Facilitator [0:09]
So first I want to just say let’s give, give ourselves a big round of applause. This is the last session of the conference. Yeah. And we were just talking back there on if you would all like this to be a boring kind of ramble session, or do you like this to be a fun session? Fun, fun. Sounds good. All right, we can bring the energy. We have a very esteemed panel here. All these guys are very accomplished and very bright. So we’ll get into some topics, but I want to throw a curveball off the bat and have a little bit of fun. I’d like each of you to share with us your favorite binge-worthy TV series that you like when you need to unplug.
Tony [0:58]
Actually, the mindset of British motoring show called Top Gear now they have the grand tour. It’s I learned nothing about cars there. It’s all about goofing around. So that’s, that’s my binge-worthy show. Excellent.
Nanda [1:13]
Okay, yeah. For me, it’s Game of Thrones. It’s because you know, of who I am also, you know, we, obviously I’m from India, and we grew up with a lot of mythology and wars and kings. And so for me, I have everything in it. So that’s my favorite. Excellent.
Vivek [1:32]
It’s my favorite is the money heist. It’s based on Spanish men that get hacked. It’s more of strategy and the seamless execution that’s shown in the program. That’s, that’s what I really like.
Facilitator [1:46]
Fantastic. Thank you for sharing that. How about you? Oh, but you’d never ask. I have many. I love Game of Thrones. There’s a TV show that I love comedy called shits Creek. Does anyone see that? It’s very, very funny. It’s a really funny TV show. And like many others I could, I’m right now I’m restarting madmen. I, many years ago, watched it and loved it. And now I’m falling in love with it. It’s really, really excellent. Yeah, that’s good. All right, so on to AI and machine learning. So you know, this is a very hot topic, as a HighRadius, we get asked about this all the time, everyone wants to know how they can leverage RPA, machine learning AI and all these things. And everyone at this stage has deep experience with doing that. So So I wanted to start by talking a bit about digital transformation. All of you have been with one or many organizations where you’ve taken these journeys, digital transformation, at a high level, what can you share with us about those journeys and what you’ve seen at your organization’s?
Vivek [2:51]
Well, you know, it’s difficult to start and I’m sitting right in front of the guru of digital transformation. But the way I think about transformation, it’s a journey. It’s a journey from being digital from doing digital to being digital. When you do digital, it’s about how do you implement your system of record your system of differentiation, and your system of innovation? To where are you really digital Are you are, you know, your business operations have digitized data or AI capabilities or not, the way I look at it is, as a company or where we are today, we are somewhere in the middle, we are still investing a lot into a system of records like ERP or even data lakes, and then making sense out of those systems. How do we streamline our business processes? How do we train our users? How do we train our customers to start using a digital platform for customer collaboration, supply collaboration, so it’s more of a journey and somewhere we feel in the middle of right now?
Nanda [3:50]
For me, you know digital transformation in our could be different things could mean different things to different companies, you know, I mean, but I think at the core of it is as Vivek was saying is the digitization part right, where we are starting with the data digitization, get your data kind of lined up, you know, then have all these tools that we have outside, which maybe you know, part of the data sciences or the machine learning or the BI tools or the RPA just implement add on the data which has already been digitized to make sense out of it and you know, use it the right way to for to advance the ultimate purpose of any company to you know, increase productivity or make it more efficient or put it on the right path. That’s what I think it is. So it’s to me it’s it takes different forms and shapes that like an RG our digital transformation right now is focusing on customer experience because an RG for those of you know is also a company that owns reliant energy. So we hope some of you are our customers. So we have to have customer service difference becomes very important. So our digital transformation is focusing on what is important to us. Very important to us right now. And so that does the reason I say, you know, it means different things to different people.
Tony [5:11]
Actually, that’s, that’s a really good point because digital transformation is the magic word for software organization companies, you throw that in, and you get 20%, more sales, because everybody wants digital transformation. And which is really a pity, because it’s actually an existential crisis. Me, I talked about that in my talk on when we’re up to two days ago, I guess, on transitioning from the, you know, third industrial revolution to the fourth. So what I do is, I tell people to think in terms of stages of digital transformation to nonetheless, point stage one is basically beginning or automation. So you are converting all of your stuff, which may be, you know, processes or even physical things into digital, right. So, when you’re doing SAP when you’re doing, you know, much of that stuff, that’s stage one, right. And that’s really not transformation. To be honest, it’s basically, you know, automation. But, but that’s okay. Stage two is when, you know, parts of the organization, it could be the finance function, or it could be a business unit in Asia says, I’m going to disrupt myself by using digital technology, right. So that’s that siloed, stage three is partially synchronized, where the company, you know, like General Electric, did some, some years ago says, we are going to be a digital company, right. And that’s very, very hard because you have to change your entire business model. But it’s not complete. Stage four is fully synchronized, where, like with Netflix, when Netflix went from mailing DVDs to basically stream, they changed the entire business model across the company, right? That’s stage four. But even at stage four, you haven’t changed your organization, culture to become, you know, startup-like, and that’s really what stage five is. So some of the work that I did at P&G was trying to essentially define and create a stage five transformation. It’s very hard to do. But you know, my key message across all of this is when anybody wants to sell you digital transformation run because digital transformation cannot be bought, it has to be learned.
Facilitator [7:36]
That’s a good point. When you’re starting these types of initiatives, do you have any suggestions on where I think there’s when you look across the company, there are many opportunities of what you could potentially tackle with software technology? Do you think it works better to start with something low-risk smaller, where you can prove out the concept? Or do you go big swing for the fences?
Tony [8:03]
So I’ll start this time, I think my recommendation is actually to do things in parallel. Where you start is always, you know, an assessment of your biggest loss area. So for example, in shared services, at Procter and Gamble, where I started was, was actually looking at different organizations’ budgets, and also seeing how many people they have. Because that’s always a good indication. You know, you have a lot of people and a lot of money. So so there are things that you can digitize, yeah. But you know, what, again, start with the biggest opportunity, you have to do things in parallel, you have to have a portfolio just like with your personal finance, things that are safe, and things that are high risk, high return. So do the typical, you know, basic automation, do the next upgrade of you know, do the, you know, RPA, but then also have a really small little bet on artificial intelligence and the crazy stuff, right? Don’t spend too much money, because you have to actually do things in parallel these days.
Nanda [9:15]
Well, it’s very difficult to cover anything else after Tony’s actually our simple thing. So, but Thanks, Tony. So one thing I would say is, you know, of course, no, no bets on, you know, starting with low risk, high value, really, we can prove it successful. But one thing I’ve seen is, while we do that, I think when you start any digital transformation journey, it’s extremely important that somebody at the top of the companies created already this vision that this company is going that path, you know, we are going down that lane, there’s no loopback and we are going to do some kind of digital transformation because in my experience that kind of help and empowers people who just started that thing
And the success from there actually, you know, really translates into, you know, think that everybody will start putting their own roadmap and you know, getting to that vision which has been laid out at the top. So I would say the combination of somebody declaring that we are going there, and definitely starting with something which is small, good. And which can really prove quick also quickly, also that it is successful and generate that positive energy around the conversation around digital transformation. Good
Vivek [10:30]
thing, in my opinion, any AI initiative or any digital initiative, should be tied directly to the outcome to be tied directly to the business productivity, what are the benefits you’re going to get if you do these initiatives? So for example, GE is a large manufacturing company. And the two biggest things that we care about the problem that we are trying to solve as one is cash, are we receiving our cash on time from our customers? Are we paying our suppliers on time, so any initiative that can accelerate cash management is the way we want to focus, we are solving problems? The other piece is for manufacturing purposes, you need a huge amount of inventory. And you don’t want to make inventory go obsolete without utilizing it better. And you want to deliver on time. So a lot of inventory management requires insights into actions, a lot of AI capabilities, in order to make sure that your cycle time and lead time are taken care of. So any digital or AI initiative that we do is directly aligned towards how we can reach the customer faster, how we can manage our cash faster, and how we optimize our existing sources or our materials in a much better fashion. So those are the big initiatives that we are aligned towards.
Facilitator [11:48]
With these projects, they don’t always succeed, right. So it’s kind of interesting because I think a lot of times you think, let’s go for the big thing, let’s really go after it. But the fact of the matter is a lot of projects fail, there’s not there’s a risk that projects will fail. Any lessons learned from trying something and it fails. And what do you take from that? How do you prevent it? Maybe things you could have done better? Any thoughts around the failures?
Vivek [12:13]
100%. So I think, if you go to any search engine, and you start typing two words, robots will, robots will, robots will, or robots will just start typing these two words. And the recommendations we’ll get will be robots will destroy humanity. Robots will take away my jobs, robots will destroy the world. So there’s a lot of negative press or media around what AI or robots can do. That’s the challenge we also faced. at GE last year, we started our automation organization, which we’re looking at solving problems that we’re talking about. The initial first robots we did, people were very reluctant, I was knocking on people’s doors to come and partner with me to do one single automation. We did feel some of them failed because people thought that they will lose their jobs. We deployed robots as digital workers in our organizations, these digital workers will come and do your daily mundane, repetitive tasks. So we introduced them as your personal assistants. But then human beings started feeling scared that they will lose their jobs. And they stopped using the robot. So we had to kill a few robots. So there were hire-to-fire policies for each and every automation. The lesson learned was when you’re introducing any automation, it has to be complemented with the human being human plus machine, they together make AI so there is no AI without humans or without machines. There was one lesson learned that is going to compliment you and give you your valuable time back so you can do more meaningful jobs. And the second was process the process has to be standard, you cannot continuously build AI which is on a non-standard or customized process. So we had to first standardize the global processes, and then automate that those are the two biggest lessons that we learned from our journey last year.
Nanda [14:07]
I think, you know, just again, building on what Vivek said, in my experience, the big thing is change management, and stop. You can say many things, you know, your processes as good as what you put into it, right, like Vivek example, we have experienced that too, we have if you have a and b as a stakeholder, who completely bought into this whole transformation narrative, and he was feeling secure to get into it. So he basically explained to me and made sure all cliques were in whether intentionally or unintentionally, you wanted this to be successful because he saw at the end of it, he is going to be empowered, and he’s going to go into, you know, get out of this mundane task and go do something different and something which is going to be exciting, and really empowering or, you know, several different good things that could happen. On the other side, change management was not very good. Where you know, We found the skeptic. And I would say somebody who has been doing this forever saying that, okay, now this is going to go away, I’m going to chain. So to me, you know, I will not follow the resource, I will not follow the person or whoever he or she or the organization, which are the resistance, I would follow the change management process you put in to get that message out saying that this is good. This is going to solve and, you know, compliment you, as Vivek said, So, hands-on, I say, you know, change management is where most of the organizations fail because it requires a lot, a lot of coaching, a lot of conversation, a lot of convincing. So, to me, I feel, that’s where I see the biggest lessons learned is, please work with your people and make them understand that this is, again, also on AI say, We there are no quick wins in the automation. First of all, there are no quick wins in AI. Yeah, I would say is a marathon, you know, never look at it as a sprint. It says, just ongoing. process continues,
Tony [16:05]
the only thing maybe I would add that that I’ve learned the hard way is that you really need to have different processes and methodologies for running your, you know, core projects from your High-Risk High Return projects, you can’t treat them the same. Yes. So if you’re acquiring a company, or you’re upgrading, you know, your SAP to the next version, you know, you have a low tolerance of failure, you have to succeed. So change management and everything, you know, but the whole rigor. However, if you’re trying to do an edgy AI experiment, and you go and tell people, there is no failure, you know, you’re not going to succeed. Yeah. And so you have to change the reward systems, you have to change the language, and you have to change the processes of running those. Maybe even the people that are running those, because you don’t want people that you know, want to check every box, you want people that will take risks. So the lesson there other than separate that was, for example, don’t call those high-risk things projects, because projects have a connotation of I have to succeed, call them experiments. Okay. measure success differently, not about, I have one experiment, and it’s got to be successful, but I have 10 experiments. And together those 10 experiments have got to deliver so many dollars, I might kill nine out of those because they’re not working. But the 10th One may give me all the returns. And so you have to like I was saying you have to treat those very differently.
Facilitator [17:47]
Totally agree that you mentioned some of the bots you had to fire or the robots kind of sad when you fire them.
Vivek [17:57]
Or the unionized yet.
Vivek [18:00]
Absolutely, absolutely. So, fortunately, the first two bars that I coded I own them as my babies are still there. If the first one was Max. Max does customer invoicing. Okay, it’s still working. The second one was olive. That comes from my favorite animation show Popeye. So one night, I was watching Popeye and I saw all I said my next point is all so both olive and Macs are doing just fine. Okay, but we had to fire Khaleesi and from Game of Thrones. So, yes, so what we do is every automation we do, it has to give you certain productivity, productivity for the next 12 months, this productivity could be the number of man-hours to be saved or quality improvement, or it could be cycle time reduction, or it could be in one inventory turns improvement. So we established these automation solutions, it could be bought a complete AI solution combination of a board machine learning OCR and vision. So we build an AI solution and we set a target for productivity. The Productivity is tracked every week. If it is supposed to do so much work in 365 days, we track every week. And if it is not performing for six months, we fire them, we decommission them with a clear indication to the stakeholders and why this is being fired. And the underlining issues I mentioned about the earlier lesson learned one, the humans are not ready to adapt to it. They are scared. So how do we drive OCM organization change management and second, the process will not be standard and the business is not ready to use it. So yes, we have to fire we had to fight Khaleesi
Facilitator [19:44]
Khaleesi may rise again though. Yeah. So under you. You mentioned something really good Earlier you said that. It very much helps if an executive has laid out a vision and everyone’s kind of going down this path you know it’s got everyone in the right direction. In all of your experiences, how do you go about getting that, that buy-in from, you know, the senior people to say, everyone we’re going with? There’s no looking back?
Nanda [20:11]
Well, you know, buying from everybody down you or how do you get a pitch? Why, exactly? To my seniors? Yeah, how to get into it. So, so to me, I think, you know, it’s, it’s, well, that doesn’t happen often. But when it happens, I would say, normally, you know, we see organizations telling you where to go, but I would say, you know, too, if I have to convince my executive to say, Okay, this is the right path to go, I would definitely, you know, basically teach him the process, which are low risk and high ROI and which are ready, which are very quick to succeed, and also, basically show him the whole vision of enough where it would be, or, or why I’m doing, why I’m recommending what I’m recommending, in the sense, you know, in the current world, you know, you should be able to maybe articulate and say this is, this is the real technology, and this is where things are going to, you know, change, you know, part of that, you know, we did, as part of, you know, I had the RPA billed through change management for NRG. And we have more than 70 processes, which are automated across the organization, and its HR operations, we also have a bot which is named Rosie, of course, it doesn’t do any rosy work in that CRC knots basically. So which is not a good thing, but Well, that’s it but But I realize that’s a lot of things, you know, we have like level variations of C naught t naught. So if you, if you’re not aware with the acronym, it’s about, you know, how you basically onboard people from the organization into it, it has a huge, you need to go and take out the user ID passwords of them from every system they ever were accessing, within a certain time, because there are Sox compliance issues. So Rosie does the job. So so to me, and I think, you know, I would just definitely go with a very solid, I would say clear cases where I know there is success and also have a clear vision, which I can go and tell them which blends with what my boss wants or the management is looking for. And I think one more thing I would say is, I think it’s very important in any of these things, you do that to be clear about what you’re trying to solve in what you’re trying to really what, where you’re aligning with the company’s vision of like, for example, technology, we say one of the biggest things is customer first, you know, so anything if I can say I have a process, which is going to add value to my overall vision of the customer first, and I’m sure it’s an easy sell.
Facilitator [22:44]
I wanted to ask you a little bit of practical question about AI. So I’ve read a lot of cases about how you train AI, you have to have a lot of data, you have to have a lot of information to let them learn and you know, do that seems to be one of the bigger challenges around AI is getting that information set so that they can learn. I know you’ve dealt with a lot of big data-type projects, what is your experience been with gathering that and being able to execute something like that?
Tony [23:12]
Maybe two lessons. One is, yes, of course, it is a challenge. But the sooner you start, the sooner you’re going to get there, right. I think in many cases, people use the whole process linearly, which is I’m going to have, you know, six months of cleaning up data or nine months of getting the data. And that’s a huge mistake because AI-type systems are very different from other software are programmed. And there, you know, determinant, I mean, you know, you know, you just have to implement it, and it’s going to work this way. AI is different. Every AI implementation is a modification project because you’re tailoring your custom building the clothes to the environment, right. And so you can’t take a linear approach of I will clean up all of my data. And then I will figure out how this works. In my case, the reason you can’t is that when you implement it, you might learn that you need more data or other data. And so, you know, that’s why Lesson number one, don’t wait, you know, just start with what you have. Lesson number two is actually funny. You may actually need less data or there may be more creative options to training AI than you think. So you guys might be familiar with Google’s DeepMind project, you know, and how DeepMind basically defeated the Go champions, the Chinese game champions. The next version of that, basically, beat Stockfish which is a chess program DeepMind learn how to play chess and beat the best chess software in the world in four hours. The difference between go and this one was that go was trained over years and years by humans to play Go DeepMind version two was not trained at all. It played out against itself. And it learned the rules of the game of chess, and it beat the best champion within four hours, okay? So you want to be careful about traditional paradigms of learning and training, because now with alternative models of, you know, self-learning, there may be other approaches to train your AI.
Facilitator [25:42]
It’s interesting that you kind of touched on a few topics around ethics of the bots, AI, so forth. And I was wondering if you might elaborate a bit more around that subject because it is very important to people as well as just how these how this if you could elaborate on that?
Vivek [26:01]
Definitely, I think, as an organization, what we believe is, people are the biggest asset to the organization. And we operate globally in different continents and regions. And we do have to abide by different regulatory requirements, legal requirements. So the way we are doing AI, we are definitely following a lot of regulations. I can talk about three, three, those examples, one comes from the US White House, it was a report that was published in 2016. That said, Any AI or the way America is investing in AI, has to be towards people’s good. So you have to benefit human beings. The second piece is the EU Commission. In Europe, the way they’re talking about AI ethics is it has to continuously invest in technology, as well as the economic condition of the country, both have to be taken care of, you have to abide within the legal requirements and regulatory requirements. And the third one that I like to refer to is the UK Lords report that came out late last year. That’s, that’s very detailed, and it talks about the five major AI ethics that we typically fall follow as well in our AI program. The first ethic is any AI solution should benefit the human being or should do for the good. And if your solution is doing that, we go with it. The second piece is any AI should not diminish any data rights or privacy of an individual or a family or a community. The third is any AI solution has to be designed of burled with all fairness and non-biased. For pieces, all citizens, whether they are UK citizens or international citizens, they have the right to education, for improving their emotional and technical skills alongside AI. So AI should not come and overtake their education rights. And finally is the autonomous AI, it should not hurt or, or even destroy, or any human being or any humanity based on these five principles. That’s what we imbibe in our organization as well any automation project we are doing, make sure these five at least the five principles are embedded, keeping in mind our employees or people are globally spread out.
Facilitator [28:29]
So thank you. And now the switching subjects a bit. You mentioned that NRG has its own RPA AI practice within the company. Any suggestions on how that evolved, how that was set up? And what you’ve learned about being part of that group?
Nanda [28:47]
Yeah, so we started our, what we call our RPA journey, about two years back. And as I said, you know we are 73 processes strong in terms of being in production and we are looking at being about 150 processes. And I basically through this journey learned that 150 processes lot of process which too is automated across the organization. And, and so one thing we have learned, we did have, I think, one, we did have very supportive management. So this couldn’t have been done, as I said, a very important thing in the journeys, especially on any digital transformation or recall a transformation program is complete buying from management and just saying that go fail, you know, it doesn’t matter. So that I think has helped because it was not easy. But one thing we have noticed, and I can share that in maybe in the same order or a little bit here, or there is the bind from the management from day one saying that this is it, we are going to do it, and it’s for good. And we definitely had I would not say perfect, but a good change management conversation. Very quickly into the RPA journey, we realized we need to scale down expectations, right, you know, we, we basically, we basically, like any vendor would sell you your their product, saying that we are going to solve all your problems, all you have to do is sit on sleep, or it will tell you when it is done kind of stuff. So, so we had to scale it down very quickly, we said, well, let’s realize that automation is not easy. First of all, there are no magic solutions, it’s hard. And I think part of change management was to set expectations low, saying that, you know, be ready to do your manual process. And so change management definitely helped. Third I would say is, you know, we have been, again, this we learned very quickly that we need to be very diligent on the process, we are picking for automation, just because somebody just said okay, this is very difficult for me. And because you know, somewhere you say, okay, everything can be automated, that me human does, you don’t go and just jump there, you know, put your due diligence. And now there are several other things that you can put together to filter some process or qualify a process. So I think the qualification of the right process for automation is extremely important. The fourth and I would, as I said this not in exact order, but they’re all very, very important is any automation doesn’t happen without I would say very, very tight collaboration with it and operations. So the business and IT, which I would say enormous Tony would explain in the industry today, they are collapsing in it, there’s no more like silos now they’re all like becoming flat, but in our experience, have seen the extremely good. The way we have structured is it and operations, they would have been those of you if you’re purely into the RPA play, you know, there is this big conversation about owns RPA isn’t it or operations. So for us, you know, it was a good marriage, you know, we’re 5050 It’s, it’s, it’s extremely important, it and operations work very closely for successful automation or an RPA implementation. And third, I mean, finally, I would say is it’s just that, you know, just getting that culture and empowering people that are good and getting that positive positivity going I’ll share with you an example. Again, it’s it at least was for me it was very revealing. So we had this customer who had a process obviously there was resistance and with some skepticism, he got it in it was a finance process. Something to do with equity settlements, a very manual process. So mostly Excel and all that stuff. So long story short, it went on it worked well but while we transition, we always say hey look, this is not perfect, it might fail when it fails what is our option is okay, we’ll invoke the manual process right so there are some SLA s and all so a second month, the first month the trans second month we had some issues the first one went out saying that just be ready with the manual process you will not believe the same person so the team which was working the same things there was no nobody was let go there they were doing different stuff. For 10 years they were doing the same thing. This is the second month of the change you know what the request comes Nanda this has to work well, of course, we are doing our best to make it work said no it has to work said well you have it but what if it is doesn’t work? You know this is part of it, you need to do it manually said no. Just forget about it. We are not going to do this anymore. Just do it because the comfort that the customer has got in one month that this process is out of that all the Indian work and all it was created for him they just wanted this to work. So the point I’m trying to make is you know when we do this, you know we need to really be very engaged with the customer. And at the end of it, it’s going to be a very beautiful thing you know, of course, I was via the team that built and everything, they were feeling very important, very happy. None of you built something with somebody is so much value. They really wanted to work all the time.
Facilitator [32:04]
Absolutely. It’s a great anecdote. I’ve seen that many, many times over the years where, you know, we’ve set something up that to be automated. And then if something happens, and it fails, suddenly, no one wants to do, they might have resisted, but then it’s, it’s they can’t believe it failed. And they have to, you know, go back. And so it’s, that’s, that’s very, very true.
Tony [34:29]
This stuff is also disheartening. Do you mean, it doesn’t work? 100%
Facilitator [34:36]
That’s, that’s, that’s technology. Absolutely. So what any questions from the audience I know, we’re gonna run out of time here very soon. Does anyone have any, anything they’d like to ask the panel? Everyone’s ready for lunch? Oh, there we go.
Audience [5:59]
Just a silly question. How many people FTE do you need to create and dismantle a robot. This question, how many FTEs? How many people do you need to create a robot? Robot? Yes. And how many people do you need when you do the dismantling?
Nanda [6:23]
I think this is a very relative question. I think one of the things that we need to understand, you know, at least in the RPA world, and we can also endorse or just tell, tell me, if I’ve missed somewhere, something there. You know, for us, a robot is a machine, right? The robot is a computer because it’s a robot, right? it can work for 24 by seven, it doesn’t work, which doesn’t need any HR benefits. It’s, it’s there 24 By seven to work for you, that’s your digital worker. But that worker can now work 24 by seven. So a process is one process like you have something to do with, okay, I go to the website, scrape some data, come in, put it in a spreadsheet and go into SAP or whatever your back end system is and upload into some and do some transaction. And just give an email out saying that this is successful, right? That’s a process. That process could be running only for two hours. So there could be tools such as process running on the bot? So it’s very difficult to answer that particular question of how much time to take to put a robot it basically, it’s a function of how many processes you are putting there. And you can and the bot? Is that physical person ready to work for you for 24 hours?
I don’t know if I answered your question right or wrong.
Tony [36:46]
But how many lawyers did it take for you to fire your robot?
Vivek [7:50]
Well, none. So I think, the way I like to address this is, fortunately, the AI technology or a board is the only technology that I’ve seen, that gives you ROI instantly, as soon as you turn it on, you don’t have to wait for three years to see your returns coming back. Every single transaction process through a robot or AI is $1. Saving. That’s one way of looking at it. The second way of looking at it is if you hired an experienced mechanical engineer, to install a turbine, or to build machinery, you want that person to completely focus on why this person was hired to do installations, not to go back and search for spare parts on an ID application or an Excel or something. So what we’re trying to do is we are keeping these highly skilled employees focused on what they were hired to do, and build bots and AI to do purchase order requisition creation or, you know, make a call to the supplier or send a notification to the supplier. So all those mundane, repetitive jobs are being automated, and highly skilled employees, whether in the finance world or engineering world, or even operations world, are tasked to do what they’re supposed to do, you’re giving them time back. So it’s a win-win situation from both ends when you utilize your existing workflow much more efficiently. And also, you get your ROI the next minute.
Facilitator [9:24]
Excellent. So I first of all, I’d like to thank all of you for being here at radiants. I hope you’ve all really enjoyed this. We’ve really tried to put a lot of effort into the content and make it also a lot of fun. So to our team that put it together. So thank you so much for making the journey to be here to all of you. And also thank you so much for this great panel is a great way to end the session. So thank you all so much. Thank you
