AI Can Create Meaningful Change in Traditional Industries
Artificial Intelligence (AI) is a field in computer science dealing with intelligent programs and machines. When it comes to traditional industries, there are some challenges that make the adoption of AI more difficult.
The physical world can be messy, chaotic, and unpredictable and technology requires consistency and predictability. In applying AI in physical industries, the best results come from engaging AI for predictable cases and leveraging humans in unpredictable and dynamic situations where judgment and intuition is needed. Here are some of the ways traditional industries can use AI to create meaningful change.
Agriculture and farming industry
Growth in agriculture is required to meet the needs of a growing world population. Farming is very time-sensitive and with access to real-time data and information, farmers can make more informed decisions. The use of AI in agriculture and farming allows them to maximize their productivity and reduce costs.
Farmers can monitor the health and growth of their crops and predict changes in weather and in insect behaviors. With predictive analytics, they can determine future yield and make decisions on how to manage resources best based on historical data.
One of the use cases of AI and machine learning in the manufacturing industry is workplace screening and safety. It can identify workplace safety events before they happen and speed up the analysis of causes post-incident.
According to a technical UK assignment help, AI and machine learning can also modernize maintenance management, allowing managers to quickly identify imminent failures and provide predictions of when failure may occur. Overall, AI can lead to increased efficiency due to the reduction in human error, more uptime and a reduction in costs.
Using AI to analyze complex medical data and research can help to inform a practitioner’s diagnosis and increase the accuracy and speed of treatment. A predictive approach to healthcare is much better than the reactive approach currently in use.
In healthcare, AI can be used to identify high-risk patient groups, predict diseases, automate diagnostic tests, and improve drug formulations. With deep learning, it’s possible to process the data for 3D imagery, allowing for much more detail, color and dimension than ever before. AI can comb through scans much faster than humans and enable an easier analysis of scan results through image recognition.
Autonomous cars are still in the process of development but they represent one of the most advanced ways to use AI. These cars can monitor the human driver, the distance between the car and other objects, detect what’s around the car and respond to different weather and road conditions. Using these cars can reduce accidents, lower traffic congestion and reduce energy costs. Companies like Tesla, Uber and Google are currently exploring intelligent driverless cars.
Goods transportation will also benefit from autonomous driving. Self-driving trucks will not require rest stops and cost less than human drivers. Delivery will be quicker, less error-prone and more cost-effective.
In the retail industry, retailers who implement chatbots can increase the amount of data they collect about customers, which gives them a competitive advantage. Chatbots can provide analytics associated with the emotions and mood of customers, while online and retailers can tailor and personalize the customer experience.
Besides using chatbots, retailers are also using recommendation engines to generate recommendations for customers based on their browsing habits. Immersive product catalog visualization is also going to grow, allowing shoppers to experience products before they buy them. All of this can improve customer satisfaction and retention.
The construction industry can benefit greatly from AI and machine learning to make processes faster and safer. For example, it is possible to gather data from sensors on equipment and manage the flow of materials and workers around the site.
Cost overruns are almost expected for construction projects and using AI means being able to generate reports for past projects to pinpoint cost overruns and make better budget decisions for future ones. AI can help with designing better buildings and with many other aspects of project planning, from scheduling to material sourcing.
Financial industry (BFSI)
Health insurance companies are having clients wear devices to monitor their blood pressure, heart rate and daily activity levels. By making use of these intelligent devices, they can give health-conscious clients discounts on their health insurance premiums. Some vehicle insurance companies are using monitoring devices inside cars to monitor driving habits and offer discounts to safe, responsible drivers.
Banking is a sector where documentation and paperwork abounds. AI can automate manual paperwork and decrease the time required to solve issues which will help banks serve customers better.
AI enables banks to identify high-value customers using predictive analytics and retain them longer by providing additional services based on their financial habits. Banks can predict the likelihood of customers defaulting on loans and also detect fraud.
The shipping industry’s use of predictive analytics is optimizing supply chain economics. Shipping companies benefit from implementing AI with image recognition algorithms and intelligent automation that can enable customs officials to conduct checks more seamlessly. They can accurately keep track of shipments and cut down on time spent in ports.
Predictive analytics can optimize routes to minimize overhead costs. Machine learning can help with analyzing historical data and the consideration of factors like weather patterns or slow-busy shipping seasons. Using AI in shipping has many benefits for ports, customers, the supply chain and the environment.
The IT industry needs to drive innovative initiatives and grapple with traditional infrastructures, which can be a difficult balancing act. IT infrastructures have become more complex and it has become essential to enhance operations management and accelerate problem resolution. Three of the main areas where AI-driven applications are having benefits are in quality assurance, process automation and service management.
In quality assurance, AI-driven applications are helping to reduce human error, identify possible defects and reduce running test time. A deep analysis of errors can define areas most likely to have defects and provide possible solutions for further optimization. AI-powered automation allows IT companies to automate many of their operational processes, which minimizes manual labor and reduces costs. By leveraging AI for service automation, companies can utilize resources more effectively and make service delivery cheaper, more effective and faster.
Traditional industries have a wealth of historical data from operational processes already in place. In spite of a perception that suggests older industries find it harder to employ digital transformation, the volumes of historical data actually give them an advantage in applying machine learning and AI. Futuristic and flashy use cases may be enticing but the transformation of traditional industries is sorely needed. Improving production levels and using predictive analytics can be most beneficial in traditional industries.
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