Artificial Intelligence (AI) and Robotic Process Automation (RPA) are crucial in driving operational efficiency in today’s modern businesses. According to Grand View Research, the global AI market size was valued at USD 39.9 billion in 2019 and is expected to grow at a compound annual growth rate (CAGR) of 42.2% from 2020 to 2027. Similarly, the global market size of RPA was valued at USD 1.40 billion in 2019, with a projected CAGR of 40.6% from 2020 to 2027.
Yet despite the widespread adoption of AI and RPA across multiple industries on a global scale, there is still some confusion and conflation which persists relating to the defining characteristics, differentiating factors, and applications of each.
To help clear up the wire-crossing confusion, your friendly tech nerds at WeAreBrain are here to explain the key differences between these two technologies, from their definitions through to their specific uses.
Let’s get started!
What is RPA?
Robotic Process Automation is the use of specialised software programs, or ‘robots’, to perform and automate repetitive business processes. They augment human workers by performing predefined rules-based tasks derived from algorithms, and do not deviate from their intended purpose unless otherwise instructed. They are virtual assistants with the primary goal to assist in handling straightforward, mundane tasks otherwise performed by humans, with optimal efficiency. They are fast, error-free, and able to operate 24/7.
What is AI?
Artificial Intelligence is the structured simulation of human cognitive intelligence processes by computer systems, or ‘machines’. Artificial Intelligence works with extremely large volumes of data, which it gathers, synthesises, and converts into concise insights by detecting underlying patterns and connections. This is achieved via the machines’ ability to learn as humans do by acquiring information and its associated rules of context in order to use the information correctly. AI also allows machines to conduct various forms of reasoning by using context and rules to reach conclusions. Additionally, AI possesses self-correction by learning from successes and failures. AI is utilised for various applications, including chatbots, speech and image recognition, and machine vision, among many others.
What are the key differences?
Simply, RPA mimics back-end office functions usually performed by humans. It can be considered as a ‘doer’ of tasks. Whereas AI’s primary objective is to emulate human’s decision-making capabilities – it aims to be a ‘thinker’.
RPA is a process-driven technology, as in it automates rule-based processes that often require simultaneous interaction with multiple IT systems. If you need a process automated to work in the background while you and your team focus on more complex tasks, RPA is the way to achieve this.
AI focuses on making cognitive decisions from large amounts of good quality data used to develop Machine Learning (ML) algorithms which can predict various possible outcomes – boosting operational efficiency in the workplace.
Businesses looking to gain a competitive advantage deploy RPA to augment their staff’s output by automating repetitive processes. On the other hand, AI aims to replace human labour (and its associated costs) via end-to-end automation. While RPA runs on structured inputs and logic, AI functions using unstructured inputs whilst creating its own logic.
AI or RPA?
How do you go about deciding whether to use AI or RPA? And what processes call for a tactical combination of both? A simple approach is to use RPA for processes which are simple enough that you can easily map them out in your head or on a piece of paper, and leave the more complex workflows for AI to handle. RPA works to refine your underlying business processes to provide an easily integrated framework on top of your existing digital systems. According to Prabhdeep Singh, head of AI products at UiPath, “without this underlying foundation, the barrier to entry for integrating AI is much higher, as AI would need to be manually woven into your core processes”.
In contrast, AI should be used for tasks that are too complex for RPA to handle alone. These are workflow processes which: contain a variety of unpredictable outcomes, are multivariable processes that are not governed by predefined rules and structures, and processes which rely on unstructured data (videos, images, emails, etc.).
But why choose one when you have the option for both?
Combination is key
Most businesses today utilise both structured and unstructured data in their daily operations, thus it is essential to combine RPA and AI to create fully automated end-to-end processes. In order to optimise a sound automation strategy, businesses must ensure both AI and RPA work in tandem to drive operational efficiencies. Together, these technologies are able to drastically reduce most business processes, which serves to not only reduce operational costs and improve output, but also to improve employee satisfaction by streamlining their workflow.
As you can see, both AI and RPA are innovative tools which help to drive operational efficiency across entire organisations through the power of automation. With machines and robots used to tackle both simple and complex tasks around the clock, businesses are able to focus on human-centered processes to drive optimal efficiency. The benefits of automation are seen in the reduction of operational costs and improved efficiency in output. But be sure not to conflate the definitions, processes, and use cases of AI and RPA, as doing so will only serve to undermine their abilities – and your efficiency 🙂
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