Here are three of the best ways to use AI to meet sustainability goals.

As noted in this study, an overwhelming 87% of private-sector leaders believe artificial intelligence (AI) will play a key role in fighting climate change. However, only a paltry 43% have a vision for how this will happen.

Small wonder, too: “Existing AI-related climate solutions are scattered, tend to be difficult to access, and lack the resources to scale,” says one of the study’s authors. Matters are complicated by the fact that AI and machine learning, computationally intensive as they are, themselves contribute to climate change.

The Cognizant take

Generally speaking, says Dr. Rouzbeh Amini, Cognizant’s Global Sustainability Offerings Lead, the transition to net zero is impossible to imagine without widespread employment of AI. At the same time, he is also quick to call for specificity when discussing how AI would best be used.

“There is a tendency to call pretty much everything related to analytics ‘AI,’ which is wrong,” he says. “AI’s true technical definition is ‘intelligence demonstrated by machine,’ and intelligence typically involves decision-making.”

With that in mind, Amini identifies the following three categories in which AI can best contribute to helping businesses meet sustainability goals:

1. Automating data management. The net zero transition is all about capturing data from the environment and the business to understand the current status, and then identifying approaches to reduce negative impacts, optimize the business or design new business models.
   
   Data volumes are huge, as they are generated by everything from Internet of Things sensors monitoring the environment; supply chain data from vendors and ecosystems; operations data; external data regarding weather, for example; social and human resources data (for social sustainability); market data; and business decision data.
   
   Manual data management is no longer feasible—AI is necessary for acquiring and managing such large data volumes.
   
   
2. Enabling or fully automating decision making. Scenario simulation plays a big role in net zero transition. This is because there are so many key performance indicators involved in designing interventions to achieve sustainability goals in the shortest possible time, with the highest impact, without breaking the financial rationale.
   
   Analyzing different scenarios and providing decision support is an area in which AI can help. AI can also be used to optimize supply chain planning and procurement decisions for sustainability, and design the best path forward for adapting to climate change.
   
   
3. Automating analytics, as well as monitoring and actuation. Some of this work is tactical, such as automating reporting or using robotic process automation. But AI can also help balance the use of cloud and network resources with an eye toward minimizing energy use.
   
   It will become increasingly important to automate remote sensing applications, such as those that monitor deforestation, air pollution and weather reports. Without AI solutions, doing so at scale is not feasible.