AI has many applications in business. Machine learning (ML) and other AI techniques are quickly becoming the norm in many industries. It can improve business performance by improving efficiency and automating a wide array of tasks. It may seem difficult to implement AI in your business, however, there are many easy options.
AI has been successfully used by plenty of businesses, large and small. We have collected some examples of successful applications of AI.
AI has long been associated with chatbots. One of the defining landmarks in AI, the Turing test, is a chatbot test. AI-driven live chat has come a long way.
As a result, it is now easy to implement a chatbot in any business. This service can provide an easy way to improve customer experience, without hiring new employees.
An AI chatbot from swivl allows your business to take advantage of AI. This AI-driven solution allows more interactions to be done without a human agent. In addition, swivl’s chatbot provides a far better experience than older rule-based chatbots.
Amazon Recommendation Engine
Modern consumers expect personalization in shopping. Online services provide a huge number of options. This selection is overwhelming to many consumers. AI can be used to provide product recommendations, simplifying the user experience (UX).
These techniques are particularly important for a massive retailer like Amazon. Amazon offers an enormous array of products and options. In order to match customers to the products they want and need, Amazon uses an AI-powered recommendation engine. These predictions make their site easier to use.
However, Amazon goes even further, integrating AI throughout their business. They combine data from Amazon Alexa, Go Store, and their recommendation engine. Amazon combines this data to create a unified experience.
The Nest Learning Thermostat is a great example of a useful product built on AI. Nest uses an ML model to efficiently manage home heating and cooling. Nest learns from the user’s habits and choices.
This technology is just one of the many smart home and Internet of Things devices now available. These products use AI to make your home more efficient, intelligent, and comfortable.
Banks are making banking more convenient by combining AI and mobile technology. Today, you can deposit checks by snapping pictures with your phone. This is made possible through AI technology.
Digital identity verification provided by companies like Mitek Systems makes this possible.
This provides techniques to reliably verify documents and people. By combining many data points these services prevent fraud. This makes it possible to provide convenient online services for financial institutions.
Artificial intelligence is having impacts throughout the transportation sector. For example, apps like Lyft and Uber use ML and AI in many areas of their business to make their users’ experience more seamless. Among other uses, AI predicts destinations, estimates ride costs, and matches drivers with customers.
The number of drivers and users on Uber’s platform make this possible. AI can take advantage of this massive amount of data to improve efficiency. Therefore, Uber claims to save years of aggregated driver and user time every week by using AI!
Coca-Cola Gets Four Times More Clicks Using AI
Coca-Cola is the largest beverage company in the world. With their enormous reach comes enormous amounts of data. Coca-Cola has embraced the use of AI to leverage this data and improve their brand.
One enormous source of data for Coca-Cola is social media. Coca-Cola is tracking its products and those of competitors on social media platforms. Coca-Cola combines computer vision and natural language processing to understand where, when, and how their brand is mentioned and perceived.
This data collection results in a huge amount of data. They use this data in order to target their ads. Coca-Cola claims using this data has led users to click on ads 4X more often. This huge improvement shows how much AI can do to improve performance.
The likelihood that a person will repay debt is a complex question. For better or worse, credit agencies have been claiming to answer this question for decades. The complex data involved makes this an obvious application of ML techniques. Since as early as the 1980s, credit bureaus have used the ML technique linear discriminant analysis (LDA) to calculate credit scores. This ML technique is similar to the classic statistics techniques like regression and ANOVA.
Though these techniques are a form of ML, they lack the complexity of more advanced AI. Deep neural networks (DNNs) are one of the most popular forms of AI today. DNNs are now being used to greatly increase the accuracy of risk assessment.
McDonald’s Shows Personalized Ads to Pedestrians
McDonald’s is another huge brand that has found a practical use for AI. Using big data to target ads is one thing, but personalizing ads in physical stores is another beast. McDonald’s has created kiosks in the windows of some stores that serve targeted advertisements to pedestrians.
These devices use computer vision to estimate demographic information. The model uses this information to select the advertisements that are most likely to lead to a sale. This real-world use of AI shows how companies are using AI in the physical world.
British Petroleum Uses AI to Inform Subsurface Engineers
BP has invested in an AI company that uses knowledge graphs to answer complex questions. This system takes in data points like project history, geophysics, and geology. The knowledge graph links this information together autonomously. It can then answer questions posed using natural language. This allows engineers to quickly understand the needs of a project.
Google Cools Its Data Centers With AI
Google AI research firm, DeepMind, has successfully reduced the energy required to cool Google’s data centers by 40%! These data
centers house all of Google’s servers. Therefore, these servers are responsible for running Google services like Gmail and Google Docs. In addition, they power the Google Cloud servers use to host many other companies’ websites.
These data centers require a lot of cooling to keep them running efficiently. In order to save energy costs, Google has used a deep neural network to manage this cooling. As a result, the system is capable of predicting and responding to changes in server usage. This allows them to cool the data center 40% more efficiently.
To see how you might integrate AI into your own business, feel free to schedule a demo with us! We invite you to learn more about our solutions that make it easy to use AI to improve your business.
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