Till now, technology has made many things go automated though as the competition rises, organisations need innovative ways to manage complexities, and thus based on these organisational needs the concept of intelligent automation has occurred. In this blog, we will focus on the concept of Intelligent Automation and its various components. The role of RPA, AI, and ML in intelligent automation and how they all together shape intelligent automation. We will discuss steps by which one can implement intelligent automation.       

What is Intelligent Automation?

Intelligent Automation can be defined as a unique or human way of automating things by machine with quality of least errors and high efficiency. Earlier used to do just what they were programmed for, but with intelligent automation, they can adapt to changing situations, contexts and learn from past data and interactions and make smart decisions just like humans.

Intelligent automation has evolved from basic automation by integration of new evolving technologies such as Artificial intelligence, Machine learning, Natural language processing, Generative AI, Robotic Process Automation (RPA) and more to have human-like but more productive work efforts.

Now machine learning is been used to learn from past data apply learned algorithms in new systems and apply data patterns in new decisions. NLP allows us to understand human commands and provide related creative solutions for automating tasks and needs no human interpretations.

Applying AI and other technologies in RPA allows for ensuring the processing of huge data, and changing its processing commands as per the situation done by AI and machine learning.     

Key Components of Intelligent Automation:

Below are a few of the key components that make the automation process in any business intelligent such,  

1) Robotic Process Automation (RPA): RPA is just the command-based automating systems or the software robots that automate routine tasks as programmed. They automate data entry, form filling, data selection based on filters and more. Due to their programmed commands, it can process huge amounts of data with high accuracy and efficiency, but its application is limited to specific usage based on its programming. RPA is designed to process huge data in the modern world where organisations process huge amounts of data daily. And RPA processes them to bring in the relevant information.

2) Artificial Intelligence (AI): This concept or technology is been made to analyse huge amounts of data, analyse it interpret it, and make decisions or create creative content based on the data is process. It is designed to analyse data patterns and their changes to forecast changes and make creative decisions. It analyses previous such queries and provides solutions or responses based on them. It makes the system intelligent. AI allows them to make some decisions on their own based on available data and GPTs and prompt training given to AI models. Different AIs are trained in different manners to make relevant decisions.      

3) Machine Learning (ML): This is a subset of AI which focuses on learning from existing data, trends and patterns. ML focuses on the evaluation of existing data, and identifying patterns, it makes the systems learn, adapt and improve to be more relevant to human queries and provide accurate data which is relevant to changing trends and times. Machine learning is the major component that allows the system to be relevant and accurate with changing times and trends, as it makes the system, analyse huge amounts of data learn from changing data provide the most relevant and updated data, and make the system learn and adapt to new systems. An intelligent system with old data is not worth it.   

Benefits of Intelligent Automation

There are various benefits of intelligent systems such as;

Efficiency: It enhances overall efficiency as the optimal utilisation of the resources is been done, and ideal resources are utilised for creative aspects, and this reduces cost per unit and enhances profit. 

Accuracy: Consistent results are promised by RPA while AI and ML ensure accurate results which are relevant across the time, and as per the situation. There is high accuracy unlike manual tasks, and this improves quality.

Productivity: Overall productivity has been raised because the time taken for the competition is reduced with AI, and the accuracy of the output is high as well for overall quality and thus the productivity is high. 

Data-driven decisions: Qualitydecision-making is possible with AI and ML as AI allows to analysis of data and brings in the most relevant information while ML ensures the data the information is accurate and up to data and suits best the decision, and that the set picks relevant information. Emergentech is one of the best intelligent automation online training courseproviders to enhance your data-driven decision-making capabilities via Intelligent automation. 

Improved customer experience: AI can use natural language processing, ML and GPTs to ensure the best customer service possible by providing human-like responses with accurate data and relevant information which suits the query best.   

Steps to Implement Intelligent Automation

Steps to implement intelligent automation include;

Identify Opportunities: Start to analyse the bulky tasks which are routine and repetitive and need no human intervention such as data entry, sorting data from different sources, etc.

Evaluate feasibility: Consider whether intelligent automation is feasible for your business or not such as return on investment (ROI), implementation cost, complexity, employee training, changing trends, etc.

Selecting the best tools: Select the right tools to implement intelligent automation such as ML algorithms, AI tools, RPA platforms and more.

Test automation solution: Test the system to check for accuracy and efficiency, and whether it meets the deadline or not.

Monitoring: Monitor performance for smooth operations and make changes in algorithms.