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AI Use Cases

A collection of over 250 uses for artificial intelligence

A continually updated list exploring how different types of AI are used across various industries and AI disciplines,including generative AI use cases, banking AI use cases, AI use cases in healthcare, AI use cases in government, AI use cases in insurance, and more

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Emissions reduction

Introduction

Transportation is one of the biggest industries that contribute to emissions of greenhouse gases globally. The Environmental Protection Agency reported that it was the biggest source, contributing for about 29 percent of the total U. S. greenhouse gas emissions in 2019. This has been as a result of the growing global concerns on climate change as well as the increasing global efforts towards adopting sustainable measures to tackle emissions in this sector. ML and AI have come to be important assets in this fight given that they are bringing new approaches based on data to improve performance, cut down on fuel usage, and therefore decrease the impact on the environment in the transport business.

Challenges

There are several issues that the transport industry is facing in its efforts towards reducing emissions. The first one is the concern of fuel efficiency. Standard cars are improved in other aspects but not in the aspect of fuel consumption thereby emitting unnecessary carbon gases. Second, there is the problem of choice. This is because there are many routes and schedules that one can take, thus making it difficult to find the most effective one. Third, there is the issue of predictive maintenance. This causes delays which are inconvenient and may also result to carbon emissions. Also, there is the problem of data gathering and processing. The industry is known to create large data sets which can be challenging to manage and interpret without the use of software.

AI Solutions

There are several solutions to these challenges that can be addressed with AI and ML. In terms of fuel efficiency, for instance, AI can help in enhancing engine management and driving behaviors real time, hence cutting on fuel usage. The potential of these possibilities is being studied by IBM and Mercedes-Benz among other companies. In the case of route optimization, ML algorithms can consider traffic and weather information to plan the best route that would take the least time on the road and hence reduce emissions. Such technologies are being embraced by companies like UPS and FedEx to optimize their delivery routes. For example, in predictive maintenance AI can be used to forecast when a vehicle will require service before it occurs, thus avoiding downtime. Uptake Technologies is one of the companies that offer AI solutions for predictive maintenance. Also, for data analysis, both AI and ML can analyse huge amount of data within the shortest time and with high level of accuracy to support emission reduction measures.

Benefits

There are a lot of advantages of using AI and ML in emission reduction. The primary benefit is that they help in pollution control by decreasing the emission of greenhouse gases. It also results in cost savings since the fuel usage is optimized and the cost of maintaining the vehicles is also reduced. Also, they improve the operational efficiency through effective management of routes and schedules as well as through avoiding unexpected failures. Lastly, the knowledge derived from the data analysis can help in making proper decisions which will result in overall enhancement in efficiency and sustainability in the long run.

Return on Investment

There are significant return on investment (ROI) of applying AI and ML in emission reduction. A study by Capgemini has revealed that AI has the potential of reducing greenhouse gas emissions by 20% in the automotive sector. This is in terms of costs, which are a significant part of the transport industry as they are concerned with fuel and maintenance expenses. In addition, the above-mentioned technologies provide the following benefits in the organizational context: Besides, the operational efficiencies that are achieved through AI and ML can also enhance the overall performance of the organization. Although, there is a high initial cost that is required to be invested on AI and ML, the long term advantages and cost effectiveness can be advantageous.

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