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Courtesy of Nividous Shvetal Desai below is a transcript of the webinar session on 'Real-World Use Cases of Intelligent Automation Part 1 - Classic examples of IA across different business functions' to Build a Thriving Enterprise.
Real-World Use Cases of Intelligent Automation Part 1 - Classic examples of IA across different business functions
In this second webinar of the Intelligent automation series, we will discuss classic real-world use cases that you can relate to regardless of your industry and company size.
Whether your employees are wasting valuable time on data entry, or you are experiencing a process bottleneck that is impacting sales, customer service, or product delivery, Intelligent Automation can help you effectively navigate through these common business challenges.
We will talk about the application of intelligent automation across business functions ranging from Finance and Accounting, Human Resources, Customer Service, Supply Chain, Customer onboarding, and other cross-functional processes.
Our focus will remain on showcasing how IA enables you to automate more and more tasks within an entire process workflow delivering improved process turnaround time, increased process visibility, and better compliance and process control.
Join this webinar to gather insights on:
Welcome, everyone. Great to have you onboard here, today.
for the next 30 minutes, 40 minutes that will be presenting, top, Ashley mentioned.
We'll be talking today about, Use Cases of Intelligent Automation, across different business functions.
And, to begin with, let me, either linear overview, although Shendi covered a little bit, I'll just touch upon it one more time, for a minute.
So, our company, ..., basically, has a product for intelligent automation, again, as Elly mentioned, and this allows your food, business transformation.
So, in your entire enterprise, your end to end business transformation can be enabled on such a florist, intelligent automation, Garth, which covers your business automation, which will be encompassing, robotic process automation, business process automation, for humans to us, and wants to work together, and artificial intelligence.
So necessarily you need, in our opinion, these three components which formed intelligent automation to do an end to end automation of your enterprise.
We're, again, as Shirley mentioned, a 10 year old company, growing very quickly, a lot of very successful implementations, whoever, and won multiple awards in the industry for the same.
We cover customers across multiple domains: banking, Insurance, Healthcare, financial services, life sciences, manufacturing, logistics heikki, BPO, and many more.
Shown you mentioned, we are covering a code by multiple analyst for, uh, starting from Gartner to Forrester to average group for our implementations, as well as our product reviews.
So Gardiner as uh, has covered us as one of the top RP products worldwide.
Forrester has covered the customer of ours for a very unique implementation for RPA and then an intelligent automation and average group ranks as it is one of the fastest-growing companies.
In fact, the average group has, also named us as a Star Performer.
In 2020, and recently, we just had the new release, 4 to 20, 2021, Peak Matrix was released for 2021, where again, we have made significant progress from even this particular ranking.
We also covered under a separate product ranking, intelligent document processing.
And that is for no machine learning, AI capabilities as a separate product other than RPA, and they're also we've been covered as one of the major contenders.
So to recap, what is intelligent automation? So we, we heard about this a little bit.
Let me recap what it really means.
So, as, as an enterprise, you are focused on, you know, enhancing your productivity, you want to scaling rapidly, you want more employee engagement through and greater insights into what's happening to your company.
And, of course, granted, accuracy, these are some of you are no roadmap of what you want to achieve.
And this is exactly what intelligent automation is.
It's created for the purpose of intelligent automation, is to give you all of this transparently, and allow you to grow bottom-up.
Rather than having to do everything at once and bite off more than you can chew right away from this talk, we realized that that could be a problem.
In many cases, where we're below an approved product engineering, then trying to approach things from top down might take much longer for you to get your return on investment, and also won't allow you to make changes as you go along.
Because not everything that you envision, maybe actually what you had, for what you imagined to what you actually trying to achieve, there may be gaps. And going bottom-up allows you to do that.
Police allow you to you don't understand where you're going and correct problems along the way.
So uh, like I mentioned earlier, robotic process automation is normally what you would, you can start with either worry process automation, artificial intelligence for, for any kind of data extraction, Document classification validation.
You can start a business process management, you have any of these three areas where you can start.
Normally, what we see our customers start with are, this is an area of robotic process automation, There are siloed processes that can easily automate, and it's very quick to do this and very easy to achieve.
Then, as they want to achieve more automation, they go a little further by augmenting their bots to get more intelligent using artificial intelligence. So they start mimicking human behavior.
And then you want the Business Process Management, where you have multiple such watched that you want to streamline into one end to end portion of your enterprise process. And also combine it with human performers who will do our reviews. Validation, necessarily. things that a human would need to take decisions on.
So as I mention, you are starting with RPA.
You know, plug the holes in your enterprise, forever to do manual tasks.
You make your data movement across inflexible systems and outdated and flexible systems, easier by using bots, rather than having humans do the work.
And of course, manual labor based processes are, or something that you would necessarily not want your, your employees to waste time on.
So, these as well as, you know, data sources which are inconsistent or, you know, having your employees work on more value, add tasks, the millions of rows are some of the reasons why people would start with RPA, then you augment widmore, human intelligence.
And then finally, give the visibility, your end to end, enterprise visibility of your, your end to end process by giving, by stitching them together through, then BPM orchestration.
So the products of, then we have allowed this to happen. So, studio allows you to do processes to low code platform.
There, you know, you can do something to grow into the control center.
It could be VPN, could be RPA processes, there'll be executed on your bots, Are smart voice, which are our AI ML servers, which allow you to give the intelligence to your boss, your RPA processes, Then you would stitch it together, necessarily by, by adding human tasks to this entire end to end process.
There are teams that require people to validate.
They have no data that may have been either extracted by a bot or a smart board.
So, at this point, uh, I believe you, Shelley, we need to run, um, a poll.
We're going to be offering a poll at this point, and we'd like you to participate before we move on.
We'd like to know some feedback from you, so they can understand, uh, that or this would be, what would be the ROI that you would be achieving from those who are, What do you expect to gain from this webinar?
So, the questions that we are sharing on the poll, Are you currently running any form of automation, in your enterprise, in your business enterprise?
the questions, the choices over there are, we are currently running it in production, We have it in the pilot or proof of concept space.
We're planning to explore it in the next 12 months. We have no plans to explore at this time.
So, we really look forward to getting your feedback on this, ah, and it would be interesting to see how, how businesses are approaching it. Of course, we are seeing these from a product vendor point of view.
We know what our customers are looking towards, but we also, um, no surprise, many times, why some of the insights we get from, from some of the webinars.
Do we have the results already, OK.
But, that's. As much as the highest question.
OK, great, so that's kind of consistent with what we know, uh, so let's continue where with what we were, there we were at.
Let me just share my screen again, then they can continue.
So, it's interesting, because this is what I expected.
We have it in the pilot, got proof of concept phase, is one of the more popular answers. And we are planning to explore it in the next 12 months.
The most popular answer.
So, you know, it's something, which is important, and being approached are, it isn't the pilot.
I'm surprised that many are not already having it in production.
I thought that that would also come up a little higher, but, but that's interesting.
So, let us go through a few case studies because there's a lot of, uh, conception is people understand that intelligent automation is something they need to do. But what does it really mean?
What does it mean? Sorry.
Right, OK, let me share it again.
OK, Let me know if you ever received it.
Yeah! You can check out.
OK, so let's, let's look at the first one we have: I can't say, well, this is something that will resonate with most people because this is something that is, that cuts horizontally across all enterprises.
So most of us have the whole procure to pay that we want to automate at some point.
But that's, you know, trying to attempt doing everything at once, maybe that more than we want to start with right away, and may not be ruslan Ottawa right away anyway.
So, uh, perhaps a smaller piece of that.
What I want accounts payable, that's one of the pieces in the puzzle that, is, is effort that has, it can do the amount of effort and particularly remember that we have what is extraction.
So, customers get you know, anywhere from a few thousand too.
Maybe ignore 10, 15, 20, 50,000 invoices a month, Depending on the size of your enterprise.
These are ready No, very difficult to automate, because it requires extraction from from different kind of vendor invoice document structures are not the same. The glass necessarily document extraction and intelligent machine learning based document extraction product or two. So, H, talk to that, you know, once the document has the data extracted from it.
When you need to necessarily compared, uh, lot of other data you might have in your system. Because you want to do attempts straight through processing, and that necessarily will come in, then you are able to get the line items extracted. And then compare it with your ERP systems, that it's a purchase order Bayesian wise, or a non people are afraid or CapEx.
Each of these have to be properly segregated, and you need cognitive capabilities to do that, you know, cognitive understanding of what this invoice actually means, and then, go into the right system of record, too, match the information in that.
There may be one to many relationships where the PO could grow into, There are multiple line items that can be matched against different invoice line items.
So, all of this requires some intelligence. So, there's RPA, which will, which can necessarily do that matching for you after the data is extracted.
And, there were being necessarily some humans also. So, you will have some humans who will have to approve. The data, will have to review some of the information, some exception who's going to them.
So, this is a classic use case that you will need.
Some are humans, lots and AI ML, which is smart box.
In our case, working together, two, solve this problem.
So, this is something that cuts across all different verdict there'll be no vertical or domains.
So, we've seen this a lot, and this is, uh, an area that we have been able to automate very successfully.
And, again, just, before I move on from there, it is important to know that it is not just about Ottawa, in terms of how many people know how much effort and my saving. There is much more to that.
There is first time, right? SLAs that I need to match, my vendors need to be happy that I'm not having rework happening there. I'm not asking them for more information, then.
I require, and they get the status. They have visibility into where things are for them.
So, it's not only your own enterprise, it goes beyond your enterprise to your customers, your vendors, or other than your customers, in this case, vendors who also benefit directly automating billable hours and invoice.
Oh, You know, basically, you want to make sure that your invoices, that you're charging to your customer. So, this is a professional services firm.
They require this automation, and every, every two weeks, they would be, you know, sending out invoices.
And every resource, every employee that they had on the billing projects were, depending on the project, would have maybe a slightly different role and a slightly different role of rate to match that.
So it was very important to make sure that there were no errors over here, and at every time, the, but we're going to do an intelligent match to see what is in the contract, what isn't there, so W, does it match with actually, with the actual detail in the, in the system for generating invoices before the invoices are generated and flat off all this problems to make sure that you don't end up charging a customer incorrectly?
So, a very interesting use case, again, there, no contraction SO.
So, W is, as you know, are very unstructured in nature. The none of them look alike.
Every customer either different want to be able to go through all of that intelligently make sense of it.
And then Extract creates from it is, Is something that is the next level of automation, which is what we call intelligent automation.
Purchase order, sales order processing for large US. Logistics company.
This is again, no, it was no PR as well as AP.
Some partial V plus, It is a little, it touches upon some of it, but basically turns out of processing, um, no, when, when a work order was received, it had to be, you know, there was a lot of manual effort.
You know, there was a lot of logging into system, filling out details, no.
And the sales orders were really complex to read. They were not your regular, I mean, the quality of all the documents were bad.
As well as the structure was very, very different for each, for each a different format.
So again, what is requiring some kind of an intelligent automation to read, using intelligent document processing, the purchase order, and then we're able to get the right information from a pellet And again reconfirmed from internal systems. The other use cases and case study is over here.
We'll talk through some similar, uh, there's similar implementation requirements or extracting and then validating inter using machine learning or using business rules. So this was very similar to that.
Service Desk automation is slightly different use case. And very interesting, because this is, again, something that cuts across all different verticals.
So whether you're in insurance or you're in no banking, or for the matter in manufacturing, there are many use cases where you would need handling of a customer request or an e-mail.
Normally, these kinds of requests flow into your CRM system.
And in the event, there are many, many such e-mails flowing in data acquired to be segregated to the right team to handle it.
And it's not always easy to do that, especially when you want a large volume.
You have a lot of people actually visually seeing the, you know, understanding the context by reading, not only the current e-mail, but the the trolling e-mail, and then routing it to the right people.
So, that is how it happened in the Agile process.
But if you were to automate this, this could be done by, you go, again, combining an RPA bot to read an e-mail.
and then apply machine learning to understand the body of the e-mail and the intent.
So, looking at the body of the e-mail, understand the intent of what the customer is asking.
Also, read, do a sentiment analysis, to understand the sentiment of the customer, To, you know, prioritize certain e-mails in the event that someone, customers, really, I read and needs special attention at that time.
So, I use these to combine, to give a human cognitive behavior in interpreting e-mails, and then not only routing them to the right individuals, but also in your organization.
But also going beyond, and, uh, they basically seemed, in many cases, handling the response itself.
So, if somebody just wants a policy, updated policy detail.
in the case of insurance, you would just go insight into this, you know details of rental system, pull it out and send it out. Even for example, that I just had to be updated.
And the required document for validation is also attached to the e-mail. Then the document could be extracted from the validation done updated, the policy could be updated and then the policy will be sent out.
So, that is things that could be handled by the automation itself, the intelligent automation.
The good generation in wealth management, again, is another very interesting use case where there are other portfolio reports that need to be generated from different websites, so likely to go out.
And, and, you know, look at all the different third party websites, get the detail login in order to get the details off.
The customer, in the management, of course, you end up investing in different, uh, products, and then to be able to download them from third party sites, and there may be some legacy systems also involved in this kind of interaction.
Then combine all of this information into customer reports, we then would, We signed, in many times, you know, In many cases, some automated signature has to be done, on those documents and then sent out to the customer.
So, again, very interesting use case where, you know, your cycle time can be reduced, and, of course, productivity and manners.
Manaus can be saved and we will increase your productivity.
Bank reconciliation is very common.
You know, you have your general ledger statements and your bank statements and you have yaar, VRS which is something that you know, you will use for reconciling each peak day.
So, so you have a bank reconciliation statement, then your general ledger and bank statement, all of these board, be combined together and every day's reconciler issue would happen.
by comparing the line items to make sure that there are matches.
And those matches, which are not found would be, would be it would be passed on to the next day, where the match would be again tried on the next day.
So, it's, the reconciliation statement that would continue to be match against an RPA bot, would be able to do that.
This is, this is, again, a little tricky, because some segments look different, and in order to be able to do this across different bank statement is different.
Um, no line items and debit credit entries, all of these Ken, is of course, a very time intensive process.
And to be done via an order, we hand it off to a bot, ah, is a very important use case for for many customers as well.
Customer onboarding using AI, ML?
This is, again, interesting, because there are ... agents.
They receive four, many different domains, insurance banking there.
The food agents grow that tablets are called their phones and then they are having the phone apps from where they will need to scan the information.
For KYC and other, uh, other documents, such as the account opening forms, for example.
So all this data entry normally would happen at the way that, you know, back office. So, normally, as this process, if somebody would go out there, collect all the forms of documentation, there wouldn't be that the checks would not be that thorough.
This would go back into, you know, that is, this will go to the back office and then the problems will be caught.
And then you would have this whole process repeat, where somebody would go out in the field again and yet gather all these forms again.
So, why not? Why not the workforce time right?
So, instead if you want to just scan it upfront and the documents are already extracted from and if possible the fuse agent can connect it right there.
So, what you are ingesting into the system are already good quality documents that are extracted from, rather the good data that is extracted.
And, instead of go into the back office and being, you know, send back.
It is, it's just going to flow one way, and you will cut down on your asset types.
You want to cut down your back office requirements.
So, normally, you a few legion's now are also helping you ingest data and reducing some of the efforts right upfront for you.
So, your process gets streamlined, and you are also, you know, really sinjar, mean, you are your, within your entities and being able to approach your timelines, so, processes, uh, uh, can be automated easily, and of course, accuracy improvement, like I mentioned.
Uh, the possibilities are limitless.
Ah, Ben, and tell you, before we move on, I think this would be the right time for another poll.
So, the next poll we have here is.
Which of the following business functions are you targeting to automate?
So, which of the following business functions are you targeting to automate?
Finance and accounting, customer service, supply chain, human resources, can others.
So, they want to help you.
That car I'm touch upon many of you here, including Sorry. Ken?
Mistakenly, put it at this point.
Can I see?
So, again, yes.
Right, OK, great, OK, so that's interesting, because, uh, know, we, we touched upon a few of those when we walk through some of these case studies, Supply chain is important, and we hear you loud and clear, and the have gotten distributed requests also from a customer.
So, it's interesting, But we've also seen addition to that, uh, no use cases that cut across, uh, different domains, like service desk orchestration, is also another very important.
one, are very repeatable that we see, uh, very good actually compared to, I mean, I would say supply chain, and, and that would kind of be the top areas that our customers are trying to automate.
So, oh, let me make sure my screen is visible again.
Yeah, I can see it.
Yeah, OK, great, So, no.
As you automate your process, 1, one very interesting thing I'd like to mention here, is, as a business, you, you are going to automate the process, and the reason I say that, I mean, there is no way out for you, because.
As a business, you're competing with your other customers, your other competitors, and you are doing better than your competitors, because you are executing them.
Your execution is your process, and you go on re-inventing it, you make it even are more tied, more optimized and, uh, that's what keeps you in business.
So that process, that keeps you in business, is necessarily something that you will automate a few.
If you automate it, you execute even better.
So, if you are constantly improving your processes, you need to constantly keep improving your automation of those processes, and that's where intelligent automation is very important Because it allows you to do this quickly.
You don't have to.
you can do it incrementally, you can do pieces of it, then when we upgrade to more versions of partial processes, the old entire end to end process may remain the same.
So the flexibility that intelligent automation gives you allows you to compete better.
So, we see that, you have, uh, all the way from finance and accounting all the way, No.
Through human resources, sales, and marketing, we talked about some of these supply chain external vendors, partners in all the way from the left to the right. You see so many different, I'm not gonna go through all of these. But, you will see so many different processes that can be automated based on where you are seeking your ROI.
So, let's go through a quick demo to give you a little insight into what this automation can actually achieve free.
So, this is going to be an accounts payable process automation, which is something that we're going to start with.
And we have a vendor portal here in our, in our screen, where I'm going to upload as a vendor, I'm going to login to this worker and I'm going to upload a new invoice and I see that it has been accepted, and the date and other information.
And also, some basic information is extracted from this automatically, and the system now kicks off a new business process.
two, manage this invoice to processed. So this is the actual workflow.
All the way, hello?
Pause it a little for a second, and try to explain what we are seeing here.
So the the process starts with OCR extraction. There'll be a data review, duplicate Chegg, rejecting Openvoice in the event that it is found to be a duplicate, geocode and cost center extraction, in the event of a non PO.
Then it moves on to processing and then approval.
And then manual handling then an SMP step. Very good Update this into ....
So this process is a little simplistic, but it can be matching exactly your process.
And this is a dashboard where you would see the invoice amount of who, the vendor information, the food vendors, all the other information that you choose to see. All of this is completely customizable.
I'm going to now, as you're going and as a human, I have a task assigned to me to review this task, and I'm going to look at it to review this contextually.
The information on the left and right is already shown in the right context to me, so that I can quickly review this, and complete this task. In the event that all of those data match correctly, It would be a straight through process. I would not have to necessarily reveal this, but we're showing you this to also give you an idea as to how line items for your invoice would be matched up against your purchase order.
Line items, unit, price, and quantity of pure quantities, all of these, can be masked from your HTTP or ERP system.
There you have this data stored, and as soon as that is completed, it triggers the bot automatically the proof of this data into your ERP system.
So what has happened is it has now approved co-payment, and it has been posted into your ERP system.
So end to end process automation is what you saw there.
It involved botch too, extract data.
And then Cocom documents, it started with a vendor uploading a document and then data was extracted from it passed on, two human to review it, and then finally, of course, it went through many steps. There are different logic was executed business logic, and at the very end, after everything was reviewed and all in place, it was posted into the ERP system.
So this is one such example, all other use cases would have pieces of this kind of automation in different parts.
This brings me to the last slide, so a lot of customers ask us how do we know, how do we know if we're going to be able to achieve the Ottawa that we expect?
This is something new to us. What is intelligent automation, is not pure? You know, are we, are we going down the right path?
So, we have a quick start offering designed for exactly those kind of questions where we take the guesswork out of the decision making, we take the risk out of the decision making.
So, there's no we offer a guaranteed rollout overtrain Bar in 3 to 4 weeks and we're able to do this because we're able to build very quickly on our platform.
So we have this Quickstart which includes the train, but as I mentioned, it will be a fixed price with no obligation to purchase, then put it in production.
You can use it, you're convinced that it works for you Israeli, when you decide you want to know, and I'll discuss the commercials.
So it comes with a one-year subscription if you so desire to then continue with it.
And it's not just, we walkaway, no questions asked.
So this allows customers, you know, some of the customers maybe using other forms of automation, and this may be a way for them to validate how limited intelligent automation works against, as compared to those other products.
In many cases, they may have no automation, but they may not be able to prove or understand what kind of returns they would get. So this is a very easy way for them to do this.
And it, of course, covers all the way from workshop to scoping to implementation and rule with the proper handler.
So, this concludes my largest I mean, this is my last night, to conclude, my webinar with this, of course.
It would not be complete without getting some feedback, some questions. I'd be happy to take questions at this point.
Shelley, do we have any?
Hi's readily. It's Brian from ProQuest just stepping in a little bit, shady, because I think she's got some audio problems.
Very good presentation. Thank you very much for that. Got some initial questions for you.
Some more might come in as we as we move forward through it, but I'd like to start by asking, one of the questions that came in, how, how do you measure the Roi of a of an IEP project? Sorry. An intelligent automation projects, lots of different methodologies seem to be out there, but from your perspective how do you measure that ROI?
That's a very good question and a question that we've been asked all the time.
So I come from various forms.
Most people don't look at Ottawa as just a number, so Ottawa is not about cost savings alone. I mean, that forms a big part, in most cases. At how much am I really going to say by automating this. But there is ROI beyond just cost savings. It could be ROI in terms of your timelines, that you need to meet them in the Ottawa, in terms of getting it right the first time.
Because the errors could be very, very costly. one example I'll give you for something like that is there in the logistics domain, We have customers shipping large containers from your.
And, they have to be absolutely 100% bang on in terms of all the information that they need, in terms of documentation bank, as he is everything that suppose the bank and have to be in place on the documentation has to be correct. All the, you know, that the containers have to be validated.
So, the the error in such a case was just humans because if anything went wrong there, the cost was huge.
So, it was not only about Ottawa for them, in terms of human effort, shaped, in automation, others, because they already had Maker Checker, kind of use cases where, you know, if somebody was validating, and then somebody else validated, really, validating it. So, it was not about just the saving of that labor. It was about getting it right, and making sure that they don't have to do the work, and once we're meeting their SLAs in time. So, SLAs.
Getting it right, your ROI, there can be multi multiple ways. It can be measured.
Fantastic. Thanks, because I've read a lot, and I'm sure any of the audience that wants to follow up on that particular aspect of shredded will make himself available to you. Either via social media or direct contact following the women are another question, which is coming, which is quite an interesting one, from my perspective: this is something I'm a periphery aware of in terms of this area, but could you share more details about the IDP capabilities of your platform?
What is the sort of IDP, sort of, information technology for those that are not as familiar with that, that acronym?
Sure. So, I do P stands for Intelligent Document Processing. So, we have various kinds of documents and organizations.
Invoices is something that we talk about right now, but it could be bank LLCs. They use case I just gave for the logistics company of extracting from bankruptcy is it's a completely unstructured document. It's their lines. There are no table. There is no named unintentional. It's very unstructured. Extracting from there and then making sense of this is important. Because something very interesting also as a different kind of use cases extracting from Nikos.
So, meter readings, during coverage, there was a very interesting use case where accompany needed to extract, I mean, come clean.
He know energy company could not really extract from their meters, and it had to be actually utilities company, and it had to be that they wanted to scan the photograph of the meter, and then extract from that.
So that's again, another extraction from a photograph, menus, extracting from menus, for aggregator or restaurant aggregator.
Another use case medical card extraction in healthcare, abstracting, from all different kinds of variations of medical cars routed through Blue Cross.
There are some in different formats, There's some of the very interesting use cases orders. We talked about those, two, all of these.
one very interesting one I forgot to mention is, in health care, in, you know, lending, there are the documents that flow in. That can be both documents using, you know, hundreds of pages.
Sometimes even that, you know, thousand pages and they have to be passed to apply to each page has to be understood logically as to what this signifies and then split documents logically.
This is a utility bill, this is alone, document, etcetera, and then extracted from our own use cases, we'll get into the realm of intelligent automation, where it's not just document extraction, but it's also context that you need to extract from the document.
Fantastic, thank you for that show.
That's very, very insightful, and I hope that cleared up the, uh, query that, the question ahead. So, building on that, I've got quite a nice one, Some ram, who was kindly said, I hope this doesn't put you on the spot too much, .... Do you have a workflow for QA, audit automation for voice, stroke checked? And if so, how does it work?
Automation for voice, chat, voice, or chat, again, requires machine learning to be able to understand the context.
Then to be able to extract logical context. In chatting or chatting.
Of course, you will need to have that whole conversational logic.
So, yes, that is possible.
And Voices is No different actually. It's different in the sense.
you have to first convert it into text and then do the same apply the same context. Conversational logic to it.
OK, back to that question correctly. I hope I got that question quick.
Well, I'm sure you'll be able to follow up with Ryan following this conversation. right. Please do reach out directly to Shred will be delighted to walk you through that process from a ... perspective.
So a couple more questions that Sundar has put it as basically, built on the first response to the right question, along the lines of C asking customer needs benefits from our from RPA or IAA. How was it evaluates? You know, I think you covered that a lot of the roi perspective, moving forward. But a more interesting development from that point is any next question and I suspect this is one we could talk about for quite some time. But if employees are relieved from repetitive and menial jobs, how else will they be utilized by the business now? I think that's one of those. How long is a piece of string questions? But What You want to take a quick stab?
Absolutely, So, this is a question that I get asked a lot, calls home.
Then, it's about so, the thing that a bot is going to take or a human job and then what the human do after that.
I never like that in any of our implementations.
Have we seen that, if there is, uh, saving in labor, is Transfigured into her, no, work for a human?
So, in fact, what it has done is, it has allowed more, more intelligent, more consuming work for the human soul.
The company, if it was handling a volume, and it's all about volume, the trading, most cases, you can only do so much as a company, because you have only so many employees are so many people, too, to do that.
Like, if you were, if you really need to scale. and if you get avant or are many boards to do a good part of what you do today. That leads all your other employees to do more intelligent work, to allow you to scale, might, be for it.
So, think of it as adding and augmenting your labor with additional employees, to allow you to still significant, even beyond what you could achieve, the limited staff that you may have.
Because, it's always limited, right?
If you want to want to scale beyond, you can't just go out and keep hiring at some point, you will, you will have some limits in terms of what you're going to fly.
So, that's one of the things that we've seen.
And I know, even in our most extensive projects, where there was a huge automation pipeline from end to end, there were more than 100 employees, and their work got automated, Actually, that's not the best use case, But, yeah, about our chair representative, hundred employees got reduced, the work for those got reduced to about 60 employee worth.
But there's other 40 employees got real, being fashioned into doing other kinds of work, which again, lead to significant advancement of the company.
Fantastic, thank you for that .... Again, please do reach out and continue that conversation. Shuttle will be more than happy to continue that with you and walk you through the processes.
And, a final question from, from the audience on this side, I know that, or, rather we know that ... is a cross industry platform, but, is there any domain specific solution that is available, for example, for health care, revenue cycle management?
So, we have no, multiple areas that we focus on.
Health care is one of them, one of our very advanced implementations, or is that, entire end to end revenue cycle management is something that we have done brilliantly at multiple customers.
In our Healthcare Eyecare, All of them have the requirements.
All right, from patient scheduling to eligibility checks To, you know, claim Sacramento, to reconciliation, payment reconciliations, to inventory management, Basically the entire end to end. You know, in some cases, customers, I've gone back position, so there is also new, new, shorter onboarding skin and especially in the case of Ikea.
That is one of the one is, medical coding, again, in healthcare related areas There.
Uh, no Quito, intensive task and to be able to do it accurately requires some automation, intelligent automation that is, there. So, there are multiple, such, you know, solutions that are industry specific?
These, in particular, are the ones for health.
Fantastic. Thank you very much for that. So thank you so much for the presentation today, very much appreciated. And ladies and gentlemen, if you enjoyed the Part one of the real-world use cases of intelligent automation, then please do not miss Part two, where we actually look at classic examples of IA across different verticals. So you will be able to build on the structure of the presentation today.
And as we say from ProQuest, please don't hesitate to reach out to either shuttle or shady or any of the colleagues on social media. Follow up. After this presentation, you will have the direct contact details that you very shortly. And the on demand version will be available shortly, so that you can download it, catch any content that you might have missed. But also, more importantly, share it with your colleagues in your organizations and a wider field. So, with that, I'd like to thank Shuttle for taking the time to join us to put this on today, and for show you, for all, have fantastic work behind. Thank you so much, guys. And we look forward to the next presentation.
And there are handouts available for you to download for this specific webinar.
There are five of them, If you haven't already had downloaded them, please don't hesitate to do so.
OK, Thank you, Thank you.
Shvetal has over 25 years of experience in the process automation space and brings tremendous knowledge to help clients navigate their digital transformation journey. He plays a leading role in steering innovation for the Nividous' a kind Hyperautomation platform. His vision has helped Nividous get recognized as a high-profile thought leader in driving digital transformation through intelligent automation.
He comes to Nividous from Progress Software where he was the Director of Engineering for their Business Process Management Division. Prior to Progress Software, Shvetal served as Senior Director of Engineering at Savvion Inc, where he contributed to the development of many products in the area of BPM software. Progress Software acquired Savvion Inc in January 2010. Shvetal joined Savvion in 1996 as one of the early developers for their highly successful Savvion Business Manager product, rated consistently for many years as one of the three BPM products worldwide by leading analysts like Gartner and Forrester. Prior to Savvion, Shvetal worked at Netscape Inc in their Enterprise Servers division.
He holds a Bachelor of Engineering degree in Electronics from Mumbai University, India, and a master of science in Computer engineering from Santa Clara University, California.
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