Intelligent applications with learning capabilities are not new. Early machine-learning programs date back to the 1950s. However, with the increase in digital information, machine learning, a branch of artificial intelligence, is gaining traction in the business world.
New machine learning algorithms help solve corporate problems using more efficient and accurate methods than outdated prediction techniques.
The power to transform data into actionable intelligence is the key for business success and first-mover advantage. This is why a range of sectors from travel to health care are leveraging machine learning to meet their objectives.
Although most consumers are unaware of it, various machine learning methods are being rapidly implemented in a wide range of everyday situations. Below are four of the most common ways that companies use ML today.
| Application | Business use |
| Natural Language Processing (NLP) | Text translation, sentiment analysis, and conversational systems |
| Data Mining | Identifying patterns, understanding customer preferences, and predicting customer churn |
| Computer Vision | Quality control, product counting, and defect detection |
| Robotics | Automating tasks in manufacturing, logistics, medicine, and other fields |
What Are the Main Applications of Machine Learning in Business?
1. Natural Language Processing (NLP)
Machine learning systems are becoming increasingly advanced, allowing them to process human language and generate responses. This is called natural language processing.
Some applications of NLP include:
- Automatic text translation
- Sentiment analysis
- NLP to improve conversational systems
- Automatically generating summaries using NLP
NLP saves time and speeds up work by automating processes that we would otherwise do manually, making it easier for us to make decisions.
2. Data Mining
Data mining tools explore large databases, automatically or semi-automatically, to find recurring patterns, trends, or relationships that help explain the data in a given context.
What can data mining be used for? Common business applications include:
- Design of strategies based on concrete information
- To better understand users’ habits, customs, and preferences
- Facilitate the search for relevant information
- Predicting consumer behaviour
- Detect the risk of customer churn
Data mining can help businesses streamline tasks and improve efficiency.
3. Machine Vision (Computer Vision)
Computer vision, a scientific discipline, focuses on developing artificial systems that obtain information from multidimensional data. In other words, computer vision includes processes for acquiring, processing, analysing, and understanding images of the real world we live in in order to produce numerical or symbolic information that can be processed by a computer.
Some of the best-known applications of machine vision in business include:
- Quality control
- Classification
- Product count
- Positioning
- Rotation control
- Pick and place
Machine vision helps achieve strategic objectives such as improving product quality, increasing productivity, and reducing production costs. The data collected on part defects provides an opportunity to identify and resolve production line problems.
4. Robotics
The term “robotics” is often associated with artificial intelligence, but not all robots are “intelligent.” Robots are machines that perform tasks on their own, following a set of rules programmed by a computer.
AI and machine learning can improve the capabilities of some industrial robotic systems.
Supply chains and companies that specialise in logistics applications are among the first to implement artificial intelligence and machine learning in their robotics tools.
Robots also support medicine and education, both in operating rooms and classrooms.
What Are the Benefits of Machine Learning in Business?
Machine learning enables companies to process large volumes of data, recognise patterns, automate mundane tasks, and make better decisions. It can also, when deployed correctly, assist companies in gaining a better understanding of their customers and increase efficiencies in their operations.
Conclusion
The number of applications and solutions offered by machine learning is enormous. One of the main advantages of machine learning is its ability to identify patterns in data and use them to make predictions or support decisions. This is why it is so effective at predicting and determining results, and thus helps us achieve our goals.
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