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This post includes the full Transcript of from May 2020 – Ashling Partners Webinar DPA & RPA Hyperautomation Webinar and Use Case on May 20, 2020 and the YouTube video of the entire educational webinar: DPA & RPA Hyperautomation Webinar and Use Case

Learn how to orchestrate hyperautomation technologies across a common use case in vendor management and procurement around vendor price changes and commodity price monitoring. Led by Ashling Partner’s Ryan Mac, this video leverages K2, UiPath and Microsoft Power BI to automate a common process to drive business results around pricing.

Introduction: Transcript from original webinar recording

Ryan Mac

Hi there, and welcome to the DPA & RPA Hyperautomation Showcase. My name is Ryan Mac, I’m a senior lead in the business process transformation group with Ashling Partners, and really happy to be speaking with you whether you’re joining here live today on video, or if you’re watching the recording later on. A little bit quickly, by way of introduction, in terms of who we are: Ashling Partners is a consulting organization that partners with other organizations to help guide them through their Intelligent Automation journeys. And so some of the key areas of focus for us are around education, building solutions, and sustainability. And so when we talk about education, we really like to make that a focus, and we encourage our clients to focus on it as well. And so we’ll do things like RPA awareness campaigns. And you see some other examples there. A lot of webinars such as what you’re listening to today. We do build and deploy automation solutions across a variety of different tools and technologies, and so I’ll call out a couple that you’re actually going to hear about today and you’re going to see in our demo. And those would be a DPA solution, specifically, today, that’s K2, an RPA solution, specifically today, that’s UiPath, and an analytics solution, which today is Power BI. So we’re going to see how these three tools and technologies can be combined together in a hyper-automated fashion. You can see there’s a couple other areas where we also build including intelligent data capture, AI, machine learning, etc. So we’re really across the whole breadth of the Intelligent Automation space. And then finally, in terms of sustainability and continuous improvement, that’s also a focus area, whether that be supporting production, automations, improving processes, instituting governance and kind of CoE type programs, and beyond really. So we’re across the entire spectrum of Intelligent Automation. 

So why are we here today? So we want to see a demo around a specific use case. But I think more importantly, we want to highlight what’s possible with these different tools and how they can be orchestrated together to really achieve significant business outcomes. So the use case today is called vendor request management or vendor price change requests. And really, the genesis of this use case comes out of the manufacturing and automotive space. So we had a couple organizations that we’ve been partnering with, kind of independently each come up with an idea around monitoring commodities prices. So you can imagine that’s huge in that space. And so we kind of zoomed out and looked at the business process, and found out that really, this is a process of interacting with vendors, and having vendors request new prices for these commodities. And there’s some work that needs to be done behind the scenes. So that’s kind of the business process that’s been identified. We actually put this into our innovation hub lab at Ashling, and we’re able to build kind of the solution that you’re going to see demoed here today. 

the future of work

So if you look at the vendor price change request process. Today, I think, no surprise, the reality is going to be you’ve got kind of a long running manual process with numerous handoffs. And so what that might look like and what we’ve typically seen it look like would be vendors communicating their price change requests, for raw materials, some for these commodities, aluminum, steel, whatever the case might be, they might use a couple different communication mechanisms. Sometimes they’re picking up the phone, sometimes they’re sending an email. The procurement team is going to receive and interpret that request, so if it’s through an email, they need to kind of normalize the data, understand what the request is all about. You might have some analysts that are actually doing the research on the data to determine if these prices that are being requested are in line with current commodity pricing. So that’s really that monitoring piece that I talked about. Then you might have a workflow, where you need an approval step from a manager to either approve or reject that price that has been requested by a vendor. And of course, finally, you want to communicate the end result back to that vendor or follow up for more information, potentially presenting them some data. So you see this process, again, extremely manual, and extremely long running, when you consider the amount of time between handoffs and you consider the amount of things that requests that could really fall through the cracks at various steps here. So when you evaluate a business process and see if it’s a good candidate for automation, and if there’s a good opportunity to really have positive business value and outcomes by automating a process, you want to look at some of the key metrics around productivity, cost, risk, employee engagement, and innovation. And so if we were to evaluate this process that we just looked at, so one, we’ve got long cycle times. We don’t actually know if we’re getting the best return. If items are falling through the cracks, or if we’re not responsive enough in our research. There’s certainly a potential for manual variants, as we’ve got very manual activity, and different actors who are executing various steps. This is highly repetitive, so it is a very rules based process, which is good for us in an automation sense. And the current process obviously, is not very innovative. And there’s a lot of a lot of human to human manual handoff. So we’re we’re trying to get is really the inverse of each of those metrics. So we’d like to decrease our cycle time, ensure that we do get the best pricing from our vendors, higher accuracy and repeatability and make sure these processes run the same way, every time and we achieve the same outcomes based on our business rules. We want to free up our employees and our humans time to really focus on critical thinking. And you know, that approval step really isn’t a piece where we should have human focus. And then of course, we want to deliver a more innovative solution, because there’s value in that in and of itself. And so if you flip the page here, looking at a hyperautomation possible solution, we can really consolidate and optimize this process. And what that might look like in this case, is having our vendors enter a request in a standard form, as opposed to different communication mechanisms, and having to normalize that unstructured data, well, we can kind of enforce structured data by exposing a web form directly to our vendors. And it’s also going to increase the speed of communication and responsiveness. So we’ve really eliminated a couple handoffs there. And we’ve eliminated a very tedious and unstructured process. Next, you still have the analysis piece. And so there’s no getting around the fact that we need to interpret this data in this request, and actually monitor those commodities prices. So you can do that in kind of a proactive or reactive fashion and we’ll show a little bit of both here today. But this is the area where RPA bots can really shine. They’re the task doers, they’re the ones performing the analysis and the research and providing back relevant information for the human. And finally, our managers, as mentioned, are going to focus on those critical thinking tasks, which is really the review approval and rejection of the request. And guess what? All along this way, we’ve got a workflow solution that’s tracking this request form end to end, you know, you can almost think of it as a case in and of itself being tracked end to end, it’s handling all the triggers, all of the notifications, all of the handoffs. And so there’s none of that manual communication, and manual handoff and instruction going back and forth. 

Awesome. So this looks great on paper. And this is kind of a process that was white boarded and designed, I think let’s take a look and actually jump into the demo portion here. And we’ll see these technologies in action.

So first things first, I mentioned exposing a web portal. So here’s kind of a fictional URL if Ashling Partners were hosting this web portal, through the kind of DPA tool here, you’d have a landing page for vendors to come Enter. And what we’re doing now is simulating a vendor entering their actual request for commodity price changes to the customer. And so there you can see they have a standard form here, they clicked on a new request to start, they’ve entered some relevant information about themselves, what their supplier name and number are, and who should be notified, what the result of this request is. Now they’re selecting from a predefined list of our commodities. You know, this can obviously be configured to, to specific business or to a specific supplier as well. But you see, we’ve got some standard commodities here. They’ve selected and they’ve proposed their price right there in the form. And we’ve given them a little bit of room to enter a comment. So they’re asking for approval on this request for five different commodities that you can see right there. So now they’ve submitted their request, and it automatically is going to come into our workflow. Here, you can see there are a couple of requests in progress. And you’re able to see the status of each of these requests. And what we’re doing now is actually opening the form. This is just sort of an interim step to see how it looks after being submitted. So you see there was the request information, and then not a lot of information there. The bots are now taking over, so the bots via the workflow, the bots know this request is assigned to them. And they’re going out to standard indices, and scraping data on these commodities to find out the kind of average pricing for this timeframe. So this is that activity that can be done kind of proactively and maintained, or reactively in response to a request. We’ll talk a little bit more about that. But you can see they’ve scraped one index for five of the commodities and now they’re on a second index first. For the last commodity, the important thing to note there is, you know, we can be looking at any number of data sources out on the web, or via API’s, and really grabbing that data and bringing it back for analysis. What you saw next is that the bots have brought that down to kind of a working interim table. And here, guess what? We’re gonna plug that data directly into our kind of analytics BI tools to be able to provide some statistical and variance analysis on those prices over time. And what we’re really trying to get at is answering the question of: is this price acceptable that the vendor setup sent us? So we can certainly set up rules in this tool, tolerance ranges, and that sort of thing, to be able to really recommend a decision to the human approval to the human approver, I should say. So you see, we’ve cycled through each of these commodities, and we’re maintaining in this dashboard, all of that analysis over time. Now what the bots can do, and that’s the activity. I should say that, you know, you can really have going on in the background proactively. And then upon request coming in, you can have that data being brought back. Since you saw the email, that would be the email to the approver, who’s going to come into the form. And guess what, what started out as a one line form. Now you see that the body has returned an analysis, a graph of the data, as well as an explicit recommendation that this request should be approved or rejected. And you can even set up, you know, a confidence threshold, and things like that. So all of the information that the human needs is at their fingertips to make a decision in a matter of seconds, as opposed to minutes, hours, days, if you account for the handoffs back and forth. So it’s really, really quick cycle times. And again, the power of information at your fingertips. So all that the approver needs to do right here is focus on kind of the critical thinking task of approving. And guess what, you can even set up confidence thresholds that, you know, if our bots based on our algorithms are, let’s say 90% confident that they should approve the request, perhaps it gets approved automatically. And then there’s a threshold that needs to go to approval and a threshold that gets rejected. So you can see the power of really having a DPA tool that is orchestrating the request and kind of managing that case, end to end, RPA bots that are plugging in and doing the various tasks that used to be extremely manual and time consuming, and then analytics tools like a BI dashboard in the background, that is really being used to harness the power of that data and provide it right back to the approval.

So you see, we’ve completed our sort of pizza tracker along the top, we’ve gone from created to bot working to under review and to complete. That really follows along kind of the workflow steps in the tool, I think that’s a good highlight of how these three technologies can work together. If we take a step back, and what we just saw, we mapped out a process that looked like this, we simplified a process, we have vendors entering requests directly in a standard form, we had bots doing the work and the research in the background. And then we had a human approval step. And so we really consolidated that workflow. But what we’re talking about here, in this entire business process is really a smaller piece of the bigger puzzle. And so what I mean by that is we really had two related activities that we just saw. One is commodities price monitoring, the act of analyzing the data kind of in the background, and that x that we saw the bots doing. Then we’ve got our vendor price change request is, which is more of that request management end to end. Well guess what? If we kind of zoom out further, and we’re going to use a tool called APQC here, which is really a leader in kind of the benchmarking and process framework space. So they’ve kind of created standard frameworks by industry and by horizontal line of business here. And you’ll see where within the supply chain, and then zoomed further into procurement along the top, and zoomed further into sub areas, and then more of that task or process level in the white space there. And so if you look at that end to end process that we just covered, where do we really fall in this spectrum? Really, we’re in those areas in green, so we’re soliciting and tracking vendor quotes, and we might be analyzing the vendor performance over time. If we start to think that these vendors aren’t giving us the best prices, and we’re rejecting a number of requests, or we might, you know, plug in processes to to start to look for another vendor or look for some competition there. And so we’re in those two areas within the procurement space within the overall supply chain space. So you can imagine that we just automated you know what we were calling an end to end process, you could really extend that automation throughout the entire business process in the procure to pay space. And so what we would recommend in this scenario would really be, you might use one of these examples as a pilot area, but then you want to look to the tangential areas that are very related up and downstream from the process that we’ve just talked about, and look for other opportunities. Because when you’ve got automations, kind of strung together, in a more end to end fashion, you really get a lot of synergy and unlock a lot more business value out of those automation junctions, as opposed to being, you know, kind of siloed in different areas of the business. That really would be the recommendation here is to think bigger, use a heat map and and really start to understand from our standard, from my organization, standard business processes, here are the areas within procurement in this example, that I think automation could play a role, and start to kind of triangulate and bring those together, where we can really unlock a little bit more power of that end to end automation. And that’s the challenge here, is to really go from pilot to a more enterprise grade automation that expands across the business process. And guess what? You see how many different standard areas of this process framework there are, I think it’s safe to say there are automation opportunities, and every single one of the boxes on the screen here, certainly the heat maps will help as you analyze which areas have potentially bigger opportunity and which areas might be a little bit more challenging, but there’s, there’s opportunity in each area. So you go back to those categories upon which we evaluated the initial vendor request process that we showed, the cycle time, the cost, employee engagement, innovation, etc. And that’s how you can really evaluate whether it’s a good fit, and your heat map is going to start to look exactly how you see it on the screen, you might see green, yellow, and red areas that are, you know, ripe for opportunity to automate.

So with that said,you might be thinking, you know, okay, I’m not in the manufacturing space, my organization does something a little bit different. What comes next? I truly believe that, you know, the importance of what we just looked at here is less about the specific use case and more about the power of these automation technologies coming together in a truly orchestrated fashion. This is the power that we have at our disposal today. And so if you’re thinking about how this might apply to you or what comes next, there’s a couple of things that we would really recommend. One: brainstorming applicable use cases, I think there’s no shortage of use cases that follow a life cycle, that require a request to be managed, or a case to be managed, tests to be done within that workflow, and some data to be analyzed. And right there, anytime you can answer those three questions with a yes, that’s required. And you’ve got, rules based sections of the process that you can carve out, well, we’ve got an opportunity to use these three technologies in conjunction.

Next, I would love to chat with you on any ideas that you do have. And I think jointly with K2, we’d be happy to do kind of a virtual Lunch and Learn. I know everyone’s or most folks are working remotely at this point and not in the office. And so kind of a lunch and learn concept might not be the reality for some folks right now. But we can do that virtually. We’d love to hear about use cases, and really talk about what’s possible right in your situation. And then finally, I think recommending starting a pilot project, I know you know, folks listening to this might be at all different phases of the automation lifecycle and journey, you might not have started, you might be well past a pilot. What I mean here is to start a pilot project that uses multiple technologies in an orchestrated fashion, and pilot how these technologies come together, much like you would have done an RPA pilot on its own. And many organizations have done that. So pick an area like we just did around vendor quotes, and start to do that triangulation via a heat map. And that’s where you can really expand and find other opportunities. So those are kind of the three big recommendations on what comes next. I truly believe that the possibilities are almost limitless for how you can apply these automation technologies in conjunction with each other in an orchestrated fashion. And so I’d encourage everyone to really be thinking about use cases that can apply to them. And really, if you want to chat more about that, we’re here to talk and here to help. So with that, I think that brings us to the conclusion of the demo. And we can open it up for any Q&A. I think the Q&A function is available through zoom right now. Your microphones would be muted, but hopefully we’ve got a few questions coming in. So anything so far?

Marshall Sied

Yeah, Ryan, we have several here. I think you may have addressed some of them throughout the conversation. But the first one was around use cases that came in prior to you saying it’s less about the use case and more about the power of orchestrating automation, because the use cases are limitless. But the question was really around other applicable use cases beyond commodity price, but within the same type of process.

Ryan Mac

Yeah, absolutely. And I do think it’s pretty limitless. You know, a good good example that comes to mind. You know, after we had sort of piloted this through our innovation hub at Ashling, we had an organization come to us and really find an applicable use case for them, which was not around the manufacturing side and commodities price monitoring, but around monitoring currency and fluctuations in global currencies, in more of a real estate context. And so I think that was a great example of, you know, taking what was seen in this use case demo and applying it to another type of business. And I really think those possibilities are limitless.

Marshall Sied

Right. Thanks, Ryan. So the next one is around the term hyperautomation, which does seem to be the prevalent Gartner coined term of the day. So any reason why you use multi technologies in this demonstration? I think you probably addressed that a couple of different ways, but that’s the question.

Ryan Mac

Yeah, yeah. And just to clarify, I probably use hyperautomation, Orchestration and Intelligent Automation, somewhat synonymously. At this point, that’s kind of where we are in the evolution of these technologies. I think there are really strong spots within each of the different types of technologies, there’s certainly some overlap and capability. But when you look at a TPA solution, where it really thrives, is in the forms workflow and rules engine and sort of managing, like I mentioned, managing that request, or even that case, you might hear the term case management, end to end. So I think that’s where that technology thrives. RPA is certainly, the RPA bots are the task doers. And so they’re taking the repetitive rules based work, and, and doing transactions performing transactions. And then of course, you’ve got BI tools with specialized analytics. And so I guess to you know, to summarize, I think there is some consolidation happening in the market in terms of various vendors and tools, getting better and better capabilities, which is excellent. But I think there are still really core strengths in each of these different areas. And the great news for us is that they play really nicely together, and so they unlock more value when they are deployed together. And in this case, like I mentioned, you know, that combo of K2, UiPath, and Power BI being deployed together is extremely powerful, and something that I think you’re going to get more and more value than you could possibly get out of using one of the tools on its own.

Marshall Sied

I’m gonna ask a subset of that question, Ryan, because more questions are coming in as you talk. So how is hyperautomation different from Intelligent Automation?

Ryan Mac

Sure

Marshall Sied

I think the difference, I guess, is a better way to coin that.

Ryan Mac

Yeah. And I just mentioned, I was using them somewhat synonymously. So if I had to boil it down, I would use hyperautomation and orchestrated automation, extremely synonymously. So that is really focused on more of an end to end business process. And usually deploying multiple tools, like we just talked about, to automate end to end as opposed to task base. So I think that hyperautomation, and that hyperautomation vision. Intelligent Automation, I would certainly say is very related. But I think the key difference there is that Intelligent Automation, you really want to focus on process optimization, and really having efficient automation. And so what I mean by that is, there’s the old quote, that you never want to automate a bad business process, right? Because you’re just going to exacerbate the issues that are already existing. And so I think, sort of that process reengineering and optimization is where the intelligence piece of this comes in how these processes are designed. And then on the flip side, or not the flip side, on the back end, you’ve got the intelligence aspect of new to new technologies that are making things like RPA bots smarter and smarter and making these processes more and more efficient. Like ML, machine learning, and of course, AI, that are being embedded into each of these automation technologies and tools that we’ve talked about. So each of these platforms has elements of ML and AI brought in, and that’s where you really get to that intelligence piece. You’re not simply just scripting and automating things over and over that might be task based, you’re really creating an intelligent business process and flow that can take different paths based on the scenarios. I think that’d be the distinction for me, but again, extremely related and working concert together.

Marshall Sied

Nice, Ryan, we have a lot more questions. So I’m going to try to get through as many as I can in the next five to ten minutes here. So change management was a topic that came up here and several of these questions. So the specific question that I got in front of me is about vendor change management with how you said that you standardized forms in the demonstration? What was the change management impact to the vendor base?

Ryan Mac

That’s a good question. So just like if you had an internal stakeholder who was a process owner or process user, you’ve got an external stakeholder in this case. And so the specific change in the process we just saw, is really around switching from an email or phone communication to a form based communication. I think the good news is you’ve got some things working in your favor as you go down that path. I think generally, more and more folks and companies want to be interacting in a smarter and more structured data fashion. And so it’s actually easier to go out to a website and enter a form that’s going to give me drop downs than it is to come up with an email and come up with those values on my own. So you’ve got that working in your favor. But yeah, there absolutely has to be kind of that proactive communication with the vendors, in any stakeholder. I would expand this to be any stakeholder for a business process in areas that are going to fundamentally change. And so I think, if we look at how change management works in an automated world, or in a world where we’re working on automation; in the past, we might have had Lean Six Sigma type initiatives that are focused on process improvement, and identifying areas for improvement. And then we would make the process more efficient, and there’d be a change management aspect. Well, that hasn’t changed. But we just are able to use automation as a vehicle. And so I think the automation can sort of identify and unlock those opportunities via two technologies, like process mining, which we haven’t even talked about here today. But it can also really help advance those conversations around optimizing processes by automating them. But you’re always going to have some aspect of change to the various stakeholders. And so that change management piece does remain critical. But I guess the moral of the story is, as we come to a more kind of innovative, more updated technology solution, by normalizing data and entering in an online form, I think that’s going to be preferred nine times out of ten. And if you’ve got those unique situations where you’re used to a customer, in this case, calling you on the phone, you can still work to manage that process and route data through the new process and kind of over time tackle that change management aspect. It doesn’t have to be overnight.

Marshall Sied

Yeah, I think that’s great. Right? I think maybe one additional item is, it all depends on the process, too, right? It’s easier to enforce change on folks that want to get paid by your organization versus folks that you want to get paid by as an example. So kind of the accounts payable nurse accounts receivable discussion. So there’s going to be different levers from a change management perspective that everybody’s got to consider.

Ryan Mac

Absolutely. I was just gonna say, we’ve automated processes around order entry from customers. And you might have a different challenge that you’d have in interacting with the change management on the customer side, as opposed to the vendor side. I certainly agree.

Marshall Sied

Absolutely. So we’ve got some somewhat industry related questions here. So one of the questions is how do you navigate around the challenges of data privacy and the use of analysis? So for example, in healthcare where HIPAA applies, that is required prior to human decision making. So in summary, how do you navigate basically, the challenges around data privacy during the rules process and the workflow process?

Ryan Mac

Yeah, so that’s a great question, and I know a big concern for a lot of organizations. You know, I will say there are certainly some organizations that would take some of those extremely sensitive processes and not consider automating them. And that’s okay. However, I would also really want to stress the point that if you look at these technologies, the work, the DPA solution, tracking the workflow end to end, and the RPA bots. These tools are not databases that are storing data. They’re simply working to access data in various source systems. And so for instance, you might have, this example was clearly a request initiated by a vendor where they plugged in information and values directly into the tool. You can have these DPA tools that are pulling data from other source systems, sort of in a real time fashion, and updating requests, and still having requests go through the workflow without actually storing that sensitive data in the automation tools themselves. And so I think they can, in a low code fashion, go across the different applications and access what they need, and update what they need, without storing it itself. So I think that alleviates some of this security concern, at least in my opinion, and what we’ve seen in terms of kind of adoption, and some of these more sensitive data use cases being automated.

Marshall Sied

Yeah, that’s great. I think maybe to add to that, as well, just from personal experience, you know, there’s different slices you can take based on security requirements. There’s certain protocols and security access points you can leverage, depending on your need for DPA or different credential management bolts and encryption that you can leverage in your RPA platform. So it just depends on the scenario, the compliance, the internal controls, and the upset process. And from there, you reverse engineer the security protocol. 

So Ryan, going back to one of your earlier statements. So one of the questions that came in and was around automating only repetitive tasks. So are you only automating repetitive tasks? Or what other tasks would you consider automating that aren’t repetitive?

Ryan Mac

Yes, that’s that’s a great question. Again, I would say there’s an element to the answer here that is extremely process specific. And really only going through that holistic analysis, can you say if something’s a good candidate, and should be automated? And if there’s a business case there, right? Because really, what we want to focus on is, is the business outcome that we’re going to achieve by automating this? Does it exceed the cost of automating it, essentially, is how you create that business case. And so I think the typical fit for RPA, in particular, are repetitive tasks, because you’re essentially introducing a digital workforce that can work 24/7 and so you’re able to grow your volume of transactions. So usually, it’s a transaction based process without adding headcount and human workers, so you’ve got this extended workforce. However, that’s not to say that everything that we automate has to be, 1,000 transactions per day or anything to that effect. I think you can look at the repetitive side, you can also look at the rules based side. So you might have these processes that are a little bit longer running and maybe less frequent. But they’re rules based, and they follow a workflow. And they take a lot of time with the handoffs from human to human in the current state. And so I think there could still be a lot of opportunity there, even if it’s not a strictly repetitive transaction based process. But it’s another area where we would want to look at the process. And then you’d want to look at the tools that are at your disposal. And, you know, maybe it makes sense to deploy DPA without RPA. Maybe it makes sense to do the opposite. But I think there’s probably still opportunities and a lot of those areas, even if they’re not, explicitly repetitive with many transactions. I hope that helps.

Marshall Sied

Yeah, I think that’s good. I think the point is to not have one t-shirt size that fits all, just understand that there’s going to be several t-shirt sizes.

Ryan Mac

Yeah, absolutely.

Marshall Sied

So I’m gonna, I’m gonna make this the last question here. Because I think we intended this to be roughly 40 to 45 minutes. And for everybody on the call, feel free to send in other questions. I think this is great. There’ve been a lot of questions during this discussion, which is kind of the idea of a collaborative community. The last question was around maintenance, specifically to your demonstration. What type of maintenance is required when you don’t own the index websites? 

Ryan Mac

Yeah, yeah. So it’s something that we typically come up around that underlying application change management. And so you’ve got all these source systems of data that are, potentially changing, potentially, you’re upgrading internal systems. And we have automation that is layered on top of that. And so when you have changes in the underlying systems, you can encounter some maintenance activities. And so in this case, it’s not an internal system owned by us. There is a challenge there, because you don’t control when that application changes, but there’s actually a couple things that we can do. So the way we design this process, and the way we automate the different pieces of it, to come together for the end to end business process, is actually really conducive to easing those maintenance activities. So in this example, if that index page changes, we only have to change one piece of our RPA script that scrapes that data. We don’t touch our overall workflow, we don’t touch 90% of the solution. And so by componentizing, the pieces of the end to end business process, we mitigate that to some extent. I’d say, the element that’s still out of your control is that the site may change. And so you can actually also use automation technologies to get proactive, and maybe on a more frequent basis, be simply checking all these data sources, and areas that are accessed in a process to see if they have changed. So on a proactive, frequent basis, before we encounter an error in our process, we could almost have a safety bot that is telling us that something has changed in the underlying process. And I think we’ve seen this deployed pretty effectively across some organizations that may not have as many controllers, they may have multiple folks touching, let’s say, their ERP system. And, they might not know when a page is going to change. Using this for an external website example, I think we’ve seen this, even with directly accessing vendor websites, as opposed to a standard index, anything not controlled by you. You can get proactive around that, and put some of those sort of insurance pieces in place using the automation technologies that were already used to automate the process in the first place. So I’d say, you know, it’s the mitigation, and then it’s kind of the proactive monitoring.

Marshall Sied

Yeah, I think that’s great. So Ryan, really appreciate you leading, leading this webinar session. For all the folks on the call here live. Let’s keep the conversation going, especially at this point of time, to Dan’s point, we are going to do a lot of these virtual educational events. We already are doing a lot of them, so check out our YouTube webpage. We’re happy to have direct conversations, conversations with K2, UiPath and the likes. So don’t hesitate to reach out. We want to hear from you guys in regards to other topics you want in the future. We will send this recording to everybody that attended live as well. And don’t hesitate to reach out and sign up for our newsletter. So Ryan, I really appreciate the time. This was really helpful.

Ryan Mac

Awesome, thanks so much, guys. Really appreciate it. And looking forward to hearing some of these ideas that everyone’s gonna come up with and brainstorm after this. Yeah, feel free to reach out.

Marshall Sied

Thanks, bye.

Length of Webinar: 38 Minutes

Originally Presented: May 20, 2020

At Ashling Partners, we work with leading technologies in the RPA and broader intelligent automation markets.The result is a more efficient, agile, and engaged workforce and a business-outcome driven company.

Ashling provides education on the evolving automation market opportunity, implements and operationalizes your process and technology capabilities, and structures a sustainable business model using four major pieces of intelligent automation.