Introduction to Intelligent Automation
Intelligent Automation, abbreviated as IA, is an advanced concept in business process automation that makes use of artificial intelligence (AI) and machine learning (ML) to automate and optimize workflows and processes, and it provides enhanced decision-making capabilities to achieve desired business objectives. Therefore, intelligent automation can be considered a modern solution for various business problems and challenges.
In this chapter, we will have a detailed discussion on fundamentals of intelligent automation and its significance in business process automation.
What is Intelligent Automation?
Intelligent Automation (IA) is a modern technology used for automating business processes and streamlining operations across organizations. It makes a combined use of various advanced technologies such as artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), and business process management (BPM). These technologies together provide capabilities to make automation decisions cognitively, hence IA is also referred to as cognitive automation.
Most business organizations employ intelligent automation for following main purposes −
- Simplify and optimize processes and workflows
- Free up resources from low-value, repetitive tasks
- Improve operational efficiencies
- Reduce risks of human errors
- Speed up processes and operations
- Reduce costs, etc.
How Intelligent Automation Works?
Intelligent Automation uses multiple cognitive technologies such as AI, ML, process mining, RPA, BPM, etc. These tools together are capable in automating from simple business tasks to entire end-to-end processes within an organization.
Since, IA can perform cognitive decision-making, it eliminates the need for human workers in many tasks, so they can be engaged in more strategic and high-value activities.
By combining right technologies together, intelligent automation can automate highly complex tasks or processes as well. It also provides following capabilities to an organization −
- Allows to identify processes suitable for automation
- Provides data extraction facilities from data sets and organize them into customized formats
- Helps visualizing information
- Addresses problems and issues without need for human involvement
- Allows people, technology, and processes to share a workspace collaboratively, etc.
How Intelligent Automation is Different from RPA?
RPA (Robotic Process Automation) is an automation technology that uses software robots to perform low-value, rule-based, repetitive tasks within an organization. It provides basic automation facilities by simulating human actions. RPA does not have intelligence like humans; hence it can execute tasks and process data, but cannot interpret them.
On the other hand, Intelligent Automation performs tasks automatically like a human worker. This is possible because it uses multiple cognitive technologies like AI, ML, etc. combined with RPA. Therefore, IA can interpret data and handle the end-to-end processes independently.
The following table provides a comparison of intelligent automation and robotic process automation by considering several important aspects −
| Aspect | Intelligent Automation | Robotic Process Automation |
|---|---|---|
| Function | Intelligent automation provides cognitive automation capabilities. | RPA is used to automate rule-based, repetitive tasks. |
| What it does? | Intelligent Automation simulates human intelligence in automation activities and provides enhanced automation and decision-making capabilities. | RPA simulates human actions to automatically execute simple tasks without any human intervention. |
| Data interpretation | Intelligent automation provides data interpretation capabilities and supports decision-making. | RPA can only download or move data, but incapable in interpreting it. Hence, it does not provide any support in decision-making. |
| Scope of automation | Intelligent automation can automate both structured and unstructured business processes. | The automation capabilities of RPA are limited to rule-based, repetitive tasks. |
| Technologies used | Intelligent automation uses AI, ML, BPM, and other cognitive technologies with RPA. | Software robots are key technological components of RPA. |
| Efficiency improvement | Intelligent automation optimizes entire processes and enhances efficiency to a significant level. | RPA reduces errors and increases efficiency in repetitive tasks. |
| Use cases | Intelligent automation is used to end-to-end automate processes and business operations. | RPA is mainly used to automate simple tasks like data entry, report generation, invoice processing, and file transfer. |
Eligible Processes for Intelligent Automation
Business processes that have the following characteristics are considered eligible for intelligent automation −
- Processes that involve the use of high volumes of data and multiple repetitive tasks are best suited to automate using intelligent automation.
- Processes in which rule-based decision-making is required for execution can be automated using intelligent automation.
- Intelligent automation can automate processes that involve manual data entry.
- Complex processes that use unstructured data can be automated using intelligent automation.
- Processes whose results required to be highly accurate and error-free are suitable for intelligent automation.
- Intelligent automation is also a valuable tool to automate time-consuming and time-critical processes.
Examples of Intelligent Automation
The following are some common examples of intelligent automation driving business excellence in various industries −
- Mortgage Cross-Selling − Banks and credit institutions use intelligent automation to analyze their customer data and determine opportunities of mortgage cross-selling.
- Employee Onboarding and Offboarding − Organizations also use intelligent automation in their HR department to streamline operations like paperwork and formalities related to onboarding and offboarding.
- Quote-to-Cash − Business organizations also utilize intelligent automation to automate billing, quoting, and payment operations. IA provides automated facilities to extract data from invoices and contract documents.
- Customer Relationship Management − Intelligent automation also helps to automate CRM operations like data entry, contact management, etc. It frees up sales employees to focus on customer interactions.
- Inventory Management − Intelligent automation can end-to-end automate supply chain operations, order tracking, and inventory control.
- Quality Management − Intelligent automation provides comprehensive data about performance and automates testing. This helps improving accuracy in quality management and reduces cost involved in quality control.
Conclusion
In this chapter, we provided an overview of intelligent automation concept, its working, and examples of real-life applications.
In conclusion, Intelligent Automation is a technology that transforms business processes and workflows by making use of artificial intelligence, machine learning, and many other cognitive technologies. It allows organizations to automate their repetitive tasks, end-to-end processes, and improves decision-making in business operations.