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Artificial Intelligence has been around for years but it has only recently begun to show commercial potential. But why now? It’s because of the exponential increase in data and our dependence on it. AI focuses on algorithms that make sense of enormous amounts of data, complete tasks while keeping this data in mind and make decisions accordingly. And it is opening opportunities for large enterprises.
To understand the potential of AI at the enterprise level, let’s see how the technology has infiltrated enterprise applications. In its simplest form, artificial intelligence can be defined as human intelligence as exhibited by machines. An early form of these machines were “expert systems” that followed rules defined by domain experts to solve highly specific problems. However, they were limited in their ability to learn on their own and relied on human supervision. Fast forward to the present and we now have AI technologies that mimic human intelligence a little better.
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This brings us to a more advanced and statistical facets of AI – machine learning. Unlike expert systems, machine learning can make more strategic decisions according to inputted data. A more advanced form of AI, deep learning does the same but without human intervention. An example of these algorithms in action can be a messenger app with inbuilt AI “intelligent assistants”, like Google Assistant, programmed to remember what users search for to optimize future search results based on data gathered from previous conversations. Another example are self driving cars that can be “taught” how to drive, retain what they learn and use this data to drive without further human input.
But does AI have the potential to improve enterprise processes? Let’s look at a few scenarios.
Businesses can leverage machine learning to predict system failures by detecting patterns in which they occur. This can be in the form of an AI program that has network visibility, monitors activities, and alerts businesses regarding when and where they need to focus on remedial solutions before crashes happen.
To describe this better, consider algorithms that draw connections with how patients describe symptoms to predict their likelihood of having certain diseases. Similarly, AI enabled software can be designed to detect “symptoms” in a hardware infrastructure that may result in a system crash.
An example can be an intelligent application that uses artificial intelligence in industrial IoT to monitor a factory’s machine performance with the help of sensors placed on vulnerable parts and alerts users as these parts begin showing signs of wear before they compromise on operations.
Another area where AI can be beneficial is security. Security software of today focuses on detecting threats when they happen through various methods like firewalls or intrusion detection systems that monitor transaction privileges.
As we know so far, artificial intelligence holds great promise in its ability to learn. In the case of cyber security at the enterprise level, this technology can be used to find out whether a file is legitimate or malignant by looking for security vulnerabilities in large amounts of code and taking actions by applying its learning from past data.
One industry that is vulnerable to cyber threats and can benefit from such technology is retail. Retailers are always under threat of data breaches and may benefit from AI enabled software to detect anomalies in credit card/ debit transactions as they happen and launch defensive techniques that shield valuable customer data.
The medical industry has made some leeway in artificial intelligence, examples of which are applications in hospitals that recommend best dietary choices for patients, based on recent changes in their medication, medical histories and individual preferences. However, this technology is limited since it requires practitioners to define what it should evaluate such as modifying data as new cases are judged and can only be applied after medical cases have already happened.
If enterprises learn how to harness the predictive capabilities of AI, the possibilities regarding patient diagnoses and treatments are endless. An example can be an application that predicts how likely it is for patients to contract certain diseases based on individual factors such as the patient’s age, the history of the disease in their family and how often it recurred before, along with learning from data of other patients. This eventually will help doctors diagnose patients before they even start exhibiting major symptoms.
If businesses hope to harness artificial intelligence at the enterprise level, they must keep in mind that it is data that fuels it. The technology is still evolving and the time to map its processes and see where it can be applied is now.
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