumption and emissions.
  • Vendors throughout the food supply chain use AI and analytics for predicting demand, thereby reducing waste and losses.
  • The fashion industry suffers from high return rates, and returned clothing ultimately ends up in landfills. AI-driven consumer analysis provides demand forecasting and inventory management to avoid waste.

Companies may not have the resources and expertise to implement several AI applications at once. Following these criteria can help project prioritization. First, tackle projects that align with the values and interests of key stakeholders, such as customers, investors, employees and local communities. Second, look for projects with the greatest potential to reduce emissions. For example, improved energy efficiency in manufacturing processes may have a greater impact on the company's overall carbon footprint than redesigned packaging. Finally, pick areas that support strategic priorities and those that can be easily integrated into existing business processes.

By incorporating AI to optimize operations and reduce emissions, companies can take meaningful steps toward net-zero goals and a sustainable future. However, it often makes more sense to form partnerships than do it yourself. To supplement in-house resources, partner with organizations that have sustainability expertise. Technology companies, government agencies and research and academic institutions can forge industry-level collaborations to bring in the necessary skills and knowledge to tackle complex environmental challenges.

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