{"id":2332,"date":"2026-04-01T23:04:45","date_gmt":"2026-04-02T03:04:45","guid":{"rendered":"https:\/\/blog2wrkdev.wpenginepowered.com\/intelligent-automation-guide\/"},"modified":"2026-09-30T08:24:23","modified_gmt":"2026-09-30T12:24:23","slug":"intelligent-automation-guide","status":"publish","type":"post","link":"https:\/\/blog2workato.wpengine.com\/intelligent-automation-guide\/","title":{"rendered":"What Is Intelligent Automation? A Complete Introduction"},"content":{"rendered":"\n\n\n

Its previous form\u2014where it involved using robotic process automation (RPA) software, a business process management (BPM) tool, and AI capabilities (e.g. intelligent document processing)\u2014was riddled with issues.<\/p>\n\n\n\n

RPA has proven to be brittle and unscalable; its bots can break with any small UI change and the process of building and managing more bots requires adding personnel with technical expertise. <\/p>\n\n\n\n

Add to this BPM\u2019s inherently limited functionality, and it becomes clear that intelligent automation is not the transformative solution that organizations once imagined.<\/p>\n\n\n\n

\"Performing<\/figure>\n\n\n\n

Performing intelligent automation today requires your organization to integrate all of their applications for a given process at the API-level, including AI and ML-based tools. Your organization should then build workflow automations that work across these connected systems.<\/p>\n\n\n\n

We\u2019ll show you how intelligent automation can work in practice, but first, let\u2019s take a step back and formally define the term.<\/p>\n\n\n\r\n

\r\n\t\t
\r\n \"\"\r\n
\r\n

Ready to implement intelligent automations?<\/h4>\r\n

Learn how Workato, the leader in enterprise automation, can help you leverage AI and ML when building end-to-end automations.<\/p>\r\n <\/div>\r\n\t\t\t\t Schedule a demo<\/a>\r\n\t\t\t\t <\/div>\r\n\t\t <\/div>\r\n\n\n\n

What is intelligent automation?<\/strong><\/h2>\n\n\n\n

Intelligent automation is an automation that uses AI or machine learning to optimize or transform a business process.<\/strong> This requires using two separate tools that can interact with one another: one is capable of building and managing automation, and the other(s) is an AI or ML-based tool.<\/p>\n\n\n\n

How different is intelligent automation from automation?<\/strong><\/h3>\n\n\n\n

You might wonder what the differences are between automation and intelligent automation. Let’s take a look.<\/p>\n\n\n\n

Features<\/strong><\/td>Automation<\/strong><\/td>Intelligent automation<\/strong><\/td><\/tr>
What does it do?<\/strong><\/td>Uses technology for performing repetitive tasks.<\/td>Combines automation with machine learning and artificial intelligence to make decisions.<\/td><\/tr>
Complexity<\/strong><\/td>Works to perform simple repetitive tasks.<\/td>Works with dynamic and complex operations.<\/td><\/tr>
Adaptability<\/strong><\/td>Follows certain predefined workflows and rules.<\/td>Learns, adapts with scenarios, and improves over time.<\/td><\/tr>
Decision making <\/strong><\/td>Can’t make any decisions.<\/td>Uses AI for analyzing data and making decisions.<\/td><\/tr>
Example<\/strong><\/td>Rule-based automationScript-based data entry<\/td>Predictive analytics.AI-powered chatbots.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

We will discuss some more examples of intelligent automation in the coming sections. But before that, we will learn about the key components of intelligent automation.<\/p>\n\n\n\n

Key components of intelligent automation<\/strong><\/h2>\n\n\n\n

As discussed, intelligent automation integrates advanced technologies to make the systems adaptive and smarter by enhancing automation. The key components are –<\/p>\n\n\n\n

1. Robotic process automation (RPA)<\/strong><\/h3>\n\n\n\n

RPA<\/a> enhances efficiency by automating rule-based tasks.<\/p>\n\n\n\n

2. Process mining and analytics<\/strong><\/h3>\n\n\n\n

Optimizes workflows by identifying inefficiencies.<\/p>\n\n\n\n

3. AI and ML<\/strong><\/h3>\n\n\n\n

AI enables systems to recognize patterns by analyzing data and make decisions. ML, on the other hand, helps to improve automation<\/a> over time by learning from user interactions and data.<\/p>\n\n\n\n

4. Natural language processing (NLP)<\/strong><\/h3>\n\n\n\n

This allows machines to understand human language and respond accordingly.<\/p>\n\n\n\n

5. Intelligent decisioning<\/strong><\/h3>\n\n\n\n

IA uses insights driven by AI to enhance decision-making and automate complex tasks.<\/p>\n\n\n\n

Now that we have learned about the components, let’s take a look at a few detailed examples of intelligent automation.<\/p>\n\n\n\n

Examples of intelligent automation<\/strong><\/h2>\n\n\n\n

Here are just a few examples of intelligent automation.<\/p>\n\n\n

\n
\"You<\/a><\/figure>\n<\/div>\n\n\n

1. Leverage machine learning to classify support tickets<\/strong><\/h3>\n\n\n\n

A machine learning model is only as powerful as the quality and quantity of data it receives.<\/p>\n\n\n\n

You can ensure that your ML solution receives a high volume of valuable data by using an enterprise automation platform to integrate it with a variety of source systems. <\/p>\n\n\n\n

Once connected, the enterprise automation platform can pull data from a variety of sources, cleanse it, validate it, and then load it to your ML solution. <\/p>\n\n\n\n

Once it receives enough data, the machine learning model can\u00a0draw predictions or make a decision, which can trigger the appropriate workflow automation.<\/p>\n\n\n\n

For instance, say your organization has thousands of employees across the globe. As a result, your IT team receives a high volume of support tickets, 24\/7; this makes the prospect of classifying and routing tickets to the appropriate IT help desk quickly near impossible.<\/p>\n\n\n\n

To help streamline this routing process, you can connect your ITSM tool (e.g.\u00a0ServiceNow<\/a>) with a storage solution like Amazon S3. You can then send countless tickets to the latter so that Amazon SageMaker<\/a> can deploy an ML model that accurately classifies each ticket.<\/p>\n\n\n\n

Once a ticket is classified by Amazon SageMaker, an automation gets triggered: the ticket gets assigned to the appropriate IT help desk, and to ensure that the personnel within that help desk are made aware of the ticket quickly, they receive a message in a channel within your business communications platform (e.g.\u00a0Slack<\/a>).<\/p>\n\n\n

\n
\"An<\/figure>\n<\/div>\n\n\n

2. Provide an intelligent helpdesk<\/strong><\/h3>\n\n\n\n

Your employees will likely have the same set of questions over time\u2014whether that\u2019s related to insights on a competitor or your company\u2019s holiday schedule.<\/p>\n\n\n\n

Instead of having employees ask one another for the answer or forcing them to search in your knowledge base, you can provide them with what they\u2019re looking for in a matter of clicks within your business communications platform.<\/p>\n\n\n\n

At the foundational level, you can use an enterprise automation platform to connect a solution that uses natural language processing (NLP), like IBM Watson, with your knowledge base software platform (e.g. Confluence<\/a>). You can then implement the following automation:<\/p>\n\n\n\n

\n