#26 More great RPA Use Cases for your business

Hello and welcome back to another episode of the Process and Automation Podcast with the Automation Guys. In today's episode, we will be looking at RPA use cases. Recently, we had a couple of sessions about chatbot use cases, and we received plenty of feedback. And this is why we're covering RPA use cases today. So use cases seem to be very, very popular because it applies to real business problems and where intelligent automation tools like RPA or process mining and chatbots can actually solve these problems. And yeah, so let's get started with a few. I think there are so many we could cover, and we selected just a list of sort of very common use cases. And yeah, Arno, what do you think will be sort of the number one or one of the number one use cases we see?

Well, I think it's, it's hard, Sasha, to actually put one at the top. Of course, for our listeners, just as a refresher on RPA theory, of course, RPA can be used to automate repetitive tasks, you know, both in your, your back and your front office where human intervention is required. And, you know, and this is from kind of data entry type activities, data extraction, to invoice processing, batch processing. So I think it's important to, to realize that RPA is, um, can be deployed across the business. And if we look at some of the kind of common areas where we see strong use cases for RPA exist, that's in processes like your, your quote to to cash, you know, and of course, you know, every business has a quote-to-cash process in some sort of way, even if they don't know it. This is where you sell either a product or a service, and you know, what happens is that you provide that service offering, and there's a lot of steps that happens in between that's kind of related to providing that product or that service. And at the end of the day, when you deliver that, that's when you obviously produce your revenue. So, you know, we see quote-to-cash as a really important one. It's kind of like a core use case for every business. And of course, automation within that process is really important because that reduces the rate of manual errors. It provides, you know, provides you faster service to your customers because, you know, automation can speed up this process. Customers will, for instance, receive their products, their services quicker, they'll receive their invoices quicker, and that would lead to sort of earlier payments and improved cash flow and overall reduce, you know, the errors in this process, which reduce your business cost, for example.

Absolutely. So this is an important one. And as we cover covering lots of these use cases. The more structured data you have in your business, I think the easier it is to automate those things. And RPA, you could say, yeah, it's like, yeah, it's your digital worker. And you as a human worker, you look at the document, you can immediately identify patterns and structured data out of maybe a letter or invoice or something else. So, and in general, if your bot needs to do all that work, we need to support these bots, these digital workers. And if you can then convert unstructured data to structured data, then, then you can really accelerate that automation even, even more. And that's so, so important as well in the other process. So Quote to Cash is you sending your invoices, you, you sending your quotes out to customers The other very important one is you're procuring stuff for your business to maybe produce things, and/or you just receive services from other companies. So the purchase, procure-to-pay, source-to-pay process, it's very, very important. And some might think, hey, these processes are around for years, all the problems around that process should be already tackled. No, I think that's I think that's far from it. Companies— and we have also growing companies who are just starting to tackle the procure-to-pay. When you just think about a growing startup, so maybe 10 years ago or 5 years ago, they just had a few supplier invoices, but suddenly they become sort of a market leader in providing services, and suddenly they have to procure a lot of stuff and they have to handle this influx of invoices. Procure-to-Pay really involves extracting invoices and payment data from multiple systems like supplier emails. So this is a very typical one. Email comes in, it will be maybe forwarded to a manager to approve. Someone is approving it and then it goes somewhere, goes to the finance person. But doing all that stuff manually, going into the ERP system, CRM systems, going into the bank, into logistic systems to just collect all that data, which is related to procure-to-pay. Yeah, so I think that's, that's, that's very, very slow and error-prone. But I think that's, that's where, where RPA comes in. It can sort of build that, that glue between all these systems, can fill these integration gaps. And yeah, it can, and it's a, it's a light touch. You don't need to need complex projects to, to get it done because it's mainly working on the front ends. It can provide that, that easy way to to do that automation without involving too much developers, etc. So extracting data from invoices, intelligent document processing, I think that kind of stuff is super important.

Yeah, absolutely. And I think, you know, this is cash leaving your business, so you want to have a really good handle on that. You want to have a kind of single source of the truth for all of these transactions. You want to be able to look into those and and make sure that those transactions are really robust, but as automated as possible with the right sort of governance around it without throwing too much people to it to do manual processing. The third one we wanna look at, we dealt with sort of receiving money, paying out money in your business. And of course the third one is all to do with customer onboarding. So, you know, most businesses do have a customer onboarding process that's really critical to get right, get those customers onboarded to make sure that their products and services can, you know, can be offered very quickly to them. And, you know, these days you can use very sophisticated OCR and cognitive automation, you know, to automate the customer onboarding. Activities. And, you know, some of these things can be done instantaneously where you even have got legacy systems in the background. When you plug in those right automation technologies in front of it, then your customer onboarding journey can be really, really lean. It can be very fast. In one study we see done earlier this year, it was done by WorkFusion, This was for a major bank and, you know, their customer onboarding process reduced from 20 days to only 5 minutes because what they did is they used all of their legacy systems, but they plugged in all of this progressive automation tech in front of it, including RPA, to absolutely drastically reduce that onboarding journey, you know, from weeks to minutes. Which is incredible. And I think that is the kind of expectation and the benchmark out there for these types of processes. And I think in a day we live today and with kind of customer retention and customer satisfaction being such an important thing, it is in my view, a sort of a no-brainer in terms of use cases.

Yeah, very often these use cases, you just mentioned the 20 days to 5 minutes, and very often you then hear Yeah, all those programs are only there for reducing FTEs within businesses, and very often that's what you see in ROI calculators, isn't it? So how much FTEs you might save. But we both are really strong believers that these initiatives or these technologies shouldn't be focused on reducing FTEs, It's just really offloading this mundane and repetitive work, isn't it, from your FTEs. And then because businesses generally have the desire to grow, to use the sort of the freed up time to grow the business where and use the manpower, the human power to really on the topics just humans or humans do best. So sometimes you read these very impressive figures, yeah, you save FTEs, but you You probably would better describe it like you free up time for 67 FTEs to do something else. Sometimes I think some of the statistics are a bit negative you read. So the results are impressive if you're a major company and you can reduce it from 20 minutes to 20 days to 5 minutes. But yeah, it should be all about how can you reduce your sort of capacity in the business or not reduce, improve your capacity. With your workforce. So yeah, other really common areas and business functions we look at is data migration and data entry. So we know this all, there are legacy systems. I think there's probably no real big enterprise out there without legacy systems. But these systems perform critical functions at companies. And if you, even at banks, there's the old COBOL systems and old terminal systems still available. And they do some of the critical number crunching. So billing systems, so all that stuff, yeah, can't be really pulled out sort of with APIs in a modern sense. And in the modern world, you have a nice API, but if you can't really get to it, you need to find other ways. And the old way was, maybe exporting a CSV file or exporting data really, really difficult, just copying it manually from one system to another. And with RPA, you can now really use— or you can use RPA for going into these old legacy systems, let the digital worker do that work. And yeah, and you can transfer that stuff, that data out error-free, and you can do it all year round. No one has to work in, in the basement somewhere where this old terminal belongs. Yeah, you can get that data out really, really, really reliable and then you take that data and you maybe update this into other more modern systems you have and that there could be hundreds of different systems you need to update regularly and your RPA can then go similar like lots of lots of arms going into these different systems and just making these changes. So sometimes, yeah, so you might forget even about your legacy systems because you put this very powerful glue in between. At some point, these systems probably will be migrated and there will be proper APIs, and then you need to look at other things, how you connect this. Then you look probably at integrated integration platform as a service. But until then, RPA is is a good friend and to get these use cases covered.

Yeah, and you speak about sort of data migration and data entry into various systems, but I also think another really, really good use case for RPA is in the sort of data maintenance space where, you know, you might have various departments in your business including HR, customer service, and marketing, and, you know, they need to routinely update data. And this is data spanning your customers, let's say personnel data, And this is data that's constantly changing. And you know, you could set up RPA bots that automatically update those relevant data from either inbound emails you receive from customers or online forms, whatever that source of update is. And really to ensure that all of those systems used by these departments accesses that single source of truth and that kind of error-free data, which is the latest data. And also data validation. Data validation is really important because, you know, we, from a traditional perspective, a lot of the sort of data validation routines are embedded within databases. So what we want to do is we want to kind of check data against sort of publicly available data sources and to make sure that if that data is still current. And, you know, RPA is a very, very good tool to do so where, you know, you can train this tool to go to various other data sources including sort of online data sources where it kind of scrapes data from websites and make sure that that data you've got is still valid and go update your sources of truth in your backend, for example. And also, you know, if you have inbound data where you have to extract data from PDFs, scanned documents, and, you know, any other types of forms, including sort of Excel spreadsheets, you could, you could use RPA really to extract that data, you know, use pattern recognition, for example, when you look at that data. And when you need to go key that data into other systems, you know, you could use sort of machine learning algorithms to, to make sure that these, this data is valid. And, you know, you could at lightning speed really from extraction to transformation to loading very quickly, get that data into your business, into organization, and also maintain it. So again, just around the sort of the data extraction, data validation updates, you know, bots or RPA bots are really, really useful in doing so. And of course, you know, they can do this 24/7. Without any errors. So it's a very, very, very useful use case for RPA.

Yeah, and I think so, you, yeah, you mentioned customer satisfaction a couple of times. And if you think about maybe insurance claims again and insurance contracts and all that stuff in general, legal documents, sometimes if there would be a claim or some, some some event where someone needs to go through this huge amount of data or these documents, I think it is not— it doesn't really make sense anymore that, that a human is going through thousands of pages or just an army of, of people going through thousands of pages and documents. So the— with RPA and with the relevant connected systems like OCR, that kind of stuff, you could, you could have your digital worker going through document to document finding relevant information and and getting through maybe claims processing, medical documentation, doctor's statements or whatever in sort of no time. And then it might be presented to some other, to maybe to human to verify lots of things. That whole process then so reduced and you can maybe pay out to a critical ill patient much earlier. So yeah, so that will be customer satisfaction. You could really do you have a big impact. So those kind of scenarios are out there and wait for being processed. I think more than 50% of business already thinking about doing RPA and probably doing early things of RPA. But if you think about the other 50% just globally on companies who are not tapped, who have not tapped into that opportunity. They can, yeah, they can use these use cases to look at to get started really. There's more, I think there's more like reports. I think there are people in businesses that they're doing reports at the end of the week, at the end of the day very often, and end of the month, so very typical month-end reports. And then Usually what happens is you copy data from one spreadsheet, another system is producing another spreadsheet, and then someone is creating a nice Excel macro. It's merging information together, and that can take a couple of people in the business a couple of hours per, per week, per month. And, and this is not something we should do, we should do manually. So these reports The report compilations, they're also based on very strict rules, and this is a great area where digital workers can take over. When it's rules-based, you can replicate it easily, and yeah, your digital worker can just do these reports and merge data together, hundreds of spreadsheets if that is the case, and it will be done. Do the analytics maybe behind it in another tool, and produces the output in, without errors and on time. Yeah, and you have the, the time back for your, for your, for your team.

Yeah, absolutely. And I think reporting, mass generation of management information, you can also train your bots to obviously react to specific issues that's highlighted with Insight to Report, you know. So if there is some alerts that you train the bots to pick up on, then those reports can actually be very valuable to draw somebody's attention to the fact that there's something specific in the report that needs somebody's attention. You know, whether or not there's a critical issue with the provisioning of services, for example, you can actually train the bot then to not just generate those reports, but also actually to, to react to something that's with Insight to Report and get the right people involved to be able to solve this issue. You know, and also let's just look at communication since we're talking about it. It's not just about creating and sending these reports, but if you have got any requirement to generate mass emails where you have to rely on data from multiple systems to be sourced and to produce these emails to be sent out to potentially clients or prospects, you know, depending what you want to use mass emailing for. It's a very easy task to do that with RPA because you can actually source that data from very disparate sources. And if you send these emails frequently, you know, this is a perfect use case to actually automate that process, you know. And it kind of brings me to activities with inside, um, you know, commercial functions including sales and marketing. You can automate something like lead nurturing where leads arrive from various channels including, you know, lead collection forms on the website, from vendors, emails, LinkedIn, and all of that can go into your automated lead nurturing RPA process. If you look at sales, of course, you know, we want salespeople to focus on building relationships and, and selling, but a lot of their times is spent on operational activities in, in most organizations. So, so with inside your, your sales function, you could automate those operational activities out of the picture and allow your salespeople to focus more on selling rather than the administrative, but, you know, burdens that comes with the day-to-day job of selling, for example.

Absolutely. And that specific areas or these specific areas in sales functions could be creating the invoices.

Yeah.

So as part of the sales process, at some point you have to send out an email. Yeah. With an invoice or even send a letter these days, but even that could be handled by a bot. Yeah, I think the key thing is that the bot will help the salesperson in collecting all that data and making sure that data exists correctly in the CRM system and in the finance system, accounting system. Someone, maybe the salesperson, only typing that stuff into the CRM, and for some reason on a Friday afternoon, they might forget to press a button button or do something, it might not transfer properly. So the bot can make sure it's always run consistent CRM data, accounting data is all up to date, and then invoices are sent out automatically. I think that's, I think is an easy one. Usually salespeople, they don't like all that admin stuff like you mentioned before. Salespeople are good usually in selling and not with this admin stuff. We all demand them, I think, as business owners that they do all that stuff. But if we can help them with a bot, I think they will be very thankful. And that as well with updating the CRM in general, it is time-consuming. Sure, sometimes they have to enter very specific information in to just track what's going on with this customer, but it's generally very time-consuming, unproductive time for them. And Yeah, so, so this is a way where the, where the bot can support the, the sales agent and the salespeople to, to collect all that information and to make sure it is all updated in the right, right places. And, and sometimes even the sales agent doesn't need to go into the CRM system, so the bot can take, take care of, of stuff from, from outside, maybe getting information from other systems. But this is, yeah, one, one very big area in sales. And if this system is not right, it produces all sorts of errors, which is not helpful for the business. And if you can get it error-free, yeah, then everyone benefits from this one.

Yeah, true. Yeah, I mean, it's interesting that you mentioned the kind of the hand handover between the Salesforce that actually looks after kind of selling, and then also from an accounting perspective, which does the billing. We do see that there are gaps out there in some of the use cases we see where that kind of, that smooth handover from your customer relationship management system to accounting system. There are, like I said, gaps in that process, and RPA is a perfect place to sort of fill those gaps and really make sure that the transition between, you know, your sales cycle to your billing cycle is really as smooth as it can be. And of course, you know, you talked about, Sasha, about updating CRM, making sure that, you know, your CRM is updated with all your customer interactions. Of course, that, you know, there's a wealth of use cases that you can deploy RPA with, that it gets all of those those different sources of information that pertains to customer interactions and go and update your CRM so that that information is visible to the rest of the business. And in terms of kind of just on this track of keeping data updated, sometimes there is a lack of integration between HR and CRM systems. And, you know, RPA bots can actually ensure that, you know, CRM changes are uploaded, you know, to kind of like the scorecards that the sales reps use so that they can see their progress in real time. And so, you know, so that's a really important use case as well, you know, that— and it all kind of just boils down again to the fact that, you know, people want to see up-to-date information. When we talk about customer relationship management, you know, we all rely on, on, on, on, on sort of good data points that comes from a variety of different systems that we need to, to ensure that our customers are happy. And, you know, of course, RPA is, is a, is a very, very a good tool to be able to source that information and keep it up to date. So it is available, gives you a very good picture of, you know, your customer record. We always call it the 360 review, you know, whatever you call it, know your customers. All of that information is really important so that everybody has got the information at their fingertips. And if they do engage with our customers because, you know, it does feel like from a customer engagement perspective, we know our customer, we know what to sell them, we know the history with us. So again, very, very important.

Yeah, definitely. This is one important area. So we have to make our customers happy, but at the same time, we have to look as well at sort of some of the the other departments, and one very important department which comes up in our conversations and is, I think, sort of waking up more and more was HR automation. So the HR departments, how could we sort out the problems for them? I think a big one in HR is candidate sourcing, so the whole process of finding people and making sure everything is collected. I think that's, that's something we can really support them. I think there are some, some sort of systems out there, but generally our companies have their, have quite old and legacy HR systems. And yeah, we can use, we can use the bots to, to help them there with everything around these legacy HR systems, for example, aggregating CVs. Collecting assessment results and interview notes. And we can put this all in then in the right places in the HR system because ideally, yeah, we need to document most of the stuff we do with our recruits. And yeah, we need to, we need to have all that information available so we can, we can also act on GDPR and all that stuff. So yeah, the bot can help us to, to make, to make Yeah, to fill the gaps of these old HR systems.

Yeah, and you know, you talk about HR, and I think another important use case is of course employment history verification. And you know, obviously that process has got numerous routines and steps, you know, such as, I don't know, arranging interviews and, you know, everything that follows up from that. And also maintaining the records of— that we verify this employee, their previous employer. And all of that can be drastically reduced using RPA and stringing that process together and automating that process. A recent study done by UiPath where they provided a case study and they rolled out an automation program, you know, with Insight, this use case, in roughly over 8 weeks. And they calculated that they reduced around about 40% of the manual labor that went into these activities, which is a very impressive, you know, reduction, you know, in the sort of the time it takes, you know, for this recruiting process.

Yeah, I think, um, there's so many use cases on the chatbot side. We recently covered just on the HR side, I don't know, 40 different use cases, and we see maybe some, some very similar candidates here where, where then RPA can jump in. And the hiring, onboarding, and headcount reduction— so these are very typical use cases especially for growing or shrinking firms, unfortunately, since the pandemic. So, yeah, shrinking firms, hiring and firing is significant in terms of workload for HR and everyone who is supporting them, yeah, as well the IT departments, facility management. So, all these things of the process can be quite costly. And then putting this in sort of solutions. Yeah, I think RPA can be again the glue between all these different things, can streamline the process. And I think the big benefit of RPA is it can do that very quickly. So instead of putting lots of solutions in there, big, big systems which cover all these areas, with RPA we can really see fast, fast results. I think with the most RPA platforms use low-code, so that helps us as well to get these solutions already defined very, very quickly. So yeah, by automating these kind of processes— hiring, onboarding, and headcount reduction— I think, yeah, you can, can really speed up the whole process and, yeah, free up again the typical, the typical one, free up time with within these departments.

Yeah, and of course, once you onboarded your employees, then it's the sort of typical run-of-the-mill processes that you have to do periodically. And, you know, that includes payroll automation. Now, there's a lot of software out there that does automation of payroll, but there's always going to be gaps where you have to transfer data from one system to another system. And typically what people do is they do that using Excel spreadsheets and, you know, mirage of other type of tools. So I think with, with, for if you look at payroll automation in general, you know, RPA is a very, very good way that you can actually increase the automation within Cytus payroll management process, you know, by sourcing information, running those processes, and also ensuring the accuracy. And, you know, this is also true for, for other types of processes within site HR, for instance, absence management, you know, where there's absentees, you know, either vacations or, you know, all of these things that a member of staff is not within site, your business, how does that process look like? What is the types of systems that you need to actually interact with to log the absence and manage that? And, you know, I think A lot of businesses out there probably have got various places, maybe a core HR system. But again, I think RPA is just one of those technologies that bring all of that stuff together into a single process. You know, we, we look at things like workers' compensation claims, and again, you know, there might have been an injury at work, um, or illness needs to be reported. And, you know, there could be a claims investigation that happens, you know, for compensation claim, for example. So again, quite a complicated set of activities that needs to happen, but very standard, you know, very easy to actually describe. And, you know, RPA is a really, really good technology to automate that. And if we look at things like expense management, for example, again, the same thing where you might have an expense system or you might not have one, but either way, you know, you could use the RPA bot to sort of, you know, source the data from an expense submission, you know, perhaps send an automated email to somebody that needs to approve it, or if it's part of a bigger workflow, but really just kind of be, like you said, Sasha, that glue that actually brings things together in terms of providing that nice end-to-end process experience.

Absolutely, yeah. And I think to wrap up on the HR use cases, we discussed this in this other episode all about chatbot use cases. So if with an RPA we have the opportunity to put an HR virtual assistant in place for our business, and we can then allow our staff to, to communicate with the chatbot for lots of these things we just went through, and then the chatbot will then do the communication behind the scenes through RPA, maybe into an old expense management system, into an old claims management system, into the old legacy HR system. But all through this virtual assistant, it can be a quite engaging conversation. They can ask questions, they can book days off, and all through this chatbot channel. And yeah, it's a very, very exciting topic in in general, and it's growing. So, so the combination chatbot and RPA is definitely something to look at. And yeah, so if you have your RPA processes already in place or you're thinking about this, also think about chatbots, definitely. And I think that's, I think that's, uh, that's a lot for, for one session. I'm sure we will cover far more use cases. Please submit your ideas on our website, theautomationguys.net. We are always looking, looking out for great ideas to talk about. And if you have a specific area like operations or so, you like to learn more about use cases in that field, or maybe in finance, or specifically in financial services and banking. So there's so much more we we can cover logistics. I think, yeah, so there are plenty, plenty of use cases. And yeah, let us know. Hopefully you could take a few points away and maybe you can jump straight into, into action. Yeah, just feel free to reach out to Arno, myself, get a trial, and then we can get you, get you, get you going very quickly. Yeah, so I think that will be it for today. Thank you very much for listening. And yeah, let's automate it. Unfortunately, that's it again with this episode of the Process and 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! [MUSIC] [MUSIC]