Artificial Intelligence allows companies to leverage capabilities they previously did not have access to. For example, energy providers are able to predict energy prices and reduce the cost of maintaining renewable energy projects with condition monitoring. This means that Artificial Intelligence could be a driver in the green revolution. However, the companies that succeed in using Artificial Intelligence must be aware that the implementation costs can be very high and that there are risks involved in investing in new technologies.
SwissCognitive Guest Blogger: Kilian Woods, AI & Sustainability Educator, Analyst, Facilitator
Implementing Artificial Intelligence
The ability of industries to adapt and mitigate climate change depends on the capabilities the industry developed to adapt to change. This includes how well the company has used digitalisation to adapt to market trends. For example, the insurance industry is automating its processes with AI to increase efficiency and improve its abilities to manage risk. The industries that are able to successfully adapt and digitalise their assets also develop capabilities that make them more resilient to change. By expanding their technological abilities (such as investing in AI), the companies that are leaders of these industries can face and deal with risks and adapt to change. Moreover, this will affect how they respond to climate change. There are many uncertainties involved with AI and the ability to use AI in assessing risk will improve how companies can adapt to climate change.
Artificial Intelligence and Sustainability
Following from what was mentioned above, this means that companies that have developed digital assets could be very valuable in solving the climate crisis. Artificial intelligence is a digital asset that can be used in conservation, prediction of catastrophic weather events, used to influence policymakers, etc. It is a tool with the ability to solve many issues. However, companies must have developed the capabilities to integrate digital assets within their organizational ecosystems to succeed. Otherwise, they could be wasting precious time and money implementing Artificial Intelligence solutions.
Artificial Intelligence can improve the profit of renewable energy companies
Machine learning can increase the profit from wind energy sources by improving the prediction of wind power production and the prediction of supply and demand. This allows energy producers to control the production of energy according to the demand. The other benefit is to lower operation costs. For example, I worked as a machine learning engineer on the condition monitoring of wind turbines. By using artificial intelligence, I was able to successfully predict six months in advance if a component of the wind turbines was going to break using SCADA data. This meant that the energy provider I worked for could order the components necessary to carry out the repairs in advance. This reduced the cost of downtime involved in waiting for new parts. And the energy provider was able to prepare for repairing the component and choose days with less wind to carry out the repairs. Thus, reducing the overall cost. Moreover, this reduces the risks insurance companies take when ensuring a renewable energy project and can lead to a lower price of insurance for the energy providers who use artificial intelligence.
The risks involved with AI
There is an increasing risk of cyber-attacks. Unfortunately, AI can be used to orchestrate these attacks. This means that companies must be aware and take the necessary steps to protect their assets. However, it also means companies are fearful of implementing AI in their companies. To adapt and grow, companies are adopting AI yet they are finding ways to do it without compromising the security of their assets. An example of this is by hiring machine learning engineers in-house instead of getting consultants to do it. This means companies are having to develop their capabilities to be able to use the technologies. This means improving the networks of information systems within organizations. Such as involving the AI engineers with the end users. For example, getting artificial intelligence engineers to work with underwriters in insurance companies. This improves processes and means that companies improve their capabilities to adopt artificial intelligence to deal with the climate emergency.
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Future of Artificial Intelligence in Sustainability
Artificial Intelligence is an important driver of change. Although there are risks involved, companies transitioning to a more sustainable model or working in the sustainability sector should adopt artificial intelligence. This is because they are improving their ability to deal with risk and adapt to climate change.
About the Author:
Kilian is passionate about driving systemic change towards solving the climate crisis. He has facilitated workshops with decision makers, schools and universities as an Climate Ambassador. He communicates the importance of artificial intelligence to transition society to renewable energy sources and for countries to achieve their climate goals.
Artificial Intelligence allows companies to leverage capabilities they previously did not have access to. For example, energy providers are able to predict energy prices and reduce the cost of maintaining renewable energy projects with condition monitoring. This means that Artificial Intelligence could be a driver in the green revolution. However, the companies that succeed in using Artificial Intelligence must be aware that the implementation costs can be very high and that there are risks involved in investing in new technologies.
SwissCognitive Guest Blogger: Kilian Woods, AI & Sustainability Educator, Analyst, Facilitator
Implementing Artificial Intelligence
Artificial Intelligence and Sustainability
Following from what was mentioned above, this means that companies that have developed digital assets could be very valuable in solving the climate crisis. Artificial intelligence is a digital asset that can be used in conservation, prediction of catastrophic weather events, used to influence policymakers, etc. It is a tool with the ability to solve many issues. However, companies must have developed the capabilities to integrate digital assets within their organizational ecosystems to succeed. Otherwise, they could be wasting precious time and money implementing Artificial Intelligence solutions.
Artificial Intelligence can improve the profit of renewable energy companies
Machine learning can increase the profit from wind energy sources by improving the prediction of wind power production and the prediction of supply and demand. This allows energy producers to control the production of energy according to the demand. The other benefit is to lower operation costs. For example, I worked as a machine learning engineer on the condition monitoring of wind turbines. By using artificial intelligence, I was able to successfully predict six months in advance if a component of the wind turbines was going to break using SCADA data. This meant that the energy provider I worked for could order the components necessary to carry out the repairs in advance. This reduced the cost of downtime involved in waiting for new parts. And the energy provider was able to prepare for repairing the component and choose days with less wind to carry out the repairs. Thus, reducing the overall cost. Moreover, this reduces the risks insurance companies take when ensuring a renewable energy project and can lead to a lower price of insurance for the energy providers who use artificial intelligence.
The risks involved with AI
There is an increasing risk of cyber-attacks. Unfortunately, AI can be used to orchestrate these attacks. This means that companies must be aware and take the necessary steps to protect their assets. However, it also means companies are fearful of implementing AI in their companies. To adapt and grow, companies are adopting AI yet they are finding ways to do it without compromising the security of their assets. An example of this is by hiring machine learning engineers in-house instead of getting consultants to do it. This means companies are having to develop their capabilities to be able to use the technologies. This means improving the networks of information systems within organizations. Such as involving the AI engineers with the end users. For example, getting artificial intelligence engineers to work with underwriters in insurance companies. This improves processes and means that companies improve their capabilities to adopt artificial intelligence to deal with the climate emergency.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Future of Artificial Intelligence in Sustainability
Artificial Intelligence is an important driver of change. Although there are risks involved, companies transitioning to a more sustainable model or working in the sustainability sector should adopt artificial intelligence. This is because they are improving their ability to deal with risk and adapt to climate change.
About the Author:
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