AI tools like ChatGPT are making headlines, but other AI techniques and tools specially designed for businesses quietly helping companies achieve their sustainability goals. Classical AI is already widely used in a variety of use cases today, and generative AI is rapidly evolving to address new classes of use cases.
Previously, I led technical teams that helped customers with their AI implementations. When I started as a sustainability leader in Expert laboratories, our professional technology services organization, I saw the potential of AI to help with energy efficiency, decarbonization, and waste reduction. Discover current and emerging use cases for AI in waste management, optimization, energy reduction and ESG reporting.
How AI is helping companies accelerate their sustainability journey today
- Asset management: Whether it’s utility infrastructure or machinery on the factory floor, timely intervention can extend the life of an asset; reducing the amount of waste sent to landfill and the environmental impact of creating a replacement. AI solutions work by collecting data about asset performance and feeding it into machine learning models to predict asset health and risk of failure.
- Inventory management: Transport costs energy; in addition, perishable goods may require refrigeration during transportation and storage. Inventory optimization It is important to ensure you have sufficient inventory while meeting customer demand. At the same time, you want to reduce the carbon footprint associated with moving and storing inventory. AI helps tackle this problem by combining aspects such as demand forecasting, last-mile delivery and route optimization.
- Planning optimization: This use case is similar to inventory management, but focuses on the challenge of ensuring you have the right talent alignment. For example, when we think about asset maintenance, the questions are which technicians are available, where and how their work should be prioritized. It’s not about minimizing travel. Instead, it is better to prioritize an item that is further away for repair because that item has a higher cost or could break sooner. AI can efficiently tackle problems such as asset maintenance.
- Irregularity detection: Some manufacturers are pursuing zero-defect goals. If a part is defective or installed incorrectly, it may not be possible to salvage or recycle it. Image and video recognition systems can use AI to monitor and capture every stage of production discrepancies as early as possible. In addition to wasting materials, additional energy is also consumed when parts have to be reworked or recreated. This use case shows how AI can help by processing unstructured image and video data in addition to the structured data from the previous examples.
- Calculation optimization: Data centers consume a huge amount of electricity. Using AI to understand computing demand over time makes it possible to do that optimize the use of computer and cooling resources. By better matching resources to demand, energy is saved.
Where to now?
In the coming years, I expect companies will deploy generative AI applications that help with a new class of use cases to achieve their sustainability goals. Some companies are already working on it.
The first of these is the use of intelligent document understanding to process sustainability information. Companies use different frameworks to report their environmental impact in a standardized way. It is a time-consuming process to collect and produce relevant information ESG reports. Generative AI software ingests text information from various business systems, including supplier systems, summarizes it and links it to reporting frameworks, with the ability for human review.
On the other hand, AI streamlines the processing of information already collected in environmental, social and governance (ESG) reports. A company could combine purchase order information with a supplier’s ESG report. For example, if you know you are responsible for half of a supplier’s revenue, you can use their ESG reports to estimate your responsibility for scope 3 emissions.
For investors interested in green financing, AI could process ESG reports in bulk to create a recommended shortlist of companies with a stronger environmental stance. In an advanced use case, generative AI models, aligned with a company’s sustainability policies, could power an advisor application for activities such as supplier selection.
Foundation for Major Language Models (LLMs)tailored to domain-specific data, are likely to play an important role in intelligent word processing applications such as this.
Using foundation models geospatial data will probably also make their mark in the coming year. These models will be valuable for to predict flood zones, forest fires and other climate risks. Companies in industries such as agriculture, retail, utilities and financial services will be able to use these models for risk assessment and mitigation.
As companies adopt generative AI in these new use cases, they must also pay attention to a new set of risks that are emerging, ranging from potential privacy concerns to a lack of factuality. a Responsible AI approach and a AI management framework are both necessary to ensure that guardrails are in place for the responsible use of both classical and generative AI.
Sustainability goals and other business goals disappear hand in hand. For many of these use cases, there is a close relationship between sustainability and cost. Reducing energy, avoiding waste and optimizing resources have both financial and environmental benefits. With the help of new sustainability applications powered by AI, companies will find it easier to make decisions that are in line with their sustainability goals.