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Automation is a blanket term used to describe any task or process that can be automatically done by a program or robot.
But what happens when the process is complex and the rules aren't rigid or fixed?
When that happens, different layers of technology, or intelligence, need to be added to help the software bot, or digital worker, to complete its task.
You don't have to be an engineer to understand these differences. In this article, we'll spell out the differences between artificial intelligence and machine learning, what robotic process automation is, and at what point robotic process automation becomes intelligent automation.
Types of Digital Process Automation
Digital process automation is exactly what it sounds like: automation software that carries out entire digital workflows through the use of custom programmed bots, or digital workers. When a digital process is thoroughly mapped out, a digital worker is able to complete it from start to finish, in record time, with no errors, and no fatigue.
We call this robotic process automation, or RPA, and it's how companies are keeping up with back-office work at a time when skilled labor is running at a simultaneous premium and scarcity.
What is robotic process automation?
Robotic process automation (RPA) is a type of automation that can emulate human actions on a computer, such as navigating software and inputting keystrokes. It’s tailor-made to fit the specific needs of the task it’s performing, and can run autonomously, uninterrupted, far beyond the hours and capabilities of a human worker. These digital workers are excellent at performing logic-driven, rules-based workflows with multiple steps.
At their most basic level, digital workers using RPA can do the following:
- Read and scan documents using optical character recognition (OCR) technology
- Send and read emails
- Log in and out of multiple programs and portals
- Move information back and forth between systems (copying and pasting)
- Generate reports between multiple dashboards
These digital workers operate in much the same way as a digital assistant, often reducing the overwhelming workloads associated with medical billing, back-office paralegal work, and accounting operations.
What is intelligent automation?
On the other hand, intelligent automation (IA) uses several automation technologies combined. This can include RPA, artificial intelligence, machine learning, and other technologies paired with business process management tools. Businesses using IA can reduce the manual labor required for mundane tasks and scale to higher-function tasks that may require strategic decision-making and analysis.
Imagine the “sandwich-making game” you might have played in grade school, where your teacher asks you to give them detailed instructions to assemble a sandwich. If you tell your teacher to put mustard on the bread, they’ll just put the whole bottle on the loaf.
RPA operates similarly, requiring precise instructions to perform a task. However, it will continue to make the same sandwich, in the same way repeatedly, and there are no problems with it generally as long as the process documentation is thorough and specific.
With IA, you can take that sandwich-making system and scale it to include the ability to make strategic decisions on what to put on a sandwich, or non-logic based decisions that may vary depending on a wide variety of circumstance.
What’s the difference between artificial intelligence and machine learning?
When we talk about intelligent automation, what we're really referring to is some form of artificial intelligence or machine learning layered over top of RPA. Where processes are a little nuanced, these technologies can enhance the capabilities of RPA to even further remove humans from the workflow.
But artificial intelligence and machine learning are very similar concepts, and the differences between the two aren't exactly black and white.
John McCarthy originally coined the phrase 'artificial intelligence' in the 1950s and would later define it as “...the science and engineering of making intelligent machines...”. In simple terms, artificial intelligence (AI) combines the speed and efficiency of computers with logic and reasoning that can think and act rationally without human interaction. AI can be found in everyday technology, from virtual assistants like Siri and Alexa, to how Netflix decides what shows it thinks you’d enjoy.
While AI can be used in automation technology, it isn’t always necessary. Digital workers using RPA can utilize AI to make informed decisions while working, or they can be simple processes that don’t require AI. AI is imperfect, and there are plenty of logic-based processes that don't require it to be effectively automated.
One of the ways AI can make these informed decisions is through a system of algorithms known as machine learning (ML). These algorithms take data and form them into a model and use it to make decisions or predictions without being programmed to. These tools have been widely adopted into today’s technology and are often seen hand-in-hand with AI technology.
What’s the difference?
While some may argue that machine learning is merely a part of AI, others believe it is its own technology field. In reality, only some aspects of ML should be treated as such.
Machine learning relies on comprehensive and reliable data to create models, and is prone to biases or poor results if the data isn’t thoroughly vetted. This is especially true in the case of Microsoft’s AI-powered chatbot Tay, who used ML to learn from other Twitter users, who ended up feeding her unsavory information to be added to the model for future responses.
On the other hand, AI is a broad umbrella comprised of several different fields and ideologies. It incorporates natural language processing, learning through ML, speech and facial recognition, and problem-solving.
RPA doesn't always mean AI – because it doesn't have to
A lot of people approach RPA assuming that it's using a bunch of intelligent and fully autonomous digital workers. In some ways, that's really sexy, and in other ways, it can be a buzzword that adds an unnecessary and ultimately very expensive layer to a solution that might not require it.
The truth is that RPA is not always synonymous with AI or machine learning, and that these are just layers of capability added on top of RPA.
Most often, RPA doesn’t need any form of AI input to create an efficient digital worker. When it comes to logic-based tasks such as data entry or invoicing, RPA can solve these issues without utilizing AI.
The strongest use case for AI at the time of this writing is for digital assistants, and even those capabilities are still very early on in their development. For intelligent process automation and automating complex digital workflows, automating the rule-based and delegating the strategic to humans remains the most effective utilization of this technology.