Artificial Intelligence in traditional industries shouldn’t be a surprise. From manufacturing to farming and food production, AI brings improvement! In this article, we will highlight some of the most interesting, significant, and consequential applications of AI in traditional industries.
Government & Public Sector
The use of AI in the government and public sector varies significantly by nation. This is largely due to the advancement of technology in each country. For example, given that homegrown universities and companies in the US are in the business of technology and AI, they are more likely to use it. But of the countries that manage to use AI, the use cases are both interesting and powerful.
Social Welfare
One of the most powerful use cases of AI for social welfare is the analysis of fraudulent benefits claims. According to Statista, in 2020, payment card fraud losses per 100 U.S. dollars of total sales is at 6.8 cents. And fraud losses reached their peak in 2016 at 7.2 cents per 100 U.S. dollars of sales. Cents-wise it may not sound like much, but that is every card payment, of every person in the US. In the end, it amounts to a significant loss.
AI is able to tackle this problem by taking advantage of large data sets. For fraud detection, interpreting a large dataset is crucial as larger datasets provide better insights into customer preferences and behaviour, as well as fraud trends. As such, AI/ML models can help companies distinguish fraud from regular transactions.
Transportation
Fully-automated vehicles on all public roads remains a fantasy of the future. However, with the application of AI, fully-automated vehicles in predictable environments is a possibility. For example, automated vehicles can move people at sub-50km/h speeds along predetermined, learned paths like industrial campuses, city centres, or suburban neighbourhoods.
Administrative Work
Ask any employee in the public sector what bugs them about their job, and it won’t take long before they cite the paperwork. Now, with the use of AI, these troublesome tasks can be made easier. Computer vision coupled with a machine learning model has the potential to automate documentation, which includes the extraction of data from invoices, architectural drawings, certificates, charts, drawings, forms, legal documents, and even letters. Additionally, natural language processing and natural language generation can be used to draft documents and announcements. An ability already used by some newsrooms in the US.
Manufacturing & Construction
Unfortunately, when people think about AI, they rarely consider its application in manufacturing, let alone construction. This makes sense considering the sector has resisted technological disruption for years. Fortunately, the advancement of new digital tools is changing that. Building information modelling (BIM) is at the forefront of this transformation, with 73% of construction firms now using it. Now AI is also being applied in the sector.
Predictive Maintenance
Manufacturers leverage AI to identify potential accidents through the analysis of the sensor data. The AI systems help them forecast the lifespan of an equipment, so they can predict when it might fail and plan maintenance and repair accordingly. Moreover, manufacturers also use edge analytics, a method of analysis of the non-central components, such as sensors, switches and various connected devices. In doing so, they can improve quality, yield, and track worker health.
Generative Design
Generative design utilises machine learning to essentially mimic an engineer’s approach to design. Designers or engineers enter parameters of a design: materials, size, weight, strength, manufacturing methods, and cost constraints into a generative design software. And then the software generates a rendering of all possible outcomes from the given parameters. In this way, manufacturers are able to quickly create thousands of design options for one product.
Process Optimization
Manufacturers can take advantage of AI-powered process mining tools to identify and eliminate the different bottlenecks in the chain of production. For example, one of the most important things in manufacturing is keeping the accuracy and timing of a delivery intact. However, if the company has several factories in different regions, building a consistent delivery system is difficult. But with a process mining tool, manufacturers can compare the performance of different regions down to individual process steps: duration, cost, and the person performing the step.
Last Thoughts
AI has affected the traditional industries much like it has affected the modern industries. The ones we’ve highlighted in this article are by no means the only ones. The application of AI in traditional industries is vast. However, the ones highlighted above are some of the most significant in terms of output. So stay tuned as we witness more development in the area in the coming years.
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Author: Michelle Diaz
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