Automation 101: What is automation?

Automation has been gaining ground across industries for many decades. With advances in AI and robotics in recent years, the era of automation is now firmly embedded across sectors from manufacturing to healthcare to banking to cybersecurity.

Why use automation?

Automation refers to programmatic systems that perform repetitive and/or rule-based tasks without human intervention. These rule-based systems are being rapidly augmented by applying data analytics and artificial intelligence. Automation engineers are usually part of automation systems, planning, implementing, and monitoring an automated process. Automation increasingly uses technologies such as robotics and artificial intelligence to automate the production of products and services. Modern automation is now sophisticated and powerful because of these intelligent technologies.

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What are examples of automation and automation processes?

Automation is found across all industries in some form or other. Below are some examples of the types of automation regularly used across industry and business:

BPA (Business Process Automation):

This is an automation technology that is typically used in the back-end processes of an organization to help run the business more efficiently. Examples of BPA use include social media management software tools that automate post scheduling and insurance verification decision automation.

Attended bots:

Mundane, routine tasks such as form-filling, customer account queries, healthcare, insurance claims, etc., are ideal for automation. Attended bots link back-office and front-office tasks such as these together. Using these types of bots for back-end routine tasks allows for more complex cases to be dealt with by human workers.

RPA (Robotic Process Automation):

The difference between RPA and BPA is that RPA works at the user interface. Chatbots are an example of RPA. RPA bots perform large-scale repetitive automated processes, such as help desk interactions. Chatbots are moving from a simple rules-based automation structure to more intelligent interactions using AI.

Industrial/manufacturing processes automation:

The industrial automation market is worth around $190 billion worldwide. Industry 4.0 makes full use of automated processes to improve efficiency, productivity, and quality. Automation takes many forms in the industrial and manufacturing sectors; some examples are programmable automation in batch production and fixed automation used to produce large volumes of a single product. Industrial robots are an example of programmable automation.

IT automation:

A mix of tools, practices, and measures are used to automate batch processes and other IT-related manual processes. IT automation can help accelerate software development and IT infrastructure deployment and delivery. Container management is an example of an IT process that automates container creation and deployment.

What are automation trends?

Automation is pushing the limits of complexity. Rules-based automation is being updated by self-learning algorithms and more inclusive data sources, including unstructured data, e.g., emails. Some trends in automation are:

Machine learning (ML):

Automation systems need data to become more intelligent. ML comes in several flavors, but unsupervised ML offers a powerful way to create self-directing automation based on large data sets. Unsupervised ML algorithms do not need rule-direction from a data scientist. Instead, they learn from data as it is ingested, adjust as they learn, and make decisions based on patterns and trends.

Cognitive RPA:

Cognitive RPA transcends routine tasks with its ability to learn and self-correct. Cognitive automation can handle exception paths and edge cases without the need for human intervention. Cognitive automation can work with unstructured data, such as emails and documents, to expand its capabilities.

Internet of Behaviors (IoB) and automation:

The IoB is an extension of the IoT used to track user behavior and patterns to improve customer experience (CX) and better inform digital marketing. The IoB is based on a combination of behavioral science, data analytics, and technology. Automation plays a significant role in applying IoB insights.

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