How AI can cater for environmental sustainability benefits

According to IDC, spending on AI hardware and software is increasing at a CAGR of 24%. AI-driven projects will rapidly become a substantial percentage of any company’s investment in technology.

AI can be a net positive contributor to environmental sustainability in many industries. Here are some examples:

• In agriculture, AI can transform production by better monitoring and managing environmental conditions and crop yields. AI can help reduce both fertilizer and water, all while improving crop yields. Companies in this sector include Blue River Technology, Harvest CROO Robotics and Trace Genomics.

• In energy, AI can use deep predictive capabilities and intelligent grid systems to manage the demand and supply of renewable energy. By more accurately predicting weather patterns, AI can optimize efficiency, cutting costs, and unnecessary carbon pollution generation. Companies in this sector include Stem, ClimaCell and Foghorn Systems.

• In transportation, AI can help reduce traffic congestion, improve the transport of cargo (supply chain logistics), and enable more and more autonomous driving capability. AI will eventually help with the “last mile” delivery problem and reduce the need for delivery vehicles. AI can help businesses with demand forecasting, helping to reduce the amount of transport needed. Companies in this sector include Nutomony, Nauto and Sea Machines Robotics.

• In water resource management, AI can help reduce or eliminate waste while lowering costs and lessening environmental impact. AI-driven localized weather forecasting will help reduce water usage. Companies in this sector include Innovyze, Kurita Water Industries and Plutoshift.

• In manufacturing, AI can help reduce waste and energy use in production facilities. Robotics can enable better precision. AI can design more efficient systems. Companies in this sector include Drishti, Cognex Corp and Spark Cognition.

• In facilities management, AI can help recycle heat within buildings and maximize the efficiency of heating and cooling. AI can help optimize energy use in buildings by tracking the number of people in a room or predicting the availability of renewable energy sources. Companies in this sector include Aegis AI, IC Realtime and IBM’s Tririga.

• In materials science, AI can help researchers find new materials for solar panels, for turning heat back into useful electricity and to help find absorbent materials as components of CO2 scrubbers (taking CO2 out of the atmosphere.) Companies in this sector include Citrine, Matsci AI and Ansys.

Also, companies should consider allying themselves with any cloud provider that is committed to reducing their carbon footprint, thereby reducing their own. Instead of focusing on major internal projects to reduce environmental impact, it's possible to shift a company's AI training and processing to a data center cloud provider that can do that for you. For example:

• Google's DeepMind division has developed AI that teaches itself to minimize the use of energy to cool Google's data centers. As a result, Google reduced its data center energy requirements by 35%. Google's public cloud offering is called Google Cloud Platform.

• Microsoft has committed to be carbon negative by 2030. Microsoft also runs massive public data centers (cloud offerings) under the name Microsoft Azure.

• Amazon has a long-term goal of powering its global infrastructure using 100% renewable energy. This includes its cloud platform AWS.

Furthermore there are areas where AI is likely to have an impact on environmental sustainability:

• Error reduction. When humans make errors conducting manual tasks, that work often has to be reviewed and re-done. The effect of addressing these avoidable problems is more energy use. AI can be a factor in reducing human error in many tasks.

• Greater efficiency. By combining several types of AI, including machine learning, NLP, and computer vision, a company can create more efficient processes, reducing energy use. Also, AI can be used to remove unnecessary steps from the current process by contributing to the re-engineering of processes.

• Raw materials. When AI is focused on monitoring raw materials use, it can create opportunities to use less. AI can also be used to drive the creation of low-carbon materials for your products.

AI uses a great deal of energy, and most companies have no idea how to measure environmental impact. We should drive the discussion around the awareness and measurement of AI's impact on the environment.