Session Information:

Why RPA and examples of successful RPA Implementations that have saved my company millions of dollars

RPAs are commonly used to replace processes that are highly manual, rule-based, and repetitive. RPAs can also be used to connect different technologies or systems.

I will provide several examples of how we utilized RPAs to save millions of dollars. I will also list some steps I would do differently next time I implement an RPA project as sometimes this tool can come with a big bill.

 Key Takeaways:

• RPAs are fantastic, if they are done right and if the right resources/partners have been utilized
• Many RPAs can last for years and save significant cost and time for your organizations.
• Monitoring and validation can come with big bills
• Next time I implement RPAs I would probably invest in internal resources

Session Transcript:

Guest speaker today, I'm very, very excited about our next speaker. We have Reyes apart with us. He's going to talk to us about why RPA in examples of successful RPA implementation that have safe companies. Millions of dollars, are very practical. Point an approach to RPA. And Reyes is a business strategy, a transformation, expert, and operational excellence leader, and project in change management leader.

He is a book author in an industry award, winner recognized for his ability to strategize business architecture, champion, ambient, continuous improvement efforts, and deliver challenging, cross functional programs while leading and inspiring winning teams. He's the former VP of Transformation as Caesars Entertainment. And currently, he's the President of his own business consultants called, say, eight plus Reyes. It's always a pleasure to have you with us. I know that I'm going to have the best theoretical and most important practical approach to all that's going on with  and intelligent automation. Thanks for sharing your wisdom with us today.

Thank you, Jodi.

All right.

I think that you guys can hear me. Thank you again for, for joining this session.

So, so I want to start by, by going back to the basics, right. You know, we, we hear about RPA and, and we hear it's like a, so it's a new thing that's coming in.

And RPA actually isn't interesting tool and interesting solution that you can utilize, because it is kind of combined that people process technology, data, elements altogether. In, in this kind of tool. that, you can, that, you can use when, when I got introduced to RPA. Initially, I was a bit skeptical. It's like, OK, now what does this do for us? And so on and so forth.

So I'll tell you a little bit about my journey with RPA, and why we started doing it, and some of the examples, some real examples that actually saved us millions of dollars. I actually put that in my book that I published a couple of months ago, called, Change to Win. The RPA is one example of change. You're changing something. You're moving from one condition to another, using certain tools. So, it's a change, but it's the change to when we want to increase value. We want to reduce cost, we want to increase revenue. Whatever it is that we're trying to do is a change is to change, to add value, Change to win. Feel free to reach out to me or just to get a copy of this book. I'm sure that you will find it helpful. So, I wanna start with this quote by Alvin Toffler.

Oven said the illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn.

You see, the thing is sometimes we get so comfortable with what we are, what we know.

And, and then we, we get so complacent, We use, like, OK, now, we know it all. So we're comfortable.

So this is kind of where, know, we probably sometimes need to get out of that comfort zone. And this is kind of what's happened to me when I got, you know, just as many as, also as as other tools that I have seen. Because like every day, we get certain people to tell us about certain solutions. But RPA was like one of those, like, OK, well, then it's good. With this, specific to what do I need to use RPA.

So I kinda started moving from discomfort zone where I felt like familiar and easy and peaceful with what I have to this kind of danger zones. Like with us against unfamiliar, I feel exposed and stressed, because it's something new, but as soon as I kind of get into this, like learning zone, I started to see the opportunity as I get excited.

And then now we're kind of re benchmarked, and that became like my, my new threshold, right? And then, then I kind of realized that growth zone.

And this is kind of where, it's like, alright, but that's again, like we were able to do this and this and this length scale this, and expand to other other areas of the business.

So, I want to start with this, because that data is power and RPA is all about data.

But I want to kinda give it a couple of, you know, maybe five minutes or so, just to talk about data and artificial intelligence.

So, as, as we probably know, data has several forms, You know, actually started with just basic raw data, which is just facts, numbers, characters We then kind of put this data together and aggregate this data to, and that kind of moves into information. So the data tells us something. So. It's the information, because an aggregation at the data has been processed and validated.

then when we look at this information and the human beings usually would just go in and say, OK. Now I understand what this data and information is telling me about and giving me some ideas, and, and, and, and the facts right?

That's kind of move them to a wisdom where I now I can make a decision. A a sound decision about certain thing using this data, because you know that data more like very raw to like creating a knowledge, and now a decision wisdom.

So, however, when we'd have like now in this time, we'd have so much data and Herbert Simon once said well, the explosion of data, you know, the the the data kind of exploded. So he said one's wealth of information creates a poverty of attention because now we have so much information, we don't know what to do with it.

And that's kind of where artificial intelligence come in, coming in place.

Until recently, really, I mean, the wisdom that we talked about, was, unique attached to humans, but, but, now, with the advancement of machine learning and AI, machines, have been able to kind of catch up and, and reach that kind of intelligence level, Where, actually, the machine, by themselves, can mimic the human ability to make a sound decision and simulate that human intelligence.

So, and when we talk about AI, there are also like several levels of AI.

I mean, the basic, basic formation of, of artificial intelligence, it's just basic.

What if algorithm, mean, it's very basic remembering even Excel. I mean, with that, we, you kinda program the machine to say, if you see this number, or this fact, or this color, do this. And then, you put that in the sheet, or in the, in a program, or wherever, and the machine was just do it based on what? I tell that machine, Or what I Program. The machine It, just so what is very, very basic.

The next level is a BI BI is, it is, is, is, you know, processing and aggregating the facts and information and, and produce some sort of like reports or dashboards that utilize those facts and numbers.

The next level of even machine learning, where the machine starts to kind of realize certain, certain trends and come back to you with some facts, and it's like, OK, well, this is I've seen this, I've seen those anomalies, or, or, or things that there are certain trends and I want to alert you that this has got this is happening and so on and so forth. And then the next level is the cognitive automation.

It's the highest level of artificial intelligence.

Where the machine, by itself, would be able to, you know, process, information, make decisions, and sometimes make, take actions, and then kind of learns and adapt as as it grows.

So when I talk about RPA, RPA like that, that level of artificial intelligence.

In my experience, you know, I've used RPA very effectively when, when I found that the processes that I apply RPA at are highly manual, rule based and repetitive. So that's kind of one element where RPA can be unbelievably powerful.

The other element here is we used RPA to connect kind of a pseudo API, kind of, you know, connect between legacy and cloud or certain legacy systems or or different types of systems that do not interact with each. other's.

RPA is unbelievably powerful in that space because it can, it can act as a human being.

You can program it and you can build it in a way that you like as an employee or as a person would have done to log off log. And take information from here. Put it there, I'll explain this to you.

And examples the next few slides.

So I want to start by, you know.

OK, so let's say, you heard my presentation, if somebody saw my presentation today, it's like, OK, I want to do it.

So what, what is, how? How should I do this. Where should I start.

The RPA is basically bots like robots.

And when you build the RPA, is, it's like any software. You have to, like. It's an idea, you assess. And then design, build, test, deploy.

Basically, and this is kind of what I put here on this slide, it's, it's just another software that, you, can, that you can initiate and, and, and build, and deploy in your, in your organization. So it's kind of, you know, very simple to kind of understand the cycle of the bot.

The, the, the way you kind of, it's works, is in the organization, usually what, you have to make a few decisions. So. because at the beginning, you get that RPA requests.

You assist, you do, the process assessment, you build the business case, the feasibility of those RPA is get like leadership sign off. And so on, so forth, and you start building and developing that RPA.

And so that's kind of the first element, which is like, OK, I realize that, Fortunately I have the, you know, the, then, the, the will, and now I wanna develop, the next decision point here that you need to make, is, OK, what, where should I put this RPA.

And, and normally, you would have to put it in the cloud. So it depends on like, your environment, which environments you have.

You can probably choose a cloud system that you can, you know, put that RPA at.

The next decision point is, like, OK, so what's triggers that?

RPA, it's a software. So it's triggered based on certain elements.

So it can be either scheduled like every day at six o'clock AM run, and that RPA will just do something, right, based on what you have built. Or you can have it as automate at automatically triggered.

So you can say the RPA will, gets triggered automatically, if, if something happens, If something happened.

No matter what you can say, if the stock reached certain level, if the stock price reached a certain level of venues, did this.

If a system, if a system, reacted this way, if the electricity level reached, whatever it is, that automatic trigger that, that are, that, are, that trigger, that automatically happen.

You can have the, the RPA to initiate.

Then you have to kind of have a another decision point, which is the input. So where the RP as getting the information from. So this is like endless. You are again, I think as a human being, you tell that human being to do certain things.

And then that that person kind of does it every day or whenever you want. So what are the files, or the inputs are coming from? Is it like a flat file, Excel is a PDF. Is it the website? Is it an application? Where it is that The computer.

The machine can go and bring it, And then the action, you know, the action will be, OK, well, here is the RPA, and then you may want to throw some exceptions. You will do everything, but if you see this, do that. Again, it's, it's, it's part of like as if you think you work with any software but the RP is that next level up?

So kind of, this is what we did when, when, when, or those are, like, the decision points that we had to go through when we develop our RPA.

So I'm gonna talk to you about a few examples that actually saved us a few millions of dollars in the RPA space. So one of them was and some of you who maybe know the casino industry, the casino industry, kind of, you know, it was like five industries. In one, we do. We have hotels. We have retail, we have gaming, We have convention. We have food and beverage, you know all of this plus plus plus thrice you know, offer everything.

So, the thing is, that, we, we, and because of the scale, when you talk about like millions of people, visiting your properties every every day. You know, if you have like you know, 40, 50 properties and you have many, many people. So, you have many areas that could leak from a productivity perspective or from a, from an efficiency perspective?

And this is kind of where, you know, you can, you can find many areas to improve your business. And RPA came to the rescue for us a few times.

I will give you a first example here, is in the marketing space.

So when we send offers to customers, we have customers who spend $50 a day at our properties, you know, eating, dining, gambling, whatever it is that they are using the money for. Or we have people who spend a million dollar a day.

Right, so I mean, And then there, are more. And then there's like the, the, the spectrum between between those two numbers. So when we want to send offers to those people.

I'm gonna send personalized offers you don't want to stand like a $10000 gift or promotion to $100 worth customer, so when we sent like mail?

or e-mail offers We have to be very careful of how we send it and who and who we send it So we had a whole department that what the whole thing they do was was just to validate the the the offers because honestly, for us, it's worth it. Because, you know, if you put like 10 people or 15 people just validating that offer, it pays their salary and, you know, a few months because if one error, you know, We gave like thousand and TEN's of thousands of dollars offer $100 worth customer. It would not really worked well for us so the RP. So what we did, we have people actually go there and validate every offer, like they would actually look at every PDF before it gets printed of that postcard, Vincent, or the e-mail being sent to specific people or to people, and we have 50, we have 50 million people in our database. So just imagine how many, how many mail and e-mail we had to prove. So what we did, we build this RPA to prove those offers. So, think about it. This way. I put the flowchart here, but the RPO is actually a log in one system.

Say, OK, this is the e-mail, and then looks into the, what we call, you know, the, the, the hotel system, or the gaming system. And say, OK, this is the worth of this customer.

And then this is the PDF that you said, OK, you're saying $10000, but here the worth is like $550 the full. It rejects it.

Instead of like a human doing this or this. This, the PDF is saying this person's name as this, but in the database of saying that even the name is this or not, our ejected and I will notify, you know, the user, whatever. So basically, this is what he did. I mean, the RPA was above, that was just validate, and, again, like opens the PDF of the system, figured out this information opened The other system. Put the password and username and whatever.

Check out the, but this certain, like, customer information, figure out the worth of that customer based on our database and then go back and match with a specific offer that you had. So that, that was extremely powerful. We weren't able, like the, but we'll do within minutes.

Or, you know, also, I imagine, like, you know, humans doing this for hundreds and millions of e-mails and, and, and direct mail versus like the bot doing this, like, split second, you know, kind of open data to that check. But everything, you know, the double-check on the on the worth and then go back.

and and vetted A So that's like one of the example we used in the marketing space.

The other example I wanna tell you about was in the hotel space.

So in the hotel space, sometimes, we don't use direct sales, so sometimes, you know, we don't, you just people, do not just call our front desk. Or they just go to our website and book a room. Sometimes they use what we, what we see a third party.

You know, companies like Expedia, like booking. Many of us like IU's Expedia, because you know, I can do everything there.

And then if they give me points and whatever, and honestly, most of the time, when I, when I check their race and the hotel rates that I wanna go through. Usually, it's about the same.

So I usually just go to Expedia, but the thing is sometimes Expedia, and the interconnection between ... system, and the hotel system, may not be always, always correct. So sometimes the Expedia rates would, would show, for example, $100, $50, But the hotel rates, that is changing every second, or every every every minute, they may say, you know, $300, or $250.

So we use the RPA to validate the information between our third party companies that we use that expedient bulking.

And then our systems and our systems were, you know, again, like fairly old systems from a hotel management perspective. So it's not always like let's say Expedia systems maybe, like advanced. But our systems were like very old.

So it was the RP. It kind of was able to link.

Or validate this information between like a very, very old like a dos, like a AS 400 kind of a system. And like a cloud system like Expedia and kind of, again, be that validation points.

So we did that for hotel. For retail, We, we launched Microsoft Dynamics across the retail space, our organization a year and a half ago, or so. And the.

The difficult element was, we wanted to, to create, purchase orders and, and link those purchase orders with another system, which is a river, and then send, those are orders to vendors.

So that's one element, which is the PO creation, and they were doing this manually, but the RPA has helped us too, to automate this, and it's funny, because the, the system kind of learns, so it starts to kind of automatically generate and like a voice certain, certain elements that they used to call, cause some issues.

The other thing was new items creation, because, again, we're going to have like 50, 60 properties, and you have like hundreds and thousands of items and your retail space multiplied by the number of, of properties that when you create a new item and you want to, you want to assign that item Specific property. Or suspended specific property is sometimes the time consuming. So the RPA has helped us with, with this element. And then and then the financial elements from the backend as well. Not only the front end, but also the back end. So we did that in the retail space.

The next example I want to tell you about was centralized scheduling.

so So when you, when you have employees coming every day to work and then they are clocking in clocking out and you have again like tens of thousands of people coming every day every you know for 365 days a year.

Sometimes things do not really get captured correctly because some people some people get sick know the 11th hour So or they you know clock and then they leave because for any reason or the clock and at certain time, But the but the system did not capture for any reason. What happens, so the like when we when, when we were like scheduling people to come in, so the schedule would say something. But the actual lead from the ADP or the clock or whatever system that we're using. It's showing something else, because we scheduled this person, but this person did not show up.

This person was supposed to come at eight, but then, they came at it at 8 15, or they can either 10 or whatever.

So sometimes, you know, with the scheduling, and actual, and then which one you have to pay, you pay the actual one, but sometimes there are some, some reasons why that that change happen.

So the RPA helped us with validation and exceptions. Because again, like when you, when you build exceptions in the system, the RP has to be able to learn those exceptions. And then again, go back and alert you of like, hey, is this correct? Is this true?

And so on and so forth.

Um, the next one here is about HR.

So, in the HR space, I think, with we use, our effort was to, again, like, validate the offers.

And the information between three different systems, between our own system, between our third party system, which was to leo between, you know, our hotel management system, HCM system, and the report that goes to the HR leader and the senior management team. And, and, that validation was, was very, very critical.

And so that's basically, I just gave you some examples, some other examples in the IT space, we were able, again to to leverage the connection between legacy systems and cloud, where APIs were not able to kind of always, you know, get into those those elements. So that's, that's kind of where we leverage RPA for. Now, when we talk about RPA, I just want you to be aware that there will always be support and maintenance required.

It's not always, like, a magic bullet that you think it will just always operate in a certain way. It needs to be maintained.

So I want to kind of give you a high level of like, what the type of support you may want to consider when you do RPA and what type of mentors elements that you may want to consider them.

So from a support perspective, you know, there's like a monitoring aspect. That, that, you need to monitor the platform itself. And the process. So, the platform where the, you know, the RPA, or what the RPA platform is, all about.

And then, the process within of those r.p.c.s, there is you can build an alerting system that could be automatic.

Are generated based on certain criteria's that are based on like system or process or something.

Or errors already have like manual alert that the people actually can, can raise.

Because maybe the business changed or the criteria changed or the performance ratio level that, you know, we need to, you know, go back and revisit those ... or something like that. Right.

And then you have to figure out like what other change requests that that you need to always, as, again, like, you know, do some support on, which is like the whole function changed, or the there are some errors. Or the capacity of each, just maximum.

Or maybe this bot is kind of, you know, doesn't, doesn't make sense anymore. So you want to sunset this bot and build another one.

So, those are some of the support elements that, that we always had. Had a team kind of helping with the ..., or end of a maintenance perspective. I think the number one will be the environment change.

Uh, as most of you know, for example, if you're like Office 365, or, or, or any of the RP system, wherever there's sometimes like, they used to do, like monthly batch upgrades, and software upgrades.

I think now they're doing quarterly, most of them, but sometimes they will do that monthly, sometimes even more frequent.

So sometimes when there are those those upgrades happen, the bot and the environment where the bot work in changes and then started to produce errors or the or the result that they produced may not be accurate.

So you want to kind of pay attention to this, because there will be Upgrades and patches, and, and, and, and those are things that you may want to consider, there were things like, You know.

Sometimes we tell the bots to go a certain a certain website to a certain screen, and then the website changes, or the screen changed, or something like that.

So you always want to go back and kind of maintain and make sure everything is, is clear, because you put it in a certain state, and then something happens along the way. So, you want to make sure that you have a proper maintenance. And then, of course, there's the performance maintenance, you know, again, maybe, maybe the, the, the, the cloud environment that we have the bot on having certain either capacity or speed limit or something.

So, so we want to kind of make sure that it is suitable for that working environments. So, I guess the key key takeaways from, from my presentation is that, honestly, from my, from my perspective, I have had a great experience with our VAs. If you do them right. It's the software that they set the garbage in, garbage out, if you do not.

If you do not have the proper solution, if you do not have the proper people that you're working with, if you don't have validation and monitoring, you will not really have a good experience and RDF as with any other software.

My recommendation also is when you RPA, is basically taking a certain process and automate it.

So sometimes the process that the human being would do, may be different from what a, a machine would do.

So you may want sometimes to re validate or re engineer a certain process artifact, or sometimes a certain, you know, solution based on, again, like, what's going on.

What advice do before, you know, before you just jump and just do everything, you know, and the RPA spacious, re validate your process. Make sure you have a good partner. You're working with one of the best in the, in the industry, because, again, like when you need, like support and maintenance, all those people will come to the rescue.

And then, and then don't forget about the people, Because at the same time, RPA is, you know, it's a process, it's a technology utilizing data.

But make sure also you, you do not forget the people, the audience, the stakeholders that you work with, too, kick off RPA, maintain RPA, and kind of also the seniors who actually see. And maybe just bring to their attention, oh, this is what we have done. Maybe you can do somewhere else. And maybe we can scale it even more. So on and so forth.

So again, most of this and other exciting stuff I talked about and my books, So feel free to check it out on Amazon or up or Apple or other book retailers.

So, I'm hoping that, you know what, what a, what I presented today would inspire people about about technology and artificial intelligence and would would allow them, would allow you guys to kind of also again like learn from those examples and experiences.

And see how, how maybe you can apply to your own environment.

Again, like what when you, when you, when you introduce a new element, you will have a lot of resistance and your organization? So, what you can probably do is just to do a pilot.

Start with like something and make sure that you pick a stakeholder, a sponsor, who is, who is willing to take a little risk, but also would probably be able to bank the most from, from the RPA. So when you have a good partner.

Or a sponsor that you kind of work with to build and and, and implement RPA.

With, there will be a great advocate for you when when you grow your, your business or when you grow your, your RPA usage. Right.

So so I think this is kind of a no the end of my presentation today, I'm open for questions, Josie.

If you feel like I didn't know, I don't think I see in the chat and my chatbot is feel free to ask any questions that audience very well very well. Read is always always great to choose C are very practical approach to chew intelligent automation. And I have a couple of general questions that came up here that I want to make sure that I, I assume is some people who log in a little bit later in the session. Jim? I'm a T You have questions about the, the recordings of the sessions. Again, remind everyone that you, as one of the 2000 blows register participants in the conference will have full access to the recordings of all sessions next week.

We'll be sending out an e-mail with the link and password for that. So Gina ... That's the answer for you. And Robert, Robert was trying to send something for the chat. And Robert, I apologize. You can only send things through the question box. And the chat doesn't work from the audience back to us. So if you have any comments or any questions. Go ahead and use the questions box on the right-hand side of the goto Webinar interface. And then you can submit your questions right now for four years and that. And as we have a few minutes here to talk about, you, know, any anything that's important for me or in your context? How how?

How do the concepts and applications that RIAs discuss here apply to your context? Don't be shy, asked the question. I'm going to be monitoring all of the questions that come in.

And as in the and make it very contextual.

Make sure that adds value to your place of work to your own personal and professional experience and and that don't worry if you're a novice or if you're an expert. ask the questions that are meaningful to you. So, he is, tell us a little bit, just to set the stage further.

What, what was this journey like at a place like Caesar's such a diversified place as you said?

You know, people don't realize how many businesses you have within that business, and you, and you talked about that, there is a casino, there's retail there is all sorts of things going on there in entertainment.

And so what was the journey like, you know, how long does it take for RPA implementation, should get traction, if you will.

Yeah, good question.

Just a, we started with Latin one RPA because, again, we wanted to test it, we weren't sure what that was a couple of years ago when we started. It was something new. I mean, now, it's like, more popular, with like two years ago, when we started doing .... It's like, OK, well, what does this RPA, is all about. Is it like a robot? Like, literally, I've had, I have, had, senior leaders in the senior leadership team asked me, is it like a real robot?

that actually does something?

I was like, It's just a software that acts like, So, so, there is the limit of education that you have to do, so you have to educate you. You have to get the buy ins.

And that's not easy as part of Change, right? So, change management is big here. And then when you go to the organization and you want to apply it in a certain area, you want to have a good sponsor who's willing to let go a certain certain systems or processes that they've had for many, many years.

So, so, so, some, some people are big advocates for change, so you want to kind of work with those people, because they would kind of help you.

They would give you, they would give you this type of environment where you actually can succeed.

Because they will, they will, they'll give you all the resources you need, the attention you need. So, you go and pilot, we did the pilot for the RPA. A couple of areas, and then, restart when we saw the results, They became our best allies, and he started to be the biggest, biggest, biggest advocates. And they started talking to other leaders in the company. That, hey, use this system by, you know, this department or continuous improvement department helped us with, with, you know, this specific RPA. We didn't know what it was about. And now we've saved millions of dollars, hundreds of thousands of dollars. We were able to reach our budget, and, and, and use the money somewhere else, or our apply it to the bottom line.

Very, very well, you know, the ERP. Vendors hate, when I say this, But when we first started experiments with RPA, there are a lot of people were very excited about. using RPA. They're using RPA, they're spending $15,000 on a robot to solve a $5000 problem.

And, and the, and, of course, that's not the right thing to do, you know. And I'm curious about, in your case, how did you go about identify the right opportunities where RPA would be really creates significant value versus being just a novelty that we can experiment with?

Yes, Great question, Jose.

I think, I think the, what, it was, it was a little bit difficult for me at the beginning. Because I wanted to understand what he was. So I was like, OK, well, what other uses. And then again, like, there's an excitement that happens at the beginning, when, when you apply a system, or a new system or something.

But a failed RPA, I feel that RPA would be like most useful when the processes that you're trying to replace our very manual. And you have like people like manually doing something, they are rule based, that you can apply a certain rule to that. So, I have to go to the specific website As it goes a specific system, I have to put my username and password to pick this up to go to this specific screen, hit F one shift, if for whatever, whatever, and then get the specific number. And then, now go to the specific system. And then apply that specific number. And then, I get this fact, which is a dollar. And then go to the third system and validate that this dollar makes sense.

I think go to the website and the CDC on saying, so, it is like a rule rule based and repetitive, like, so, you're doing this for hundreds of thousands of times per day and you have like a team of, I don't know, tens and hundreds of people doing this, you can replace that with one bot.

If you can do this right. Or when you are trying to again get one information from a very, very old legacy system, a a cloud system, and then based on the information, make a certain decision. about a specific rate, or a specific behavior. Or opening or closing. Or, or whatever it is that you want to do. So those are, like, the area that kind of triggered.

kind of where we can use it. But again, that came with experience, because we didn't know what it was capable for.

We tried something, and honestly, it's not every RPA is successful, We had some failures.

Nothing, nothing is perfect. And, to your point earlier, I mean, sometimes, you just may want to do things very easy. And, so, maybe, you don't need to kill a mosquito with a bazooka.

You know, sometimes you just, you kill a mosquito, right?

That's right. And then there are some great questions coming in here, and I want to make sure I get to all of them. Keep your questions coming. I'm going to get to as many of them as possible. So, let's let Robert ... has a, has a follow up on this. So, what is the learning curve like for the internal staff? I don't assume that you had a whole bunch of data scientists were just sitting there, ready, and process. People. Who are just sitting there. ready to deploy RPA on day one. So you know, you come in, you know, we're looking to RPA. It's not a hardware robot. It's a software robot, So learning number one.

And then people started learning that, wow, this thing can automate across applications, it's kind of interesting.

And then, how, beyond yourself, as a leader and re-use? How did you do? What was the learning curve like for the internal staff?

Well, some people.

So it depends on who we are talking about. From a stakeholder perspective, some people are very skeptical. Of course, resistance always is the case at the beginning.

But some people feel the threat as well.

You know, when, when, when, when you talk about RPA, is and what they do, and whatever. But from a stakeholder perspective, and specifically, leaders who are trying to improve their businesses, whatever the learning curve is, so so let me separate separate. The sponsors from the users.

The sponsors to be clear here, I think this question is not on the sponsor side, is just on the user side.

They're curious about, how do you go, like, did you have some person in your group who, like, kind of caught onto it and wanted to do it? And what from the internal capability building is what I think the question is focus on.

That was good. So, thank you for the clarification. Yes.

So, so we did get a couple of people, a couple of directors, to kind of shepherd the efforts internally, because the third party or the, the, the sulfur implementer, you know, the RPA companies that we did with, they can bring the solution. But they need some sort of, like, internal resource to direct them and get them what they need from the company. What we did, we actually outsource the Autobot build and support and maintenance to the third party.

But my plan was to, potentially at a place the third party support and maintenance over time.

We didn't have the luxury to do this because we had other priorities at the time. But, if I had to do it differently, I would Probably, after like, a few months, I would invest in people who actually can develop the bot internally using the platforms. And make sure that the support and maintenance is done internally.

I did have a couple of people, again, just, who are we're shepherding the, the, the efforts and they were. They were like leading the efforts internally to ensure that it's getting traction. Is getting the approvals is getting the everything we need.

But, if I wanted to do it differently, I would probably, I would probably invest internally, because again, like the, with all due respect to all the RPA implementers and softwares and the system is one thing, but the support and maintenance can be costly.

So as, as, as with any other things, I mean you outsource something at the beginning and then when you learn, you kind of can bring it, bring it internally.

So we did not reach the phase where we were actually building bots internally and supporting them internally, but that would have been like the preferable solution.

If you come to me, understand, I understand that that helps a lot to understand the Sandy. And that, Jim has that Jim Walker here has a question about, if you had to guess how many hours annually are required for each automation. I think that people are looking for kind of order of magnitude what you know, and I know that there's a very wide range based on the type of applications you are automating.

But for kind of an average application that you used, how long does it take to get something like that up and running?

Well, that's quite difficult. Question because, I mean, if you're talking about, like, getting the, the company, the RPA accompany approved, paid in a RIBA and, You know, being there, and That's true. That's true. No, I don't think, so. Let's, so let's narrow. The focus has been, I think it's more like, oh, I have an idea. I have an RP event already that I'm working with. And I have someone internally who's going to partner with the RPA vendor. And we're going to automate this function here, Retail. That, that seems like, you know, kind of on average. Something that looks like on average application that you did. Order of magnitude, I was talking hours, weeks, months. What does it look like to, to implement?

So if you have the RPA vendor already and if you have that platform selected already and if you have the Software Implementer or, you know, the, the company that built the RPA for you, then, to your point, it's really depends. Some of those r.p.c.s are quite simple, like, for example, the RPA that we did for HR was quite simple.

It took us, maybe less than a week, two to have it, like, ready and published and and like running.

SL arc's are extremely difficult. I mean, like, if they, but it's not, it's not like months. I mean, because it's like a software, and the more your resources you add to it.

The faster, usually until you reached out, you know, a lot of diminishing return or whatever.

But, but usually, I would say, one week to four weeks maximum For any, you know, RPA and then I'm OK. Again, like, if you have the vendor ready, if you have the system ready. But also if your processes is proper because sometimes you may want to go back and re engineer or refactor something to enable it to allow RPA to work. Because sometimes RP is a little bit different from people. So sometimes you may go back and look at that process, like, why do we do this this way? In the first place, you know, we need to redo the whole process, and then we go and bring the RPA.

So so and then the engineering process could take you weeks, Right, so I don't know but like if you have everything ready the RP is, you know, I would say days to like maybe a few weeks, Maximum for the, for, again, remember the cycle that I talked about, which is the needs assessment.

A design, build, test, deploy. So kind of the same thing. So you can do this all in, like a week, or it can be like four weeks, again, depending on the availability of sponsors and people, and the process itself.

Yeah, I love that answer because it's such a practical answer.

The right technology is so cool.

You bring it in and often in different parts of the business. And when you're looking to the business now, it's not a technologist at your for the often. You are now taking a comprehensive look at a process that's all messed up, and you do not want to automate that. You fix it before you automate, and, and that's often is where we spent a lot of my time, isn't it? Just getting people aligned and making sure that we have the right process to automate. Tell you how many times we would go and try to redesign, or a fixed price, like why do we have, this is the first I couldn't get it out. Let's find somebody else. Right, so, so, just because there has been there for like 30 years, it's like, yes, but the environment changes, whether this in the first place, right.

So sometimes you come, yeah, that's right, always remember the rules of eliminating or reducing and only then automating. Very good, RIAs, we're out of time. This time, goes by fast when you're having fun, it's always fun to be with you and learn from a real practitioner. Your experience cross industry is fantastic, and you understand deeply the business side, and the tech and and the technology side application. And we are very grateful to have you share your expertise with us.

Thank you Jodi. Thank you for having me.

Ladies and gentlemen that was real atar. Always great to chat with ... is about what's going on in the world.

Excellence, innovation, culture, business and digital transformation. Tremendous experience and insights. Check out his book. He captures a lot of that on his book.

And, and the very great way of starting our day together, We're going to be wrapping up this session, and at the top of the hour, we're going to be joined by two experts on artificial intelligence and machine learning. And RPA in the, in the session will be pushing RPA to the next level with artificial intelligence and Machine Learning.

And after that session, I'm going to have a session with you on how great enduring organizations accelerate excellence, innovation, culture, business, and digital transformation in times of uncertainty and rapid change. So, two next sessions coming up, I will see you back at the top of the hour with doctor Jackson and Brian ..., who are going to be discussing with us the mix of RPA, artificial intelligence, and machine learning, Susan?