What is Intelligent Automation?

Intelligent Automation (IA) is a hot topic in the automation space (for good reason). The benefits of incorporating IA into your existing operations are vast – so vast, it warrants an entire blog series. In a 5-post blog series that will publish on Wednesday of each week, I’ll share key insights about why intelligent automation is far more than a technology trend, but a game changer.

The blog series will go beyond defining IA, illuminating the impact it can have on your efficiency, productivity and ultimately, bottom line. Here’s what you can expect each week:

1. What is Intelligent Automation?
2. Signs that you need Intelligent Automation
3. How Intelligent Automation improves efficiency and productivity of your operations team
4. How to design your Intelligent Automation Journey
5. Do’s and Don’ts of Intelligent Automation

This week, we’ll explore the fundamentals – what exactly is IA?

Intelligent Automation is a relatively new term, made popular by Robotic Process Automation (RPA). IA brings together multiple automation technologies to automate a process and is supported by AI/ML to provide a more effective solution.

IA has encouraged the re-emergence of technologies that have been around for decades, like workflow management, OCR, analytics, etc. More importantly, the word “intelligent” highlights the advent of AI/ML in these technologies to solve seemingly unsolvable problems.

IA helps you understand incoming data, whether it is structured or unstructured from multiple sources (email, images, voice, SMS/text, video or chat), and extract meaningful information from it. This allows you to orchestrate the process workflow, that now comprises of human workers and digital workers, to act on the extracted information. Even more, it allows you to make increasingly complex decisions using complex business rules engines or decisioning systems, powered by machine learning, to reduce exceptions.

The diagram represents an Intelligent Automation framework. We also refer to it as Digital Operating Model.

Intelligent Automation Framework

What are the 4 Major Components of IA?

The four major components of IA, as shown in the framework above, can work independently or together to accomplish the following automation objectives:

1. Data Comprehension and Extraction: The data can come from various channels and in multiple formats. The first step is to understand and extract this incoming data using OCR (digitize), Natural Language Processing, templates (structured data) or text analytics.
2. Robotic Process Automation (RPA): Once the data is understood and extracted it must be acted upon by entering it into applications, performing data manipulation/enhancement, approving based on preset rules or sending it to external systems. RPA can be used to accomplish this task, if the actions are structured and rules based.
3. Workflow or BPM: If the work must be orchestrated or needs human involvement, a workflow/BPM tool must be used to orchestrate both Digital Workers and Human Workers. A workflow tool also increases visibility across the process and helps you manage & optimize the process.
4. Artificial Intelligence (AI) or Machine Learning (ML): This aspect is embedded at various places – data extraction, decisioning systems, pattern recognition, Chatbots, text/voice/vision channels for data initiation etc. AI helps reduce exceptions and increase the level of automation across your processes.

Do you really need Intelligent Automation? I will address this in my next post titled - “Signs You Really Need Intelligent Automation.” In the meantime, visit for more information about how IA can benefit your organization.

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