Automation to hyper-automation
Integration automation is where machines can mimic human tasks and repeat the actions once humans define the machine rules. One example is the “digital worker.” In recent years, people have defined digital workers as software robots that are trained to work with humans to perform specific tasks. The use of a repeated set of processes can increase its productivity and efficiency and reduce human errors.
AI is programmed with logic and rules to mimic human decision making. AI can be used to detect threats such as changes in user behaviour or increased data transfers. Automation is the use of technology to perform tasks with reduced human assistance.
Any industry that encounters repetitive tasks can use automation, but automation is more prevalent in the industries of manufacturing, robotics, and automotives, as well as in the world of technology—in IT systems and business decision software. The most complex level of automation is artificial intelligence automation.
The addition of AI means that machines can “learn” and make decisions based on the past situations they have encountered and analysed. For example, in customer service, virtual assistants can reduce costs while empowering both customers and human agents, creating an optimal customer service experience.
Here we are talking about the next generation of automation, that is Hyper automation. Hyper-automation is a business-driven, disciplined approach that organizations use to rapidly identify, vet and automate as many business and IT processes as possible. Hyper-automation involves a mix of artificial intelligence, machine learning, robotic process automation, integration platform as a service and low-code or no-code tools.
We have seen there has been a great shift from Automation to Hyper-automation. When companies use RPA with IT Automation with AI, the impact is mainly positive. However, there is still a need for integrating different software approaches and applications to get even better results.
Hyper-automation integrates RPA, process mining, machine learning, and artificial intelligence. The automation process, in this case, is even more efficient and in-depth. It can help experts or teams focus on other critical services. It also helps to ensure that experts have enough room to work with and essentially expand their scope. The market for hyper-automation services and software is currently booming.
According to Gartner, companies can expect it to reach $596.6 billion in 2021. Through hyper-automation, companies can access data analysis reports within brief periods. The insights are accurate and allow increased precision in decision-making. Since algorithms are present, they can be used to check the correlations between available data and then analyse them to obtain the metrics that are impactful and actionable.
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