[00:00:02]Sascha Cutura: [00:00:02] Yeah. Hello and welcome back to another episode of the process in automation. As most of, you know, um, to all the listeners of our podcast is that we cover all sorts of different areas of automation. And today, when we would look, we would like to look at the intelligent process automation. And, um, so what is intelligent process automation?

[00:00:42] Arno Van Rooyen: [00:00:42] That’s what we cover and why does it matter? Um, welcome, welcome to the show as well. And why does it matter? Well, I guess it is a buzzword that’s out there. What we need to do first is not to look out what is [00:01:00] intelligent process automation, because we’ve heard the term out there. We’ve heard the term RPA, which is the process.

[00:01:08] And, you know, today we were going to sort of differentiate between those two things, intervention process versus automation or IPA as it is. It refers to tasks that are automated or optimal. In part by artificial intelligence and machine learning algorithms, um, you know, intelligent process automation can reduce human intervention and a variety of business processes out there.

[00:01:38] Um, and you know, IPA submissions go far beyond simple rule-based costs. For example, uh, IPA tools can, um, apply artificial intelligence to process unstructured data. And this is, this is something that many our RPA tools cannot do, or, um, you know, to, to provision it resources, [00:02:00] to ensure critical SLS are maintained.

[00:02:02] For example. No example can include the use of machine learning algorithms that enable, uh, intelligent process automation tool to improve task performance over time, for example. So, so really it’s it’s it’s it’s, it’s taking automation and applying cognitive behaviors to it to make it intelligent. Um, so it’s, it’s not just rule rules-based predetermined, but really it works on sort of machine learning algorithms where you train training to do things to and learn by itself.

[00:02:41] Um, and in this instance, you know, one of the examples I’ve mentioned this unstructured data, like an invoice, for example, and invoices could be different. Depending on, you know, you’re extracting that structured data from the invoice, which has got the line items, the invoice number, [00:03:00] doing that for a variety of invoices, for the view, to give that to a robotic process automation bot that needs structured information to do some processing.

[00:03:15] And so something was, um, you know, in some of these processes, when we look at them, you know, just RPA is not efficient enough, isn’t it though. The bot needs eyes and, um, and these eyes. Nowadays, um, uh, is, is one capability of, uh, of artificial intelligence. Isn’t it? Um, division, division capability, uh, of, of, of AI.

[00:03:38] And so it’s, that’s a very, very common one. Um, when we look at intelligent process automation and how we can enhance, uh, RPA, um, Yeah, really, a lot, lots of people are sort of using these terms a bit like the same isn’t that, so that they say intelligent process automation is RPA, but, um, yeah, as you [00:04:00] mentioned is just a combination of it where yeah, exactly.

[00:04:05] And I think, you know, intelligent process automation is often regarded as you say, as being the same as robotic process automation and it’s sort of, it’s sort of just half true. Um, robotic process automation, um, is a key component of intelligent process automation. Um, but intelligent process automation.

[00:04:28] She doesn’t necessarily have to include RPA, you know, so sort of intelligent process automation. You might just have an AI, all that sits there. That’s not dependent provided outgrew to RPA bots, and indeed RPA bots can, can exist without. You know the intelligent part, but of course the data needs to be, we be very structured and, you know, robotic process automation really refers to, uh, [00:05:00] tools, applications, platforms, and all the scripts that automate simple rule-based repetitive tasks.

[00:05:07] And these stars are generally quite, you know, what I’m consuming when you do it. You know, so for instance, instead of collecting phone numbers from applications or RPA tool can be, we’d be trying to automate that spot. Um, but the inherent problem with RPA is the fact that DD still rules based. And if you update your, that your contact form or the customer interest information in, in the wrong row, on the form, um, you know, the RPA two won’t be able to successfully complete that class because of course it doesn’t have that intelligent capability.

[00:05:41] And this is the way of robotic process automation is frequently used, you know, at that point where RPA is now no longer effective, because it leverages artificial intelligence. You know, these intelligent process automation tools can complete more [00:06:00] complicated processes that incorporates, uh, you know, uh, a variety of new and emerging technologies.

[00:06:07] Okay. So yeah, let’s, let’s look at the artificial intelligence and, um, and also maybe machine learning and, um, how that can be used, um, was intelligent process automation. Um, yeah, so the use of AI and ML machine learning, uh, Uh, in process automation enabled the intelligent process automation platforms to, to go, as you mentioned earlier, um, further, um, um, than just, um, front office and back office tasks, um, uh, is, um, that goes further than just RPAs used to automate these, these tasks, artificial intelligence, for example, makes it possible for, for these platforms to analyze, uh, This unstructured data, as you [00:07:00] mentioned.

[00:07:00] So the semi-structured data and the unstructured data, uh, necessary for language, um, natural language processing, so, and detecting the intention, the intent and other cognitive technologies. So th this, this combined is allows users to build really complex workflows for. And now the topic comes up again and for our FOSS, again, more and more so to chatbots or, um, or responding to customer requests.

[00:07:26] So as soon as we understand what it is, um, the bot needs to do, uh, it could be, could be not just, um, text for example, it could be also images. Isn’t it? Um, and, um, video clips also sorts of use cases are available, um, um, to, to give the bot a little bit more information to process more in, in this end to end process.

[00:07:49] So, so where some, um, Or where you usually use RPA tools to automate tasks that already exist, intelligent [00:08:00] process automation. So together with tools, these tools give users the opportunity to, to, um, to sort of rethink the capabilities of existing processes or to, to really look at the processes and to optimize them with, um, was, was additional learning.

[00:08:17] So deep learning as a, as the phrase here, or use some new technology such. Such as some NASA intelligent decision making, um, to create really innovative processes. And, um, yeah, it was those, um, capabilities in your platform. Um, when you, when you start using machine learning algorithms, that also looks at all the historic data, um, and the real time data, um, you can, you can optimize these processes, your process even farther.

[00:08:50] So. We, we, when we look at this machine learning and looking at real time data and historical data, we, we, we touch overlap slightly probably with, [00:09:00] uh, he, and there was a process mining. Um, but, but this really allows us to optimize processes, uh, in, in real time and then predict what might happen in the future.

[00:09:09] So very, very powerful. When you think about predictive analytics and that kind of stuff, um, for example, you could look. Um, uh, work workflow and, um, automatically route, um, the work, um, uh, on the predicted run times and all the content you have discovered, or the understanding you have, um, um, historically, um, you can manage how you hold the whole, um, How the process flows to, to, to solve, to solve problems.

[00:09:41] And, um, yeah, so, so this, this is where, uh, where you can improve your processes with artificial intelligence and machine learning. Um, yeah. So that’s something you should really look at. And of course, [00:10:00] I think we, we, we have spoken about, you know, how important data is. A lot of people say data’s new gold. It feels like they’ve been saying that for years, but I think it’s, it’s, it’s so much more, um, important now to look at your data because for end to end, um, orchestration, you really need those there’s data sets across your business.

[00:10:26] And of course, you know, in today’s business environment, um, these platforms, we use the SAS platforms. They don’t exist as a single island. Um, and you know, uh, if, if we deploy, uh, intelligence personnel, intelligent process automation tool, um, you know, it will have to access and manipulate data from a variety of sources.

[00:10:52] And, you know, a lot of this access rely on disparate technologies, you know, from different vendors. So [00:11:00] if you look at intelligent automation and sort of having access to this, this, this, this really valuable back in data, uh, a level of extensibility is, is required, you know, on the backend, you know, either through your, sort of your workflow automation and exposes data or your enterprise stop scheduling.

[00:11:18] Um, that, that, that does ETL jobs, for example, um, you know, service orchestration, um, you know, we, we, we talked about data, data API and data platforms quite extensively in the past. These solutions provide sort of these kind of universal. So most of them provides these universal connectors, um, and API accessibility to seamlessly manage data and dependency.

[00:11:45] Um, between disparate systems, you know, with inside your, um, your, your business and that truly then makes end to end, uh, process automation possible. And that thing unlocks, um, you know, the, the, the [00:12:00] ability for you to use intelligent process automation tools, um, you know, to find that right, that data. On your infrastructure, um, that you could, you know, deploy these automations, you know, that, that do require, um, and provide an unlock that value intelligence versus automation can, you know, provide you, uh, you as a business.

[00:12:29] Um, so again, it’s, it’s that kind of. Thinking where, um, you know, we, we, we have to have our data, um, in, in a good place. We need to expose it in a way that it can be consumed easily. Of course they are. These different silences that exist inside business can sometimes be a bit scared when you look at the different disparate data sources, but they are options know.

[00:12:56] Um, almost all of the modern SAS [00:13:00] applications provide connectors, uh, even with each other legacy systems. Um, there’s, there’s ways to connect that to, um, to your data sources. Um, and if you can’t, of course you can use RPA to do that. Um, and again, you know, once you’ve got this sort of this data liberated very powerful place, then to, to overlay your intelligent automation tools.

[00:13:25] Um, where let’s look at chat bot, for example, a chat bot can actually, um, index your back in data. Um, we’ve seen examples where the chat bot understands data structures and understands, let’s say your customer entity. So it knows how to bring that data into, um, a conversation. With with somebody. Um, and you know, that, that, that that’s a fairly, fairly basic example, but that, that can get to the single source of truth very easily.

[00:14:00] [00:14:00] Um, just by using the chat bot connectors, for example, to connect to your, um, yeah. Look at a few other examples. Or maybe before we jump into the examples, how good would all that stuff be? If it’s. Uh, possible on a low code platform, isn’t it? So we did, we discussed that a lots of times and sometimes, uh, our listeners might think, um, uh, artificial intelligence machine learning end to end process automation, orchestration.

[00:14:31] All this stuff might, might be very complicated. Um, But it can be also done, um, on very, very powerful local platforms. Everything is part of that platform at the RPA, the AI and lots of stuff. So it doesn’t need to be difficult. Um, everything can be done, uh, was the power of local these days. So, um, yeah. So then let’s look at a few examples here.

[00:14:57] Um, you mentioned this shepherd example [00:15:00] already a good use case. So, if you look at it into a few more use cases, these, um, IP, uh, IPA tools or intelligent process automation tools, uh, use to automate, as you mentioned early on or time consuming and routine business processes, enabling all the employees to, to really look at the more valuable tasks, uh, in, in.

[00:15:25] Uh, in their, in their departments and spent more time on those instead of feeling like a, um, like a robot. Um, and by, by freeing up this time from your employees, um, Yeah, your business, it will be more efficient. We’ll be more productive and save a lot on, um, on your full-time employee costs. Um, so this, this is, this is a good advantage.

[00:15:51] Not everyone is necessarily looking at saving, um, saving full-time employees. Uh, that’s the most case it’s actually the [00:16:00] opposite. They, they, they never want to get people, uh, want to get rid of people. Um, they just want to extend it. Sort of the real valuable capacity of people. So that’s, um, that’s very, that’s why it’s so important.

[00:16:11] And, um, yeah, so there are lots of different use cases in lots of different industries and lots of different departments. So it could be finance, finance industries, healthcare manufacturing. So we, we covered so many use cases as well in the past, on, on different angles, on more chatbots on low-code on RPA.

[00:16:32] So yeah, feel free to. Um, to just go back, um, on our episodes and catch up on all this good content we have produced over the, um, yeah. Have produced in the past. But let’s pick them one here. Um, first we really like, uh, always it’s financial services. Um, since it’s a very busy, busy and innovative usually industry, sometimes it feels like they are a bit old [00:17:00] fashioned and not moving fast, but they actually have the pockets to try all this new stuff.

[00:17:05] And when they look at it, they, they come up with really good use cases. Um, so customer support, for example, um, Those professionals in these departments have to gather customer data from, from databases. Um, they, they have to retrieve that information from, you know, they have to do phone calls, emails, and online chats.

[00:17:25] So, so this is very time-consuming, um, and can impact sort of the whole customer journey, the customer experience. Some, some topic we really passionate about as well. Uh, This, this tool can be an intelligent process. Automation tool can be used to pull data from, uh, automatically data from different databases and updates.

[00:17:45] These records, um, was, was all the information. Gathered from emails and phone calls automatically. So it can sort of transcribe and classify and identify intent and [00:18:00] maybe the mood of the person calling. So to store that extra information in a database and, um, Yeah. So you can gain so much more, um, from, from your data.

[00:18:12] Yeah, exactly. Yeah. I think financial services is, is still a very good place to find use cases because of course there’s, it’s, it’s transaction rich. Um, and these transactions tend to be high volume, low value. The, like you say, you know, identifying intent, um, A transaction, for example, from different sources, extracting structured data from that out into a database, and then presenting that to a person with a index of some description, a risk index or a mood index.

[00:18:47] That’s very, that’s very valuable. And you know, we see it, for example, in insurance where cleanse the body might spend hundreds of hours a year entering data from claims forms. [00:19:00] To the insurance management system or their CRM system, or back in system and, um, you know, deploying an intelligent process automation tool, um, have used to describe it from the forms and automatically put them onto your insurance system or your CRM system.

[00:19:19] Um, and again, this task can be included in a larger end to end process that delivers that relevant information directly to the customer or the end user or wherever is looking. Um, I mean, I saw, I saw an example of a implementation of a AI driven low-code platform where, uh, that, that the telephone call was automatically transcribed and intend extracted from the telephone call.

[00:19:47] And, um, the, the attended bot actually navigated to the, to the right place to go and update the information all the agent had to do is really just verify. That was correct. [00:20:00] Um, so I didn’t even have to type the information in, um, so it’s really, really powerful sort of cognitive services that’s coming out and that’s available as part of these, uh, You know, intelligent process automation.

[00:20:15] Um, you mentioned them, so the bot is identifying the intendance, then doing all that stuff. And then the human workers purifying it. So this is the kind of deep learning, which at some point happens, isn’t it or occurs. So, so the learning flows back into the system or can flow back into the system. So then at some point the bot is rich achieving like 90% of our 99 on or whatever night or success.

[00:20:41] Then at some point you don’t need a verification from the human worker anymore because you then start trusting that algorithm, um, based on that learning the historical learning. Um, so you can, uh, exactly, exactly the point. And this is the thing that [00:21:00] differentiated from normal robotic process automation is that the machine learning algorithms in the background.

[00:21:08] Thus constantly learn. So they, they do learn that for these specific phrases, the intent was extracted, maybe a human actually confirmed that that was the correct intent. And if that happens again and again, a couple of times, because of course the data was extracted, nobody changed it on the phone. So it means that the algorithm was correct.

[00:21:32] So that just ensures that the learning of that. Um, sort of, sort of the elephant that the accuracy level elevates point where, um, you know, you don’t need to have somebody that needs to review it because it’s got enough, um, sort of knowledge and intelligence based on previous learnings, but that is. Off a customer that phones in that wants to change the [00:22:00] address, for example, exceeding and very many to often or many times actually the accuracy.

[00:22:06] Um, you have, uh, from your, um, from human workers in these departments, because at some point, um, uh, this is what we see in here. Some point, yeah, if you do this day in, day out, some points, you get blind for some things. So, so you make probably more mistakes than the bot would do. Um, Uh, does doesn’t mean that we, we as a human obsolete, but I think for some tasks, um, it’s definitely makes sense to, to get it done by, by a button and, and AI.

[00:22:38] Yeah, exactly. So let’s throw another example in there. Let’s say in shipping, for example, where you could use a intelligent process automation tool to analyze shipping data, for example, to optimize your shipping roads and schedules in order to reduce bottlenecks and prevent delays and, you know, optimize valuable resources.

[00:23:00] [00:22:59] Now you can, you could, you can, you could automate that process or try and automate it without. Using machine learning, but of course you have to look at all of their variations, um, quite difficult and time consuming. If it’s rule-based or something like this, if you do deploy the deep learning and you can actually create a model that would simulate, how do we optimize assets, how to optimize the routes?

[00:23:26] Uh that’s when the, the sort of benefits comes in. So, so to three very simple new spaces, again, you know, financial services, insurance and shipping, quite, quite, um, you know, different types of scenarios where this could be beneficial. Um, but you know, hopefully it’s clear to us. They all massive benefits of deploying intelligent process.

[00:23:54] I guess the main benefit I would see, um, [00:24:00] You know, efficiency, optimization, and innovation. And I think that’s generally true for process automation at a whole, um, you know, where you, you automate these routine processes that you save employees times, um, and make their lives easier. Yeah. But when you introduce kind of machine learning algorithms, you know, that these algorithms can discover new ways to optimize your processes for even further efficiency and productivity games.

[00:24:30] Um, and you know, when you’ve got these capabilities with inside your business, intelligent process automation capabilities, and the tools, you know, both your sort of your business and your IP by to use it. And can use those technologies to develop even more innovative solutions and more importantly, to improve those customer experiences that we always do.

[00:24:54] Um, yeah. And that’s, uh, [00:25:00] these benefits are quite tangible if you put to put us in numbers, um, um, So you can automate like 50% of your manual tasks or more around 50% is just sort of on the lower end, what we have seen and some areas that it’s really going up to 90% and which is fantastic. Um, so the processing times were reduced by 50% and, um, our, I, um, Achieved, um, uh, is over a hundred percent on this, usually in very short timeframes.

[00:25:32] And, um, so I guess, um, that’s quite, uh, quite appealing. If, if you look at this, um, you still need to find out what processes are worthy, um, to, to, to improve and to automate was, um, intelligent process automation, but, uh, If you can just pick a couple of those to get started in, in, in your business, I think, um, [00:26:00] yeah.

[00:26:00] Um, you want to do more and then hopefully then the pipeline fills up with, uh, lots of processes to automate and, um, yeah, so very, very good, good benefits there. And good reason to look at, um, and, uh, Yeah. Like, like we said earlier in our clubhouse session, um, in a week we see the gap between it and the business is closing.

[00:26:27] Um, you know, where it is deploying more and more of these low code automation tools, including intelligent process automation tools, robotic process automation to local, low code automation tools where, you know, it does accelerate. Digital transformation initiatives, because it is easier to deploy these solutions and they innovate is it is reducing costs across the business and increasing the satisfaction.

[00:26:56] So, so there’s alignment, you know, where artists kind of [00:27:00] aligning very closely with the business, meet, um, their, their needs and to orchestrate these is. End to end processes for the users and meant to, you know, to streamline processes across the business. It, you know, it, it’s a really sort of, um, exciting future, um, you know, seeing those things happening more and more.

[00:27:26] With intelligent automation in the mix. Um, you know, that, that will go to the next level where, you know, we’ve got our processes automated. Now we’ve got our robots in the background that’s been doing, do things rule-based activities very fast. They can work 24, 7 misconduct just brings a new level to our business process orchestration where it really intelligent.

[00:27:52] Um, and you know, by combining all of these, these sort of automation tools with machine learning applications, [00:28:00] Um, you know, we, we, we, we, we really kind of getting to that, that sort of hyper automation space where it’s basically very clever things that can be done very quickly. The big, the big, uh, thing to look at is really the end to end automation.

[00:28:16] Isn’t it. So we had, we had lots of, um, point automation within the business and, um, But I think you’re looking at the end to end Sundance. It’s sort of the really, really big one to look at. And these are where the, where the really huge benefits are, um, and where it gets, uh, it gets really interesting to have this, this big mix of capabilities.

[00:28:42] Yeah, absolutely. So, very exciting. Uh, lots to talk about, um, We’ll try our best to, to try and cover all of these different disciplines. Um, but you know, a very, very, very good topic, intelligent process automation. [00:29:00] And I think as has AI becomes more accessible to people, um, you know, this would become or will become, um, you know, I would say very mainstream very soon.

[00:29:14] You know, w you know, when people realize its benefits and, and, and you know, how it, how it can sort of take automation to the next level. Right. So, yeah, we, we recommend to our listeners to catch up on the previous episodes. If you, if you, if you’re not a regular listener, um, uh, follow us on the automation guys.net.

[00:29:38] To just, um, uh, you know, be informed of what’s going on in the space was events and content. We share, uh, webinars, um, our podcast episodes, um, yeah, everything you will find there. Um, and as always, if you need sort of help, um, want to sort of, um, some advice on how to get started. [00:30:00] Um, your honor, and I, we are here for you to, um, Yeah to, to, to go on a session and, um, just show you what is, what is possible, um, thinking outside of the box.

[00:30:12] And those kinds of sessions and yeah. Feel free to reach out to us at any time. And let’s automate,

[00:30:24] unfortunately, that’s it again, with this episode of the process and automation podcast. If you liked this episode, please give us a five-star rating and don’t forget to subscribe to this podcast. So you don’t miss any upcoming episode. We hope you want tune in next time. And until then let’s automate it.