Abstract
Automation is an indivisible part of recent computer applications. It reduces human efforts, operational cost, and produces results effectively. There are many applications where ‘automation' plays a very crucial role like healthcare, e-governance services, education, logistics, and manufacturing. Recent technologies like artificial intelligence (AI), machine learning (ML), and cloud computing play vital roles in developing automated applications. Data is at center stage in these automated applications. Therefore, one can focus on ‘data', i.e., integrating data from variety of sources, handling the missing data, and processing it to get the relevant data. Classification and clustering methods can be applied to get better results. As discussed earlier, ‘automation' must benefit the end users in terms of time and operational cost. Being researchers, the aim should be on developing the ‘ease of living' applications. With the help of recent technologies, ‘paperless' applications can be developed.
Top1. Introduction
Automation with the help of recent AI, ML and other technologies has gained popularity in every sector. There are many advantages of automation as it significantly saves time and efforts of the users. It is possible to introduce the automation in routine tasks of the organizations. Organizations can maintain the customer and employee retention rate as automation saves more time & providing better quality services. Management and employees can have maximum time for better planning, management of the routine tasks. With the help of automation one can optimize the routine tasks to increase the throughput (output ratio). As compared to the manual approach, it became easier to streamline the tasks contributing in the overall growth of organization. With the help of recent technologies, intelligent automation can provide significant and accurate data used in better decision making. Hence, many organizations are providing training of intelligent automation to their employees. Models adopted by many organizations are based on learning, adapting, and taking better decisions. Being human it is quite difficult to process the large volume of data (big data) but automated tasks (software robots) can easily process it within less time with higher accuracy and better insights.
There are many advantages of intelligent automation like, developing an efficient model, managing the risks, savings the cost, and higher scalability, better experiences, improvising the strategic decisions, and ultimately maximizing the revenue. There are many advantages of the ‘intelligent automation’ in the e-services - it doesn’t require citizens to wait for the approval long time. Concerned authorities can sanction the application if it fulfills all the requirements. However, if everything is digitized and automated it becomes necessary to provide ‘security’ to the personal data (maintaining the privacy). Automated fetching and storing of the data, automatically processing the documents, giving automatic responses to the customers, automatic sanctioning the insurances and medi-claims (medical claims), and automated e-tax filing are the few recent trends of intelligent automation.
1.1 Components of Intelligent Automation (IA)
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Artificial Intelligence (AI)
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Robotics Process Automation (RPA)
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Natural Language Processing (NLP)
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Character Recognition
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Text Mining
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Machine Learning (ML)
A key challenge in intelligent automation is ‘change management’ i.e. training employees to understand the automation. Modifying the existing organizational structure, infrastructure and automating the routine tasks. However improvising the applied automation is taking feedbacks from the stake holders constantly. If required, based on those feedbacks, one can modify the automated tasks in the next iterations.
Key Terms in this Chapter
Neural Network: Structure that resembles human brain structure.
Deep Learning: Processing the information like human brain does.
Intelligent Automation (IA): Speed-up the business processes with the help of AI and robotics process automation (RPA).
Supervised Learning: Classification of data to train the machines.
Big Data: Large volume of data used in data analysis and decision-making processes.
Data Mining: Extraction of different patterns from the given data.
AI Ethics: Responsible use of AI tools is known as AI ethics.
Natural Language Processing: Understanding the human verbal and spoken language.
RPA (Robotics Process Automation): Is a software robot applied to build, deploy and maintain the processes.
Artificial Intelligence (AI): Methods enable computer systems, and machines to behave like human and helps in critical problem solving.