How Will AI Impact Various Industries in 2024?
Over the last few years, the influence that artificial intelligence has had across the economy is staggering, with almost no industry left untouched. Now, as it continues to scale and enter the daily lives of most people, the technology is rapidly changing the way we live and work. In 2023, we saw AI continue to make its mark on various industries, from healthcare and retail to manufacturing and finance.
So let’s take a look at how AI is changing some key industries, and find insights on how AI will shape the future.
Healthcare
AI is having a major impact on healthcare, helping to improve patient care and outcomes. For example, AI-powered medical imaging tools can help doctors detect diseases earlier and more accurately. This is seen in the subfields of personalized medicines where AI can analyze massive datasets of genetic and medical data to create predictive models that can predict individual risk factors for certain diseases. There are also new AI algorithms that are helping with medical imaging analysis. From CT scans, and X-rays, to MRIs, the AI programs can detect subtle anomalies that can often be overlooked by the human eye.
Another way AI is transforming healthcare is through the use of AI-powered chatbots. These chatbots help patients with their questions and concerns, and AI-powered robots can assist with surgeries.
Case Study: DeepMind and Retinal Disease Detection
E-commerce & Retail
Other industries that are being transformed by AI are e-commerce and retail. One of the main ways this is happening is with the increase in personalized shopping capabilities. AI programs can provide users with product recommendations by analyzing data (browsing history, purchases, etc,) and provide customers with excellent recommendations. For the retail industry, this provides avenues for upselling and based on predictive behavior metrics, introduces new customers to products based on their buying habits.
Chatbots, like with healthcare, are also providing a supercharged experience. With the ability to be responsive 24/7, these AI programs can provide customers with on-demand information, support, and more which allows for human workers to be free to be shifted to other priority areas within the company. Finally, there is operational efficiency. This is primarily seen in the real of inventory management.
By analyzing sales data and other market metrics, AI programs can predict future demand, which allows stories to optimize inventory levels, reduce waste, and ensure an excellent shopping experience.
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Manufacturing
There are two main avenues in which AI is changing the world of manufacturing, enhanced efficiency and optimized production. In the case of enchanted efficiency, AI algorithms analyze sensor data from machines to predict potential failures before they happen. This level of predictive maintenance allows for the minimization of downtime and reduction of liability based on unforeseen damage. Then there’s robotic automation. AI-powered robots are taking over repetitive, and often more dangerous tasks, with greater levels of speed, and precision freeing their human counterparts to handle more complex tasks.
With optimized production, AI is assisting manufacturers with dynamic scheduling and planning. AI programs analyze real-time data and create optimized production schedules for resource allocation and inventory controls. This allows for continual manufacturing with the reduction of waste. Then there is defect detection within the quality control world of manufacturing. AI-powered vision systems can inspect products with accuracy and identify small flaws that can be missed by the human eye.
Case study: Siemens MindSphere and Predictive Maintenance
Finance
In the world of finance, AI is helping to overhaul the industry in a couple of interesting ways. One of which has to do with what’s called “High-Frequency Trading. Here, AI-powered algorithms are analyzing vast amounts of market data at the speed of light. This allows for sophisticated trading strategies and automated execution of trades in only milliseconds. Then there is quantitative investing. AI models analyze both fundamental and technical data to identify investment opportunities and predict market trends.
But one of the biggest shifts is within risk management. AI is taking a two-front approach to fraud detection and credit risk assessments. AI algorithms are detecting anomalies in financial transactions and helping institutions to identify potential fraud. Something that once was only caught if caught by a sharp enough eye going to transaction histories. Then there are CRAs, where models analyze loan applications and use data to predict creditworthiness with greater accuracy, to help reduce bias and lead to fairer lending practices.
Marketing
Like with retail, AI algorithms analyze vast amounts of customer data, including demographics, browsing behavior, purchase history, and social media interactions, to build detailed profiles. This has become a new tool for media buyers to create highly targeted ad campaigns that resonate deeply with individual customers, increasing engagement and conversion rates. Another way to use consumer data is by dynamic content optimization. AIs are tailoring website content, landing pages, and emails in real-time based on individual user profiles and preference
Finally, there is the world of social media intelligence and engagement. Here, by monitoring social media conversations, AI programs are identifying customer sentiment about brands. This allows for marketers and other professionals to be proactive in their engagement with users; handling both negative and positive feedback with greater ease, allowing users to feel heard and as part of the solution.
Case Study: Netflix and Personalized Recommendations
Supply Chain Management
One industry that is accepting AI with open arms, has to be the supply chain ecosystem. There are several ways that AI is impacting supply chain management, so let’s start with the first, logistics optimization. With the use of AI algorithms studying data related to routes, traffic patterns, fuel efficiency, weather patterns, and other metrics, transportation routes can be optimized to help reduce the cost of moving items across the supply chain. A related avenue is demand forecasting. Using similar data, combined with sales history, and inventory data, AI algorithms can predict with great accuracy demand trends with great precision.
This allows businesses to avoid dealing with costs associated with overstocking or running out of products; reducing multiple forms of waste including fuel, and products collecting dust. Then there is inventory management. Similar to what we saw with retail and eCommerce, AI tools are monitoring inventory levels and using data sets that detail demand history and other metrics so helves can stay stocked while reducing instances of waste.
Finally, quality control. Similar to manufacturing, the supply chain management sector employs AI-powered robots to help keep an eye on products and issues that can come from problems the human eye may miss. With certain programs, defects, or other issues can be detected. Reducing costs associated with waste and potential liability.
Case Study: Maersk and Optimizing Through AI
Conclusion
As you can see, AI is having a great impact on just about every industry. Though it’s too early to measure the overall impact, one thing is clear, AI is only getting started. Now if you want to keep up with the latest and greatest in the world of AI, then you don’t want to miss ODSC East 2024 which will share co-location synergy with the Ai X Innovation Summit.
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Originally posted on OpenDataScience.com
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