Three Examples of Effective Machine Learning Use in Organizations

programming machine learning

Machine Learning (ML) is a bit difficult to define. That’s because you will likely get a different view of ML depending on whom you ask. 

So, what’s machine learning? 

Machine learning is a branch of artificial intelligence (AI). It is the process of programming computers to take their performance higher by learning from data. ML is at the core of disruptive technologies as it has made dramatic progress in the last decade.

Regardless of your definition, the goal of ML, at its most basic level, is to adapt to new data independently. Decisions can be made based on innumerable calculations and analyses. This is achieved by infusing AI with the ability to learn from the data input. 

The systems make decisions with minimal human intervention. ML is regarded as the best thing to happen to business management. They help increase accuracy and efficiency. Also, they reduce and even eliminate instances of human error.

The Importance of Machine Learning

Many factors have propelled the growth of Machine Learning. Among these, powerful processing, unlimited quantity of data, and affordable storage are the aspects that occupy centerstage. 

In modern business, many industries are developing ML development services to analyze complex and compelling data faster. They can get highly accurate results. 

ML tools help organizations quickly and accurately identify lucrative opportunities and potential risks.

How Machine Learning is Becoming an Indispensable Business Tool

Organizations that master the practical applications of machine learning can achieve better business results. It can boost the bottom line. New ML techniques keep evolving rapidly, creating limitless possibilities. ML is seen as the best way of building business models, strategizing, and planning.

Machine learning is enabling companies to swell their growth and improve processes. It also helps improve employee engagement and customer satisfaction. 

Here are some examples of how machine learning is creating value in organizations. 

Personalization & Search

In the last few years, a few organizations have completely changed how people leverage online experience. Leading names in the online content business, such as Netflix and Spotify, deliver truly compelling customer experiences. They come with a high level of personalization.

Personalization is a vital element in the entertainment industry today. It allows every subscriber to have a different outlook on the content. With the right machine learning and recommendation algorithms, the website or app can create content matching subscribers’ interests. 

Music and entertainment apps such as Spotify and Netflix rely heavily on personalization. The app uses AI and ML to create curated playlists that appear differently for different viewers. They get custom views depending on their past viewing habits, trends, and interests. ML helps these companies automatically adjust the playlist to each subscriber’s preferences.

Customer Service

Every business, regardless of size or industry, can succeed if it offers the best customer service. You cannot achieve much-needed customer retention and business goals without providing quality service.

Many companies use support-focused customer analytics tools. So, when combined with machine learning, these tools can create a higher level of convenience for customers. 

Likewise, recent research indicates a marked preference for the self-service mode of customer support. Customers prefer helping themselves instead of going through the painful and excruciatingly slow process of speaking to an agent. 

Companies are using ML to provide support to customers instantly. This is done through Virtual Assistants, Chatbots, and Artificial Intelligence-powered tools. These tools are designed to stimulate conversations with agents. They help customers find faster and more accurate answers to problems.

Zendesk, the company that provides Software-As-A-Service specific to customer support, uses ML to drive content creation. Data from support tickets is comprehensively analyzed, and the outcome used to create actionable tips in help articles. 

The company also employs machine learning to add a predictive element to some support analytics.

Security and Fraud Detection

The number of people providing companies with their data has grown exponentially over the years. Unfortunately, the instances of data breaches and hacks have also increased significantly. 

Over the past four to five years, the accounts of many leading and reputed organizations experienced hacking. Companies looking to solve this enormous problem believe the answer lies in investing in Machine Learning and Artificial Intelligence.

ML has the power to inspect millions of files. It can help detect and eliminate threats before they can wreak havoc on a company’s data. 

With its power to examine millions of files, companies have heavily invested in machine learning. It helps them uncover risks and automatically remove them before they get out of hand. 

Cisco is a fine example of companies using ML to improve security and fraud detection issues significantly. They continuously analyze how a network acts for anomalies. Additionally, this helps detect potential threats with a high degree of accuracy. MI also offers malware protection by evaluating the behavior and properties of existing malware. 


Machine learning is revolutionizing the business world in unimaginable ways. Artificial intelligence and machine learning drive their strategic decisions. 

By providing personalized experiences to delivering quality customer support through virtual assistants, MI is making everything possible. Certainly, it’s making lives easier and more entertaining for customers and service providers. Due to MI, businesses can reduce risks and remain relevant in highly competitive settings.

However, MI is a highly evolving field. Companies must find ways of remaining current with these changes to enjoy the full range of benefits these technologies offer.