Intelligent Automation: Everything you Need to Know in 2021

In today’s digital age, people are increasingly expecting quick, if not instant, results. As such, efficiency is more important than ever before. 

And even though a lot of businesses have digitized their front office, that is the part that customers or clients see, their back office where their personnel works every day, are often still very archaic and outdated. 

As a customer, you can easily fill out an online form to request a consumer loan or make an insurance claim. Yet, it often still takes several days before your request is handled by a person. This is because even the most basic tasks, such as checking if all the information is there, are done manually. 

And let’s face it, with people expecting quick results, this is no longer something customers are going to accept from their bank, insurer, accountant or many other businesses. 

That is why the rise of intelligent automation (or hyper-automation) has made it onto Gartner’s list of the ‘Top Strategic Technology Trends’ for the second year in a row. 

But what exactly is ‘Intelligent Automation’ and how can it help your business?

To answer this question, we’ve put together this guide. And below, we’re going to look at the following chapters: 

  • What intelligent automation is – a definition 
  • The three key concepts of intelligent automation
  • How intelligent automation differs from RPA 
  • How intelligent automation will power the next wave of automation
  • The benefits of intelligent automation
  • Examples of intelligent automation

So, let’s not delay any further; it’s time to get started. 

What is intelligent automation? A game-changer.

The world has already become familiar with automating some simple repetitive tasks thanks to classic rule-based automation such as robotic process automation (RPA) tools.

These RPA tools are software or technologies that make it possible to build, deploy and control robots that can emulate human actions. For example, data entry, data extraction or invoice processing. 

This makes RPA tools good at handling the same structured information over and over again.

However, intelligent automation helps to automate more than just these repetitive tasks. It also helps to automate thinking (or cognitive) tasks. With Artificial Intelligence at the core of intelligent automation technology, there’s no more need to hard-code specific rules for the machines to follow. 

Instead, you just need to feed the machine information and it automatically learns from feedback and new examples. 

This is why intelligent automation is such a game-changer. Thanks to this technology, a machine can also apply what it has learned to unstructured information. 

You just need to feed the machine information and it automatically learns from feedback and new examples. This is why Intelligent Automation is a game-changer. Thanks to this technology, a machine can also apply what it has learned to unstructured information. To help you understand this further, we’ve pulled together a list of the differences between classic automation and intelligent automation:

Classic AutomationIntelligent Automation
Repetitive tasksThinking/cognitive tasks
Structured informationUnstructured information
Hard-coded rulesLearn from examples

As the vast majority of the data, we use today is unstructured, you can easily imagine that this is a game-changer with huge potential to automate more of the basic administrative tasks instantly and 24/7. Something which would take humans and even classic automation much longer to do.  

Intelligent automation is a steppingstone towards general artificial intelligence (AI) 

Artificial intelligence is no longer confined to academic laboratories and can now be found in several applications we use every day. 

For example, the facial recognition software you use to unlock your smartphone or the software that moves some of the emails that you receive directly to your junk folder. 

These, and many other examples that we know today, are referred to as ‘narrow’ artificial intelligence. The individual applications have learned to be very good at doing the one specific task they were programmed to do.

Then we also have something called ‘general’ artificial intelligence. Visionaries and dreamers explain general artificial intelligence as the ability of a machine or computer program to think like a human.

Simply put: one machine will be able to do anything a human can do. According to famous futurologist Ray Kurzweil we are still two to three decades away from reaching general human-level artificial intelligence or ‘Singularity’.

Just like the steppingstones to cross a river, there will be stepping stones to go from narrow to general artificial intelligence in the future. Intelligent automation (aka hyper-automation, intelligent process automation, or cognitive automation) is one of those steppingstones. 

Intelligent automation combines all the basic capabilities that any office worker needs. Autonomous driving is a good analogy that also combines various narrow AI applications that any driver needs, such as road sign identification or finding the optimal route.

The three key concepts of intelligent automation

Intelligent automation systems are made possible because of three key concepts. These are broken down into the three Rs of automation. These include: read, reason, and rely on. 

These make up the technological foundations for intelligent automation. Now let’s take a closer at each of these three concepts in more detail. 

ReadDiscover (information) by reading it in a written or printed source
ReasonThink, understand and form judgments logically
Rely onDepend on with full trust or confidence

Let’s make this concrete on a typical email from an insurance use-case:

Automate indexing of incoming email

To help you understand this further, we’ve put together an intelligent automation example of how these three concepts are used in a typical email from an insurance case. In the example, the individual is trying to end their policy. 

We’ve broken this down into the three steps below: 

1. Read

The first concept or stage of the process is reading. This consists of discovering the relevant pieces of information found within the email. Data extraction technologies are used to find the relevant information. For example, the insurance policy number. 

In this case, the policy number literally appears in the digital text from this email. 

If this had been a paper letter, Optical Character Recognition (OCR) would have needed to be applied first to transforms a picture of the letter to a digitized text. OCR and data extraction are used in most intelligent automation use-cases.

2. Reason

The next concept is reason. This means the system needs to understand what this message is about and make a decision as to what needs to be done next. 

As the concept ‘car insurance cancellation’ or ‘end of vehicle ownership’ do not literally appear in the message, the system needs to work this out for itself. 

This means technologies such as Natural Language Processing (NLP) as well as Machine Learning classifiers are used to achieve this. 

3. Rely on

The final step is to rely on. This concept was created to answer the simple question:

Can we trust the machine?

If the answer to this question is no, a person would need to get involved in the validation of the decision and correcting this if necessary. Asking a person to validate a decision made by AI is often referred to as ‘bringing a human into the loop’.

Therefore, each decision made is given a confidence score, reflecting the trust in the machine. 

How intelligent automation differs from RPA

Robotic process automation (RPA) has been hyped enormously in the past few years, giving birth to several unicorns. RPA can best be described as the old excel-macro on steroids, relentlessly executing repetitive macros across not just one but several applications. 

A typical example of how RPA is used is when you type information received via a web form into the database of an ERP system. RPA manages to automate many low-value-adding tasks like this that are based on repetitive tasks, with structured input and fixed rules. 

This makes RPA one of the more advanced forms of classic automation that we described earlier. Yet, as it does not learn from its mistakes, it can’t handle exceptions that it hasn’t been programmed to deal with.

The focus of RPA is more on input/output between systems via screen scraping and mouse/keyboard automation and less about the actual content itself. As such, the typical shortcomings and drawbacks of RPA include:

  • There is no solution for semi or unstructured content
  • There is no solution for rules that are somewhat ambiguous
  • Screen scraping and emulation is not future-proof and breaks easily

Contrast this with intelligent automation, which incorporates artificial intelligence (AI) technologies like machine learning, natural language processing (NLP), structured data interaction, and intelligent document processing (IDP).

Because these AI technologies can simulate types of human intelligence, this means that automation can process higher-function tasks. That is tasks, that require more reasoning, judgment, decision, and analysis.

In fact, intelligent automation is focused more on the heavy lifting of the actual content than RPA. 

This is why intelligent automation will power the next wave of automation.

How intelligent automation will power the next wave of automation

Despite intelligent automation being the next big step, this doesn’t mean RPA is on the way out. Rather, RPA, intelligent automation and machine learning will go hand in hand to enhance one another and power the next wave of automation technologies. 

It’s estimated that the future of automation will provide cognitive systems that can self-learn and handle processes better. This will improve the overall workflow by making the necessary modifications and optimizing the processes to be able to extract and organize more semi-structured or unstructured data. 

This means that intelligent automation tools will be able to handle a wider range of more complex activities like never before. 

That being said, while intelligent automation RPA is getting smarter and will eventually supersede basic automation, many of these levels of superior cognitive technology are yet to be realized. In many cases, they are still in the early stages of development. 

The benefits of intelligent automation

So why would you opt for an intelligent automation solution instead of other (often cheaper) process automation solutions?

Well, intelligent automation can have several benefits for your business. Some of the key benefits include: 

Increasing the efficiency of your processes 

A robot can achieve in just a few microseconds what it could take a human minutes, and perhaps even longer to achieve. This means you can save time and make your business functions more efficient.

Not only this, but humans can only work for a limited amount of time in the day. Intelligent automation can work 24/7 because the software does not need free time or sleep. What’s more, as lots of these tools operate on the cloud, these are accessible from almost anywhere. 

All of this contributes towards increasing the overall efficiency of your processes. 

Improving the customer experience

Intelligent automation makes it possible to complete tasks in a much quicker and simpler fashion. Not only is this great news for employees, but the benefits also trickle down to the customers too. 

Think about it, if they’re waiting for a decision, response, or even something like having a product shipped, all of these automated processes can be done outside the usual 9 to 5 and can therefore speed up the process. 

This means they get what they want much quicker. Something which, as we mentioned earlier, today’s digital natives have come to expect.

Optimizing back-office operations

Automated systems can drastically improve your back-office operations. They make it possible to automate the monotonous, repetitive tasks that once fell to employees. This gives employees more time to do meaningful work and focus on more important aspects of their role.  

Boosting workforce productivity

Following on from this, you can boost the productivity of your workforce by minimizing human errors, which means less time spent fixing these issues. 

It also helps to empower employees as they don’t necessarily need special technical skills to use these programs. This can empower the employee to get their job done more efficiently and effectively.

Reducing costs

One of the biggest advantages of intelligent automation is that it can help to cut costs for your business. 

When work is automated, it is completed faster, can be performed 24/7 and you get greater output for less money. This means you tend to see a much faster return on investment (ROI) than some other technologies you might use in your business. 

Reducing the risks of an error  

Automated processes reduce the risk of human error, which can be one of the leading causes of breaches and issues in a business. Not only this but if you automate these tasks and keep them in-house rather than outsourcing to a third-party provider, you have more control over your information and workload.

Being scalable and easy to implement 

Unlike an employee headcount, automated systems can be scaled up or down quickly and more cost-effectively. Plus, it’s easy to set up as much of the underlying technology you need already exists in your business. 

What’s more, because these machines are increasingly self-learning, they continually learn and update themselves without the need for human intervention. 

Examples of intelligent automation in practice

Intelligent automation companies, software and tools are designed to help you automate various cognitive tasks. Just some of the example use cases of how this can be applied in business include:

  • Real-time insurance claims handling
  • Robotic accounting
  • Delivering information to the relevant departments
  • Document collection and validation (for example, with credit applications)
  • Digital client onboarding
  • Indexation of incoming emails (mailroom automation/email routing)
  • Pulling data from financial databases
  • Updating financial records   
  • Analyzing shipping data to optimize shipping routes 
  • Reducing shipping bottlenecks, preventing delays, and optimizing the available resources

These are just a few of the ways you might use intelligent automation in your business. As we begin to fully grasp automation of this nature, more tools are being created all the time to help automate even more processes. 

In summary 

So, to sum up, as there has been a lot to take in here, we’ve quickly pulled together a few of the key points you should take away from this guide. These are: 

  • Intelligent automation helps to automate repetitive tasks. It also helps to automate thinking (or cognitive) tasks
  • This means these tools can simulate types of human intelligence and process higher-function tasks
  • As it becomes more popular, intelligent automation is a steppingstone towards general artificial intelligence (AI) and it will power the next wave of automation
  • Intelligent automation can be broken down into three key concepts: read, reason, and rely on. This allows them to deal with unstructured data
  • Finally, the key benefits of intelligent automation include increasing the efficiency of your processes, improving the customer experience, optimizing back-office operations and reducing costs and the risks of an error  

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