When it comes to automating business processes, there exist nuances in the various technologies. From robotic process automation to human analytical automation, these tools can help save your organisation time, which you know, equals money. In this article, we’ll cover the difference between RPA and AI.
3. What is the Benefit of RPA?
7. What is the Difference Between AI and RPA?
8. When to Use AI and When to Use RPA?
10. What are RPA and AI Examples?
RPA, or robotic process automation, is software that mimics human action to complete rule-based tasks. With the robotic completion of structured tasks, you can streamline and expedite business processes. The software will complete tasks the same every time, so you can use it to standardise any process that has structured data as inputs.
Robotic process automation is made for repetitive, continuous, and clearly defined processes which would otherwise have to be completed manually. RPA has become known as the digital worker because it can take care of time-consuming and low-level work, such as: logging into applications, copying data, scraping data, and connecting to APIs.
Robotic process automation is useful across industries because every type of business has at least one type of process that is repetitive and manual. That being said, the industries that are benefiting most from RPA are banks, financial services, telecommunication companies, and insurance companies.
This is because these industries rely on massive amounts of data and require utmost attention to detail.
When you incorporate RPA into your organisation, you have the power to free up your employees’ time from having to conduct monotonous tasks. As such, you open the door to new possibilities with a more creative, actively invested, and energised workforce.
Additionally, utilising RPA will change the way your organisation gets work done. The biggest benefits of automation include:
RPA is often the incipient way for businesses to adopt automation technology. Upon getting used to low-level business processes being handled by this type of software, organisations realise the immense benefits of automation.
Then, they can level up to automation solutions like human analytical automation which can carry out workflows, deliver deep insights, and help business leaders make informed decisions.
There is a difference between RPA and AI. AI automation, or artificial intelligence, is a computer’s ability to resemble a human’s intellectual and mental abilities. Artificial intelligence creates the ability for computers to intake data, be it structured or unstructured, and make sense of it by knowing what to do with it. This allows AI automation to execute tasks that are not just rule-based (like those that RPA requires).
AI captures data that then can trigger judgement-based responses and actions. Some examples of AI in action include facial recognition technologies, data analysis, and voice recognition. AI is behind tools that you use on the daily, like Siri on your iPhone or Alexa on your Amazon Echo.
AI is used for a multitude of purposes. It’s common nowadays for websites to have chatbot assistants, which is one use of artificial intelligence. Additionally, finance teams can leverage AI to perform advanced analytics, such as being able to complete tax forecasting with immense accuracy. AI can help to optimise products, manage inventory, plan logistics, and more.
While AI is not an inherent part of robotic process automation, the two technologies can come together to maximise outcomes. When paired together, artificial intelligence and robotic process automation create a third type of technology known as intelligent process automation (IPA), or smart process automation (SPA).
Intelligence process automation carries out repetitive tasks from the RPA inclusion, but it can do even more through the addition of machine learning. Machine learning models are an underlying force of artificial intelligence which allow the software to learn over time through access to more data and the recognition of patterns.
As such, intelligent automation can make decisions as if it were a human because of algorithms and insights gleaned from data.
When paired together, AI and RPA transform organisations immensely to reduce costs, streamline workflows, and achieve operational efficiency.
Since both artificial intelligence and robotic process automation are technologies that can execute tasks and mimic a human’s abilities, it can be easy to confuse the two. However, when you think of one as the brains and one as the body, the differences become more clear.
In essence, artificial intelligence is like a human brain in that it carries out the “thinking” process to make decisions and judgements based on the information that it has available (i.e. data, patterns, trends, analysis).
On the other hand, robotic process automation completes tasks based on rules, a.k.a. like the human body that is all about “doing” things.
Robotic process automation is focused on getting a process done, or being process-centric. The RPA tools are made for repetitive and rule-based, simple tasks. RPA must be trained to execute a process and relies on structured data that is clearly defined.
Artificial intelligence is data-centric and can utilise machine learning and natural language processing, for example, to approach unstructured data. The system can train itself through algorithms to recognise incoming data and take immediate action based on what needs to be done.
Both RPA and AI can hasten the time it takes to complete business processes, so how do you know which to deploy for what purpose? For starters, understanding the difference between RPA and AI helps.
The best way to get started is to implement robotic process automation for processes with the following characteristics:
RPA is the entry-level of automation, so once you have it deployed to manage your simple processes, you can use artificial intelligence to take care of any process or action that requires more complexity. For example, AI will be necessary when:
Machine learning refers to the technology that can learn with data without having to be programmed explicitly. Machine learning does this by leveraging semi-structured and structured historical data.
However, when looking at machine learning vs artificial intelligence, machine learning provides slightly less capabilities than artificial intelligence because machine learning works in specified areas of knowledge. You can create a machine learning model to handle a certain type of task within a defined purpose, but the same model won’t be useful for a different end goal.
Thus, machine learning sits between robotic process automation and artificial intelligence in terms of its ability to handle complexity.
RPA and AI can be witnessed at work in tandem in many settings. Let’s review some examples to get a better understanding of the possibilities:
Finance transactions have become ubiquitous. Digital transactions require utmost security, and as such, RPA and AI can save the day. For starters, robotic process automation makes it possible to capture a user’s data from different sources.
This helps to detect fraud if something seems to stand out from the normal usage of funds. Additionally machine learning and AI can allow for predictive analysis to spot red flags and prevent fraud from taking place in the future.
Chatbots have sprung up everywhere, and they are making for a more seamless and expedient customer support environment. Robotic process automation and AI empowers these chatbots to perform actions (i.e. add more to a customer’s order) and also to pre-empt customer complaints by addressing problems before a customer has to spend their time providing the details (thanks to historical data).
Automation is more than a buzzword, it’s become a necessary way of operating organisations and supporting people. Now that you know the difference between RPA and AI, it’s clear that using both technologies is beneficial.
Alone, robotic process automation can complete rule-based and defined tasks, but when it is paired with artificial intelligence, the software has the power to make decisions, predict future outcomes, and execute high-level tasks.
Balance sheet reconciliations are needed in every business. Here's a guide when you’re looking to streamline balance sheet reconciliation.
Account reconciliation software streamlines your workflows and reduces the chance for error. Take a look at the best solutions available.