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Knowing the right mix of technology to solve your business problems is key for building a successful shared services strategy. In this webinar, industry veterans Rakesh Sangani, CEO, Proservartner, and Gwyn Roberts, Vice President EMEA, HighRadius, discuss the fundamentals of RPA and AI technologies. They share insights on how shared services leaders can make these technologies work together and get the best outcomes for their respective organizations.

On Demand Webinar

Why Your Shared Services RPA Strategy Needs Artificial Intelligence

Session Summary

Knowing the right mix of technology to solve your business problems is key for building a successful shared services strategy. In this webinar, industry veterans Rakesh Sangani, CEO, Proservartner, and Gwyn Roberts, Vice President EMEA, HighRadius, discuss the fundamentals of RPA and AI technologies. They share insights on how shared services leaders can make these technologies work together and get the best outcomes for their respective organizations.

Key Takeaways

Challenges for shared services technology implementation
[05:14]
Highlights
  • Lack of understanding to use the right set of technologies to address the right problems
  • Lack of clarity regarding the potential of automation and other technology solutions
  • Organizations need to be open to experimenting with proof of concepts and technology pilots
Automation trends in shared services
[10:34]
Highlights
  • Leveraging a combination of attended and unattended automation
  • RPA vs. AI – Focusing on the outcome than the technology in itself
  • Investments in ERP maintenance is declining
  • RPA in outsourcing vs. insourcing
  • RPA vendors hierarchy is changing
Tips for running RPA Pilot for your organization
[15:28]
Highlights
  • The 9-Steps journey for running RPA for your organization
  • The top areas to focus on for automation in the finance function
  • How to run proof of concepts
Choosing the right mix of technology - RPA vs. AI
[23:50]
Highlights
  • Understanding the difference between RPA and AI
  • Expectation vs. reality for RPA and AI
  • The past, present, and future of technology implementation with RPA and AI
  • How can AI in RPA empower order to cash process

Rakesh Sangani  [0:03]  

It’s great it’s a very knowledgeable audience, then normally when we ask that question, there’s a way to move towards P2P. Lots of businesses initially think that accounts are payable. invoice processing is the easiest one to automate. And the place to start. Actually, what we find in our experience is that because Accounts Payable relates to such unstructured data, because your suppliers bring in that data in many different ways, as maybe not the best place to start and I know actually audit to cash, there’s a lot of low hanging fruit if you want to get some quick wins. So it seems like we have an educated audience here today. So that’s lovely to hear. So audit cash and MPTP were the results. Thank you, Tanya. I’m going to touch on the evolution of shared services. When we talk about shared services, we refer to this market really evolving over the last 30 years plus. So it was really in the mid-1980s, that shared services emerged as a concept. I think a lot of organizations were centralized before them. But this concept of shared services emerged in the mid-1980s. And then what you found is, we’ve really gone through a number of evolutions in shared services. So initially, shared services were around moving things near shore. So within the UK, for example, for UK companies, to Ireland, for example, then there was a big use of India and the Philippines. And more recently, so Eastern Europe, then there was a big focus on Lean and Six Sigma being the prevalent tools and techniques to allow organizations to generate more efficiency from their processes. And really now the focus is on outcomes, and how you can deliver the right outcome in your shared services marketplace, leveraging a combination of technology, RPA, AI, but process improvement together with that technology, and leveraging the right location together with that process improvement and technology. So we talked about the four evolutions of shared services. And this gives you a bit of history around how shared services evolved to where we see the marketplace today. So where are we today, as I mentioned, it’s really around new technology is the disruption within shared services and outsourcing. If you look at the term business process outsourcing, which many of you will be familiar with, that term really doesn’t exist anymore, if you look for BPO companies, so if you go on to the website, so the likes of TCS and Genpact and Accenture and IBM, you will struggle now to find the words business process outsourcing, simply because they are now BPM companies, business process management companies or their cognitive technology companies or they run digital operations. Really what we’ve noticed is the technology’s changing not only the way that outsourcing companies work but the way that shared services are increasingly being run. And it’s very much rebranding in the outsourcing world to focus more on how technology is driving better improvement within processes. So social media and mobile solutions, also changing the way organizations are working, because they’re looking for data quicker. They’re looking for accessibility 24 hours a day. And so technology is helping to drive better solutions in the Shared Services marketplace, to ultimately not just reduce cost, but provide more value internally.

So what’s holding us back? Well, what we think was really holding the market back today was the lack of understanding around automation and technology. So we seem to have gone from having not enough technology to fix all of our problems in shared services, to suddenly today, we have this plethora of technology, all of these automation solutions that exist, and a lack of understanding around how to use the right technology to fix the right problem. So you know There’s wonderful technology out there. And what it’s forcing organizations to do is to become educated a lot quicker than they’ve ever had to, because its technology is also changing very quickly, and to be a lot more open to experimentation. So I think the days where you do a nine-month analysis of all of your processes to identify the propensity to automate, and finally, choose technology over those processes, I think those days are over. I think the days today are really running proof of concepts and pilots, and doing quick checks of whether that technology fits within your business. And if it does, scale it up, and if it doesn’t throw it away. So I think, really, that experimentation is one of the things I would say is also holding us back. Moving on to our view of where the market is today, we do this survey focused on the UK, every six months around the actual level of automation that exists. What we found is, in the last time we ran this survey a few months ago, 88% of UK firms had automated some aspect of their business in the last six months. And 42% of UK companies expected to leverage some form of RPA in the next six months. So there’s certainly a desire by many organizations to leverage technology to help them deliver more for less. We asked the question about what they think about automation. And this is also interesting. So I think initially when we started talking about RPA, machine learning, and AI, the focus for organizations was very much around cost savings, and how we can reduce headcount. Whereas I think, in reality, what organizations have found where they brought in this technology is that there’s a lot more benefit than just cost reduction. It can enhance the productivity of teams, it gives more control to organizations and those benefits, and really came out in the survey that we did in the UK. So what we found was that, whilst the top reason still cost, increasingly, organizations were automating, to make their employee lives better, but also the mechanism to standardize processes and increased our productivity. What we also found was when we looked at, you know, 1000 organizations in the UK, and we asked them the question, functionally, the levels of automation, what we did find was that actually finance was a bit of a laggard, compared to the other functions when it came to adopting new technology. Now, you know, being a finance practitioner, myself, and a chartered accountant, I can, I can definitely see that sometimes finance waits to see technology being successful before it really adopts it. Well, I think we’re, we’re seeing that in the level of automation being leveraged in the finance function today. So 24% and this, this automation is very much led by it, which is lovely to hear because you’d believe that they have the competence, followed by customer services, data management, and sales order processing.

So we also looked at industry, what level of automation is really being taken up and what finance, for example, is set in that industry perspective. And we found in financial services, organizations tend to go to Finance first for automation. But in manufacturing, they aim for more manufacturing processes, retail, more sales, order processing, education, and IT and telecoms more around the IT function in total. What we found also, is if you look at the results in more detail, that 100% of financial services institutions said that they had automated some of their processes in the last six months, and they were very much the leader from an industry perspective. Compare that to one of the laggards, which was education, which only had 60% of education institutions that automated one of their processes, at least in the last six months. So that’s a little bit around the market perspective. I’m going to touch on the trends now. So what we see around automation and shared services, when it comes to trends, is increasingly a combination of different types of technologies. So in particular, when it comes to robotic process automation, there’s a combination of attended and unattended automation being leveraged to deliver the right solution for the process. So attended automation is the type that sits on your laptop and aims to make an individual more productive. Unattended sets in a back-end server require no triggers from an individual and can be scheduled for 2- 3 am in the morning, or whenever you’d want to schedule that robot to work. So what we’re seeing increasingly, actually, organizations are using a combination of attended bots, which are cheaper, and can be better from a change management perspective. And unattended bots, which can work independently, anytime, 24 hours a day, seven days a week. And we’re also seeing that trend relating to other technologies as well, where organizations are looking not just to find one technology, and try to fit it everywhere in their business, but to find the right technology that fixes the right problem. We’re also seeing the lines of AI and RPA being blurred. So in reality, the functionality of the RPA vendors is improving. So they’re acquiring AI capability. And actually UiPath, recently in the media record, is an AI company, whereas I would have called them an RPA company, or you’re finding that there’s a lot of confusion around these terms. So our advice to organizations is normally don’t worry about AI and RPA, you should be worried about the outcome you’re trying to generate. And tools like HighRadius and RPA. And the technology that exists today should be focused on how it can deliver the outcome that we want, rather than whether it has AI and RPA capability. So it seems very much that those lines between AI and RPA are blurred. We also predict that the investment in ERP systems, in terms of the maintenance of those systems will decline over time. Because you really don’t need to invest in expensive APIs to connect your ERP to other systems in your organization’s because there’s actually lots of alternatives, lower-cost technology you can use today to do that. And we’re seeing that more and more of the trend. We’re organizations that are introducing user interface tools, because the RPS genuinely have poor user interfaces, and using robots to take that information back into the ERP. So certainly we see that the amount of investment in ERP maintenance will decline.

We talked about this already. Outsourcing is not dead. But it’s certainly diminishing. There’s a number of organizations like Smith and Nephew like Zurich Financial Services that have brought their outsourcing back in-house because those things that they were outsourcing, typically had good characteristics for those elements that in today’s marketplace, you would automate. So they bring it back into the house, they feel they have more control. And they aim to automate some of those processes. We certainly see that as a trend. And then finally, I think if we talk about RPA. Specifically, we see that the vendor hierarchy is definitely changing two years ago, the blue prism was the number one blue prism today. And number three, when it comes to the split of revenue, headcount functionality, all of the mix and forecast wise for 2019. I think UiPath is aiming for a growth of 300%. Blue prisms are aiming for a growth of about 50%. It just gives you a view of how quickly this marketplace is changing, which also means that organizations need to be able to move a lot quicker to adopt this type of technology in their businesses. And there’s a quick case study here, which just talks about how we’ve implemented RPA from the start all the way through to creating the center of expertise. I won’t go through that in detail, but if you would like to have a look at that, please feel free to reach out with the only other thing that just quickly wanted to touch before I hand it over is when we talk about robotics in general. We talk about a nine-step journey to really ingraining bots into every aspect of your business. And the first four elements of this are really the key. So the first is gaining awareness. So I imagine some of you are on this Call to generate more awareness around technology and what you can do for your businesses. The second is to really select the processes that you want to test these concepts on. The third is to get the right product. And that does depend on the process. And the fourth is then to run that low cost, very quick proof of concept or pilot, where you build this in a live environment. And we say, you know, once you’ve done that, and you can prove internally, that this technology is really a goer, for your business, it’s at that stage, you should build your strategy and your operating standards and really plan for the future. Once you’ve proven it, within your organization, once you’ve built the strategy and impact, you can then scale it up. And really steps eight and nine, optimizing that scaled-up model and ingraining it into your business. So when it comes to financing specifically, I know we’ve got lots of finance attendees and participants on the call today, we have a view around the top 15 no brainers when it comes to automation in the finance function. And these are things we’ve done many times and lots of good examples. And they’re quite mature. So if you’re sitting with a perspective that you’re very well, you have very manual processes and your finance function, or you’re in a very early stage of your maturity, and you want to test it on one particular area. These are the areas that we would suggest looking at first. And again, if you want the slides, we can certainly share those with you. And examples of how you run a proof of concept. It should take you no more than eight weeks. And you go through a series of stages really understanding the robustness of your current state, designing your future, building it in a test environment, building it in live before, stabilizing that we have the saying, don’t celebrate when it’s live only celebrate when it’s seven stabilize. And on that note, I would like to thank you for your attention, and I will hand it over to Gwyn.

Gwyn Roberts [17:22]

Thank you, Rakesh, for that insightful presentation. So I’m going Roberts, I’m the VP of EMEA for HighRadius. What we’re going to look at now is a couple of audits to cash processes and understand how RPA and AI can help automate and improve efficiencies. So before I start, I’d like to sort of briefly introduce HighRadius tea. So HighRadius was founded in 2006. And we provide an integrated platform for accounts receivable. And this helps automate manual processes and drive efficiencies across all of AR. So one of the points I also want to make is that we’re not an RPA company. What we have is a platform for credit to cash, and we have RPA and AI embedded within our solutions. And we’re always looking at new technologies, whether it’s something like blockchain to further enhance our solutions. So from our HighRadius perspective, we have over 1000 employees based at three key locations. So Houston, Texas, which is our global headquarters, London, which serves a European market, and we have a large office in Hyderabad, which is where all of our product development takes place. So we currently have over 400 customers using our technology globally, the majority of our customers are using solutions within a shared service center. And we’ll share some of our clients’ names towards the end of this presentation. So firstly, let’s understand at a high level the difference between RPA and AI?

Okay, I’ll open the polling now. So the question here is, there’s obviously a huge amount of buzz around AI today. But how do you as leaders within your organization’s feel about this technology? Okay, just 15 seconds guys to answer a couple of you haven’t answered yet. So let’s get those answers in. Okay, let me share those polling results. Hopefully, they’ll do the work this time. Okay. So the majority of people have answered that an AI can be a game-changer. And I would absolutely agree with that. I’ve been on panels for the last sort of a couple of months with various organizations at the City Bank and AI is an incredible enabler for organizations. So it’s really finding the right use case, to understand how AI can actually impact a business and the processes and understand what the value that’s going to bring. So what we’re going to go through are just a couple of processes. So we’re going to focus on credit and collections and understand how RPA plus AI can help those within those processes. The right way to advance the slides for you is just to say Next slide, please. So be on the RPA versus AI.

Gwyn Roberts  [22:44]  

Apologies, my screen is stuck on my picture much as I like looking at myself.

Facilitator  [22:53]  

Okay, I will guide you, do not worry. That’s what I’m here for. Okay. Okay.

Gwyn Roberts  [23:00]  

The first slide we’re looking at is RPA versus AI. So let’s understand at a high level what the difference is. So RPA typically needs regular human intervention. On the right-hand side, we have AI, an AI that can function without regular human intervention. So RPA simply mimics the user’s actions as it’s been trained to do. But when you look at AI, it mimics the human thought process based on patterns. If you look at processes, typically RPA deals in structured data. When you look at ai, ai can also deal with data that’s fed in a structured, semi-structured, or even in an unstructured form. And when you look at RPA, it’s also strict rules-based automation. AI can actually learn over time based on user experience and exposure to the data over a period of time. And lastly, when you look at RPA it’s very definite and static. An AI is probabilistic, probabilistic, and variable. So if we advance to the next slide.

When we look at the expectation versus reality RPA one of our expectations is that RPA will automate complex tasks. The reality is that RPA can only automate tasks where there isn’t a requirement for any creativity. or emotional intelligence, or something that involves a complex decision strategy. And another expectation is that RPA will run 24/7 with no stops, no brakes, etc. Again, the reality is that RPA will fail, where there are no expectations or unexpected events. So without expectations, it will fail. So, that said, artificial intelligence can broaden RPA scope, scope, and suRPAss its limitations. So, what we’re going to do now is just a glimpse of what artificial intelligence can do for credit operations. So we’re going to look at how a credit analyst can be empowered by an AI digital assistant or something very similar to Siri or Alexa, specifically focused on credits.

Hopefully, you can see how that technology can actually help both credit analysts and collectors and other users within the order to cash process, that technology is actually available now. So it’s something that HighRadius launched at our user event in February, in Houston. So we call that autonomous receivable. When we look at technology and AI implementation, there are lots of studies out there. We’ve looked at the light global RPA survey from 2018. And back in 2018 53% of organizations stated that they’d already started that RPA journey. So by 2021 87% of organizations will be using AI in their finance operations in some form or other. So the survey was of 400 individuals across many different industries, with a combined value of $1.8 trillion. So it’s here. RPA has been around for a while. Now, as Rakesh mentioned, there are many RPA organizations, whether it’s UiPath, blue prism, or automation anywhere, but AI is moving very, very quickly. Lots of organizations are trying to understand the value that AI can bring.

And if your organization has not started its Intelligent Automation journey, you are officially behind. So how can artificial intelligence Empower RPA in order to cash so as I said, we’re going to look at two processes, credit, and collections? So when we look at the credit management process, when you automate with RPA, you can achieve something like 25 to 30% automation. If you actually add artificial intelligence with RPA that dramatically increases to up to 60% automation. And the same with collections. So collections with RPA 30 to 40% percent are automated with AI 60 to 65%. So in the life of a credit analyst, the first scenario we’re going to look at is where there isn’t any automation at all, it’s going to be completely manual. So hopefully not a lot of organizations, although we do come across somewhere most of the processes are currently manual. But if you look at we’ve selected some of those processes for credit management on the left-hand side, so that’s aggregating credit information, going off to credit agencies pulling the information, aggregating that together, they’ll and making decisions, same with any credit groups, public financials, credit insurance, etc. Also capturing that credit application data, so from new customers, how do we actually then use that data to do credit scoring, or make any risk assessments, the approval process, perhaps for customer onboarding, and what that new credit or what the new credit limits should be, and then periodic reviews and blocked order management’s. So based on introducing RPA, on the left-hand side, you can see in green, some of those processes that RPA technologies can actually influence. So RPA can be used to go out to credit agencies to pull the information automatically and bring that back so that when the credit analyst comes into work, all of the data is there ready for them to make a decision. However, if we can go back, sorry, one slide. However, all the credit application reviews are still manual. And blocked order management is also not automated. So when we then bring artificial intelligence in, we can actually increase to 45 to 60%. Automation, the credit analyst is only focused to focus on exceptions, and more than half of the processes are automated. So when we look at bringing in artificial intelligence, one of the use cases that we’ve seen, organizations very, very interested in is being able to actually be proactive in looking at blocked order management. So the ability for a solution to use artificial intelligence to actually understand when order is potentially going to be blocked. So again, that’s just one use case. But the use of AI is increasingly influencing how efficient and how the process for credit management and audit to cash is going to become more and more efficient. So in summary, when we look at credit management, obviously, I don’t think there’s any organization currently with a fully manual credit management process. But when you then introduced robotics processing, you can actually automatically capture the credit application data, you can actually auto retrieve credit reports from the likes of Experian and Dun and Bradstreet, whichever credit data agency that you use, or whether it’s credit insurer, and then you can also collaborate and use RPA for collaboration for credit approvals, for creating periodic reviews, potentially on the basis of risk. And also do use RPA for blocked order release, etc, and customer correspondence. What artificial intelligence can allow you to do is reduce the volume of blocked orders processed on a daily basis, by predicting there’s going to be blocked orders in advance. So rather than waiting and being reactive, you’re using AI to be proactive and making sure that those orders are not blocked. And most of the credit reviews are also done by the system rather than by the analysts themselves. So again, you can choose the criteria as to how AI can actually help. So that’s the credit management process, let’s have a quick look at the collections process. So again, manual process, where the actual analysts look at creating an aging list, then creating a prioritized work first, who they’re going to call first. The analysts then update promises to pay, and then either send emails or make phone calls, depending upon the worklist that they’ve created. And then obviously, when they speak to the customer, there need to be some updates. And whether somebody uses I’ve seen people use Excel documents, Word documents, exit data, Access databases, etc. So a lot of manual processes, a lot of work involved, to be able to actually make a call to a customer at the right time. In that cycle, when we introduce RPA, you can create some efficiencies within that process. And some of that is about the linking of the ERP to a particular system. So you can actually take information, take the open AR information, automate that process, create an aging list, and create that prioritized worklist. So the collection post-process is still not fully automated. But organizations are still depending on individuals to implement credit policies. But it can who’s me. RPA can bring that process up to 30 to 40% of automation when we bring in artificial intelligence as well. Artificial intelligence can be used to actually predict when an invoice is likely to be paid. So if you can actually understand when an invoice is going to be paid, you can actually focus your resources on collecting the 30 Day Past overview. So it might be that somebody is always paying you three days late, but they always pay. And that might be to do with the payment cycle. But again, cash is posted automatically Once payment is done, customers can access all account-related information on a single portal pay. So there’s a lot of processes that can be automated within collections management. And you can dramatically increase the efficiency within that process itself. So again, in summary, in robotics process automation, you can get towards 30 to 40%. If you then use artificial intelligence as well. And that’s, you know, really key from a prediction perspective. So how can you actually history based on historical data and other trends? How can you predict when that invoice is going to be paid, you can then also have a dynamic prioritization of the workflows. So based on a number of factors, you can have clients that perhaps were low risk three months ago, move into medium risk, move into high risk. And then depending on that risk category, the way that you would actually make the collections from that company would change. So it might be that you started off with just an email strategy, when you get to high risk, you’re probably depending upon the value, you’re probably going to make a call, and you’re going to use a more aggressive collection strategy.

So if we’re looking at sort of RPA and AI, we’re looking at 30% with RPA. And then you can actually double that if you’re using artificial intelligence in combination with RPA. With collections, again, similar uplift, so from 40% to 65%, of the sort of higher-end. So it’s really, really important to look at the processes within audit cash that you want to automate and then also look at the technologies that are out there, too. rushing, making those efficiencies. So don’t look at just RPA but look at artificial intelligence as well, and really deliver the efficiencies within those processes. So we have another polling question.

Facilitator  [39:26]

Okay, if I can get you to we got 50 seconds, I think, when the questions are going on the polling questions, we’ll carry on going through the slides. Okay.

Gwyn Roberts  [39:34]  

So just to go through a sample list of customers, we have HighRadius to have approximately 400 Plus customers. Many of them are operating out of shared service centers, whether it’s a single global Shared Service Center, or in the main, whether it’s regional. So we have lots of organizations with a shared service center in Europe and a shared service center in the US, so it might be in the Philippines or somewhere that’s looking after the US and also in the Asia pack. From a HighRadius perspective, when we started, I said that we were a platform-based company, we have an integrated receivables platform, it’s built from the ground up, I’m not going to spend too much time going through the five solutions, but credit cloud credit management process making that more efficient EIPP a self-service portal, a lot of our clients will use that to potentially use a self-service portal for their small and medium-sized organizations. Cash application or cash allocation cloud, again, a process that is typically manual that might have been offshored. Again, as Rakesh said, we’re seeing people bringing this back in the house and using technology to six significantly decrease costs, deductions, very much a solution built from the ground up for CPG companies. And again, one of the key use cases for AI there is predicting whether or not a deduction is valid or invalid. That’s something that we launched at the beginning of this year and is getting a lot of traction with CPG companies. And then by no means least, collections cloud, which we covered during this presentation. And that’s really having a single version of the truth from a collections perspective. We do have some customer success stories. But given the time, we want to be able to let you ask some questions. So I’m sure that these slides will be made available to you. After the webinar. We can probably turn your stop here. And we’ve got sort of a few minutes left to answer questions,

Facilitator  [42:24]

indeed. But I have the first question here is, where should we start with these latest technologies? RPA AI? What’s the first step?

Rakesh Sangani  [42:38]

So I can answer that question. I think the first step, from our perspective, is gaining awareness. So you know, we talked about that journey around ingraining, this type of technology, whether it’s all AI, or even in the future, maybe even something like blockchain. I think the first step is really to understand, educate yourself and gain awareness of that technology. And then And then really making sure that you choose the right area then to execute that technology. But awareness would be my starting point.

Gwyn Roberts  [43:18]  

From my perspective, I think that when we’ve worked with large, complex organizations, they tend to look at the processes that potentially give them the most business pay. So either business pays from the number of people that are involved in that particular process. So whether it’s collections or cash application, and then really choose a market where you can get your biggest bang for your buck. So, you know, looking at whether or not you deploy in the US first versus Europe. It’s really getting that quick win within the organization, and then being able to sort of have a deployment strategy for the rest of the regions.

Facilitator  [44:09]

Okay, great. Okay, a couple of questions over how do you differentiate between a fake AI solution from a real one?

Gwyn Roberts  [44:24]

I think that sort of AI is incredibly powerful. Strap word currently. So there are lots of organizations sort of saying that they are AI, companies AI, sort of, so that their valuation is becoming larger? I think that when you look at specifically AI, what you really need to do is to understand what that capability is, you know, by understanding what algorithms are being used by understanding what the use cases are, are there any other organizations using the solution? Can you talk to those organizations? There are lots of organizations out there, where automation, automating a process, and they’re actually rapidly going through lots of data that isn’t really AI is about learning from, from the data that you have, and actually being able to actually use AI to mimic human decision making. So yeah, it’s worth understanding where the technologies come from, who’s using it? And how is it being built within the actual application itself?

Rakesh Sangani  [45:56]  

Yeah, and I would add to that, that it’s worthwhile understanding what that technology then can do for you. So I agree with a grin that there’s a number of companies that used to be very different types of companies now calling themselves AI companies that can create a lot of confusion in the marketplace because these terms are being openly bandied about. But I think the key is, well, what does it do for your business? How does it get you to the level of automation you want to achieve? And as Green said, does it have the reference ability with different organizations?

Facilitator  [46:35] 

So some of the great bases for the last question online, but I know you applied for cash, so maybe you could verbalize the response. So the question is, I’m hearing proof of value rather than the concept being used by RPA. Providers. What is the practical difference?

Rakesh Sangani  [46:54]

Yeah, so yeah, there are two ways you can look at that experimentation that I spoke about. One is very, very quick and dirty. This is what it could look like on a particular process, which takes about half a day. And that’s what the RPA vendors call a proof of value. So they just demonstrate they can work on a particular process, we have a lot of assumptions. So that’s where David must have come across that term. What we call the proof of concept, or the pilot, is when you have it in a live environment. So that means you’ve gone through having it in a test environment, making sure you do the user acceptance testing, getting all of the bugs out of the particular automation, and have it in a place where it is ready to go operate effectively within your organization, but for a small area, so that we would call a pilot or a proof of concept. And that’s the difference. It’s really around the maturity of the automation in those two examples.

Facilitator  [48:05]

Okay, great. I feel that. Okay, everybody. So I think that’s all the questions. So thank you very much. For everybody that joined the webinar today. I know we’ve had a few technical difficulties, and I can now hear my voice itself, which is even better. So just let you know, there will be a recording of the webinar. So if you didn’t happen to miss some of the slides, then you will be able to rewatch the recording. So I’d like to thank cash and Graham for joining today. And thank you all for joining and we look forward to it again. Thank you so much, everybody.

Rakesh Sangani

CEO and Founder
Proservartner

The real focus is on outcomes, & how you can deliver outcomes in your shared service by leveraging a combination of technology via RPA, or via AI.

Gwyn Roberts

Vice President EMEA
HighRadius

AI is about learning from the data that you have & actually using the AI to mimic human decision making.

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HighRadius Integrated Receivables Software Platform is the world's only end-to-end accounts receivable software platform to lower DSO and bad-debt, automate cash posting, speed-up collections, and dispute resolution, and improve team productivity. It leverages RivanaTM Artificial Intelligence for Accounts Receivable to convert receivables faster and more effectively by using machine learning for accurate decision making across both credit and receivable processes and also enables suppliers to digitally connect with buyers via the radiusOneTM network, closing the loop from the supplier accounts receivable process to the buyer accounts payable process. Integrated Receivables have been divided into 6 distinct applications: Credit Software, EIPP Software, Cash Application Software, Deductions Software, Collections Software, and ERP Payment Gateway - covering the entire gamut of credit-to-cash.