As AI becomes more powerful, it’s starting to change the way we do business. These changes are coming from a wide range of use cases across different industries and sectors.
Some of the most common business processes that will be taken over by AI are those that involve repetitive, rule-based tasks. This includes order processing, data handling, and customer service.
Natural Language Processing (NLP)
Natural language processing, or NLP, uses machine intelligence to interpret natural human speech and text. It’s a fast-growing field that draws from a variety of disciplines, including data science and computational linguistics.
NLP can be used to automatically detect keywords, names, places, companies and specific phrases in large amounts of data to find trends and useful insights. It can also be used to automate workflows and streamline operations.
NLP models require massive datasets, and they’re typically built using statistical methods or neural networks for machine learning. However, these methods are limited by the nuances of human languages.
Vision systems are used for a variety of applications in industry including quality control, sorting, packing and inspection. They also help robots obtain the precise positioning they need to complete their tasks in a factory.
These solutions use a combination of cameras, lenses, lights and a controller to capture images. This information is then processed and converted to a digital format.
The information is then communicated via discrete I/O signals or through a serial connection. This allows for quick action on the part being inspected.
The ability to monitor manufacturing equipment for signs of damage and wear helps reduce maintenance costs and keeps production running smoothly. This can lead to a decrease in scrap, increased revenue and higher quality outputs.
Machine Learning (ML)
Machine Learning (ML) is a sub-domain of AI that uses algorithms to learn from large amounts of data. This process helps ML programs identify patterns and predict outcomes in a self-learning manner.
Various industry verticals are tapping into ML technology to derive insightful information from large volumes of data. These include life sciences, energy, law, financial services and manufacturing.
Unlike most other types of AI, Machine Learning has the capability to derive useful insights from unlabeled data without any supervision. It is also adaptive, and improves its analytical accuracy with each run.
Predictive analytics is a type of AI that can identify patterns within data and use them to predict what’s likely to happen. It can also provide business users with information on what they can do to improve their operations or reduce risk.
One popular predictive analytics use case involves detecting a medical condition in a patient before it worsens. This helps doctors avoid complications and reduce healthcare costs.
Another example is predicting the wear-out of industrial components. A machine-learning algorithm that has access to a large data set can predict which parts may fail before they do.
Predictive analytics can also be used to improve quality control. This can help you detect and fix flaws in your products or services before they cause damage to your customers or affect the overall bottom line of your business.
Chatbots are an AI use case that can help you automate a variety of tasks in your business. They offer a more hands-free way to handle customer requests, reducing labor costs and providing a better overall experience.
In the banking and retail industries, for example, chatbots can help customers perform routine activities such as checking balances or transferring funds. They are also becoming more popular in the HR industry, where they can share company information and documentation with employees.
Context-enabled bots use machine learning and Natural Language Processing to understand a customer’s context. This helps them provide answers that are tailored to the user’s requirements.