#36 The Intelligent Automation Roundup

Hello and welcome back to another episode of the Process and Automation Podcast with the Automation Guys. As always, with me here is Arno. Hello, Arno.

Hi, Sascha.

So today we have talked about automation over the last 30, 40 episodes, and yeah, let's, let's go into this topic again, do a roundup on, on what we have covered so far. What do you think about that?

Yeah, that sounds good. I think we've, we've spoken a lot about a lot of the different disciplines of the automation, specifically around hyperautomation and intelligent automation. You know, we've covered a great degree of topics, and of course, you know, we've gone into a lot of use cases as well, which has been well received by our listeners. And thank you for all the feedback you guys have given us, given us some ideas for sort of future podcasts as well. People do tend to like use cases because I think it's a, it's a great great way to, I guess, put the art of the possible in a sort of a practical sense with an example. Of course, you know, throughout these couple of months, a lot about RPA. Of course, there's still a big buzz on the RPA space, Sasha, as you know. You see a lot of things coming through, you know, now that things sort of post-pandemic is starting to open up. And people are sort of getting back into project modes. They are certainly sort of reaching out to us and starting to look at more RPA initiatives that potentially can safeguard them in the future against some of the disruptions we've seen.

I think RPA is one of the big topics we have discussed. And alongside that RPA, comes this topic low-code, isn't it? So building applications with speed of light. So basically drag and drop, coming up with innovative ideas and put them into solutions very quickly in days, weeks, rather than months. So that was a big, big topic we discussed as well. And we see actually daily all these projects coming up currently. It's RPA, it's workflow, it's user interfaces, getting sort of away from Excel spreadsheets and emails, building even tiny business applications to solve sort of tiny departmental problems. But that is really as well one of the big topics which came up and probably something we will see more and more of.

Yeah, a lot of use cases, of course, for low-code. I think low-code has truly found its place now. It's very mainstream. You know, the people we talk to, you know, a couple of years ago, they wouldn't know what low-code is, but you know, now when you have conversations, people get it, they understand it is a way to develop software using drag-and-drop type capabilities. We always talk about the Lego blocks. Other people say it's like the flatpack version of software, but it is true. You know, it's components you buy. And you put it together. And depending on the vendor, what type of components you require for your type of application you want to put together, obviously different vendors offers, you know, different capabilities. Um, you know, I think there's not a lot of difference between the sort of the mainstream vendors. Like you said, Sasha, I think with, with low-code, especially low-code workflow automation, very easy to create these productivity apps solving a very small problem or enterprise-wide problems. And of course, combined with the RPA, really, really compelling offering for people out there that wants to start with automation. Very easy to get started, dip your toe in it. There's a lot of sort of free trials you can download just to get started, you know. And I guess the message is Challenge yourself. It is what we call sort of innovation technologies. Take a problem, any business problem, find a workflow tool or RPA tool, or inquire with either myself or Sasha. We can always give you some guidance and solve your first problem. And from there on, you'll see that the kind of the the speed of execution and how quickly you can start to automate these processes and add value to your business.

Yeah.

And of course, you know, we've got AI and chatbots. That was quite a big thing that we've discussed in many episodes. You know, very, I would say, a very sort of progressive technology now.

Yeah, very popular topic. Absolutely. Yeah, AI is progressing, and that's, that's probably why, why it becomes more attractive, because chatbots we had for a while, but now enhanced chatbot capabilities was, was, was AI, conversational AI it is called as well. So that's, that's something which came up just recently, and seems to become more and more attractive. Microsoft did the big acquisition as well into the AI about speech, that kind of stuff. So there is probably far more to come in the next couple of years. It's just very nice to add a few more channels into your business process. Now you can use your WhatsApp, your MS Teams, Slack, Facebook Messenger, all sorts of communication mediums and channels. Panels to actually interact with the process. So basically run the process just on a chat window. And this is something really new, probably was the younger generation actually, they probably not really want to do processes differently going forward. And something everyone who is customer-facing has to look at. And yeah, we discussed other use cases, right? Lots of external, internal use cases covered like 40, 50 different ones and then several episodes. So yeah, huge, huge pool of opportunities.

Yeah, I think between AI-driven chatbots, low-code workflow, and RPA, it's so easy to put a really meaningful solution together. So for example, you might have a complaints management workflow that in the back office you want to string together the process. How do you deal with a complaint? You might have a bit of RPA in the background that wants to interact and link that complaints process with perhaps a third-party system. So that's the glue between your workflow and your, your backend system. And it's then very easy just to plug in an AI-driven chatbot in the front. So you can have a solution just for that particular scenario, probably ready in a couple of days. Whereby you've got a channel in the front where somebody can engage an AI-driven chatbot. That chatbot obviously understands the intent of the person that is actually logging the complaint, for example, or it could be a suggestion, you know, it could be anything. But in this instance, the AI chatbot would be able to then route that case to the back office. That transition is seamless. And someone in the back office would get a task, pick up the case, and be able to deal with that very quickly. Now, if you, if you look at a sort of a traditional way of doing that, either having somebody take a phone call or somebody sending an email to a generic inbox, you're going to have all sorts of garbage arriving in that inbox, or you're going to have a very expensive resource having to sort of man a telephone. And there's gonna be a lot of sort of manual emails being sent, disorganized process, something like this, very easy to string together, days, and you've got your first automation. It just, just with those 3 components, how you can really string together a sort of a meaningful automation. And it doesn't stop with complaints. You know, we, we talked about use cases for HR. You know, a lot of companies has already got HR processes. Maybe it's employee leave requests, it could be expense claims. So there might be already some internal forms and processes that exist. Maybe there's a SaaS system, a cloud-based system. It's so easy to then actually, you know, elevate the type of functionality that those systems, those pre-existing assets give you by providing an extra additional channel via chatbot, because again, the chatbot, you could train it, plug it into your backend systems. You know, chatbots like Druid, for example, they're very good. They explore your object models and your data structures in the back. Machine learning modules understand your data structures of the data. So it's really easy for it to understand if, if the intent is to log a leave request for example, load the employee record to see how many available leave days they've got. And like you say, Sasha, you could surface this through a sort of a web interface. It could be WhatsApp, Teams, Slack. There's just so many, so many potentials to almost bring those existing functionalities that you've got forward. We touched on HR, but think about finance, Sales, marketing, sales. Exactly. You know, and I think if there's a shared inbox somewhere or there's a spreadsheet floating around that gets emailed across, you know, to different people, you know, by plugging in, I would say these really 3 strong automation technologies, you know, the possibilities is really endless, you know, and the sort of efficiencies you can gain. And I guess once you get started with it, that's then when you could explore further. And we talked a bit about process mining. Then you go out and look for other opportunities, maybe things you've never discovered that doesn't exist, but you know there is a problem, right?

Yeah, absolutely. So process mining is a big topic as well. I guess that one very big problem with automation in general before going into process mining, I think a very big problem is finding these opportunities within the business. So this is what we have many discussions. So maybe we identify the first one, a really nice candidate for automation, low-hanging fruit, so that kind of stuff. And then we do the project very often with businesses and everyone is happy. But then sometimes sort of initiatives slow down a little bit. And the mistake very often we together with businesses maybe had in the past, we didn't have, we didn't look for out for a huge amount of sort of opportunities within the business. So it is, it is really critical for huge success or good success within, within the business for automation. When you find out like 10, 12, 20, 30 different opportunities, you have an idea where you should, what you should address next after you have done maybe your first initial MVP with something and everyone is happy. But then, yeah, to keep up the momentum, you need to have this bigger picture. And process mining and some other process discovery technologies could be really helpful there to to uncover what you should address next, because sometimes you concentrate on the nice and sexy things you want to tackle, or the really big one, big problems. They may be good projects in its own, but very often they may be not the best one to tackle next. And process mining might bring up problems by identifying all sorts of timestamps in systems, systems, ideally within the whole end-to-end process, identifying all these steps someone is taking or the whole department is taking over time, and then comes up with an answer of maybe inefficiencies here, problems there, and maybe it is just an organizational problem, or maybe it becomes very obvious that this it is a problem we need to solve with automation, with a chatbot, with workflow, or combination of all of it. Process mining can be very, very helpful.

Yeah, we always say it's using your existing digital footprint to understand how processes really run. And I think that's really important because there's so much data out there and that, you know, the digital footprint might be very large that it's very hard to comprehend exactly, you know, what is running smooth, what is running not so smooth, and, you know, where those, those actual problems are that, like you said, Sasha, that you need to uncover. And, you know, this is almost that ultimate way to point process mining tool at this digital footprint and, you know, uncover those problems and, and really give you the opportunity to, to really drill into the ones that's really problematic and to, to address those and to then make really good technology decisions to, to say, okay, perhaps we need an automated process here, perhaps we need an RPA bot process there. You know, this particular issue will be fixed by plugging in a bit of AI. And I think process mining is a really, really good catalyst for unearthing these opportunities. Of course, like you said, a lot of people start with, especially when they're new to automation, they might start with some of the obvious automation challenges, you know, with inside departments, you know, some of the operational processes, finance processes. I think process mining just gives you that ability to really go very granular and really understand some of those underlying process problems a lot better that would be otherwise really, really hard to uncover by analysts, for example. So we covered process mining quite a lot. It's really, really important. Topic. And, you know, one of those topics will have a lot more episodes and a lot more discussions on, you know, and we actually think it is a really, really fundamental, you know, part of hyperautomation that gives hyperautomation, you know, really sustainability in terms of your business because you can always uncover more problems. You can always look to further streamline things. And, you know, once you're in that zone, it's really then when the sort of the true benefits, you know, of automations become transformational.

And it is very often, it's not necessarily a procedure process sort of flow problem. Sometimes it could also identify within that process, a specific supplier, a specific product, a specific invoice or whatever, just more the item which is going through the process is actually that problem. So which, for example, someone is approving an invoice which is sort of not, not sort of in the normal sort of metrics and the more normal thresholds. So then Process Mining can even uncover that. So at the specific step in the process, the process is running fine. So we're getting from approval to the next step super fast, or from first approval, second approval. This is all super efficient and really good. But then with what's going through the process, it's also analyzed by process mining, and then you can identify maybe, oh, this supplier, this kind of supplier in this country is actually not very good for the business because it's breaking rules here and there. So it's— and then you can in real time, if it runs through the process, identify, okay, or we need to act. And similar like with, with, with chatbots, if something sort of comes in, you need to then connect it to lots of systems. And this is what process mining can allow you as well as a business. So while it goes through the process, you can drill down anyway through all the data and find out where are the inefficiencies, capacity problems, and you can immediately kick off an automation to maybe correct a specific thing, notify a person if a specific situation happens. So you can immediately react, otherwise 3 months later you're going through your reports and think, oh, it happened, we should have spotted that one. And this is also what process mining is doing in real time. Is really going through every little process step and the actual item which is going through the process.

Yeah, yeah. I mean, it could be as simple as that. There's not a purchase order attached to the invoice. And exactly, you know, people just naturally assume that they will, you know, because there's not a purchase order, then they need to chase somebody else to actually retrospectively raise a purchase order. Whereby you might want to prevent, isn't it? Ideally, yeah, fix, fix the process at the start. So, you know, no invoice submissions unless there's a PO attached. Very simple example, but I think that, you know, within it, inside of the finance department, it is such a, a small thing that could go unnoticed, and people are just trained to deal with it and manage by exception, whereas process mining is something that it You could literally tell somebody, well, this process is taking a very long time. Why is it taking a long time? Okay, let's drill into that part of the process. Okay, there's a huge lead time from an invoice being accepted to an actual invoice being paid. Well, why is that? Because there's a usually time where there's a handoff to somebody else to actually chase for a PO and then raise the PO. Then we realize there's no purchase requisition. So it is sort of a cascading effect. That, you know, this sometimes unearths. And like you said, Sasha, it's— it could, it could be if, you know, if you have a multinational company that, let's say, this works well, for instance, in Germany, but it doesn't work well in Italy, or works well in Italy but not, not well in Germany, but it's the same process, what's going on, you know. So there might be some standardization that you thought was brought in and it was working. So technically the processes should sort of, you know, behave the same and have the same sort of process times. But if they don't, then you can understand what the reason is for that. Exactly.

Yeah, so there's a really good case study sort of done by Uber. So Uber is using process mining heavily to identify just for customer service. So usually process mining comes out of very often sits more in the financial departments, accounts payable processes, but Uber is using it for looking globally how their customer services are running. And they obviously see, okay, here in the US we have those kind of touchpoints to solve a problem. And then suddenly if you branch out into different regions, because from my understanding, so Uber, when they grow, they grow very quickly in a specific region. Make a decision, okay, we're going into this region and then we grow. And with everything what comes with it, it grows maybe not as efficient as maybe someone thought. So then to just overlay what actually the real picture is globally, yeah, you use then process mining to then see, okay, so we are so far off within these regions from maybe a global standard and Impressive.

Very impressive. Nothing— think probably impossible to get to that place if you were to deploy sort of normal traditional business analytics to it, right?

It would take a very long time.

So, so this stuff's all data-driven. It analyzes your digital footprint. And like I say, it's worth giving process mining a lot of airtime like we did in our past episodes. So check it out if you're new to it. It is certainly something, like I said, which is very valuable to your automation initiatives and, you know, give it that sustainability, you know, that kind of project pipeline you need to really get to those transformational effects that you're after.

Yeah, and I guess lots of enterprises, lots of companies really agree. So not just as we thinking these are the benefits So this is why Celonis is one of the big players in the market. And they just received sort of a billion, billion dollars investment just last month, which is fantastic news and clearly shows there's a huge demand. Not just now, because these, those kind of investments are sort of looking into the future massively. So, so there will be huge demand and every company will probably look at it at some point and come up with Yeah, I guess even every, every small to medium company, they— you want the something to, to just analyze what— whatever you do, and they recommend you should do things here, you should do things there to improve. And ideally with AI and automated, how brilliant would that be?

Yeah, we also talked a lot about integration and, um, you know, the need for good integrations with legacy systems, integration between systems, avoidance of data silos. So, so there's a lot of work being done at the minute by various vendors, vendors we work with, really create easy-to-configure data hubs and, you know, easily integrated data hubs that gives a single source of truth for your automation initiatives. Very important Of course, because when you create new automations, you don't necessarily want to create new data silos. It's very important to, to have these single source of truths, whether it's a customer record, employee record, you know, financial records, orders, or, you know, all of these records where, you know, having a single data point, a single data object for for these records are really important, you know, not just for maintenance of data, but also for all of these automation technologies that we speak about in our podcast to be able to connect to, so, you know, so they can consume that data. And, you know, all of them actually looks at the same, you know, data across the business. Of course, that data can then be also opened up for, for reporting, analytics, insight. It can be combined with analytics that's produced by your automation software, workflow software, your RPA software. And so it's really a very, very important component as well that we touched on in the past in some of our episodes. And, you know, undoubtedly, you know, a lot of work will still go on, you know, in this year and also I think next year in the future. Like I said, I see a lot of vendors are spending a lot of time, you know, to create these enterprise data lakes for their products. So it's a It's a very convenient place to bring a lot of data sources together.

Yeah, and now with everything what's happening these days, so we have hundreds of cloud applications even in small companies, but in the bigger ones you have your Salesforce in the cloud, you have your Workday, ServiceNow, so all these big enterprise services as well are sitting in the cloud as a SaaS offering and you have your workflow systems nowadays, customers are opting in for deploying things into the cloud. But these systems are not sort of complete systems to cover an end-to-end process. And that's the situation we are in. Many years ago, we always said, okay, you need to buy this platform and then we deploy everything on site, everything needs to go into this platform. But this is, that one maybe was an interesting idea a while back, but so far from reality. Because we have hundreds of applications internally already in many, many companies, and we definitely have even more applications out there, really good SaaS offering small cloud solutions to tackle problems, HR solutions which are in the cloud, and payroll solutions which are in the cloud, and then they're all part of the overall process. Some elements are on-site, some elements are manual, And to just connect that, because we will never bring everything into one system, so this is utopia. So we only have the choice to come up with some really powerful— to bring all that data and orchestrate all that data and all these different silos to make one overall nice flowing process. It could be when we talk about the workflow side, obviously we have maybe a nice workflow system, low-code system is on top of it. And then we have lots of these little arms and grabbing all that data to make that overall process. But without integration, without API connectivity and in a secure way, so like really enterprise-grade security to speak, to all these different systems from on-site to, from on-premise to cloud and back and forth. So yeah, you can't build a modern system. And yeah, this is where we are with that, and this is why integration becomes a topic for, again, not just large enterprises, it's really starting for small business as well. They have their Xero finance software, they have their BambooHR HR system, they have their documents maybe in G Cloud. So these problems are not just problems for enterprises. We talk obviously around lots of big company use cases, but that is really, really important when we look at the full end-to-end automation capabilities.

Yeah, and I think, you know, a data hub for your business where you could have a data service or provision a data service. Really, just if you approach that from the start in the right way, you could service so many consumers of it, including people that would potentially layer workflow solutions on top of that. So if you get it right, you could really sort of open up the floodgate for automations because you know that you've got a really sort of single source of truth of your business data, and providing it's secure, it's descriptive, other teams can then look at that, consume that, and build their workflow systems on top of that without really worrying about the sort of implementation and, you know, where all of that data is persisted. As long as it can be consumed, it's really easy to, you know, to plug in these automations. And like I say, reporting as well. So business insights analytics. You know, once you have that sort of data hub up and running to a degree, and you know, if you are a small business, it might be a smaller scale, and if you're enterprise-grade business or enterprise-sized business, might be a larger scale. But the point is, you know, it is a place where you could make data available for people and really sort of liberate that data so people can create these reports and create these automations and and mine that data and start to draw those insights and intelligence out of it so they can make better decisions, they can automate their processes, they can understand where the gaps are, where perhaps RPA needs to be plugged in. So it's a really, really important topic.

It's a bit of an overlap with process mining, isn't it, here and there potentially? So if you look at them, maybe a bit of the same data, But if you combine that as well with AI analytics, can be very, very interesting. So you can predict potentially specific outcomes. So where process mining is looking at the currently what's happening real time. So with maybe analytics, if you have all that historical data and you do a few other things with analytics, you then can build predictive data models and yeah. Really fantastic things can appear, can happen. You know, every time when the sun is shining, your trains will be delayed because of the doors have a sensor problem which only kicks off after 35 degrees. And if you collect all that data and then use proper analytics, maybe with AI, so you as a train manufacturer, that you can then sort of predict and maybe find or need to adjust your maintenance schedules and specific times of the year to just figure out, yeah, or to just prevent disruption in service. So all this can be done with the area of analytics. It can go really, really complicated on that one. Yeah.

So I guess the question is, for some of our newer listeners that's new to automation, where do you start? Where do you start with all of this? What's the first thing you would do?

Yeah, I guess you would, um, you know, just look at your current situation, um, what's, what's happening in your business, and then define sort of where you want to be. I think that's, um, and that, that sort of determines, um what needs to happen in all these different areas. So, so what you need to identify your pain points. So this is the, this is the big thing. So otherwise you wouldn't change things, you wouldn't have a business case if you don't know where, where actually the pain is. So that there's, there's definitely some analysis to be done initially. That could be done with all sorts of methods, isn't it? So it could be technology, it could be interviewing people. Yeah, and many, many other topics or areas we covered, we covered as well in our podcast. I know, any other, any other ideas there?

Yes, I think we covered, you know, process discovery. We covered that in depth. That was a really, really good episode where we really looked at, you know, how do you actually start talking to people and understand what pain points are. We highlighted a few techniques there for mapping our processes. I think, you know, a lot of people go one of two ways. The first route is really to start small, identify really small problem and apply low-code technology. And this includes Workflow and RPA. Potentially chatbot to solve that, just to sort of test the water, how it works technology-wise, how a delivery of a low-code project looks like, and really to get buy-in from stakeholders, to, to, to, to really see it is not very hard to do these things. In my opinion, the hardest part is really just to sit around the table and understand, you know, what are our top 10 problems And out of that top 10, you know, what is the 3 that we desperately need to solve? I think once you at that point, the remaining part of is really quite simple because all it is, is then a question of just mapping those processes out. It might be that you start with one, get the buy-in from all the stakeholders, ensure you get the buy-in from IT so they understand the vision. And automate it, you know, get a minimal viable solution out as soon as possible, make a difference as soon as possible, and build on that, and do so in a collaborative way. You know, always keep those stakeholders, those end users of the processes engaged. Always ensure that what you're doing is making their lives easier, not harder. Try and reduce work from people, don't create more work, um, and, and try and target those processes that, that really consumes people's time. It could be really as simple as a shared inbox where stuff arrives and you just want to take that stuff and put it in the right places for people so that they can pick that up and do their day job. I mean, that in itself is such a, a good place, in my opinion, to start with with RPA, for example, where you go around and you just ask people, show me your shared inboxes, you know, and there's 50 of them. You're like, well, there's an obvious place to start. Let's train a robot. Let's train a robot to read those emails and actually use a bit of sentiment analysis to understand what this is. And even if it's just as simple as forwarding that email on to the correct place, you know, that, that in itself is a very, very small implementation, but it's a start. Once it arrives at that place, maybe there is an opportunity then to consume that email and create a little workflow where there's a task for somebody where they click on and they look at it and you give them the information they need to make, make a decision. Or if there's opportunity to look at it and auto-approve it because it's below a threshold limit of £100 or something whereby, you know, you can train a robot to make that decision. Take the volumes, the low value, high volume transactions out of your business and grow from there. You know, start looking at other opportunities, start looking at your channels. Like, what if we plug that chatbot in here? Employee doesn't have to send an email to HR to ask them about the leave policy or some contractual policy. Yeah, a bot can feel that.

I know there are many, there are many of those. Sometimes we, we think, okay, we need to really concentrate on these big problems. Sure, they, um, have seen— we worked on really big projects which had lots of business support stakeholders and fixing that problem. But when you look in this organization Sure, they, they may be— there was the view, okay, we look at further automation later on. But if you, if you, if you enable lots of departments to actually get almost that stuff themselves, so this is why low-code is so important, instead of just doing this complex automation and programming. So yeah, to help these departments, these, to address this huge amount of smaller problems and to really get them going with the right IT and governance around it. But this is what we talked about a lot, but then really get them going and tackle all these 80% of small problems instead of just getting through the 20%. And I think that's why With all these things we have available at the moment, it's such a great time for everyone to look at it and get started.

Yeah, and I think although the topic of automation is quite broad, if you unpack it slightly, a lot of the things we do to automate these processes are not very difficult. It is literally, if you automate the process, it is dragging boxes onto a design canvas, connecting it up with decision points in. It is quite natural for people to understand that, especially when they understand their processes. I think that's what, that's in my opinion, what makes low-code so appealing because it's easy to represent your process it's easy to model what your data should look like, whether or not it's an invoice or some transaction. Could be a, you know, a purchase requisition. It could be a transaction from a bank statement that needs to be allocated in a financial system or applied to the financial system, whatever that looks like. It's, it's so easy in these, these low-code systems to model that. And then of course, to create the user interfaces, really modern looking user interfaces without writing code. I think for me that that's one of the great benefits. And then of course publish it so it's accessible on mobile. So people can actually look at this when they're en route. So they are connected to their processes. Again, it doesn't have to be a very complicated thing. A lot of the use cases we looked at in the podcasts are standard things like onboarding, offboarding, these types of things where surprisingly when you speak to people, these types of mainstream standard process are still quite manual. So these are some of the great places in your business to start automation and to start looking at sort of low-code automation, RPA, and how that could be used to really rapidly automate these processes and make these problems go away a lot faster. Like you said in the beginning of this episode, like lightning, lightning fast, you know, compared to traditional high-code development where everything is written from scratch using, using high-code methodologies and, and, and, and, you know, the old traditional ways of doing things. Um, so we're in an exciting, um, sort of time where, uh, you know, it's certainly a lot of the things we do, um, as a business, uh, we certainly look at really complicated stuff and, and, and to this day, it's really quite surprising how mature these platforms are at actually being able to cope with really, really sophisticated enterprise-type implementations, but also how quick it is to create something very small and make— make some—

Yeah, we had recently an intake of new graduates, and they couldn't believe it how quickly they can actually build a proper enterprise-ready, fully working application. So they were just used to sort of the normal stuff, building web apps, sort of, you know, sort of hand— yeah, you have to code everything. So with other frameworks, that kind of stuff, and now jumping on a proper workflow platform low-code workflow platform, they just learn it so quickly and get results in just a week, two weeks, impressive results. So yeah, it's fantastic to see when it works and everyone is happy and everyone is really keen on finding other problems in the business, come up with really quick ideas, prototype things with these local platforms. So that's also fantastic. And we should talk more about not necessarily building applications. We can use also the, the, the using low-code for, for innovation and just ideation. It's, it's also very powerful.

Yeah, I had an interesting quote where somebody said, once you see this stuff, it's really hard to unsee it.

And oh yeah, that's good.

It is, uh, it is. It's, it's something that stuck with me and, um, It's, you know, it's something really that if you're new to low-code, it's worth checking it out, you know, getting in touch with myself, Sasha, more than happy to give you guys a demo. Obviously more than happy to give you some ideas on a particular problem and how these, the technology and the hyperautomation space would help you to solve that problem, how long it would take, You'll be very surprised with, with the timescales, especially if you're used to sort of longer timescales when it comes to traditional IT development. It's definitely something worth checking out. I know there's a lot of talk in the market about it, but, you know, challenge yourself, challenge us. You know, between Sasha and myself, we've got a really, really long history with, with low-code. You know, we've, we've, we've delivered a lot of these projects and, you know, we can certainly give you a really, really good initial start just to get you out the starting blocks with automation and sort of just to demystify things, what you need to do, perhaps where you are on your journey, what, what, what a potential good next step would be, how big you should go, what the sort of team makeup should look like, what's the culture you're aiming for in your project delivery. And really just, you know, all of the peripheral things that would make your automation journey very successful.

Yeah, this is why we have created this checklist for every one of our listeners. 50 sort of tips to really get started with the intelligent automation journey. So you can always go to theautomationguys.net, send us a message there. So just send us like automation as a sort of a keyword, just send that message to us and then we will share that checklist with you. So, and then you have sort of everything to get really started on that with success.

Great, thank you, Sascha. Been really good discussing that again. I hope our listeners enjoyed that. And I don't know if there's anything else that you want to add.

No, so that's, uh, yeah, that was a good, good session, good roundup on, on what we have covered so far and what we very likely will continue to, to cover. So as Arno mentioned earlier, so we are very happy for feedback and suggestions. So yeah, send them, send them through on, on our theautomationguys.net website or on, on our social channels, and we will, um, or join our Clubhouse session we have every week on Tuesdays at 4, and Yeah, and then we will discuss that more with you. And as always, thank you very much for joining in. 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.