Session Information:

The Evolution of the iBPMs

The iBPMs is at the center of the emerging CX, Automation, and Continous Improvement movements. The IQ of processes will continue to grow as the iBPMs leverages Composable Low-code, Process Intelligence, AI/analytics, the Real-Time Data Mesh, and Digital Twins at the Distributed Edge.

The Reason the iBPMs Emerged / A Historical Perspective :

What was the IQ of the Early IBPMs?
The iBPMs is Still Evolving?
Where will the iBPMs End Up?

Session Transcript:

Here with us, coming directly from Arizona in the United States, to the world, to the world, we have Jim ... with us.

Jim is an independent thought leader in applying these the business platforms, which are gonna call GBP, Customer Experience journies, C J M, and Business Process Management, Automation, low code, Advanced Data Management and Decision Management at the edge Apply to business Outcomes.

Jim has been involved with IT from his teen years, and he enjoys the ever growing and changing field and he is also a highly awarded, digital and traditional artist, who Moonlights writing songs for fun.

So, multi talented, cross industry experience.

Jim, what a gift to have you with us. Thank you so much for sharing your insights and expertise with our global audience today.

Well, thank you very much. I really appreciate the opportunity.

It isn't often that I get a world audience, and I'm really excited about it.

What I'm going to do is run us through a little bit of history.

On the technology side, I came at it from, you know, a Gartner perspective.

But, before I do that, in the end, I'm going to cover some of the future things, And where I think we're going, which is the digital business platform.

Um, So, what I'd like to tell you about is?

First of all, my experience level is, is half, I'm having a bit of a problem, or are my slides advancing?

Jim, no, right now, it's still the first one, so you kinda move to the eye. If you want to move to the next arrow in the bottom, tried that one more time, please.

OK, it was giving me a ring, a Dinghy sound. Yeah, because you're going backwards, hit, they want to go forward, and it's the one next to it. That's the way up. Yeah, here's a technology guy that can't even figure out the buttons.

So, my background is house with the, you know, the real-world implementing, and it worked at some very large companies, north-western Financial Network, and American Express, actually implementing processes, AI, low code.

Back then, when we had not the kind of tools that we have today, then, half of my career has been with Gartner, or as an independent analyst, and, consequently, I have, you know, the real-world experience, and sitting on the sidelines, watching, predicting, and, you know, rating vendors.

So the important part is that processes aren't going to get smarter.

But actually, everything is going to get smart.

You're going to see smarter, you know, mining smarter RPA, smarter in, in all aspects, as AI gets embedded in a number of platforms.

So intelligent processes are going to be relevant for organizations.

And they're going to be pertinent.

That Smarter Processus equal, better, interactions with customers, better results, better outcomes.

And the smarter you make them, the more you improve them, the better things are.

So I'm going to give you a historical perspective on how the Ivy PMS emerged.

But before we start, let's talk about some of the common names for processes, and you can see them here. You can read, just so I guy can, when I talk about processes, I'm including all of these versions.

You know, BPM was one of the more popular in recent ones, but as we all know, some of the bloom has gone off of that particular topic, and it's back to just workflow, and process, and customer journeys.

So if you take a look at the beginning of this, it started with the W FMC and it was a lot of the content guys.

We're, we're getting together, they say, where should this process, stuff go, when it was basically processes, followed content?

And then we came into what is known as the BPA here in the late nineties, which is about modeling and simulating and it grew very fast, You know, there were tools like, like heiress that, um, you know, drove the market.

Then the next era was an explosion of players that tried to integrate the human to human processes with the below the water line integration processes.

And at that time, I did a count, and there were up to 200 vendors that were involved from one aspect to another.

Then, we move to the BPM S era, which said, Well, what do we need to do to make this more successful on a broad scale, and what was necessary, besides methods and standards, or two things: one on the front end, which is recognizing events and patterns, so that your process could wake up and do certain things.

And the other thing was activity monitoring. What's going on?

How do we get this, you know, full life cycle of, you know, model, execute, measure, adjust, so that you can have continuous improvement.

Then, content came in, again, more as a data source, and also was the beginning of the template era, where there are templates of pre-determined processes, whether it was horizontal or vertical.

Then came the ... era, which was in 20 12, and this was a huge sea change.

This is where we went, social. mobile cloud, included rules, engines, under the covers for more agility.

Mining's started to enter in, those separate, some vendors had that built in early, like a fujitsu.

And then we were more into optimizing, you know, We had done the round trip engineering.

Now, we want to optimize, and so now we can get into what the black belts wanted to do, which is constantly optimize. We started moving to cases rather than process instances.

And IOT came in and we started getting into edge computing.

So what were the prominent features of IB PMS? And this is courtesy of Gartner back then.

And you can see that there were a lot of moving parts there, There's a modeling piece, there's a collaboration piece, there's the human interaction piece, your connectivity to systems, active analytics that are always on, and then on demand analytics.

And, of course, a business rules management system. So, you can change rules in flight.

So, the idea of, of A a suite that worked together.

The problem with those suites, most of them at that time were somewhat closed.

And what has changed, going forward, is they're much more open, and, you know, the either they grew the pieces that they needed over time, or they bought them, and integrated them, but that's where the ... first started.

And I'm going to get into where it's headed next, later on in the presentation.

So, you know, we call these things I, intelligence.
8:22
And, you know, why was it intelligent?

and how did you measure it? Well, people challenged.

And I came up with what I called in, in, in intelligent process measurement, a cumulative process, intelligent quotient, just though I could measure the cases.

So I could look at what companies were doing and it had five factors. one, was the raw intelligence, and you can read that.

The key piece, under that was the influence of polley analytics, which has many analytics, running, real-time machine learning. And we haven't really hit the level five yet, where the AI is running all the processes. And when we get to independent agents, that will happen.

Then, if we go under the social site to the right, it's all about collaboration and skills, crowd sourcing, social analytics and better practices.

And we've picked up a lot of that that seems to be moving quite well.

Moving back to the left, it's about agility, and that's the one thing that the ... brought in, was agility through the rules, engines, and through the policy capabilities.

So, the agility actually took a step up when we got into case management, where we had milestones, going forward.

We're going to see that a lot of the processes are going to be goal driven, so that means that if the goal changes, the process will change.

Now that process is going to be smaller, and it might be bots and agents that are basically coming together to get work done. On the right side, there's the autonomy right now, you know?

A lot of the processes are programmed, and so this is a freedom level.

And this is extremely important, and we're not very far down the line on the autonomy, know? We're at the well processes. We will do things and then notify you.

And we're not quite yet to goal driven processes that have constraints that said, you know, these guardrails will not be violated.

And so the notion of of that is, is growing, and then interactive, where you have bots and agents and process snippets all acting together.

So this is the intelligence, no quotient, CPI Q, that I developed. So I can look at the case studies.

So, here's an example of one dynamic Surgery Center.

I mean, you know, if you've ever had to wait for one of your loved ones, Wow.

You know, you know, they're getting operate, and it's pretty scary.

So, this set of surgery centers tagged the patients, tag the relative that was going to drive them home, tagged all the instruments.

And they were able to, before the day, started, predict the schedule.

So, you know, they're running through 4 or 5 in your procedures, and sometimes they get behind, something gets in the way.

So, what they did is they had a live dashboard that would show where each patient lives, and, at that time, um, it wasn't displayed fully, but now they have it, So it can be on your phone so you can see where things are.

So, if anything happens, they can do a visual simulation and determine everything with a positive outcome, but we all know the real-world things don't always do that.

So, once a goal is attained, it lets you know, but if a goal is out of sorts, it will re simulate and adjust the goals and give a new visual dashboard.

And consequently, you know where that is. So I use the CPI cue to show, well, how is this working while it's high in visualization?

It's very goal driven.
12:47
It uses a lot of analytics.
12:51
It, you know, basically, kind of moves ahead and notifies.

So this has a fairly high IQ for a process.

Here's another example.

This is the farming operations.

Where in air situations, and I live in Arizona, it's pretty air it, we have a lot of these watering machines in southwestern US as that as well and so does Australia, and most of this got, was done in Australia.

So it used to be the farmers had to turn on these robots, water robots that had fertilizer inside of them.

Well, they changed that, so they would work automatically, and it would dynamically determine the water fertilizer mix, by having IOT devices in the ground measuring.

And, consequently, it could determine how much water a particular field needed and a particular part of the field.

They also came up with national moisture models that showed where the moisture was.

They could determine where the mountain runoff was going to go, and they can dynamically adjust these watering feeding systems.

And they were able to increase their crop production by 40%.

Now, since I wrote this up, they are now coming up with new variants of seeds based on this kind of environment.

So, they're learning how to genetically put the right kinds of clients that respond well to this kinda system.

So, this was, you know, highly successful.

And you can see, it's very agile.

Some visualization.

The raw intelligence, again, was analytics, not very social.

Not a lot of interaction, but a lot of learnings. So, this was a fairly smart way to do things, but it was a different kind of smart.

So, you know, where are things going from there?

And that was done with early ..., I believe this particular one was Appian and the one before was was Bosche when Bosch had, um, a BPM engine that they were selling separately.

So let's talk about where processes that are going.

I mean, most people think of processes in this lower left-hand side.

Very structured, sequential, but that is still be there, but it'll be intermixed with case collaboration where you have snippets of sequence, that you pull in at the right time.

Once you determine something needs to be happen happening.

And then the third in the upper right are basically smart box that are really independent resources that dynamically come together. And create a process dynamically.

That's where we're headed.

And if you read my 2013 book that talks about the next wave of BPM that's hinted at, so where are people putting digital investments?
16:20
And so how is that going to affect the Ivy PMS.

So, you can see these are very successful: content capture, work collaboration, process automation, automation.

You see that all the time, customer journey, employee journeys, analytic focus, decision focused, AI, edge focus, Agile focused, low code focused. You've heard a lot of that and you'll hear more.

This is all, these are all on ramps to digital transformation and multiple things are probably going to be going on at the same time.

Now, the question is, how involved is process?

Well, you can see, by the green, that's all over this chart, that these things are really strong process layden, So, there's a lot of process involved. There's a number of them, that I have a medium approach to process, and then the The one, the Agile Integration Focused is, you know, minimal process activity.

So, you can see process is everywhere.

I went too far.

Here it goes with the buttons again.

So, processes can direct intelligence, applied to work.

Now, eventually, resources will direct the process. But we'll talk about that a little bit later.

So if we take a look at RPA, which up till now has been hot?

Very hot mining is working with it and it's getting hotter and so is mining.

So, if you're coming out from the tactical levels, through the business levels, and through the cross business levels, on the X axis of this chart, you'll see that a lot of the processes are about infrastructure automation, development automation, and, you know, RPA helps their ally.

If you take a look at where process comes from, they're better at dynamic resource automation, whether that be just composing static processes or dynamic processes.

And what you see is, as process provides more benefit, they're going to be the super charging of resources.

Don't be surprised if AI whispers in the year of resources, to tell them to do something different.

So, it's about resource assistance, it's about process automation. It's about better journies now, right now.

Journeys and processes can be quite separate.

So, if you go to a doctor's office, you have a certain journey, but there's a lot of things that are going on behind the scenes that are in the process.

The problem is, there's not a lot of visibility, you know. You sit in the office and you just wait and wait and wait, and you don't have any visibility, and so, the journey and the process is very separate.

So, where were things hadn't?

Today, process is a lot about taking actions. In the future, it's going to be about recognizing things that are emerging.

So, we're gonna go from static situations to emerging situations. And, you know, now that kogod has occurred.

We know about emergence and recognition and responding to that recognition. And eventually, the processes will be making decisions as it goes along.

If you give it constraints and you give it goals, it can make its own decisions. And the higher up on this chart you go, the more the benefit.

So what can processes do?

You can see that they're good at recognizing patterns and events. So they're at the frontline there and IOT.

They can help you with decisions and scenarios and let you know if there's an emerging scenario like a pandemic.

They can adjust and take actions and direct resources.

Now, you can see that there's a lot of things that a smart process can do on a right and I'm not going to read it to you.

But, you know, the idea of having knowledge and alternatives, and become goal directed instead of flow directed, I think, is the future.

So we're going to be optimizing resources and some of the resources are going to tell us how to optimize in real time using a data mesh.

That's where we're headed.

So that talks about, and the reason we're kind of changing names, and Gartner's figured this out if you change names, you can You can give things life so the ... now becomes an open digital business platform.

So, either it's a generalized digital business platform, or it's a specialized digital business platform.

And where they're coming from is there are some that are AI and decision focused. There are others that are analytic and data science and mining oriented.

There are others that are BPM or automation oriented. There are others that are collaboration, content oriented.

There are those that are component based.

There are no, that are kind of low code.

They're cloud focused, cloud data focused, digital business platform specialty, and their IOT Edge hardware.

So, is the ... dead? No.

It's going to collaborate, It's going to be open, it's going to be involved, you see more and more process getting at it.

So this is the chart that shows the digital business platform environment.

And let's start at the top.

There are a number of technologies that are business impact that are flowing into our digital world.

There are a number that are developmental in their impact and others that are infrastructural.

So these things are flowing constantly. And I'm going to get into a chart that shows you how things have, you know, flowed into our digital world.

What we find now, in the center of this chart are the kinds of digital business platforms.

There are there are, and I'm going to go clockwise.

There's the process and automation DDP.
23:15
Then there's the data science intelligent performance DDP.

Then there's the secure data mesh, DCP, and I'm gonna give you examples of vendors for these so that you can get a handle on it.

Then there's the Digital twin IOT DDP and then there's the business application pre-built, and in the center of it are all, are some that are going to have 70% on average, of all of these and become a generalized all inclusive DDP.

Well, off to the left, under the goals, you see a little, you know, red circle, which is the journey in collaboration DDP.

one of the things that process didn't do is it didn't do collaborations really well. And it was a separate world.

We're going to see that come together. You're going to see more collaboration in intelligent business platforms. You're going to see more collaboration in trying to get results. You're going to see.

You know, we know we have all of our six Sigma steps, there's going to be tools that are going to help you collaborate as you go through those steps.

And there are huge benefit pools to digital.

I'm going to give you some examples of those. So here are the influences.

And you can see, at the far right, and I started at the left, because.

You know, I was around when he did some of this stuff.

As a teenager in the sixties, I was involved with some of this early stuff. But let's take a look at what's on the far right.

These are things that are gonna be coming into your pool: Digital identity, customer excellence, digital assistants, smart bots on a technical level. Knowledge management is coming back.

Cognitive, not just machine learning, not just deep learning, but cognitive thinking kinds of capabilities.

And then, on the technical infrastructure level, it's quantum, industry 4 dot 0, and cloud, and hybrid cloud. All of these things are influencing the GDP.

And we can see, you know, collaboration to that zero is involved.

Journeys are involved, chatbots, customer experience, the data mesh, which, basically, in the cloud, gives you your own dynamic view, either as a program or as a person.

five G, coming on ontologies are coming on. These are all key things. Now, once you get past 20 30, I'm lost, or dead, one, or two, because there's more things that are, that are happening in the labs that we don't get a chance to see.

So, let's move to say, what are these digital business platforms all about?

They're going to be more resource centric.

They're going to be goal driven, journey focused.

And as we see, the process, and the journey start to converge, more collaboration, more real-time, edge, more predictive instead of reactive.

More RPA, more low code, more AI and Pollyanna lytic, which means AI and Analytics. I mean, there's a huge fight going on, and you've talked to the analytic, guys. It's all about analytics. Talk to the AI, guys. It's all about AI. It's those things working together.

The data mesh, which includes new kinds of software, voice, I mean, new kinds of data sources, Voice, video, images, and more embedded in re-usable capabilities.

So, we're seeing that the digital business platform is an explosion like the IV PMS in 20 17.

So this is getting explosive in terms of what's being added.

So let's talk about some of the vendors. Let us start on the right side.

Some of the all inclusive ones are jam packed, oracle, peg systems, Salesforce, and sap.

There, their goal is to become as good as an all inclusive DDP as they carry.

Then there are others that are more aimed at automation in the green.

We've got Abby, Appian and Blue Prism, ninh tax package systems place there as well, UI path.

Let's take a look in blue.

The, kinda lighter blue is the intelligence performance DDP, Apache Spark, Sass, tibco, and ... which comes at it from a corporate performance standpoint. That's a new name to you. Keep your eye on them.

Then we have in purple here, the Secure Data Mesh DDP.

So we're talking about vendors like crowd strike, HP micro focus, IBM, Informatica, single store, new vendor, watch them, tip Go.

And Tibco also played a role. I'm sorry, I didn't mention them for the performance piece, as well.

Let's move over to the left side.

And talk about the Business Application DDP, and these are names you recognized Oracle NetSuite, peg's system, Salesforce, sap, Workday.

And let's get into this little red one, where there's a lot of collaboration. There's a lot of video interaction.

We have Microsoft, Monday, parallel, Brand-new Little Vendor, that wants to BDP, help you with, you know, all of your process improvement efforts, Slack Zoom.

And we saw Salesforce buys Slack because they recognize that this is a way going forward.

So in our last bit of time, just checking my time here in the last four minutes, let's talk about how things are coming together.

So if you don't have a specialized EDP, if you don't have a generalized EDP, you're probably combining things.

So here's an example of an intelligent process with RPA with mining for automation. That's either a poll or a push.

Either it's content coming in, or it's a process pulling the content.

And here's an example of one is maybe very near one of them. We heard yesterday where people have NLP bots that are looking at the words.

And these, these came off of manuals that would help train new workers, or advanced workers that were in the middle of a problem. They didn't understand.

Well, now, there's a system that's being used that will pull up the right video at the right time, Show you the par, give you all of the the tasks, how to make the change.

Depending upon what you need, you might just need, you know, you know, what are what are the things that are going wrong. But this will have the full bevy of videos brought up in context of a worker trying to fix something.

Here's another one, where active shooters are often in buildings that people don't understand.

And there's a big effort to do a fast scan of three-d.

floor plans so that the emergency personnel have the ability to have facility familiarity by reviewing these floor plans live on their mobile device or their pad.

And they can respond, and you know, they can change the response time and decrease the response time by 21% so far.

This is an emergent case study, but it shows you the power of new kind of data in real time.

And that's the one nice thing about the data mesh is it's more real-time.

Here's an example of a mining situation, where there's a lot of moving equipment, and the equipment breaks, and if, and this is what you're seeing in the top left, is the, know, a front end loader where a tooth is missing.

Well, if, if the tooth is missing, the chances are that in the dirt someplace, it goes into the processing machines and break the machines, and worse, those things can get projected and create safety situations for the humans that are around these machines and manage them.

So, this is a case where, they're using videos along the way to see if there's any teeth broken. And, no, it was done for safety, but there were significant cost reductions.

Here's an example of collaborative distributed intelligence for a trader, So some of the market training is done by bots, but it's some of it's still done by people.

And quite often, the trading doesn't consider the the yield desires or the portfolio management that's required by, let's say, some high-end customers that are, you know, involved with wealth management. And you can see all these bots and agents helping the trader.

Whether it's forecasting, mining, you know, using algorithms, doing a risk assessment, real time, looking at the trading rules, and say, can I do this or not do this.

All of this is happening very fast, either for a human or a bot, and all of these things give the process, bot or agent goals and constraints, so they can move forward in a trade situation.

Here's another one.

Where somebody wants to buy bike, um, Either a kiosk or Zoom type service determines the mood of the person, look at their body build, and try to assess their personality while they're looking into the video.

Right now, this personnel is a little nervous giving a presentation, so might be a bike the right away, so they're able to, In essence, recommend a bunch of bikes and show a video of that kind of body type on that particular bike and make a suggestion.

This came courtesy Guide EM the had one of those implemented.

Here's one that has not been implemented But is been the goal of a lot of companies And I think we're gonna see this one emerge the idea instead of having a a Specialty Agent press one if you want billing. Press two. If you want to hear our latest offers, press three.

If you want real support or scream, I need help.

Well, the notion of a generalize agent that has wisdom about the customer, about the billing, about technical issues, about governments and about the products and services that you sell and the status of the account. Imagine AI whispering in the ear of a generalist or showing them something in a popup screen. This is being tried in a number of organizations. I worked at two companies that tried this before the technology was there and they abandoned it.

So, So, that's the the end of The cases that I'm going to show you here.

Some of the books that I've written, that you might want to look at This presentation is very consistent with the themes in those books, ones on BPM, the next wave, The other is digital transformation, then I thought I'd sneak in Sorry for doing this, Um, but uh, showing you a piece of Maya Art.

Yeah, that's that's pure beauty, Jim. That's fair, That's awesome.

Thank you. Yeah, fantastic coverage. Thank you so much. I have to ask about the art just to start. I mean, is there a specific technique approach? What are you doing there with the art?

Well, you know, I'm the digital, well, I'm the hand side, you understand painting and all that. But on the digital side, I use software to do two D, or three-d. fractals.

Wow.

And so what I do is, the machine will randomize based on the parameters. You give it. It'll give you images, And then you select out of those images what you want to work with.

So you can change the angle. You can animate them. You can spin them. You can stop them in the spinning.

He can change the, you know, the perspective can change the lighting, shadows, and you you have a piece of art when you recognize it.

Fascinating, Jim, I shouldn't expect any, any less from you to the true merging of science and art. So thank you for sharing that.

I'm gonna ask you, there are several good questions that came up while you you're presenting, Can, May says, from Hong Kong. Excellent presentation, Jim, thank you for that. The audience really appreciated. The kind of the angle of your presentation is different from the other presentations, which is good, the diversity, and covering IBM from multiple angles. You have more of a technology look and feel of, of of BPM.

And so that resonated with a lot of people who are interested in that component. And so, I will start with Daniel here.

Danielle is asking, Then you'll find her is asking, Do you think that process mining should be a standard tool in future BPM suites, or they will continue to be kind of dealt by the technology companies separately, Which seems to be, mostly how it's being done today?

I think over time the answer is, it will be in a lot of these general purpose DCPs.

But it takes a while for these things to be accepted, look at how long it took RPA to move before people like peg a bottom, people like Appian Bonzo.

So I think I can predict the time period, but it's speeding up.

Mining is delivering a lot of benefits, and we saw Aby bottom mining tool, So, You know, And they're trying to integrate it with their intelligence, you know, process an intelligent content platform, So that the buying has started.

The question is, do they want to build, or do they want to buy? I think they'd be smarter buying.

But I know there's a partnership between slowness and sappy. I don't know if they bought them.

I haven't been watching the market, but it wouldn't be surprised if they did very well very well. And this is very relevant, because, next week, we have a deep dive in process mining, And the, as you mention, process of mining is not brand new. It's been around the ready for a few years, but it's definitely scaling, much like RPA, I think, scale earlier than process mining. So, this is very interesting. New, new ... at mi SaaS. First, thanks for addressing the vendor landscape.

It was going to be his question, and then his question is around, if you can cover which industries you see at the, are at the forefront of this evolution, with DB piece, Are there specific industries that stand out for you?

Yeah, there are. There are ones that come there because they, they have money.

They're cash cows.

So we see a lot of the financial folks are always trying to be better. And that's why I picked the financial industry to start my career.

Well, actually, I started in manufacturing.

And then moved to the finance side because I was jealous because they had money to spend on technology.

So I see a lot of the finance companies, you know, insurance companies, credit card companies, variant. They're in it because they have money and they can afford to get better. They want to get better. The other is healthcare.

Healthcare is they want to get better because they want to save lives, they know they, they don't like what happened with covert. They want to be better next time, they want to combine real-time data with archival data.

They want to combine the patient history with the real time monitoring that's going on, so they can look at the history of the patient. Wow, you know, while they're in urgent care of sorts, and so health care wants to do it.

Not because they have the money, they want to do it, because they have to, because they want to save lives.

Manufacturing is always at the leading edge, because they're trying to cut costs. You're trying to be efficient.

They're trying to produce new products. They are always trying to do things.

Now, are they high profit?

Are they cash cows, not necessarily, But, again, they're coming because there's desire to get better and that's that, you know, and that's, they share that with the healthcare. Do they have to? Well, yeah, maybe if they want to stay competitive, they have to.

And so, you know, we see no supply chains, full supply chains being digitalized from the time that they measure, somebody's teeth, to the time they give them their choppers. They're brand-new teeth.

It's all done cross company companies.

Somebody measure's somebody, installs somebody, you know, carves out the image of their teeth and manufacturers it and if they can't do it in time, where the patient wants it, it goes to another, somebody else.

And so, you know, that's starting to happen in manufacturing.

Those are the three that I see most often. I'm sure there are others, blah, blah. And another one comes to mind is anything that has applied for.

So like oil field platforms, where there's a lot of service, we see pipelines being inspected with drones looking for emerging patterns in pipelines so we don't have spills.

We're going to see more of it in the green energy area because they get the start over. They get to, know, invent things.

So I think some of the new emergent things around electric cars, around electric, like ..., around automated driving.

All of that is, you know, is, is impactful. I think the logistics industry got on this stuff really early, You know, their own GPS, they were on reporting.

But then there are other companies that just want to do something different. Look at the process that Amazon has won the world with.

They did it with a process.

As a customer, I know where everything is pretty much.

Unless it comes from a country that hides it.

Excellent, excellent, Jim. So, I have time for one quick question and the answer here, and this one is, first of all, I want to say, Karen Kirby makes sense a note saying that fantastic presentation. Thank you for sharing this with us today.

And I'll follow that up with the question and not from Karen specifically. But one of the themes that has a merge here is just finish with, like, kind of high versus reality. So, you know, we know, we know what technology today has the greatest value creation potential across all industries, and what is another technology that, you know, can make stupid happened at the speed of light? It looks really good, but it's not really helping. And so, give me the, kind of, the good, and the ugly.

So, Best technology and worst technology right now, not to say that five years from now, we'll be ready the best and worst technologist right now, I'd say right now, it's, it's process and RPA combined.

I hate to pick two, but those two are delivering a lot of capability mining is going to come in there soon.

The one that can get you in a lot of trouble is AI. And I was involved with the first one of the first waves of AI, and we got ourselves in them.

Pikul, many times, we eventually worked our way out of it, but we put too much effort.

And AI at that time was forward and backward chaining, inductive, deductive reasoning.

We had to figure out what to do, and we spent a lot of money figuring it out, and we ended up hard coding, some of the things we prototyped in AI.

So AI can go awry, real easy.

Forget about the ethical issues. Forget about, you know, machines taking over the world, forget about, you can get a lot of trouble with AI. And that's why AI, this time, when, it came out of its AI winter plate, it's smart. Let's do machine learning first.

We know how to do that.

Let's move into deep learning next.

Then they'll move into fantastic Jen Jen signer. Thank you so much for sharing this deep expertise in technology, your historical perspective on the evolution. You have done it. You have the futuristic view as well. It's a real blessing for all of us to have that expertise share with our global audience.

Well, thank you very much. Now, if I could just work the advance keys properly, I'd be OK.

Fantastic, Jen, thank you so much for, for your time today and for your expertise.

Ladies and gentlemen, Jim Sign or directly from Arizona to the world. What a great deep dive into the evolution of technology in there and really how BPM has evolved into intelligent business process management and will continue to have to evolve. We're going to wrap up the session and when we come back we're gonna, we're gonna go to Bangalore India. And I'm gonna have the Senior Director of Platform Engineer for Target in India talking about how we enable organizations to trust their data. So very excited about welcoming Krishna Rao for, for that session. And I'll see you back at the top of the hour. Thank you.