AI Tools for Sustainability: Silent Achievers in Enterprise
AI tools like ChatGPT are certainly grabbing headlines. However, other AI techniques and tools, particularly those designed for enterprise applications, are quietly aiding companies in achieving their sustainability goals. Both classic AI and the rapidly evolving generative AI are being put to good use in diverse ways.
There is vast potential for AI in areas like energy efficiency, decarbonization, and waste reduction. AI is becoming instrumental in areas like waste management, optimization, energy reduction, and ESG reporting.
Current and Emerging AI Applications in Sustainability
Here are some ways in which AI is helping businesses accelerate their sustainability journey:
Asset Management
AI solutions collect asset performance data, which is fed into machine learning models to predict asset health and the risk of failure. This not only prolongs the life of the asset, thereby reducing waste and the environmental impact of creating a replacement but also ensures timely intervention.
Inventory Management
AI assists in inventory optimization, balancing adequate stock levels with customer demand, while also reducing the carbon footprint associated with moving and storing stock. This is achieved by integrating aspects like demand forecasting, last-mile delivery, and routing optimization.
Schedule Optimization
AI plays a critical role in ensuring optimal alignment of talent. For instance, in asset maintenance, AI helps prioritize tasks based on factors like cost and potential failure rather than just minimizing travel.
Anomaly Detection
AI can help manufacturers achieve zero-defect goals by using image and video recognition systems to monitor each stage of manufacture, catching discrepancies as early as possible. This approach minimizes wastage of materials and energy.
Compute Optimization
Data centers are known for their huge electricity consumption. AI can understand compute demand over time, allowing for the optimal use of computing and cooling resources. This results in significant energy savings.
What’s on the Horizon?
In the near future, companies are expected to deploy generative AI applications that aid with new classes of use cases to meet their sustainability goals. Some firms are already working on this.
Intelligent document understanding for processing sustainability information is one such application. Another involves AI streamlining the processing of environmental, social, and corporate governance (ESG) reports. Furthermore, AI could help investors interested in green finance by processing ESG reports in bulk to create a shortlist of companies with strong environmental postures.
Large language models (LLMs) fine-tuned with domain-specific data are likely to play a key role in these intelligent text processing applications. In the coming year or so, models using geospatial data are expected to predict flood zones, forest fires, and other climate risks. These models will be valuable for businesses in sectors including agriculture, retail, utilities, and financial services for risk assessment and mitigation.
However, as companies adopt generative AI for these new use cases, they must also address emerging risks such as potential privacy concerns and lack of factuality. A responsible AI approach and an AI Governance framework are essential to ensure proper use of both Classic and generative AI.
Ultimately, sustainability goals align with business goals. In many cases, sustainability and cost have a close relationship. Reducing energy, avoiding waste, and optimizing resources have both financial and environmental advantages. By utilizing new sustainability applications powered by AI, companies can make decisions more aligned with their sustainability goals.
