What Is Intelligent Automation: A Complete Guide

What Is Intelligent Automation?

Intelligent automation is built on two pillars: automation and intelligence. The first pillar eliminates repetitive tasks from the business, while the second tries to bring human-like, intelligent decision-making into the process.

Hence, when considered as a single unit, intelligent automation becomes a critical part of a business that helps cut costs and manage limited resources efficiently.

Intelligent automation is achieved using three major technologies:

• Artificial Intelligence: A branch of computer science that provides technologies and methods to induce intelligence into digital processes. In intelligent automation, these techniques are integrated only with the automation tools.

  For example, natural language processing is an artificial intelligence branch that works on understanding human language and responding to the person accordingly. In the case of intelligent automation, one can instruct AI with the English sentence “Generate invoice for ABC enterprises since last payment” and the invoice should get generated.

• Robotic Process Automation (RPA): A branch of automation that helps build software applications that can interact similarly to a human. A common example of RPA is the employee onboarding process in which all the onboarding formalities are individually conducted using RPA software without human help. It includes ( but is not limited to) document collection, document validation, and clearing doubts over a chat.
• Business Process Automation: A branch of automation that focuses only on automating business processes. For example, every task associated with invoicing can be clubbed under BPA, including invoice generation, invoice processing, automatic payment release, recording payments, etc.

Why Incorporate Intelligent Automation?

Incorporating intelligent automation into the existing infrastructure or while building a new business looks like a complex task. It involves multiple technologies that work in different directions but together to work as a single unit.

It requires expert resources for the inclusion of intelligent automation and maintenance during its run. The initial costs associated with it can raise questions about the benefits and ROIs a team will get in the short or long run. These benefits can be listed as follows:

• Refined System: Intelligent automation leaves very few requests, queries, complaints, and resolutions for manual inspection. It decreases the load on the support team, refining the overall process of the software system and giving them a chance to tackle something new every day instead of repetitive queries.
• Eliminates Errors and Risks: Processes that run with intelligent automation do not make human errors since they are machines and algorithms. However, the risks of such errors can be very high with sensitive processes. For instance, verifying a customer’s eligibility and issuing them a credit card is a high-risk process.

  If a card with the incorrect limit is issued, the risk of defaulting and never recovering the cost is extremely high. Therefore, it is better to intelligently automate this process and reduce manual intervention as much as possible, which can reduce the error percentage.

• Accurate and Consistent: Machines are always accurate and do not require any breaks. They can work consistently for 24 hours with the same accuracy, making intelligent automation a desirable partner in the software system.
• Dodge Compliance and Legal Battles: Many times, humans forget if any of their actions are legally problematic, or they make an exception, considering it a minor issue. This can go unnoticed at that point, but later, when discovered, it can cost an organization a lot.

  On the contrary, machines work with set algorithms. If some criteria are entered into them and the current scenario doesn’t fit, there are no exceptions or favors. It is rejected immediately, which provides a lot of peace to businesses.

• Introduces Cognitive Abilities and Computer Vision: Cognitive abilities and computer vision help software behave more human-likely and perform certain tasks that could only be performed by humans.

  For example, eCommerce platforms can analyze handwritten return request forms, understand the customer’s notes, and initiate the return or refund process automatically without requiring manual input.

• Enhance the Customer/User Experience: Long queues to connect to a live customer executive or agent or pending email queries for a long time spoil the business’s image for any customer, even if they like the product or the application. Intelligent automation can replace the agent in most queries, reducing resolution time significantly (often to a few seconds) and uplifting the customer’s experience.
• Save Costs: Businesses run on profits, which influence most of their decisions. Hence, it is always fruitful for them to include intelligent automation in the process, as it can help reduce costs.

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Components of Intelligent Automation

Intelligent automation brings together a range of technologies to handle complex tasks seamlessly. Built around a process automation platform, these technologies include:

• Artificial Intelligence: Enables machines trained on large datasets to perform tasks requiring human-like intelligence, such as perception, language processing, learning, and problem-solving.
• Machine Learning: Enables machines to learn from data and improve performance over time without explicit programming.
• Natural Language Processing: Allows machines to understand, interpret, and generate human language, enabling interaction through chatbots, voice assistants, and other conversational tools.
• Robotic Process Automation: Involves using software bots that execute repetitive tasks, like data entry, invoice processing, and customer service responses, with speed and accuracy.
• Cognitive Automation: Integrates AI and RPA to automate complex decision-making processes, such as pattern recognition and reasoning.
• Computer Vision: Empowers machines to interpret and analyze visual data, facilitating tasks like object recognition, image analysis, and autonomous navigation.
• Intelligent Character Recognition: Allows machines to read and interpret handwritten or printed text, automating tasks like document processing and data entry.
• Process Mining: Uses AI and data mining to analyze and optimize business processes, helping organizations identify inefficiencies and enhance operations.
• Integration Platform as a Service: Connects software applications, systems, and data sources, automating workflows across an organization’s IT ecosystem.

How Does Intelligent Automation Work?

Here are the steps with which you can effectively implement intelligent automation and unlock its full potential.

• Visualize the End Goal: Consider removing bottlenecks and leveraging AI to handle high-volume tasks. It helps you define what your process should look like with AI integration.
• Understand the Technology Infrastructure: Find expertise to design and track your processes using the right algorithms tailored to your needs. It is essential for building a solid foundation.
• Form a Strategy and Build Partnerships: Collaborate with in-house experts to refine business interactions, ensuring accuracy and value. Enlist IT and cloud partners for scalability and evolution.
• Prepare for Change Management: Redefine your organizational structure and prepare the culture for IA. This shift will free up time for employees to focus on high-value tasks and business relationships.
• Execute and Iterate With Continuous Improvement: Leverage machine learning algorithms to improve processes over time. Capture user feedback, monitor, and adjust for better results.

Real-World Use Cases of Intelligent Automation

Here are some real-world AI agent use cases of intelligent automation that are commonly used today to make life easier for teams and businesses.

• Employee Onboarding and Offboarding: Employee onboarding and offboarding both require multiple steps that focus on a document collection, account initiation and settlement, adding or removing employees from the payroll management system, and verifying the documents for their authenticity.

  While all of these are easy tasks, manually and repeatedly performing them multiple times a day can result in errors that pose high risks, especially when it involves finances. Intelligent automation is deployed in these areas today, where it automates the entire employee onboarding and offboarding process.

  In all these processes, the only approval from the manager step was the manual work.

• Fraud Detection: According to Tableau Public, in 2024, U.S. citizens lost over $7.7 billion to digital fraud. An extremely low percentage of this fraud is due to cyber-attacks, and most are due to human errors, such as failing to judge a person and finding them genuine enough to be trusted with their cost. Something similar is also the case with banks.

Intelligent automation acts as a barrier between both parties to verify the authenticity of the person, their documents, their usage patterns, and the intent of the transaction. All suspicious transactions are blocked, and users can be flagged for further manual scrutiny if necessary. It makes financial institutions extremely safe for both banks and users, reducing the fraud rate to a minimum.

• Chats and Support: Intelligent automation has become an intricate part of the chat and support system for most businesses. It acts as the first point of interaction for any customer who needs help or has complaints and concerns about the software product. Intelligent automation can be implemented in a lot of ways with chat and support. In addition, chat support systems run by intelligent automation are always online, which enhances the user experience and brings minimum load to the team for manual work.
• Software Development and Testing: Intelligent automation enhances software development and testing by streamlining repetitive tasks, improving efficiency, and reducing human error. It accelerates development cycles through continuous integration and intelligent test automation.

When it comes to software testing approaches like test automation, AI-native test agents like KaneAI exemplify this innovation by enabling users to create and refine complex test cases using natural language, streamlining the testing process and boosting productivity.

KaneAI is a GenAI native QA Agent-as-a-Service platform for high-speed quality engineering teams to create, evolve and debug tests using natural language commands. It significantly reduces the time and expertise required to begin with test automation.

• Smart Test Creation: Easily create and evolve tests with NLP-driven instructions.
• Automated Test Planner: Automatically generates and automates test steps based on broad objectives.
• Cross-Language Code Export: Transforms automated tests into multiple major programming languages and frameworks.
• Advanced Testing: Define intricate conditions and assertions using natural language.

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Evolution of Intelligent Automation

The journey of intelligent automation makes us believe that processes can continue evolving when they are not isolated but integrated with innovations. Intelligent automation is a product of such integrations.

Let’s look at how intelligent automation has evolved over the years:

• Eliminating Repetitive and Mundane Tasks: In the early 1980s, software started to become much more complex, but organizational resources were limited. As a result, everyone could see that it now took much longer to develop a feature or fix a bug. It was not due to additional responsibilities but an increase in manual tasks that kept piling up with time. For instance, the more features they developed, the more data was required to be stored in sheets, which was all done manually.

It raised a need for automation, whereby mundane and repetitive tasks could be automated, eventually freeing manual resources for other work. Hence, automation came, although in the early stage, and it will grow to be of greater value in a few years.

• Involving Automation in Business Processes: While automation had picked up, it was confined to individual and singular processes only. For instance, generating invoices and the payment system can be automated, but data from one cannot be reflected in another, even if they are both part of the same business.

Such multiple isolated units were then connected as much as possible making the complete process as one single unit. It was termed business process automation or simply automation in business processes.

• Bringing Humanness Into the System: In the early 2010s, automation had to be evolved to accommodate complex scenarios. Before RPAs, automation meant taking over the manual process using a fixed, standardized method. There was no scenario-based decision-making. If two scenarios needed to be accommodated, two different automation systems had to be developed. It changed with the introduction of robotic process automation.

RPA can understand the automation scenario and can perform different types of processes appropriately. RPAs also bring human-like behavior to the system in which the software can perform actions such as copying files, creating tickets for complaints, and processing refunds, all without any manual intervention. It has been a breakthrough technology for businesses.

• Bringing Automation and Intelligence Together: By the 2010s, artificial intelligence had started to pick up, and its applications in the already matured RPA systems were explicitly visible. Up to this point, RPA systems worked based on set rules and scenarios without contributing anything to decision-making. Technologies like cognitive intelligence, machine learning, and NLP seem to fit perfectly well with the business cases handled by RPA and BPA, speeding up the process even faster and making smart decisions on their own.

Aside from automation, AI applications expanded in the areas where human behavior was important. For instance, the system could understand the user sentiment through chat and handle such queries on priority. This has made automation smarter and shifted the method of working more toward the user from being toward the businesses.

Modern intelligent automation platforms are now moving toward agent-based architectures, as described in MCP and AI Agents, where AI systems coordinate tools, maintain context, and make decisions across complex workflows.

Intelligent Automation vs Robotic Process Automation

Intelligent automation and RPA both work for automating the process and, therefore may seem like synonyms at first glance.

However, as explored through different sections of this article, RPA is an entity used in intelligent automation and not a separately used technique in contrast with it. If both are compared or worked upon, the following differences can be observed:

Citations

A Systematic Literature Review on Intelligent Automation: https://www.sciencedirect.com/science/article/abs/pii/S147403462100001X