AI is the new business buzzword. But have you heard of IA? Otherwise known as cognitive automation, intelligent automation is like AI’s big-picture cousin, taking the reins when it comes to making decisions and coordinating activities for smooth operation.

Intelligent automation vs AI: what’s the difference?

Intelligent automation is a new, smarter way to automate your business processes – and, contrary to popular belief, it’s not here to challenge artificial intelligence. Rather, it enhances your current AI and RPA (robotic process automation) technologies, using its advanced decision-making powers to create a steady stream of already computer-powered tasks. Ultimately, this allows businesses just like yours to replace a series of disparate, automated activities that require human action and decision making in between with more agile and efficient operations.

How has Intelligent Automation developed?

Because AI is a core aspect of intelligent automation, it’s important to understand how artificial intelligence has developed to explain IA as a concept.

A brief history of AI

Although current uptake of digital technologies would have us believe otherwise, AI has actually been around since the 1950s. And whilst many initial AI projects were doomed to fail, we now live in a world where 90% of the most prominent businesses use artificial intelligence. From facial recognition and object detection to sophisticated chatbots capable of providing quality customer service, there’s little AI can’t help us with.

How AI operates

AI technologies run on a series of algorithms, which means they need to be trained to recognise specific inputs before taking appropriate action.

Early AI systems were therefore limited when it came to making decisions, given that they relied heavily on rule-based programming. In other words, whilst they were capable of following the explicit instructions that governed their behaviour – allowing them to perform specific tasks – they struggled to handle complex, real-world situations that required nuanced decision making. Unable to check the quality of the own their work and jump between related processes, many regarded them as inflexible and clunky, particularly when used in dynamic working environments.

Deep learning

All of that changed, however, when machine learning and deep learning entered into the picture. Both these processes revolutionised AI by enabling systems to learn from data and adapt their behaviour based on experience. As a result, computers can now analyse large data sets and extract meaningful patterns, without explicit programming. In turn, this allows them to make decisions and predictions based on the information they process.

From robotic process automation to IA

In the past, robotic process automation (RPA) was the main way of automating repetitive, rule-based tasks. The technology was largely used for labour-intensive activities like data entry and extraction, which didn’t require any independent thinking. The bots used would mimic human interactions with digital systems at interface level, following predefined rules and workflows to streamline simple tasks. Nevertheless, RPA alone lacks the advanced cognitive capabilities required to handle complex decision-based activities.

This is where intelligent automation (IA) comes in.

How Intelligent Automation works

Intelligent automation combines RPA with advanced technologies like artificial intelligence and machine learning (ML). This allows it to automate not only repetitive tasks but also cognitive functions that involve reasoning, decision making and data analysis.

Leveraging Intelligent Automation

One of the best things about intelligent automation is that it works with your existing technologies and can be adopted in a matter of just weeks or days. What’s more, getting the results you want couldn’t be easier:

1. Machine learning and AI algorithms are used to analyse both structured and unstructured data. This information will then be used as your knowledge base, from which you will make valuable decisions and business predictions.
2. To make the most of your IA technology, you will then need to look at business process management (BPM). This essentially consists of automating your business workflow to improve agility between different operational processes.
3. You can then use robotics process automation (RPA) technologies to complete any repetitive tasks.

IA goes beyond rule-based automation, learning from patterns, making predictions and handling unstructured data. As such, it is able to adapt to changing scenarios. It works in conjunction with your existing technology, coordinating AI and other systems to facilitate the quick and efficient interpretation of data, documents and natural language. Whether you use it to perform sentiment analysis or to make data-driven recommendations, it’s guaranteed to raise the performance of your current automation, bringing process optimisation and intelligent decision making to the table.