First of all, thank you so much for joining the ‘after lunch session.’ I know it’s pretty difficult. But yeah, kicking off things now. This is the session on humans and machines, changing workforce, and job responsibilities with AI. So our office is headquartered in Houston. So when I was coming the day before yesterday, from Houston to Dallas, thankfully, I came in a Tesla Model three, thanks to my CMO, would wish. So when I came over the driver is five-hour-long, right? And the five along mostly you have to come by I45. And Urvish actually put Tesla on autonomous mode, because we had so much stuff to do for Radiance. And that’s the first time for the first two, three hours or no, sorry for the first two hours I was thinking, should I look at the road, or should I look at Urvish? Because he was talking to me. And then I was thinking I think I need to trust the technology. And I think after five hours, we reached, everything safe. So I wish this is the first time in my life that I have let the technology drive right rather than anyone else on the wheel. So moving back to the AR, Garfield, and Marinko, you have been leading your teams for a while. Do you think today the execs are ready to accept AI and the changes AI can bring to the team?
[01:28] Marinko Marijolovic:
Can I go first? So I’m not sure how much the executives are thinking about AI. I think they are in a byproduct form. So what I mean by that is the people below them. So folks like us, that have to provide data in a moment’s notice and quicker. They expect us to keep up with technology such as artificial intelligence and others to get them that data. I mean, they’re making business rapidly and you hear everything about speed. So I think it’s one of those that are being pushed up. I think as we incorporate more technology, so my boss is the CFO, he knows that I’m using technology, he knows that I’m using even artificial intelligence to some extent, but he just wants the results. And he wants information immediately. And if I’m not capable of producing that, he’s either gonna find somebody else who I tell me to go get some technology or something. So it’s imperative that you know, that I keep up with him. That’s one of the reasons you know, I’m here. It’s one of the reasons we moved to automate a number of functions. But I do believe that will push up to the executive level, they’ll become more pertinent that they will actually start heading teams within the organization of instituting various artificial intelligence platforms.
So one follow up question on that and then we’ll move to Garfield. Do you think that if execs do not kind of know whether you’re using HighRadius or any other company? They are not kind of interested in that, they just want the results. They think they’re giving you the green signal or leave it to decide on the technology and then tell them the value that the technology is bringing.
[03:11] Marinko Marijolovic:
I think it’s implied that you’re going to get the technology that’s necessary. So by that they’re getting the green light is kind of like getting power, you don’t wake up, somebody says, You have the right to make a credit decision on this. You kind of do it until someone slaps you and says, well, no one told you to do that. And usually, they won’t slap your hand as long as you’re obviously right and moving in the right direction. But I think it’s implied that there’s some expectation. So like, we do a number of different projects throughout the year in the organization, finance and outside and the company as a whole continues to push advancement in technology that we need to simplify tools and reduce our costs and streamline processes. My boss constantly saying we gotta streamline as well, but I can’t do it without some technology, you know.
[03:57] Garfield Brown:
Yeah, I agree with literally almost everything that was said. That inherently in our roles, there’s a certain level of trust that’s afforded to us as leaders to make decisions that support the decisions that our CFO, CEOs are making. And as we look across the landscape today, and you see how things have evolved, and how AI integration is taking advantage of what’s out there today, we have to utilize those tools that allow us to give us information or give us information quickly. So we can go into the CEOs office, a CFOs office, onto the information that we support, that we believe in, and utilizing that trust that they have given us to say go out and find me the right data and go in and present that to myself, okay, here’s what we should do. And here’s why we should do it. And here’s the supporting documentation behind that. And in today’s world, if you’re not using what’s out there, for example, the AI information that’s there are taking advantage of autonomous modes or those types of things. I’ll be honest with you, you will be left behind.
Okay. And based on your data, they can take the modern form decisions as of now.
[05:01] Garfield Brown:
And they will. And they have been when you go in, and you have that track record that has given you this, the role that you’re currently in, and you’ve gone and you’ve data mined and you’ve gotten the information, but it’s from an AI perspective on autonomous perspective, and you present that. And you presented one confidently, you presented with a high degree of knowledge about what you’re talking about. And the decisions that they’re making based on that information is then supported by the results, it just bolsters your case and then supports the decision that you have done by using the information AI, those types of things around you.
Correct taking cue from that learning. Where do you think AI has maximum adoption? Is it more on the analyst level today? Or is it at the manager level?
[05:47] Lauren Kennedy:
I think that by default, we all seem to think that it’s at the analyst level, and I fall victim to that thought myself. But as I’ve gotten more into it, I’ve started to realize that I need to be working on automating my own tasks. My team is starting to move and shift their perspectives more into an AI-focused view, what can the computer do for me, that frees up time for me to help my customers, which means I have to think that way in my own world so that I’m here to help them. So I think that’s the responsibility that we leaders have, that we have to also think of AI in our own roles and responsibilities to make ourselves present for them.
So, as Lauren has mentioned, that it is not only the analyst level, also at the manager level, so this is a question for all of you and feel free to chime in. What do you think a future workforce looks like? Or like a future AR team looks like? What do you think about the acceptance of AI? Where are we going three years from now?
[06:44] Marinko Marijolovic:
I think that the teams will be smaller, but I think the people on the teams will be tasked with doing more with managing more than work even though you know, they’re not the managers. They’re going to be managing their functions. I think there’s going to be much more information flowing quicker. So it’s going to move to what kind of Sashi was just showing there with the various analytics, I think as many people just kind of monitoring dashboard, seeing where there are issues where there are drop-offs and things where they need to intervene. So they’re gonna need to understand the process. And you need to understand artificial intelligence, RPA and things of that nature, and get involved but I think it’s going to require more of an overall, well-rounded individual on your team. So even though you have fear, folks, I think, they’ll be capable of doing more.
[07:31] Garfield Brown:
I think you allow for a certain degree of scalability. You allow us to be able to manage your costs. It’ll offer to support organic growth or acquisition of growth by us managing our headcount, or the level of our staff and the other the proverbial do more with less but maybe what you do, you’re able to do more with what you currently have. Even though you have taken on more business your business has grown the type of thing so I think the future in my mind, once AI becomes more ingrained in our culture is that a higher degree of staff probably the same level that you have a more informed, a more educated, and more robust, a more fluid staff that you may have. But I think it will allow us to do all of those things.
[08:20] Lauren Kennedy:
I would agree, I think they’re that our teams are going to take on a more all-encompassing type of position where they know the ins and outs of upstream and downstream processes and what feeds those processes. So I think their level of knowledge in the process is going to grow. And we have to grow with that.
Do you think in today’s world, people are okay to have a conversation or have a nonhuman interaction like talking to a bot? Do you think people would rely on those messages or don’t rely on those interactions, do you think is it moving towards that?
[08:54] Garfield Brown:
So I’ll give you an example. That’s funny us actually. So yesterday booking the hotel to go to Charlotte in April. And as soon as I log on, immediately on the right-hand side is, hello, how may I help you? Ignore that I kept doing it. “Hello, how may I help you?” It just kept going and going. And that wasn’t the person. That was something behind. So whether or not we want to, it’s there. It’s just a matter of at what point in time do we become more comfortable with that acceptance, that this is the future, this is where we’re going. And that, again, was yesterday. And I’m sure it probably happens to thousands of us every day, in all different ways, and all different mannerisms and methods.
[09:43] Marinko Marijolovic:
I think to get those points, and then we communicate with bots a lot more than we think. I mean, we kind of used to calling the bank and getting a little robot to give you your balance and things like that and hit this button, hit that button, it was probably fine but I think we’d be amazed to find out how much communication is being done by bots? From like Amazon on down to a lot of the emails that you’re getting that is targeted to specifically a bot is working on that it wasn’t, there’s not like 1000 people in a room trying to figure out what kind of emails you know, to send to you to sell you various things and stuff.
But now that you talk about that we are using that in our daily lives. It’s just that maybe it’s not that visible. Do you think people will trust digital or virtual assistance with confidential data? Do you think data security is an issue because a lot of people have heard that AI is not being embraced completely because they do not trust the system or they do not trust the board?
[10:41] Marinko Marijolovic:
And I don’t think that’s any different than the credit strategy that you’ve created. So if you’re using the HighRadius tool, or I’ve got a questioning strategy, I don’t think this is a whole lot different. I mean you’re gonna have input into the programming of these bots and the analytics that they pump out, you’re going to obviously test in a test mode and things of that nature. So I think that the trustworthiness of that information isn’t gonna be really an issue. Because you’ll gain faith that through the programming that you’re doing in the testing that yeah, okay, it’s spitting out the information I want and I think it’s become like second nature after a while that you’re just gonna be like, yeah, it’s there.
[11:30] Garfield Brown:
I think if you look at which companies out there that are using or have used AI, Bank of America, all the big major fortune 500 companies, fortune 100 companies have embraced this technology. Then you look at you as a smaller company that says, okay, we’re going to partner with HighRadius to sell right? And you’ve asked us we’re going to write this algorithm that will tell you when to call this customer when to email, went to fax, this type of thing, give me three years worth of your data and now for me, to turn around and say, I don’t trust it. It’s kind of counterintuitive, is it not? You know, so I think if we’re going to go, I don’t want to say all in. But if we’re going to believe in- this is the wave of the future, to then suddenly turn around and said, I’m not sure, again to the point you just made, if you’ve done your homework, vetted the information, ran it through all the tests, and there is everything is coming back the way it should be, then inherently you have to then take that leap of faith, however, you’re doing it with data, with support with a certain level of confidence that shows to you that okay, I’ve done my homework, so I feel comfortable, just like you would feel comfortable going into your CEOs office and say, okay, let’s partner with HighRadius.
It’s funny that you talk about belief because my next question would be, do you believe what a bot would recommend to you? What do you believe if a system tells you- you know what, this is what you should do, like you said, that highly the system does, this is the customer you should email.
[13:03] Marinko Marijolovic:
We believe in analysts. So we’ve got a bunch of credible analysts, financial analysts, county analysts. So they produce some information, you’re like, I’m not buying what you’re selling here. And at least, I guess the way to look at it, a bot will be consistent. So you may not agree with the bot, but it’ll be very consistent with the data that’s being provided, you should be able to see a trend line. Yeah, this is off. But, when we get information from our human analysts, a lot of times, each one is off in what they’re doing. And which one do you trust? So you still have a trust factor? And you got to decide like, oh, you know, what, I know Garfield’s always better, you got to kind of look at that. So, I think if nothing else, it will be consistent. And if you determined that we’re missing something, then obviously got to go back and change some of the settings.
[13:55] Lauren Kennedy:
I would agree, I think they’re identical. I was gonna say, I would agree that there has to be a level of trust when you first configure that solution, you have to have tested it, you have to have said to your teams and the people who are using it, we’ve gone through these scenarios. There is a level of comfort that we have, it might be something different, but different doesn’t mean it’s wrong.
[14:19] Marinko Marijolovic:
Right now, we’ve been playing around a little bit with artificial intelligence in the payment prediction. And the team tried different strategies of trying to improve the accuracy of the prediction and payment predictions. And it looks good. And it kind of levels off at a certain point. So we’re kind of trying different things now within the test, to get us to feel better and to also see that data is going to move in a certain direction. But I think once we figure it out, and once it kind of starts trending and peaks at that level that we think is sufficient that we have faith in it. I don’t really see that a lot is going to change that will change our minds and say now, like three months later that that data is no longer valid unless obviously somebody goes in and change something, some type of setting and stuff.
[15:09] Garfield Brown:
I’m actually going to take it out of in my mind out of the office and bring it into our homes. I’m going to ask probably after this audience is live in front of us here, 95% of you have probably gone on Amazon and bought something or looked at something. 100% of you have probably gotten a recommendation from Amazon. As you looked at this or other people who looked at this like this, and I’m probably going to say 50% of you have probably then taken the recommendation that Amazon has given you or take any one of those Amazon-like places. Aren’t you trusting a bot or some automated process to say hey, if you like ‘a’ you more than likely would like ‘b’ and I’d hazard a guess that I’m probably right in my percentages what I just arbitrarily just threw out right there? And that’s the reality of it that we’re living in right now.
Yeah, that’s true because I think the last Christmas party and Elaine will know this. I ordered a Deadpool costume for myself. And for the party tonight it recommended my spandex. I guess. Yeah. And I went with the recommendation.
[16:26] Garfield Brown:
I’m staying away. Okay.
Last question to you, Marinko. Because we talked about AR. Whenever we are in the room we always talked about AR but we’re not talking about the compliment AP department right, the accounts payable. We have seen that in the last 10 years. AP has really gone the route to automation. AP departments have been automated, right? But AR is always playing catch up. Right? I still see like AR departments being very manual. Why do you think that has happened? And what do you think is the next step?
[16:58] Marinko Marijolovic:
I think part of it is that the AR function, there’s this belief by all of our organizations that this money is owed. So it’s kind of like you don’t walk into a store and just walk out with the product, you’re going to pay for it. So the assumption is when we ship product customers are going to pay us. So why do I have to spend more money on this, then, you know, I’m giving them a good price, to begin with. But I think our customers are kind of cheated a little bit, I guess because they have streamlined their processes. So much so that you know that my own AP group actually has shrunk, but we still have a manager or supervisor, a bunch of people now we have two people do an AP, just because people look at and go, the cost is just not, there’s no need for this cost. We can trim, streamline this, trim it all down to a three-way match. If everything matches up, it automatically goes over for payment, and there’s not a problem.
[17:53] Marinko Marijolovic:
And then on our customer side, like if you’re dealing the retail world like I am, there are tons of deductions that are pounding away at you. And there’s no way that our customers are making these decisions as they’re receiving goods or inspecting the goods or whatever, waiting for data to say, “Oh, I didn’t get an ASN.” So let me charge them $100 back. Now, there’s a bad program automatically creating these chargebacks back and AR to your point has fallen way behind. And we do need to catch up because you can’t fight the robot with a human I mean, it just not gonna work. That robot doesn’t quit. I mean people are saying chargebacks walling to midnight and early in the morning, there’s nobody at my place, you know, answering those Amazon. I mean, we’re not even sure at times when we deal with Amazon to Garfield’s point that we’re communicating with a person I mean, we had some contractual pricing issues. And we finally deduce that we were actually communicating in negotiating with a bot. There wasn’t really a real person on the other end. That’s very frustrating on one hand, on the other hand, it’s like, we better lift up our game as well. Otherwise, you’re going to get swarmed with data. That’s really what happens, I think, from our customers, and as they just don’t have so much data that you can’t keep up with.
I think I was talking to my Marinko before the session, and I think where it goes, I love this takeaway. I think I’ll say it here. He said that when by using HighRadius products, the role of cash app analyst has changed from applying cash to managing exceptions. That’s how technology has changed the role. And likewise for collections, and likewise for credit. So do you think this is the future, every department is going to kind of have a change in that?
[19:44] Marinko Marijolovic:
I mean, if you look at it, such as you mentioned AP is that way, payrolls are that way, a lot of Treasury banking functions are that way. I think that all of these roles, you mentioned, you asked earlier, how are the teams gonna change? Yeah, I think that’s what the teams are going be doing is managing those exceptions, and making sure that everything’s coming the way it is, kind of like the George Jetson of our time where it’s one person, just a bunch of dashboards and buttons that someone’s gonna have to monitor and press. But that’s kind of the way to look at is with the cash, because that’s an easy one for all of us to view. We all kind of remember when a ton of people posting cash, we’ll talk about a low-value task.
And that’s kind of what you ask them, what’s a low-value task, and then what is going to become a low-value task. So at one time, having somebody pick up the phone and actually call a customer asking for money was a high-value task. Now it’s become a low-value task because you can get a badge just to send a notice to them, and you’re calling the ones that either have a larger sum of money or that have a larger issue, more complicated issue that so that’s kind of how you want to look at all this is. How do you move to increase your value and eliminate that low-value stuff that you can automate? And you have to automate with that. Because of that previous presentation earlier with Sashi, you mentioned how much data is just come at us in the last two years. That’s crazy when you think about that. So how can you keep up with that unless you do some kind of automation and artificial intelligence is getting implemented all over the place? We just haven’t acknowledged that or have not been fully visible to us as the average person out on the street. Okay.
Lauren, any closing comments for this session, anything that you would like to kind of stress upon on humans plus machine and kind of changing workforce?
[21:45] Lauren Kennedy:
I can kind of echo Marinko’s comment in the previous question. Customer behavior is always a key factor with accounts receivable. We’re always responding to customer behavior. So the harder the better the bots we have to kind of help predict that customer base. I think a better position will be, I’d like to hopefully see that we continue to move into that space so that we can catch up and move ahead of the customers and catch them before they catch us.
[22:15] Garfield Brown:
So I have a weird creed if you want to call that as I kind of live by that, I can literally lead my team by. And it’s simply This is that change doesn’t mean that I’m being disrespectful to the past, it just means the future will be different. And if we think about that, look at where we were 10-15 years ago, the way we communicated to the way we’re communicating now, and to the way we possibly will be communicating in the future. So we have to, as leaders, in our organizations, either embrace that or will be left behind or organizations will get individuals who believe in that and support that change. So we have to also just get on board. But again, it doesn’t mean I’m being disrespectful to yesterday. It just means tomorrow’s going to be different. And I have to embrace that.
[23:10] Marinko Marijolovic:
One last comment since we are here at the football stadium, the Browns just got rid of the bounce at the football team, no matter who’s running it, but he’s got rid of the front office, the coaching staff because they weren’t using analytics enough. So the current group was not for analytics. And now they’ve brought in a totally unrelated group. So we’ll see what happens after the Super Bowl. Then everybody out there knows that way to go. If we bomb again, as usual, that it’s the same old.
Thank you, everyone. Thank you, everyone, for joining the session. Thank you, Lauren. Thank you, Marinko. Thank you, Garfield.
[00:01] Moderator: First of all, thank you so much for joining the 'after lunch session.' I know it's pretty difficult. But yeah, kicking off things now. This is the session on humans and machines, changing workforce, and job responsibilities with AI. So our office is headquartered in Houston. So when I was coming the day before yesterday, from Houston to Dallas, thankfully, I came in a Tesla Model three, thanks to my CMO, would wish. So when I came over the driver is five-hour-long, right? And the five along mostly you have to come by I45. And Urvish actually put Tesla on autonomous mode, because we had so much stuff to do for Radiance. And that's the first time for the first two, three hours or no, sorry for the first two hours I was thinking, should I look at the road, or should I look at Urvish? Because he was talking to me. And then I was thinking I think I need to trust the technology. And I think after five hours, we reached, everything safe. So I wish this is the first time in my life that I have let the technology drive right rather than anyone else…
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.