In this episode of the Process and Automation Podcast, hosts Sascha and Arno answer a series of frequently asked questions from their community about robotic process automation (RPA) and related technologies. They start by defining RPA as software robots that mimic human UI interactions to automate tasks across applications, then explain intelligent automation as RPA enhanced with AI/ML, and describe process mining as a data‑driven way to discover, analyze, and monitor real‑world process flows. The hosts outline a practical, step‑by‑step guide for organizations to begin an RPA journey—building a business case, selecting an operating model, planning a roadmap, and establishing governance. They discuss three major challenges: employee resistance, selecting the right processes, and setting realistic expectations. Finally, they define hyperautomation as a strategic initiative that layers complementary technologies (AI, OCR, computer vision, etc.) around core RPA to achieve end‑to‑end automation, and note that Gartner predicts the evolution from RPA to hyperautomation as a top technology trend. The episode concludes with a call to action for listeners to engage with the community, explore deeper episodes, and subscribe.
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Hello and welcome back to another episode of the Process and Automation Podcast with the Automation Guys. In today's episode, we go through a list of questions, yeah, questions we often get asked by our audience and from, from the community around the topics of RPA and intelligent automation. So yeah, hopefully our answers to these questions we have collected are helpful for you. And of course, please feel free to send us more of these questions on our community or via our social channels or via our website, theautomationguys.net. So Arno, what do we have as our first question from the audience?
So the first question, Sascha, is what is RPA? And to put it simply, RPA is the technology that allows organizations to use software robots to perform specific tasks in an automated way. So using a combination of user interface interaction, for example, descriptor technologies and cognitive processes, RPA bots can be used to mimic or to emulate selected tasks within an overall business or IT process, for example. So these may include manipulating data, passing data to and from different applications, triggering responses, or just simply executing transactions. So these RPA bots can, you know, work through one or many different software applications, and it really, again, kind of mimics, human behavior. So you can train it to do specific things that a human would usually do, but it does it really quickly.
All right, yeah, that sounds interesting. Yeah, cool. I will grab the next one from the audience. The question here is, what is intelligent automation? Yeah, so we get that very often as a question. So when talking about intelligent automation, So we, we think it goes beyond RPA. So Arno just mentioned what RPA is, but intelligent automation goes really further. So to intelligently automate means take RPA technologies and enhance it with artificial intelligence, AI, machine learning, and bringing in other technologies as well. And that will allow for more custom and intelligent design. Design of the process automation and can be implemented, of course, across departments and the whole organization. And yeah, we also very often say intelligent automation will enable businesses to move towards hyperautomation. So there's another buzzword we will cover in, in the questions later.
So the next question that's commonly asked is, can RPA work in any software application? And the answer to that one is yes. Generally, RPA is fully agnostic and it could be used to automate and integrate data in any application. And this could be applications on-premise or in the cloud and in any interface or server, including terminal server systems like Citrix Remote Desktop. So again, if a human can interact with an application, a robot can do exactly the same.
All right, so nice and short. Yeah, so the next one is what is process mining? So when we talk about process mining, we talk about setting, talking about a set of solutions that looks at event logs, at the event data timestamps and so on, that are generated by a series of systems, enterprise systems, systems like an ERP system, like an CRM system, HR system, so all sorts of other systems like maybe supply chain management tools. And yeah, to rebuild a virtual view of the processes and the process how they're actually running, that's a big, big difference to what we do a lot in process documentation. So yeah, and process mining tools, and we very often talk about they should be robust and powerful. So these process mining tools can blend data data mining with, with other technologies like AI and machine learning to generate data-based analytics. And that will help organizations discover the as-is state of business processes. As I mentioned, it will really show us how process actually running, and that will identify opportunities for optimization and automation. Also, when we talk about process mining, we talk about that it is a part of multiple stages of this, of a typical ARPA lifecycle. Very early on, we use process mining to identify, identify processes that are really good candidates for automation. We analyze it to the extent to which RPA can be implemented in, in old processes, old systems, and later it will also provide additional monitoring analysis, and we can look at that data and see how our RPA performs and really can drive further improvement on from that. Yeah, so as you all know, we have done several in-depth episodes on process mining already, so I do recommend to everyone to listen back to some of our older episodes. And yeah, we will link to those episodes in our show notes as well.
Great, thanks Sasha. So the next one is a common one as well people ask us is how do I get started with RPA? And you know, what we've done is kind of just created a little, a few steps, simple steps as a guide for business and IT leaders, you know, to develop their RPA implementation strategy. To ensure that, you know, it's successful and also scalable in the future. So obviously the first step would be to reach out to us in our community or via social media channels. You know, our community automation experts can help you and assess you or, you know, to really kickstart your RPA journey and your intelligent automation journey as well as help you to identify those opportunities. Failing this and you would like to sort of just look at RPA yourself as an initial step, I think the most important one there is to build an RPA business case. Typically look at your Aziz processes, look at how automation can improve and streamline these processes and really extract the sort of or highlight the hard and soft benefits. Hard benefits is obviously financial savings, soft benefits is things like customer satisfaction because You know, digital workers don't make mistakes, so customer retention is high. So there's numerous of these benefits. The third one in the list is to determine your, you know, most optimum operating model. So your target operating model needs to be defined for RPA. You know, how will you work with your new digital workforce? And then the fourth one is really to plan out your automation roadmap and your RPA governance plan. So sit down, you know, look at how you want to bring RPA and automation into your business and also how you want to govern this, you know, how you want to set up IT governance to make sure that, you know, your processes are scalable, they're properly managed, and they are also available, you know, especially when it's business-critical processes.
Yeah, cool. Thank you, Arno. So yeah, the next question we have, we get that asked quite often in general in the beginning of projects as well. What are the challenges of RPA? And yeah, there are indeed multiple challenges that can appear when implementing robotic process automation in your department or Yeah, the whole organization. And let's go through 3 of them. And the first one we see is that employees are resistant. And so really get them started. So that's of course like with every other technology and general technology implementation, it results into change and people do not like change. So that can result in stress for employees. So in the case of RPA, There's also the belief that automation equals to people have to leave, so firing people, layoffs, that kind of stuff, and creating some adoption resistance therefore. That's definitely a big thing we have to work when we bring this technology into organizations. Another challenge, of course, is selecting the right processes. We talked about the process mining. It could identify really good candidates, but of course, yeah, it is very vital to really find which processes are suitable for RPA so that automation can run successful. And this requires an in-depth analysis and should not be really rushed to deploy. So this, this will most certainly otherwise lead to failure. And of course, it's not meeting the expectations of these departments and organizations. And of course, that they will, and then will again, will produce trouble with employees again, so they become even more resistant if we're even choosing the wrong processes. And yeah, and of course, setting realistic RPA expectations, that's super important. Companies need to really recognize recognize the limits of what RPA can do and understand that RPA will not really suitable for every broken process in a company. So yeah, I think this, these three points are most of the sort of the biggest challenges of RPA.
Right, so the final one, and it's actually two, but I'll combine it into one. The first question is, what is hyperautomation? And I'll sort of give you a textbook definition of it whereby it says that it is a strategic enterprise initiative that can deliver a continuous automation journey with superior business outcomes by leveraging a combination of technologies that complements the core RPA adoption. So what we're seeing here is within the hyperautomation definition is it's really core RPA in the middle and then all of the sort of other technologies, complementary technologies that surrounds it to deliver this so-called hyperautomation. And that kind of blends into the next question as well. What does the future look like for RPA. And, you know, again, according to the Gartner's list of top 10 strategic technology trends, the evolution of RPA as we know it into hyperautomation is the number one trend. So again, expanding the automation from RPA to a combination of other cognitive technologies like AI, machine learning, natural language processing, OCR, computer vision. That enables this sort of end-to-end automation lifecycle. So again, I think those two questions are linked to each other. To get started, people usually get started just with RPA. But again, most RPA vendors are building these sort of progressive technologies that's complementary to AI. That really enables the, what's called this, this hyper automation.
Cool. Yeah, thank you very much, Jano. Yeah, I hope these answers to the questions from the community and from the social channels are helpful. Good summary of what these technologies, what RPA is, what hyper automation is, intelligent automation is. We will put information to other episodes we covered earlier in the show notes. And yeah, thanks for listening. And until next time, let's automate it. Unfortunately, that's it again with this episode of the Process in Automation Podcast. If you like this episode, please give us a 5-star rating and don't forget to subscribe to this podcast so you don't miss any upcoming episode. We hope you will tune in next time and Until then, let's automate it.
