AI Agents Vs RPA: What Every Business Leader Needs To Know
Two terms that come up frequently in conversations about business automation today are 'agents' and 'robotic process automation' (RPA).
They're often mentioned together because both aim to streamline repetitive, rules-based tasks that were traditionally handled by humans.
However, while they share some common ground, especially around automation and the use of 'robots,' they represent very different approaches to solving different kinds of problems. Understanding these differences is essential if you want to choose the right tool for the job.
At its core, RPA is about programming software with clear, rule-based instructions to perform simple, repeatable tasks.
AI agents, by contrast, aren't programmed; they're trained. Once trained, they're left to get on with the job using tools like natural language models and computer vision to navigate complex tasks and make decisions.
So, how does that affect the kind of problems each one is best suited to solve? And more importantly, how do you decide which to use in a given situation?
What Robot?
Robots, bots, virtual assistants, and, increasingly, 'digital workers' are all terms that have traditionally been used for any machine capable of helping us work. Starting all the way back in 1961, when General Motors installed mechanical arms on its production lines.
The term 'robot' covers any machine that can automate work for us. Whether or not it uses the algorithmic, machine-learning-based processes that we call 'AI' today. RPA, however, generally refers to software-based robots, rather than mechanical ones.
Technically speaking, RPA isn't intelligent in the same way that we might consider an AI system like ChatGPT to mimic some functions of human intelligence. It simply follows the same rules over and over again in order to spare us the effort of doing it.
RPA works best with structured data because, unlike AI, it doesn't have the ability to analyze and understand unstructured data, like pictures, videos, or human language.
It's frequently used for repetitive 'production line' work, moving data between applications, and extracting data from structured sources (such as customer databases or financial reports) for analysis.
AI agents, on the other hand, use language models and other AI technologies like computer vision to understand and interpret the world around them. As well as simply analyzing and answering questions about data, they are capable of taking action by planning how to achieve the results they want and interacting with third-party services to get it done.
If you use ChatGPT or another generative AI chatbot in 'thinking' mode, you can get some idea of how it does this by watching its 'thoughts' as it responds to queries. Instead of just thinking about one query and then replying, it can apply this ability to complex, multi-step tasks and projects.
To illustrate the difference, consider a database of customer service emails, and how each could approach this same data set differently to carry out different tasks:
Using RPA, it would be possible to extract details about who sent the mail, the subject line, and the time and date it was sent. This can be used to build email databases and broadly categorize emails according to keywords.
An agent, on the other hand, could analyze the sentiment of the email using language processing, prioritize it according to urgency, and even draft and send a tailored response. Over time, it learns how to improve its actions in order to achieve better resolutions.
While AI agents are a far newer and more sophisticated technology, that doesn't mean they're automatically the best choice for every task. So, how do you know which one to use?
So, How Do I Choose?
Here are some questions you can ask if you need to consider whether agents or RPA are right for your automation project:
Does the task involve clear targets that can be achieved by repetitive action day-to-day? If so, then RPA could be a good fit.
Is the data clean and structured, or messy and unstructured? If everything fits nicely and neatly into the rows and columns of a spreadsheet, RPA is probably the right choice.
Does the task involve making decisions based on interpretations of human language, behavior or intent? These might be suitable for agents.
Will the process change as the task is executed, or will our tools need to adapt to new sources of data? Agents will probably be more useful here.
Finally, many projects may be best tackled by taking a hybrid approach. In cases where tasks could be completed by combining both routine automation and intelligent decision-making, this could provide the best of both worlds.
For example, an HR onboarding system could involve deploying RPA for processes like setting up access privileges, processing forms and filing standard documents. At the same time, AI agents could answer questions, personalize advice, and monitor the system end-to-end to make sure it's running smoothly.
As automation strategies mature, learning to identify opportunities to deploy specific technologies or combine them for maximum efficiency will become increasingly critical to business success.
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