Intelligent apps are those that make use of real time and historical data from user interaction and external sources to generate relevant and personalized suggestions and solutions to various problems. The future of mobile apps is the product of many worlds colliding: with artificial intelligent software, the Internet of Things, and large-scale consumer surveys, all vying for attention in an increasingly crowded market. There are many categories of intelligent apps. One of them is the general-purpose smart phones that we all see and use every day; such as Android and Apple’s iPhone, and Windows Mobile on Verizon’s Mobile Subscriber Access (MSA) program. But beyond these, there are smart phone apps for transportation, business, travel, weather, healthcare, education, finance, and many more.
One category of intelligent apps is those that can gather, manipulate, and deliver data from different user experiences, with the help of online services and data sources. This category is popularly known as contextual processing or digital intuition. This is a very fast category because it requires just a small amount of time to retrieve and present the right information that the user needs. For example, when you key in your location and currency data, your smart phone app may instantly suggest several restaurants based on your recently logged-in time, and maybe even some recommendations based on your preferences for cuisine. Contextual processing relies on user data sources such as text messages, GPS tracking, and social media sharing; it also makes use of machine-learning algorithms to filter and classify the valuable insights it gets from these data sources.
Another subcategory of intelligent apps is the one that relies on artificial intelligence for its process of gathering and presenting the necessary data sources. In this case, the developers build a system that works closely with the users and with their phones. The software uses the phone’s sensor to gather and log motion, voice recognition for detecting and logging speech, as well as facial recognition for facial recognition purposes. Based on these captured data sources, the program creates an intuitive and highly-customizable experience. This is a good example of an artificial intelligence application because it requires minimal training and it can be completely integrated with the user’s decision making processes.
Machine learning has also become one of the important categories of intelligent apps, particularly because it allows developers to leverage large amounts of data. This is because a large part of machine learning relies on data analytics. Data analytics can refer to anything from Weather Analytics, Financial metrics to Natural Language Processing and much more. Basically, data analytics lets us take the analytical challenges and opportunities posed by today’s digital world and translate them into an efficient and effective solution.
The third category of intelligent apps concerns the integration of technological systems with people. This is where we come in contact with our devices and the cloud. We are increasingly creating and interacting with artificial intelligent computers and there lies the opportunity for vendors to sell us their knowledge and their skills. Deep learning is one of these areas and it enables vendors to teach their artificial intelligent phones to understand how to better understand certain human interactions that could lead to better opportunities. For example, imagine if your car can learn to adapt to your unique style of driving as you go along. Now imagine that it is capable of predicting some of the future driving habits of you and other drivers around you and it can even suggest actions that will improve your chances of having a more pleasant drive.
Another category of intelligent apps concerns the use of digital devices for business purposes. A common misconception that many people have is that they need to have an app that can do both business and entertainment. In fact, many of the best intelligent apps help you do both at the same time. For example, one of the most useful digital assistant applications helps you manage your appointments and track your productivity. Apart from that, it helps you manage your contacts and messages, organize your calendar and many other features.
It is not the extent of a particular smart application but rather its ability to give real-time solutions to complex problems. A prime example of an intelligent application is IBM’s WebSphere Application Center (WAAC). It has the ability to help your staff streamline their workflow by offering intelligent application solutions that help them collaborate better and get more done in less time. This is because WebSphere application center automates the processes of collecting customer information, storing it in one central location and letting customers directly contact the representatives of their choice. The program also allows them to access various documents, such as purchase order histories, customer satisfaction surveys and case studies. Another feature of WebSphere is its document scanning capability that enables you to electronically sign legal documents.
Clearly, intelligent apps can transform the way we live by giving us access to additional information and cutting costs by taking some of the human intervention out of the equation. They can also make the whole process of fintech and mobile commerce more interactive for the end users. Indeed, smarter fintech is the way of the future and this is just the beginning. The key to success for fintech companies lies in applying the insights from big data science, cognitive intelligence and online social networks. Given the right strategy, combining these three strategies can strengthen your company’s position and lead you to greater profitability.