Artificial Intelligence (AI) has been around for decades. But, it has only recently started to become a commercial success. AI is now being used in various industries and applications.
In this article, I will discuss some examples of commercial applications of artificial intelligence.
How do companies use artificial intelligence?
Artificial intelligence is already being used in a variety of industries today, helping to streamline operations and improve efficiency.
Artificial intelligence is the general term used to refer to any type of AI software that performs human activities, as though it was a person. Some of these tasks include collecting data, managing data, planning, and organization which can be performed by other people but are difficult for them due to their lack of time or resources.
They also have conversations with others much like humans do through various chatbots on social media sites such as Facebook Messenger and Google Talkbot while performing deep analysis on vast amounts of data making life simpler when trying something new.
One example includes automation: companies like Amazon are using AI to handle mundane tasks such as customer service requests or inventory management which frees up employees for more important work.
Another way artificial intelligence can help save time at home or on the job is by providing instant access to information about various topics with natural language processing; it’s not uncommon nowadays for voice assistants such as Siri, Alexa, Google Assistant (or even Cortana) respond instantly when you ask them questions related geography – so that research no longer requires manually typing into search engines!
Here are 4 examples of commercial applications of artificial intelligence.
1. General Electric’s Predix operating system
The sensor revolution has led to a new era of digitized machinery. The Internet of Things is not just about consumer gadgets as the rise in sensors within the industry means that hard equipment spaces can be monitored by artificial intelligence, something we have covered before with machine learning applications in industry and engineering technologies like Predix which powers industrial operations for industries such as oil and gas, aviation among many others.
Predix, the software built by General Electric that was initially designed for warehouse management has now been expanded to encompass everything from oil pipelines all around the world. Predictive analytics are used on GE’s apps as well as third-party ones such as Accenture’s Intelligent Pipeline Solution where millions of miles worth of data are collected and analyzed in order to manage safety and resource usage across a network.
GE’s Aircraft Landing Gear Prognostics app is a predictive maintenance program that helps engineering crews keep up with the safety of aircraft. The prognostics app lets airline engineers see how long landing gear can remain in service before a plane needs to be put in for servicing, and creates schedules based on this information which should reduce unexpected equipment issues as well as flight delays due to unforeseen circumstances such as mechanical failures or accidents.
GE created their new product, the “Aircraft Landing Gear Prognostic,” by leveraging Predix technology- an operating system built solely from software applications intended specifically for industrial use cases like GE’s protonic analysis programs used within aviation industries worldwide.
Domo’s new dashboard is a cloud-based software that collects data from hundreds of third-party apps to help companies make decisions. The product can scale with the size of the company, which means it could be used by small businesses or huge enterprises.
Domo, a fast-growing company that has raised over $500 million in funding, created a dashboard designed to help companies make decisions. The cloud-based software can be used by teams as small as 50 or much larger enterprises and is able to collect information from 400 native third-party apps which offer insights into the data collected.
Domo’s dashboard helps companies make better decisions by collecting information from third-party apps. The cloud-based system can scale with the size of a company and is used by teams as small as 50 or large enterprises with over 400 employees. There are more than 400 native software connectors that allow Domo to collect data, which offers insights and context for business intelligence.
Domo is a cloud-based business management tool that can be used to pull data from the applications they are already using, such as Salesforce and Square. Domo offers users an easier way of analyzing their businesses by providing them with comprehensive reports on all aspects of it in real-time without having to use different programs for each function– which could get confusing or overwhelming at times!
3. Siemens’ MindSphere
Siemens launched its MindSphere open industry cloud platform in beta, a data-driven tool to help industrial companies monitor their machines. Siemens unveiled the launch of its new application for monitoring machine fleets and evaluating equipment performance through analytics like service needs (machine tools) and drive train evaluations – this can be used by manufacturers at plants around the world; with Siemens’ data-driven product factory managers will have better access to tracking maintenance schedules as well as how they’re using assets so that operational lifespans are maximized.
Siemens and SAP are working together to create a new data-driven platform for industries that will be able to help companies make the most of their machinery by collecting all sorts of data about how it is operating. The MindSphere box collects information from machines, which can then show in great detail where they might run into problems or whether there’s something wrong with them.
The light at the end of this tunnel could mean huge savings on repairs down the line, but you have to use Siemens’ MindSphere system first!
Machine learning can help companies increase their sales channels by helping the company understand customers and what they want. Apptus specializes in using machine learning to connect customer intent with revenue, as well as offer more personalized suggestions for future purchases based on past ones.
Machine Learning has many applications that are beneficial such as those from Apptus which use it to make recommendations about actions a business might take so they may boost their sales channels. Machine Learning is also used connecting customer intent with revenue while offering more personalized predictions for potential future purchase choices depending on previous preferences.
With Apptus eSales, retailers will no longer have to worry about making the wrong guesses when it comes to merchandising. The software uses big data and machine learning modeling in order to predict what products are likely going best with a potential customer’s interests. This means that instead of guessing which product might interest someone as they search online or get recommendations from friends, you can be sure your store is stocked with goods most relevant for their needs!
The Apptus eSales solution guarantees there won’t ever be any more wasted inventory because all items on the shelves will make sense based on a predictive understanding of consumers’ tastes by combining big data and machine-learning algorithms.
The company’s machine learning solution can predict and automatically display related search phrases when a customer visits an online store. It also displays products associated with the pertinent terms that might be searched for in their system, making customers’ experiences more efficient by displaying results without requiring them to do anything but type in keywords or product names.
Artificial Intelligence is the future of technological innovations, and it has already proven itself to be an asset in various industries. It not only saves time but also improves quality as well because its nearly accurate algorithms are always learning on a daily basis.
Artificial Intelligence has the potential to be a game-changer in many industries. It is time-saving, efficient, and cost-effective so any business would want it on their side.
The only downside could possibly be that AI can never replace creativity or human judgment but once you have those two things down then there’s no reason not to use Artificial intelligence because of its other benefits.
When AI has applied correctly anywhere from business operations to research endeavors then any company will experience great success – no matter what sector they operate in!