Episode 429: Transforming Manufacturing: AI's Role in the Modern Workplace

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Welcome everyone to another episode of Dynamics Corner Brad, can AI do more than just put words together?

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I'm your co-host, Chris.

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And this is Brad.

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This episode was recorded on July 25th 2025.

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Chris, chris, chris, can AI do more than put words together?

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Ai can do a lot, and AI is all over the place these days.

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I know we often focus and talk about how AI can help you within your ERP software, but AI can help you outside of the ERP software and with us.

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Today we had the opportunity to speak with Brian DuBois about AI and manufacturing.

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Good afternoon, sir.

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How are you doing hello, hello, doing well.

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How?

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How are you Doing very well?

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Thank you, very well.

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Thank you for taking the time to speak with us.

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I was just talking with Chris, I have two new obsessions and I don't know how I got into these obsessions.

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Someone told me I'm a year late on one of them.

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I'm into making sourdough bread.

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Okay, yeah, you're way behind, man, I'm way behind, so I made my own starter this week.

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I think one day I made three loaves, I don't know.

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And the funny thing is I hey, you got to keep it alive man, you got to keep alive this starter.

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I do.

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I have the starter, I keep it and I get scientific.

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Like I measure the flour, I measure the water.

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I measure the water, I stir it, I track it.

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When you keep going, you stop caring about measuring those stuff, you just do it, man.

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No, it's going well.

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And then now I'm practicing the designs, but I'm practicing.

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I think I finally have the recipe that I like.

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I don't know if it's the flour, the air or what, but this stuff.

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I fed the starter this morning and four hours later it had already doubled in size.

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It's like a ferocious flower eating.

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Yeah they're alive man, they're alive.

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I know they're alive, but I'm not going to feed this thing frigging four times a day.

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You have to, no, I can't.

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You don't have to feed it four times a day, do you?

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No, you kind of have to keep an eye on it.

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There's a point.

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It's not really more of a time, it's more like when you see it rise and you know you got to remove some of the stuff.

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Yeah, it's when it doubles in size.

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I don't know.

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Everyone says when it doubles in size you have to take it in half.

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So this thing doubled in size in four hours.

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Like, take it in half.

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So this thing doubled in size in four hours.

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Like it started, you know, you put it in, you wait 24 hours.

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Well, 10 days I, you take whatever it's seven to ten days for it to to grow finally or to become alive.

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Okay, and then the first day I did it was like a little slow.

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And then I'm like, dang, this is fast.

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So it's like 12 hours it doubled.

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And then I started baking and then I'm doing this and now it's like four hours is doubling like it's a full-time job.

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The um.

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The other obsession that I have is I've been messing with raspberry pies.

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I have that also.

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You're like a decade behind sir listen, I am an old man so I'm a little behind the times, but at least I'm behind the times and I'm able to follow, because now I am a vibe coding ai pi app creating person.

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I am creating so many things.

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I bought all these like hats to put on it about the sense hat, but the e-paper I knew nothing about python.

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I still know nothing about python, but you should see the stuff that I'm doing because you'll pick it up.

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No, I am picking it up, but ai does everything for you, right that's true.

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You see what?

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Python is such a popular thing that ai knows a lot about it.

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You can literally yeah, like you said just vibe code with Python.

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Yes, yes, I mean, I have the sensor, I'm tracking the temperature.

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I didn't know anything.

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I said okay, write something to track the temperature from the Sense hat.

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Okay, now save it to a CSV file.

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Okay, I need to display a web page.

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So I went through, installed Apache on the Pi in a Docker container.

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I have a JavaScript that reads a flat file it's our csv file of temperatures and goes which is ai.

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And what are you?

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What are the temperatures?

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Oh, this is.

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Is it the sourdough?

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Is that the temperature?

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No, I shifted.

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No, it's the temperature I track the temperature of temperature, humidity and pressure of my house okay as well as track the temperature of the system.

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So I made little graphs for it, so it was all AI driven, and with that, mr Brian.

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Sir, would you mind telling us a little bit about yourself?

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Sure can.

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So, brian Dubois, I'm the director of industrial AI for a company called Rovisis, so we are a system integrator and we are focused exclusively on manufacturing and industrial customers.

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So, you know, I like to kind of you know I love AI, I love talking about this stuff, but I do like to kind of narrow the scope somewhat so we can go anywhere with this conversation about AI.

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But you know, I don't know anything about AI in fintech, I don't know anything about AI in healthcare, but AI in the industrial space.

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I am an expert in that, both in terms of what's possible today and how we can apply AI in the industrial space today, but also kind of where things seem to be going in leveraging AI in manufacturing.

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So, yeah, happy to be here today.

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Thank you.

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Thank you for taking the time to speak to this.

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Ai is showing up everywhere and AI is one of those terms that is like oh, I know AI, or you know AI, it's so broad.

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It's just like what people used to say is oh, you're an IT guy, yeah, and they didn't understand that IT, you know, information technology, has so many different areas and AI.

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As we've been going through this journey of talking with individuals about AI, I've also learned that AI has many different facets to it.

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There's many pieces of it that comprise AI and AI.

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As you had mentioned, it's not just helpful with using the tools to create emails or creating the tools to create emails or creating the tools to.

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We can talk about generative AI towards the end.

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I know that's a favorite topic of yours, so we'll jump on that.

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But AI in the manufacturing space can you tell us a little bit about some of the advances in AI, and even now, or what we should call it, maybe within the manufacturing space?

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Yeah, and actually what to call it is an interesting question.

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So typically when I present on this, I talk about three categories of AI in the manufacturing space.

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The first one I have dubbed traditional AI.

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Now, it sounds kind of funny to talk about traditional AI in a space in an industry that is really just now starting to adopt AI, but the reality of it is is that in this category are algorithms and ML models that have actually been around in the manufacturing space for quite a while 10, 15 years.

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So this comprises things like anomaly detection.

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So if you guys are familiar with that, that's where you hook up a model to the process.

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It needs no a priori knowledge and it just starts monitoring the process and it can start to tell you when things go abnormal.

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Now, importantly, it can't tell you why things are going abnormal.

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All it can say is is based on everything I'm seeing today, it doesn't look like it did yesterday.

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So that's anomaly detection.

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When you're looking at anomalies, are you looking at anomalies for time, anomalies for output, like what are you measuring or what is a good?

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measure.

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Yeah, an anomaly detection algorithm can really do any of those things, so it can look at.

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It could be maintenance, so it could be looking at the RPMs or the current draw of a drive or something like that to determine whether or not it's the same as it was yesterday.

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It could be looking at temperatures, pressures of the process, saying things are not going the way they did yesterday.

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And it can typically.

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It can typically pick up on those, those trends, faster than a human operate, even an experienced human operator can, because it's looking for very nuanced correlations between a lot of different variables.

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So that's anomaly detection.

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Are these IoT devices that kind of collects all that data and then that's what it's doing it's just collecting all this information and see if there's any anomalies.

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It is, yeah, and it's interesting because five, seven years ago there was that big push around IoT and you guys remember that and everyone was talking about IoT.

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What's IoT?

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Internet of Things.

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Internet of Things and then there was an industrial Internet of Things, so there was iIoT that was being marketed to my industry, and the interesting thing about it is that now, fast forward five years we are not seeing the adoption of IoT like I think that like everyone thought was going to happen, and part of the reason why is that we already have sensors, we already have instrumentation, we already have all of these things and they flow through what's called the control system, which is the system that actually makes everything move and work inside the plant, and that's all been around for 30 plus years.

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So IoT was just kind of an adder on top of that.

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And there were some very specific things where it was an interesting choice to have it.

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You know it's actually Brad you brought up like humidity and temperature.

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Those are the types of things where we could slap an IoT sensor in.

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It was cheaper than trying to network all of that through the control system and great.

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So now we've got a couple extra data points that we can use, but the vast majority probably over 85% of the data coming from the plant floor comes from the existing instrumentation, sensors and things that we already have.

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So anomaly detection oftentimes to get back to your question.

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We can oftentimes just use the data sources and the data trends that exist on the plant floor today and we can send that into the anomaly detection model without having to add a whole lot of extra sensors and instrumentation.

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Another area where IoT got a lot of play was around predictive maintenance.

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So this was the idea that I can attach one of these IoT modules to a drive, to any kind of rotating equipment, and it would determine vibration, it would determine temperature, it would you know, and so and there's been some adoption of that, but not the uptick that I think a lot of people anticipated around IoT.

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So that's anomaly detection.

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We're still talking about traditional AI.

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Another category under that would be the predictive models.

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So anytime you hear the word predictive, they're pretty much all the same.

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So predictive quality, predictive maintenance, predictive set point the idea is that you're going to take large volumes of very clean, very correlated data, you're going to send them into a model and ultimately, you're going to be able to learn how to predict a single value.

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Now, that's all it will ever be able to do, right?

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So in the case of predictive quality, what's the quality of this batch going to be before I complete it?

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Right, in the case of predictive maintenance, how many days until this piece of equipment is going to go down You're going to be able to predict a single value.

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Now, importantly built into that prediction is that someone has to know what to do with that prediction, right?

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So if I can predict that the quality of this batch is going to be low, it's going to be off spec.

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Somebody has to know what to do to be able to fix that right.

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What additives do we need to increase the temperature, reduce, you know, the pressure?

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They have to know what to do with that, with that information.

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But that's the predictive category and then the final subcategory.

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Under traditional AI I typically lump in all the vision stuff.

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And again, but vision's been around for a long time in the industrial space.

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So we've been doing vision, we've been doing object detection for a long, long time.

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Even defect detection we've been doing for 15 years.

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I will say that the vision systems have advanced quite a bit in the last couple years and so we can do more with it.

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But the other thing that I kind of emphasize with clients that I talk to about this is that vision systems should really be another source of signal.

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So you should be as typical vision system.

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You should be able to get four to ten new signals coming off of that.

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Not just I detect that there's an object here, but how many of those objects and where are they placed, and maybe heat signatures and things like that, an angle of a certain thing coming through a conveyor.

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Let's get all of that data and send all of that back to the control system so that we can make better decisions with it.

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So again, that's traditional AI, and then I can talk about the other two here in a second, unless you guys had any questions about that.

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I have lots of questions with all of this because I could see within a facility the savings that they could have with incorporating AI, and you said a lot of this could be used with the existing controls that you have on the floor.

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How does it, how would one know or begin to go down this road to see how they could incorporate AI into their existing structure?

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And then another question I'll ask.

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I like to ask a lot of.

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I stack on the questions because I get excited about this.

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I hear you mentioned the word model.

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Right, this is another one of those.

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So I hear the two biggest words.

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I hear when I, when I hear uh ai, is ai or a phrase, I guess you could say and model.

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So what is a model and how does one know which is the most appropriate model to use and where do these models come from?

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Yeah, okay, so there's a lot there to unpack.

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Yes, let's address the model thing first.

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So, a model we use these terms even I use these terms kind of sloppily so we talk about AI very broadly, but I don't even know that anyone has really categorically said here's everything that's in AI and here's everything that's not in AI.

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Right, so you could throw in things like decision trees and rules-based systems.

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When I was in college, many, many years ago now, I took a course on AI and at the time neural networks were kind of out of vogue and at the time rules-based systems were what it was all about.

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So everything we learned about in this AI class was all about rules-based systems and we barely talked about neural networks.

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Now, fast forward, many decades later, and neural networks have come back very strong and are at the heart of a lot of these models.

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But ultimately, I guess, if I had to kind of very broadly look at it, a machine learning model, an ML model, is something that you can put inputs in and it's going to leverage those algorithms and then it's going to give you some kind of output back out.

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Now, typically that's in the form of a prediction, but in the case we've seen with GPT models.

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That's going to be in the form of effectively guessing what the next word is and completing those phrases and sentences with those next words.

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In the case of autonomous AI, which is the next category of AI that we're going to talk about, it is really doing, it's perceiving the current state of the system and then it's making a decision about the next best move that you can make.

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So we'll talk about that here in a second.

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I want to address another question you had, though, about where are we getting all this data from and where do people start?

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So most people underestimate the amount of data that they already have in their plant, in their facility.

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They are typically collecting orders of magnitude more data than they realize.

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If they're not collecting any data at all, then they've kind of missed the boat and they missed the messaging over the last two decades.

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So when I started I've been with Rovis's for 25 years now so when I started my career in the early 2000s, we were still educating everyone as to why they should collect this data right, and I remember I'll never forget I had a customer who said well, we just throw out the data, why would we keep this data right?

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And now, 25 years later, that sounds kind of quaint, but that was the mindset back then, like why are we going to spend this money to collect this data?

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What in the world would we ever do with it?

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Right?

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So now the good news is is that most manufacturing customers drank the Kool-Aid, and in the last two decades, they have been collecting all of that data from the plant floor.

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So they typically have lots and lots of data and, again, everything on the plant floor, especially within the last 10, 15 years, everything on the plant floor is smart, right?

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This is smart equipment, smart assets.

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It can give you.

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You know, when I started my career, we were still dealing with very, very old equipment that could maybe give you 10 data points.

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Now, every piece of equipment can give you like 100 data points.

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It can self-monitor, it can give you all kinds of information about how it's performing.

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So we've got actually lots of data.

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It's rare it does happen sometimes, but it's rare that we have to add more instrumentation or sensors.

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We typically have all the data we need to do what we want to do with it.

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Oftentimes, though, that's necessary, but it's not sufficient.

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So we've got all that data from the plant floor.

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We've captured it all in these time series databases that we call historians.

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When I started in this career I knew nothing about historians, but they are dominant in this space.

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They're time series databases and we use them all over the place and we can gather that data from those time series databases.

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But we have to bring them into, we have to build data sets out of them, and oftentimes we're taking data from the plant floor and we're mixing in IT data.

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So think of data like from the systems you guys typically deal with the dynamic systems, the ERP systems, your supply chain, from the systems you guys typically deal with the dynamic systems, the ERP systems, your supply chain, or mixing that data in to build a complete data set, a complete picture into what it took to make that particular product.

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Now, with that data set, now we can start to use that to train those ML models that we were talking about to be able to make predictions.

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So I'll give you an example of that.

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We had a customer and they do supplements, like powder supplements, and so they fill these plastic tubs with powder, right.

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And so they came to us and they said look, we've got an issue where we'll run.

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You know, we'll fill these containers right, we'll fill these containers.

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Right, you mentioned supplements.

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I just have to interrupt.

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Yeah, when you talk to them, can you tell them I'm interested in hearing this but can you tell them, when they make those containers, to not put that little lip around it so that you can't like pull the last of it out.

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Because, you fill that up.

00:18:36.185 --> 00:18:40.663
You always you could scoop it out you can scoop it out, but then at the very end, dump it out.

00:18:40.845 --> 00:18:50.291
Nor can you scoop it out, because the scoop that they put inside is not small enough to get to the little round proper shape see this is.

00:18:50.393 --> 00:18:51.978
Everybody knows what I'm talking about.

00:18:51.978 --> 00:18:53.000
I will feed all this back.

00:18:53.742 --> 00:18:56.549
Well, they should use ai how to be more efficient with that stuff.

00:18:56.589 --> 00:19:03.981
Come on, yes yes yes, so I don't waste my supplement because I can't get it out and oftentimes I like flip it upside down.

00:19:04.021 --> 00:19:11.107
I bang it because I try to pour it into the other container, because I don't have enough left and you're getting it all over the counter and I wasted it right.

00:19:11.127 --> 00:19:14.143
So if you just pass that along, I will pass that on I'll pass that on.

00:19:14.624 --> 00:19:25.492
So they're filling these containers and they're going in there and and they're running a whole batch of the powder and no problems feels, fills fine, same batch, same skew, same everything.

00:19:25.492 --> 00:19:31.530
And they start filling you know the next batch into the containers and the filler jams up.

00:19:31.530 --> 00:19:42.951
Well, when that happens, it is hours and hours of downtime for them to clear the line, clear the fillers, get everything reset and restart the process and they're like banging their head against the wall.

00:19:42.951 --> 00:19:48.026
They're like we don't understand what's going on, why these fillers are constantly jamming Again.

00:19:48.026 --> 00:19:50.565
All indications are that we're running the exact same product here.

00:19:50.625 --> 00:19:51.648
So what is the problem?

00:19:51.648 --> 00:19:56.167
So that's the kind of problem where all the easy solutions have already been tried.

00:19:56.167 --> 00:19:58.179
Right, they've tried all the easy stuff.

00:19:58.179 --> 00:20:05.320
Now they're looking to AI to try to tease apart that problem and so.

00:20:05.320 --> 00:20:10.167
But to be able to understand what the actual problems are there and get to that root cause, we've got to look at a lot of different data sources.

00:20:10.259 --> 00:20:32.073
So we've got to build data sets, like I said that, pull together data and in the end and I don't remember exactly what it was, I'm paraphrasing, but it was basically like when the raw material was from this supplier and it sat in the warehouse for this long and the humidity was this and this particular filler had not been maintained in X number of days or whatever.

00:20:32.180 --> 00:20:33.606
That's when we see this right.

00:20:33.606 --> 00:20:36.269
That's when we get this perfect storm where the filler clogs up.

00:20:36.269 --> 00:20:48.612
Well, okay, but all those things, all those data points that I just talked about you can imagine all the different data, all the different places we've got to go to actually be able to build a data set that captures that.

00:20:48.612 --> 00:20:55.431
So now we can leverage data science to actually tease that apart and find out what the root causes are.

00:20:55.431 --> 00:21:00.049
So we give them that report and they're like, oh, thank God, this is it, this is what we needed.

00:21:00.049 --> 00:21:01.701
But then we don't stop there.

00:21:01.701 --> 00:21:24.994
We take it to the next level and now we build an ML model that we can then hook up, we can operationalize, we can hook it up to the plant floor so that it can monitor when that perfect storm is coming, so that they can now have some early warning as to, hey, hang on, you're getting into that situation where you know all the pieces are falling into place, where you're going to start to get into jams on the filler.

00:21:25.816 --> 00:21:29.117
You know you had made a, you had made a comp, you made a comment about.

00:21:29.117 --> 00:21:35.472
You know, using there's already a lot of data and you can use those existing data.

00:21:35.472 --> 00:21:44.686
I think that's a big problem right now, at least in my experience, where they want to put more data in but they fail to understand what you already have.

00:21:44.686 --> 00:21:52.468
So let's start with what you already have and get the most out of that and then, like you said, slowly bring in other data points.

00:21:52.468 --> 00:21:58.786
If you are trying to solve other things, or maybe you realize, okay, we've used all this data, we need a little bit more.

00:21:58.786 --> 00:22:00.606
How can we do that?

00:22:00.606 --> 00:22:02.507
Or what other data points can we add?

00:22:02.507 --> 00:22:11.906
So that's a good call out, because I get this all the time Like we need more data, we need more data and, as you already have plenty, let's answer that first let's take that first.

00:22:12.469 --> 00:22:21.682
It's like you're, and then you're causing more variables, and then you're not like getting the right, the answer that you may be looking for or the right questions you should be asking.

00:22:21.682 --> 00:22:24.666
So I appreciate you calling that out, because it's a big problem still.

00:22:25.147 --> 00:22:26.128
And let me build on that.

00:22:26.128 --> 00:22:30.474
So you know, I typically tell customers I've got more data.

00:22:30.474 --> 00:22:36.385
There's no limit to the amount of data I can give you right.

00:22:36.385 --> 00:22:40.603
Like I said, like everything on the plant floor is smart, I can give you more data than I could overwhelm your systems with the amount of data coming from the plant floor.

00:22:40.603 --> 00:22:42.366
That's not actually what you want.

00:22:42.366 --> 00:22:46.401
So what we end up doing is we start with use cases.

00:22:46.401 --> 00:22:57.301
So we're big believers here that use cases should lead, technology should follow right, and so we identify what the use cases are like in the case of that filler problem, we keep interrupting.

00:22:57.321 --> 00:23:04.902
I'm sorry but I like that because you have to identify the problem that you're trying to solve before you solve it with technology.

00:23:04.902 --> 00:23:14.410
A lot of times, people think I can use technology because it exists, therefore I have to use it, versus using the right technology to solve the problem at hand.

00:23:15.090 --> 00:23:18.032
Just like you, Brad, when you're developing.

00:23:18.032 --> 00:23:22.757
They're just like hey, can you add this field so we can collect this data, but it's a calculation of other areas.

00:23:22.757 --> 00:23:26.742
Well, why do you have to create that field?

00:23:26.742 --> 00:23:28.948
Just calculate it from the backend side of things if you just want that data.

00:23:28.948 --> 00:23:32.446
So it's pretty wild, yeah.

00:23:32.446 --> 00:23:33.652
And then you work backwards.

00:23:33.751 --> 00:23:35.961
Once you've identified that use case, like the filler problem.

00:23:35.961 --> 00:23:48.221
Right Now we can work backwards and say, okay, we're going to need information from your warehousing system, we're going to need information from your raw materials, from your ERP system, and yeah, so then we start to build that data set.

00:23:48.221 --> 00:23:52.941
Now the good news is is that these data sets typically can answer a lot of questions.

00:23:52.941 --> 00:23:56.551
So it's not like you're building a data set and you can only ever use it for one use case.

00:23:56.551 --> 00:24:01.981
So typically you're identifying two, three, four use cases that all are going to use a very, very similar data set.

00:24:01.981 --> 00:24:08.493
Now let's build that very clean, very correlated data set and now we can start to attack these different use cases with it.

00:24:16.440 --> 00:24:18.082
My mind goes with this as a million different directions with this.

00:24:18.082 --> 00:24:27.580
It's so practical and how it can be helpful and to see and also to make sure, as you said, you figure out your use cases, which oftentimes people have a hard time identifying, right?

00:24:27.580 --> 00:24:37.494
It's also I know what I need to do, but okay, let's just throw technology at it, let's just throw this at it and you know, hope that it is solved.

00:24:37.494 --> 00:24:40.606
So, wow, so now you can.

00:24:40.606 --> 00:24:42.474
You you have the data points, which term we added?

00:24:42.474 --> 00:24:43.097
The data points.

00:24:43.097 --> 00:24:44.223
We determine the use cases.

00:24:44.223 --> 00:24:45.570
Then you can utilize technology.

00:24:45.570 --> 00:24:57.226
So you take the data sources from the different systems, from your erp system, from your control systems, and now you have this model that you made, or this model that exists.

00:24:57.226 --> 00:25:00.852
How do you tell it what to do?

00:25:00.852 --> 00:25:02.355
How does it know what to do?

00:25:03.122 --> 00:25:04.065
well and again.

00:25:04.065 --> 00:25:07.423
So, okay, so we're still in this realm of traditional AI, right?

00:25:07.423 --> 00:25:10.892
So traditional AI effectively just can answer questions.

00:25:10.892 --> 00:25:15.260
It's just going to make a prediction, so you're going to send it inputs and it's going to predict an output.

00:25:15.260 --> 00:25:20.520
That's all it ever is going to do and, importantly, it can only ever predict one output, right?

00:25:20.520 --> 00:25:28.471
So, again, in the case of that filler system, are we trending towards that issue that we have where the fillers, you know, clog up?

00:25:29.815 --> 00:26:03.409
But you're hitting on an important point there, and that's that many, most many of my clients, when they start to think about this, and even if they've got, you know, maybe an internal AI team, data science team that has started to build some models and things like that, the problem is is that no one sitting around the table knows how to actually operationalize those models on the plant floor, and that's one of the key points that I make to my customers is is that until that model is put in operation, until somebody is making decisions based on the predictions of that model, you have not seen a lick of ROI.

00:26:04.050 --> 00:26:08.449
Everything that went into that, right, was a big science, expensive science, experiment.

00:26:08.449 --> 00:26:40.169
Until it's actually on the plant floor and people are taking action based on the prediction of that model, and what that also touches on, though, is organizational change management, because now you're talking about trying to get operators and supervisors and folks who maybe have spent a lot of time on the plant floor to and they've learned to trust their ears and how the machines sound and their smell and how things look and stuff, and you're like forget all of that, put all of your trust into this AI model, right, and?

00:26:40.209 --> 00:26:51.988
that's a big lift, really quick on that, because that has always been a problem, always been, and I don't think it'll ever go away, because we know that there's a lot of forecasting tool there for you know predictable of like when to order.

00:26:51.988 --> 00:26:54.189
So demand forecasting right.

00:26:54.189 --> 00:26:55.790
It's always that problem.

00:26:55.790 --> 00:27:06.117
Anytime that I've implemented demand forecasting, there's always that one or two people in the company are like well, I've it for this long, I don't trust it and I'll never trust it.

00:27:06.117 --> 00:27:08.842
And so you, you spend all this time and money and effort.

00:27:08.842 --> 00:27:13.742
You always gonna have that one person who's like I'll not trust it, I'll make some adjustments and stuff like that.

00:27:13.742 --> 00:27:21.903
But people need to give it a chance to like, hey, let's, let's, let's give it three months, six months or a year, right?

00:27:22.365 --> 00:27:24.667
And Brad and I had a conversation about AI in general.

00:27:24.667 --> 00:27:31.064
Where do you trust enough on AI's responses and results If they make one mistake?

00:27:31.064 --> 00:27:32.990
All of a sudden, we're like, oh, we don't trust it at all.

00:27:32.990 --> 00:27:36.250
But if a person makes a mistake, it's okay for us.

00:27:36.250 --> 00:27:38.185
Ah, you made a mistake, you're human.

00:27:38.185 --> 00:27:39.390
Blah, blah, blah.

00:27:39.390 --> 00:27:40.705
You can make more mistakes.

00:27:44.140 --> 00:27:45.104
So it's like there there's a bar higher for AI.

00:27:45.104 --> 00:27:46.972
The bar is higher for AI and I'm sympathetic to those folks.

00:27:46.972 --> 00:27:57.866
Like I really have a ton of respect for the folks who run these plants and so I think that we have a good approach there, because we've been, you know, as a system integrator.

00:27:57.866 --> 00:28:03.247
We've been around for 36 years now, so we've been implementing new technology on the plant floor for a long, long time.

00:28:03.247 --> 00:28:05.311
So I think we've got a good approach there.

00:28:05.732 --> 00:28:16.807
But part of that is getting those folks engaged right from the beginning and making sure they feel like they're part of the project and making sure that they feel like this is a solution that they helped implement.

00:28:16.807 --> 00:28:24.111
That's a key aspect of getting over those objections and making sure that there's buy-in from all of them.

00:28:24.111 --> 00:28:31.589
But to your point, chris, I'm also sympathetic to the fact that, yeah, it's a higher bar with AI than it is with humans.

00:28:31.589 --> 00:28:37.726
You're investing this money, it's new technology, and the expectation is that it's going to be right pretty much all the time.

00:28:37.726 --> 00:28:41.204
Whether or not that's fair or not, that's just how humans are.

00:28:41.204 --> 00:28:43.846
So we've got to kind of work within those bounds.

00:28:44.267 --> 00:28:44.667
Yeah, yeah.

00:28:44.667 --> 00:28:59.000
But I think there's a way to get around that and we've had.

00:28:59.000 --> 00:29:03.733
You know, brad and I have conversations of industry experts where you know there's that trust relationship you have with someone that's human and you understand that they can make a mistake and all that stuff.

00:29:03.733 --> 00:29:06.119
But from the AI side, right now as it is, we kind of just take it for what it's giving you.

00:29:06.119 --> 00:29:16.232
Like, I don't know how accurate it is, but if you have a little bit of visibility of like, okay, what's the probability of the accuracy?

00:29:16.232 --> 00:29:24.192
Or what's the accuracy of this, if it's like 99% accurate and it's telling you, hey, this is 99% accurate, then I'll have a little bit of trust.

00:29:24.192 --> 00:29:32.050
But if it's coming back to say, you know, it says I'm 60% accurate, okay, maybe I need to add more data points to make it more accurate.

00:29:32.050 --> 00:29:37.432
Right now there's nothing, there's no system out there that would give me that.

00:29:37.432 --> 00:29:39.465
Currently You're just kind of like, oh, that's the result.

00:29:39.465 --> 00:29:41.627
There's some calculations done in the background.

00:29:41.928 --> 00:29:42.249
I don't know.

00:29:42.269 --> 00:29:52.244
Know it's a mathematical calculation that goes to your whole person conversation yeah, somebody comes to do service at your house, you're going to trust that they know what they're doing and they're going to be able to fix the problem.

00:29:52.244 --> 00:29:55.915
You don't know what's behind the box.

00:29:55.915 --> 00:29:58.383
I guess you could say I understand the point of it's.

00:29:58.383 --> 00:30:02.250
It's how do you, how does one build trust in anything?

00:30:02.250 --> 00:30:04.123
How do you build trust in driving a vehicle?

00:30:04.123 --> 00:30:04.199
Forget ai?

00:30:04.199 --> 00:30:04.326
How do you build trust in walking over anything?

00:30:04.326 --> 00:30:05.136
How do you build trust in driving a vehicle?

00:30:05.136 --> 00:30:05.432
Forget AI?

00:30:05.432 --> 00:30:08.068
How do you build trust in walking over a bridge?

00:30:08.068 --> 00:30:10.048
How do you build trust in everything?

00:30:10.048 --> 00:30:16.212
And that's the question and that's what we need to come up with in this case.

00:30:16.400 --> 00:30:18.587
This is, I think, a little more.

00:30:18.587 --> 00:30:23.571
I don't want to take away from your time, but you're kind of going to shoot me down a different path here for a minute.

00:30:23.571 --> 00:30:27.026
It's new technology.

00:30:27.026 --> 00:30:37.460
It's scary technology to many Because, to be honest with you, if you look at what you can do with coding and again, we've had the conversations, chris and I, with others about vibe coding what's vibe coding?

00:30:37.559 --> 00:30:48.846
Listen, people have been taking code and reading samples and doing things and putting code together for years, so that's not a new concept the reading samples and doing things and putting code together for years.

00:30:48.846 --> 00:30:48.909
So that's not a new concept.

00:30:48.909 --> 00:30:50.145
The concept is is you can do this a little bit quicker and it's coming back and it's almost like magic.

00:30:50.145 --> 00:30:50.443
Right, it's, it's.

00:30:50.443 --> 00:30:57.351
I tell people it's magic because sometimes I start typing something and it fills out lines and lines of code that I was just thinking about.

00:30:57.351 --> 00:31:03.584
So I think there's a little bit of fear in that, because nobody really understands it or knows what it is.

00:31:03.584 --> 00:31:06.708
But then how do you come to have trust?

00:31:06.708 --> 00:31:09.807
Chris, to your point, what do you need to have trusted?

00:31:09.807 --> 00:31:12.709
Because no one's going to tell you that something can be 100% correct.

00:31:12.709 --> 00:31:24.722
Right, there's too many variables on this planet where you can't have something 100% correct, because even if something's level or not level, just have a slight shift in the earth, and now you're not level and something will go off.

00:31:24.722 --> 00:31:31.053
So how do you gain trust in a system that you use?

00:31:31.472 --> 00:31:32.755
Yeah, and I think it's.

00:31:32.755 --> 00:31:35.465
The answer is it's the same.

00:31:35.465 --> 00:31:40.984
It comes back to organizational change management, which has been around for a long time, right?

00:31:40.984 --> 00:31:47.987
So we understand how to get folks to adopt new processes, how to get them to adopt new systems.

00:31:47.987 --> 00:31:50.828
None of that is new, right?

00:31:50.828 --> 00:32:07.413
Yes, it's a new tool with AI, but that's one of the things that I feel like is part of my job and the role that I have, working for an independent system integrator, is to demystify what AI is about, right?

00:32:07.413 --> 00:32:18.788
So it's a new tool in our toolbox, yes, but it's not like we have to throw out the whole playbook, the whole rule book of how to implement new systems, new processes within an organization.

00:32:18.788 --> 00:32:20.364
We know how to do that.

00:32:20.364 --> 00:32:29.573
As humans, we've been doing that consultants have been doing that for decades now, so it's just about leveraging that organizational change management to build trust in this new system.

00:32:29.573 --> 00:32:31.319
This new system happens to be very capable.

00:32:31.319 --> 00:32:35.050
It's called AI, but it is just another system that we're implementing.

00:32:36.342 --> 00:32:48.048
I will say that there is one other aspect, though, to AI and researchers are working on this, but it'll be a ways out still and that's called explainable AI, and I don't know if you guys have looked into that much.

00:32:48.048 --> 00:32:59.384
But you know, one of the challenges of AI right now is it is a black box, so it is very difficult to understand how it came to the answer that it came to Right, and that's where, chris, you were talking about.

00:32:59.384 --> 00:33:01.069
Like, I don't know if I can trust this answer or not.

00:33:01.069 --> 00:33:02.050
How did you even get to this?

00:33:02.050 --> 00:33:09.236
And that is something where, at least with a human, if they make a bad decision, you can go back with the human and you can say well, why did you say that?

00:33:09.236 --> 00:33:12.721
Well, I thought we were in this state, but it turns out we were in this state, right?

00:33:12.721 --> 00:33:15.564
So explainable AI.

00:33:15.564 --> 00:33:21.990
There's a lot of research there, and what that will give you is the ability for the AI to go back and say here's why I said what I said oh yeah, the reasoning.

00:33:22.470 --> 00:33:24.951
It's reasoning, the reasoning of how I got to this point.

00:33:24.951 --> 00:33:38.145
It's actually a really hard problem, surprisingly hard problem for AI to do, but that's where they're working on so that we can at least get that, and I think that will help with building some of that trust I did want to talk about.

00:33:38.145 --> 00:33:40.067
So we talked about traditional AI.

00:33:40.067 --> 00:33:42.567
Let's look at the next category about traditional AI.

00:33:42.567 --> 00:33:43.530
Let's look at the next category.

00:33:43.530 --> 00:33:45.798
There's three of these categories.

00:33:45.798 --> 00:33:51.865
The next one is called autonomous AI, and what autonomous AI is is, to me, this is the future of manufacturing.

00:33:51.865 --> 00:33:52.948
This is where we want to get to.

00:33:53.348 --> 00:33:59.087
Autonomous AI does, frankly, what most of my clients want AI to be able to do, and that's that it makes a decision.

00:33:59.087 --> 00:34:03.788
So it actually looks at the state of the system and it says here's your next best move.

00:34:03.788 --> 00:34:23.467
So, unlike the predictive models, where it can recognize, maybe, that there's a problem, but it doesn't know how to fix it, autonomous AI can actually work its way out of a problem, and so what it leverages is this underlying algorithm it's called deep reinforcement learning, but it's been around now for almost a decade.

00:34:23.467 --> 00:34:34.429
Algorithm it's called deep reinforcement learning, but it's been around now for almost a decade, and it allows the autonomous AI to make decisions like a human can, it can actually build long-term strategy.

00:34:34.429 --> 00:34:36.336
This all came out I don't know if you guys remember around 2016,.

00:34:36.336 --> 00:34:37.119
There was DeepMind.

00:34:37.119 --> 00:34:44.626
It was a Google spinoff and they built some tools to be able to play a program, to be able to play Go, alphago, yes.

00:34:44.927 --> 00:34:45.809
I remember that.

00:34:46.048 --> 00:34:47.811
Yes, okay, well, that didn't go away.

00:34:47.811 --> 00:34:54.217
I mean, it made a lot of press back then but then it kind of like went underground or whatever, like that didn't go away.

00:34:54.217 --> 00:35:03.925
That algorithm has made huge impacts on a lot of different industries, but including the manufacturing industry, so we have actually been adopting that and leveraging it.

00:35:03.925 --> 00:35:12.719
So I've got DRL models that are running in plants right now that are acting like expert operators, and it is like magic.

00:35:12.719 --> 00:35:26.686
It's wild to see these autonomous AI models, these agents, and what they're able to do and how well they're able to perform and, in a lot of cases, outperforming even the experts who helped train them.

00:35:26.686 --> 00:35:29.552
So autonomous AI I'm very bullish on.

00:35:29.652 --> 00:35:34.771
I do feel like that is the inflection point in this history of manufacturing.

00:35:34.771 --> 00:35:36.503
This is going to be the next big thing.

00:35:36.503 --> 00:35:38.672
Is this autonomous AI?

00:35:38.672 --> 00:35:43.425
I know everyone thinks it's going to be generative AI and, like I said, we can talk about that here in a minute.

00:35:43.425 --> 00:35:45.572
That's my third category is generative AI.

00:35:45.572 --> 00:35:49.541
Yeah, and we'll talk about that here in a second.

00:35:49.541 --> 00:35:58.342
But I really believe that autonomous AI is going to be the thing that, when we look backwards, that's going to be the thing that propelled us forward in this manufacturing journey.

00:35:58.742 --> 00:36:03.813
I think that's the case, because that's what the big focus right now is the agentic AI, right.

00:36:03.813 --> 00:36:14.248
So now there's a term where and I read this somewhere I don't remember where where you know back in the day, there's an app for that, right, there's an app for that when you got your cell phone.

00:36:14.248 --> 00:36:21.501
Now the idea is in the next, you know five years or the next year it's going to be there's an agent for that, right, there's an agent for that.

00:36:21.501 --> 00:36:30.434
So it'd be the agentic AI where it's going to be autonomous, where it's going to do things for you, removing the tedious component of that.

00:36:30.434 --> 00:36:36.943
I think that's great, but for me, it's long-term wise.

00:36:36.943 --> 00:36:46.634
That's perfect to do all those tedious work, work, but I would love to have a little bit more of a predictability where you know, not so much generating responses.

00:36:46.634 --> 00:36:54.221
It's more like I wanted to predict my life's going to be, or if I do this, what would it happen?

00:36:54.221 --> 00:36:57.061
More so than just having a conversation, like I don't.

00:36:57.061 --> 00:36:59.108
I can have a conversation with anybody.

00:36:59.940 --> 00:37:05.362
Well, and so you're starting to hit on some of the limitations of generative AI, and unfortunately so.

00:37:05.362 --> 00:37:12.887
Forbes, I think, said 2025 is the year of the AI agent, right, and so there's all this focus on agentic AI and agents.

00:37:12.887 --> 00:37:20.012
The problem is, is that the underlying technology that they're looking at is generative AI?

00:37:20.012 --> 00:37:32.340
Well, the big problem with generative AI is that and Apple proved this once and for all last year in a study that was released October of last year generative AI can't reason.

00:37:32.340 --> 00:37:39.974
That's a pretty big limitation of any AI system is so it can't reason.

00:37:39.974 --> 00:37:41.923
It does not understand causal effects.

00:37:41.923 --> 00:38:01.460
They were giving it simple math problems and it could not reason through these simple math problems, let alone the types of problems like you're talking about, chris these big, complex problems Think about political problems, think about big legal problems, think about these big problems that humans face and generative AI struggled to reason about the simplest math problems.

00:38:01.460 --> 00:38:08.929
So all of the intelligence that we attribute to generative AI is actually us just projecting intelligence onto it.

00:38:08.929 --> 00:38:10.085
It is very, very dumb.

00:38:10.085 --> 00:38:14.206
Generative AI is good at one thing, and that's guessing what the next word is.

00:38:14.206 --> 00:38:21.612
It is effectively an autocomplete on steroids, and I hate to pull the curtain back for those folks who don't realize that.

00:38:21.612 --> 00:38:23.588
But that's all that it is.

00:38:23.588 --> 00:38:43.601
And so when we the challenge is that these agentic AI, what they're doing is they're having generative AI effectively similar to writing a program lay out a script of what it should do to try to accomplish that task, and then you can hand that script off to something else to actually run the code, right?

00:38:43.601 --> 00:38:54.742
Well, the problem is that, I mean, it's so limited in what it can generate and it really can only ever generate things that it's seen in the past Like it has to be.

00:38:54.742 --> 00:38:56.889
It has to have seen something like that.

00:38:56.889 --> 00:39:00.329
Now it's been trained on vast volumes of human information, right?

00:39:00.329 --> 00:39:06.465
So it's seen a lot, but it still is not going to be able to get creative, it's not going to be able to work around certain problems.

00:39:06.465 --> 00:39:11.030
And then you mix into that the problem that it has with what are called hallucinations.

00:39:11.030 --> 00:39:17.672
And so for your listeners who are not familiar with hallucinations, what that is is that's where the generative AI just makes stuff up.

00:39:17.672 --> 00:39:23.409
It just makes it up out of whole cloth, and you can't tell the difference between what's made up.

00:39:23.409 --> 00:39:28.260
It up out of whole cloth and you can't tell the difference between what's made up and what's real.

00:39:28.260 --> 00:39:29.541
And I'll give you a couple examples of that.

00:39:29.561 --> 00:39:31.405
I was using it the other day.

00:39:31.405 --> 00:39:32.788
We were my daughter and I.

00:39:32.788 --> 00:39:34.452
We were going to generate a playlist together.

00:39:34.452 --> 00:39:41.143
And so I go into ChatGPT, I'm like generate a playlist of I don't remember what it was beach songs or something like that and so it generated like 25 songs.

00:39:41.143 --> 00:39:44.048
And so we're we're starting to program these into a Spotify playlist.

00:39:44.048 --> 00:39:46.534
And we get to this one and we're looking, we're searching for the song.

00:39:46.534 --> 00:39:49.226
I'm like I don't, I am not finding this song anywhere.

00:39:49.226 --> 00:39:51.371
So I go back to generative AI.

00:39:51.371 --> 00:39:53.824
I go this song here, did you make that up?

00:39:53.824 --> 00:39:56.552
And it's like, yeah, thanks for pointing that out.

00:39:56.552 --> 00:40:01.987
I actually did make that up, and this happens way more than people realize.

00:40:02.568 --> 00:40:06.355
A more potent example happened in May of this year.

00:40:06.355 --> 00:40:15.914
The Chicago Times published in one of their Sunday circulars they had kind of a fluff piece.

00:40:15.914 --> 00:40:17.679
It was the summer reading list for 2025.

00:40:17.679 --> 00:40:26.400
I don't know if you guys saw about this, but it was their summer reading list for 2025, right, this.

00:40:26.400 --> 00:40:28.204
But it was their summer reading list for 2025, right.

00:40:28.204 --> 00:40:37.032
And well, someone recognized pretty quickly and posted on Twitter or X posted that it turns out that out of the 15 books in that list that it had generated, only five of those books actually existed.

00:40:37.032 --> 00:40:41.606
The other 10 books were completely made up Now remember this was not.

00:40:41.945 --> 00:40:46.121
This was an article in the in the Chicago Times, you know Sunday Circular.

00:40:46.121 --> 00:40:49.690
So of course the Chicago Times was embarrassed.

00:40:49.690 --> 00:40:54.228
They went back and they did research into what happened there.

00:40:54.228 --> 00:40:56.132
They interviewed the author.

00:40:56.132 --> 00:41:06.123
The author of the article did admit he had used ChatGPT or ChatGPT to generate the article and he had not bothered to double check that any of those books actually existed.

00:41:06.123 --> 00:41:09.753
His editor didn't bother to check that any of those books actually existed.

00:41:09.753 --> 00:41:12.568
So this is a real problem.

00:41:12.568 --> 00:41:20.170
And so when people talk about agentic AI and they're really stoked about it, I'm like, oh boy, that is very, very dangerous.

00:41:20.170 --> 00:41:31.440
To start giving this technology that can just make stuff up and go down these really weird tangents and giving it the ability to actually execute that code itself makes me very, very nervous.

00:41:31.500 --> 00:41:35.251
I'll throw a twist in this, though, just for the conversation.

00:41:35.251 --> 00:41:36.525
How is that different than a person?

00:41:36.525 --> 00:41:39.867
Because it goes back to what I was saying.

00:41:39.867 --> 00:41:41.860
We talked about the trust you talked about.

00:41:41.860 --> 00:41:44.166
It doesn't have the ability to reason.

00:41:44.166 --> 00:41:47.313
To be honest, with that question, I question a lot of people.

00:41:48.603 --> 00:41:58.653
I was going to say the same thing, and also you think of creativity and human creativity and what humans put together.

00:41:58.653 --> 00:42:04.639
A lot of times we put together things based on what we think, we know or what we remember of what we've experienced.

00:42:04.639 --> 00:42:12.994
So if you're saying you're feeding off this information to AI, is it its lack of reasoning or is it lack of being able to experiment and gauge the results?

00:42:12.994 --> 00:42:21.791
Because if you're coding, you're saying you can say create a script, give me a script, I can say that to Chris, and Chris can give me something and it may not work.

00:42:21.871 --> 00:42:23.686
It may work, it may be whatever.

00:42:23.686 --> 00:42:25.313
And Chris can give me something and it may not work.

00:42:25.313 --> 00:42:26.340
It may work, it may be whatever.

00:42:26.340 --> 00:42:30.099
And the only way we know is we have to test it to make sure that it works properly based upon the requirements that we were given.

00:42:30.099 --> 00:42:41.951
So I think I'll always go saying is AI is a tool like any other tool and people need to realize that, and then, with certain things, it may do better.

00:42:41.951 --> 00:43:07.695
I guess you can say, and some other things it may not, but you still just as if I'm not going to have a bunch of people build the jet without having somebody do an inspection to make sure that the jet was built properly or put together properly, so there are things that someone should do when it comes to AI to make sure that whatever they're using is sound.

00:43:08.501 --> 00:43:23.949
Well, so I'll say there's two issues there where I think that makes it distinct from just going to your assistant and saying can you do this for me, can you book this for me, or can you write this code for me To a, to a human assistant.

00:43:23.949 --> 00:43:25.956
I mean, there's two kinds of distinctions there.

00:43:25.956 --> 00:43:42.166
One is is the perception of performance, and right now, because we're on that you know hype cycle, all these folks that you're talking about, who would use this tool, are being told by the media that AI is the, is here, the future is here, it's going to do everything you want it to do.

00:43:42.166 --> 00:43:52.831
We've got these agents now that do these amazing things, so, and the vendors, honestly, are incentivized to say that right, these AI vendors are incentivized to create that perception.

00:43:52.831 --> 00:43:59.001
So we've got this perception, this incorrect perception, that's being pushed down to the masses and most people.

00:43:59.001 --> 00:44:13.039
It's funny like I thought this was so well known and I'm shocked at more and more, when I talked to people that they did not realize I was talking to my mom this was last month and I was talking to her about chat, gpt, and I'm like now you, you know that it can make stuff up right.

00:44:13.039 --> 00:44:14.262
She's like what do you mean it can make stuff up.

00:44:14.262 --> 00:44:23.233
I'm like it can just make stuff up, like it'll make facts up and it will give the appearance that those are correct and it will not be correct.

00:44:23.233 --> 00:44:26.126
It will just make stuff up and she's like I didn't know you could do that.

00:44:26.126 --> 00:44:27.862
So that's a problem.

00:44:27.862 --> 00:44:33.893
When you have the masses leveraging these tools without understanding what the limitations are.

00:44:33.893 --> 00:44:37.190
All they get is this little thing at the bottom that says AI can make mistakes, double check its work.

00:44:37.190 --> 00:44:37.670
Right, that's it.

00:44:37.670 --> 00:44:44.813
That's all we get, not this deep analysis of no, it can make really big mistakes and say really, really misleading things.

00:44:44.813 --> 00:44:47.402
That's the first problem.

00:44:47.402 --> 00:44:54.106
The other problem and this is really a big issue is the hubris of the models themselves.

00:44:54.106 --> 00:44:56.050
So ChatGPT will freak.

00:44:56.612 --> 00:45:00.985
I'm a heavy user of ChatGPT so I run up against its limitations pretty frequently.

00:45:00.985 --> 00:45:04.233
It will frequently tell me it can do things it can't do.

00:45:04.233 --> 00:45:13.900
Now I don't know, like if you had a personal assistant that you hired and they came in through the interview process and they said, yeah, I know how to do this and I knew how to do this and I can do this.

00:45:13.900 --> 00:45:16.023
And I, yeah, I've done that a million times.

00:45:16.023 --> 00:45:19.148
Whatever, it's not going to take you very long to realize.

00:45:19.148 --> 00:45:24.014
If they were just full of it and they were just, you know, and they get in there.

00:45:24.014 --> 00:45:27.166
And it was like fake it till you make it and they get in there and they can't do any of those things.

00:45:27.166 --> 00:45:28.826
You're like you don't even know how to use Excel.

00:45:28.826 --> 00:45:30.626
You don't know, you don't know how to use any of these tools.

00:45:30.626 --> 00:45:32.146
You said in your interview process you could do it.

00:45:32.146 --> 00:45:37.943
That's the level of hubris that these models have so frequently.

00:45:38.003 --> 00:45:40.704
Chatgpt and I mean this was just from a couple days ago.

00:45:40.704 --> 00:45:48.070
I had an existing PowerPoint presentation it's a training I do and I said I don't like the layout, the flow of this presentation.

00:45:48.070 --> 00:45:53.253
So I fed it the presentation and I said can you help me kind of reorganize this so it kind of flows better?

00:45:53.253 --> 00:45:54.773
Right, and so it did.

00:45:54.773 --> 00:45:58.737
And it gave me this nice outline and said, okay, what if you move this slide up here?

00:45:58.737 --> 00:46:01.362
And it was great, great, okay, awesome.

00:46:01.702 --> 00:46:06.961
Then it says would you like me to reorder that PowerPoint presentation for you and get all these slides?

00:46:06.961 --> 00:46:08.184
And I can do all that for you.

00:46:08.184 --> 00:46:10.489
I'm like, really, you can?

00:46:10.489 --> 00:46:14.728
Okay, sure, and it just corrupted the PowerPoint completely.

00:46:14.728 --> 00:46:28.983
It can't do it, but it, with all the confidence in the world, said yeah, I could absolutely do that For sure, give it to me, I can do whatever you need me to do with it.

00:46:28.983 --> 00:46:29.445
Right, that's a problem.

00:46:29.445 --> 00:46:30.969
That's a problem when the AI itself doesn't understand its own limitations.

00:46:30.969 --> 00:46:33.418
That's how we get ourselves into some really bad places.00:46:33.418 --> 00:46:35.083


So it's twofold.00:46:35.083 --> 00:46:36.746


It's yes, it's the education side.00:46:36.746 --> 00:46:39.420


The masses are on that hype cycle and we'll get to what is it?00:46:39.420 --> 00:46:44.132


The trough of disillusionment, eventually, and people start to realize the limitations.00:46:49.639 --> 00:46:50.521


But the other big problems.00:46:50.521 --> 00:46:51.744


The AI itself is overpromising and underdelivering.00:46:51.744 --> 00:46:52.425


Over and over again.00:46:52.425 --> 00:47:06.204


I think, from a personal use, yes, I see that being a big problem, but maybe and again, this is just my opinion from an enterprise level, from a business level is from my, just my opinion from an enterprise level.00:47:06.204 --> 00:47:16.509


From a business level, you can ground those models to know like here, here's your limit, just work within these bounds of these information that you're given so you can minimize the risk.00:47:16.619 --> 00:47:20.121


I'm not saying eliminate the risk entirely, and that's also.00:47:20.121 --> 00:47:22.148


I mean you could do the same thing with ChatGPT.00:47:22.148 --> 00:47:29.114


You can create a parameter of like this is the type of what you are, only work here in this space, feed it whatever you want to feed it.00:47:29.114 --> 00:47:37.653


So you could do that and limit some of those risks in creating those limits for that AI model.00:47:37.653 --> 00:47:44.983


But it requires a little bit of work and it requires people to understand that requires a little bit of work and it requires people to understand that.00:47:44.983 --> 00:47:49.211


Unfortunately, a lot of people were sold, like you said, in the media, where it's going to solve all your problems.00:47:49.211 --> 00:47:54.940


Well, that's not the case, because you still have to understand the tool, as what Brad mentioned.00:47:54.940 --> 00:47:59.717


You have to understand that it is a tool and a tool could be used incorrectly.00:47:59.717 --> 00:48:04.300


Can they still use it Absolutely, but you can certainly use it incorrectly.00:48:04.380 --> 00:48:04.762


And there are.00:48:04.762 --> 00:48:08.494


You know it's not like the LLM researchers.00:48:08.494 --> 00:48:10.943


The generative AI researchers don't know about this problem, right?00:48:10.943 --> 00:48:12.708


They are actively looking into it.00:48:12.748 --> 00:48:15.887


And, to your point, chris, there's a technology to ground it.00:48:15.887 --> 00:48:22.588


It's called RAG retrieval augmented generation where, basically, it forces the GPT to cite its source.00:48:22.588 --> 00:48:29.186


You have to give me where, in whatever manual or whatever you read that you've got to give me a chapter and verse.00:48:29.186 --> 00:48:40.210


You got to point to where and you've seen this now ChatGPT has incorporated this where it will occasionally give you sources, right, so that it can give you an idea of where it found that information.00:48:40.210 --> 00:48:42.722


But there are limitations to RAG.00:48:42.722 --> 00:49:03.487


One of the big limitations in the enterprise setting is that RAG relies on comprehensive enterprise search, which is a problem that we've been trying to solve for 20 plus years now, and nobody really has a good handle on enterprise search, and RAG relies on that to be able to find the sources of its information.00:49:03.487 --> 00:49:05.030


Wag relies on that to be able to find the sources of its information.00:49:05.030 --> 00:49:06.050


So there's limitations there.00:49:06.050 --> 00:49:08.554


But to your other point, chris, like when you?00:49:08.554 --> 00:49:10.476


So let's take this now to the plant floor.00:49:11.719 --> 00:49:15.110


This is why I when you go back to, can you explain what you mean by enterprise search?00:49:16.079 --> 00:49:23.663


Yeah, like I'm just saying, like enterprise search, like trying to find, like when's the last time you used enterprise search to try to find something on your network?00:49:23.663 --> 00:49:25.284


Did you find it the first time you searched for it?00:49:25.284 --> 00:49:27.027


Did you find it the second time you searched for it?00:49:27.027 --> 00:49:31.231


Did you finally give up trying to find that document and you just recreated the thing from scratch?00:49:31.231 --> 00:49:41.503


Like Enterprise Search is one of those really hard problems that we really still have not solved and we don't have good answers for Enterprise Search.00:49:41.503 --> 00:49:52.648


People just live with the fact that enterprise search kind of sort of works, and RAG, which is that technology you're talking about, where you're forcing the generative AI to cite its source, relies on good, solid enterprise search.00:49:52.648 --> 00:50:02.485


I've seen these AI vendors' architectures and they'll lay out the whole thing and there's a box there and in order for it to cite its sources, there's a box there that says enterprise search.00:50:02.485 --> 00:50:03.547


I'm like, no, wait a minute here.00:50:03.547 --> 00:50:06.813


That's not a solved problem by any stretch of the imagination.00:50:06.813 --> 00:50:11.811


Enterprise search is not very good still 20 plus years later, of us trying to solve that problem.00:50:11.811 --> 00:50:15.771


So, yes, there are things that we can do and we're going to continue.00:50:15.771 --> 00:50:16.985


It's going to continue to get better.00:50:16.985 --> 00:50:19.364


I know that, but as of today.00:50:19.364 --> 00:50:25.539


So let's take it back to the plant floor day.00:50:25.539 --> 00:50:27.063


So, let's take it back to the plant floor.00:50:27.063 --> 00:50:28.847


This is why I'm not a big proponent for leveraging generative AI on the plant floor.00:50:28.867 --> 00:50:42.423


Yet, because of these major limitations and to your point, chris, the one person who's going to be using this so let's say, you're trying to solve a maintenance problem on the plant floor the person who's going to be using this is the one person who knows the least amount about it.00:50:42.423 --> 00:50:42.664


Right?00:50:42.664 --> 00:50:46.795


Because that, obviously, if this was the expert, they would just go fix the problem.00:50:46.795 --> 00:50:47.538


Right?00:50:47.538 --> 00:51:03.510


We're talking about and the AI vendors are selling this vision of being able to get your least experienced maintenance person, who's been there for three weeks, giving him or her a chat bot that they can ask questions about how to fix this piece of equipment, and it could just make stuff up.00:51:03.510 --> 00:51:04.572


It'll just make stuff up.00:51:04.572 --> 00:51:05.335


It'll just make stuff up.00:51:05.335 --> 00:51:07.769


It'll say whether or not it knows how to fix that particular piece of equipment.00:51:07.769 --> 00:51:09.804


It'll say, yeah, I know exactly how to fix that.00:51:09.804 --> 00:51:15.101


Here's what you're going to do You're going to torque this bolt and you're going to rev this thing and you're going to add this additive in and you could blow up the plane.00:51:15.101 --> 00:51:15.963


You could kill somebody.00:51:16.402 --> 00:51:21.588


Yeah, that I always share about using AI.00:51:21.588 --> 00:51:27.376


It's like having an autopilot on flying a plane, right?00:51:27.376 --> 00:51:34.652


So you trust it that it's going to take you from one destination to another and it will adjust accordingly and all that stuff.00:51:34.652 --> 00:51:41.032


But if that doesn't work, you, as a pilot, should know how to fly it manually.00:51:41.032 --> 00:51:42.929


You should still know how to learn to do that.00:51:42.929 --> 00:51:55.103


No different than a developer that uses a AI to help develop a software, you can use AI to get all the stuff, but you should still understand what it's doing.00:51:55.103 --> 00:52:01.081


That should be the initial approach of any AI uses in your business.00:52:01.081 --> 00:52:02.806


That should be a core.00:52:02.806 --> 00:52:05.592


So that's an important component.00:52:06.541 --> 00:52:08.525


And that autonomous AI that I was talking about.00:52:08.525 --> 00:52:10.472


That's like building an autopilot.00:52:10.472 --> 00:52:18.940


It's like building an autopilot for that part of the process and it will look over your shoulder and make recommendations and say you should do this, you should do this, you should make this change.00:52:18.940 --> 00:52:25.994


But, importantly, like a real autopilot, a real autopilot will kick off when it recognizes that it's outside of its operating parameters.00:52:25.994 --> 00:52:35.449


Autonomous AI, unlike generative AI autonomous AI can say I wasn't trained to do this, you're back in control, I'm kicking off.00:52:35.449 --> 00:52:44.094


You need to take back control of the process so it can hand that operations back to the operator when it recognizes it's over its skis.00:52:44.094 --> 00:52:46.663


Generative AI doesn't it just?00:52:47.224 --> 00:52:49.824


makes stuff up, so from your world.00:52:49.824 --> 00:52:57.072


Then, brian, when you're working on the manufacturing warehouse, you know machine learning and IoT, things like that.00:52:57.072 --> 00:53:03.789


What specific model or AI model are you using?00:53:03.789 --> 00:53:13.302


If you can share that, I don't know if you could if it's a proprietary thing.00:53:13.322 --> 00:53:13.945


No, no, no, yeah, it's not at all.00:53:13.945 --> 00:53:16.277


So it's a frequent question I get and it's a misunderstanding of how we're approaching these problems.00:53:16.277 --> 00:53:18.463


We have no a priori models that we're bringing to the table.00:53:18.463 --> 00:53:23.445


We are building these models for each customer, and there's a couple of reasons why we do that.00:53:23.445 --> 00:53:34.847


A every customer's data is different, every customer's equipment is different, their processes are all different, so it's really kind of impossible for us to build this library of models that are going to apply in all these different situations, right?00:53:34.847 --> 00:53:37.686


So A it's not even possible really, anyway.00:53:37.686 --> 00:53:42.324


So we're always starting from the data, building those data sets and then generating models off of them.00:53:42.324 --> 00:53:57.748


As far as what the underlying algorithm is underneath the model, honestly, in this day and age, you don't really have to even worry about that, whether or not it's a decision tree under the covers, or it uses a neural network, or I mean there's all kinds of different deep forest search.00:53:57.748 --> 00:54:01.190


I mean there's all kinds of crazy different algorithms under the covers.00:54:01.190 --> 00:54:02.405


You don't even have to worry about that.00:54:02.405 --> 00:54:09.507


The systems themselves that train these models already will pick the right one.00:54:09.507 --> 00:54:11.217


They'll run tests against each other and find the one that's the most performant.00:54:11.217 --> 00:54:12.943


All of that happens automatically under the covers.00:54:12.943 --> 00:54:21.329


So now we've got a trained model that, again, black box inputs come in and then it's going to make predictions coming out of it.00:54:21.329 --> 00:54:25.393


Or, in the case of autonomous AI, perceptions come in, decisions come out, coming out of it.00:54:25.393 --> 00:54:31.081


Or, in the case of autonomous AI, perceptions come in, decisions come out.00:54:31.081 --> 00:54:31.623


The other side of it.00:54:31.643 --> 00:54:40.266


The other aspect of that, though, is that one of the most common questions I get is everyone asks are you going to use my data to make your models better and then hand it over to one of my competitors, right?00:54:40.266 --> 00:54:41.128


So?00:54:41.128 --> 00:54:46.242


And the answer to that is no, like we're starting from scratch.00:54:46.242 --> 00:54:47.726


We're building these models for you, mr Customer.00:54:47.726 --> 00:54:49.931


You get to keep that model when it's all done.00:54:49.931 --> 00:54:50.961


You have all the IP.00:54:50.961 --> 00:54:54.309


We don't ever leverage any of your data to train other models.00:54:54.369 --> 00:54:59.802


Now, hilariously, then, they typically follow up and ask that question that you just asked, chris, like do you have a bunch of models?00:54:59.802 --> 00:55:01.605


Then Are you going to have to start?00:55:01.605 --> 00:55:03.128


I'm like no, I don't have a bunch of models.00:55:03.128 --> 00:55:05.231


It's the same rules for you as for everyone else.00:55:05.231 --> 00:55:09.246


You don't want me taking your model and giving it to your competitors.00:55:09.246 --> 00:55:10.509


It's the same thing back to you.00:55:10.509 --> 00:55:12.784


So, no, we don't do anything like that.00:55:12.784 --> 00:55:17.903


We always start from scratch and we build these models from scratch for each customer and that's not a huge lift.00:55:17.903 --> 00:55:19.487


It's not as big of a lift as it sounds.00:55:19.487 --> 00:55:23.974


In fact, the data science to be able to get that data in a state.00:55:23.974 --> 00:55:31.233


Typically on these projects, the data science is typically 75, 80% of the effort is the cleansing the data.00:55:31.233 --> 00:55:32.983


It's the unsexy part of AI, right?00:55:32.983 --> 00:55:40.835


You're cleansing the data, you're getting rid of bad data, you're eliminating rows with empty cells in them and things like that.00:55:40.835 --> 00:55:45.141


So, getting all of that data right, that's where the effort is Actually.00:55:45.141 --> 00:55:46.907


Training the model doesn't take that long at all.00:55:47.460 --> 00:55:47.940


So you're running.00:55:47.940 --> 00:55:50.568


So is this kind of a big data world then?00:55:50.568 --> 00:55:56.367


So if you're pulling all this data, it's a big data, yeah, so what database does it go into, is it?00:55:56.367 --> 00:55:58.411


And certainly I hope it's not SQL.00:56:01.487 --> 00:56:21.179


Well, so you know, I don't know how familiar you guys are with the world of data science, so it's a pretty established world right now and so they have tools that they're using already and typically, yes, you're working with hundreds of thousands, maybe even millions of rows, but you're typically working with it in Python types of environments.00:56:21.179 --> 00:56:24.469


You're typically working with it in what are called Jupyter Notebooks.00:56:24.469 --> 00:56:27.047


So these are established tools.00:56:27.047 --> 00:56:33.639


You're using pandas, You're using some of these established data science tools and then, yeah, I mean you can certainly leverage.00:56:33.639 --> 00:56:35.347


It is a big data type problem.00:56:35.347 --> 00:56:44.972


So there's tools like Microsoft Fabric and there's Databricks and there's Snowflake and there's some tools behind the scenes that can help when you're working with that volume of data.00:56:44.972 --> 00:57:00.092


The other thing that's really interesting that most people do not talk about and I like the Databricks and, by extension, the Microsoft Fabric, because it leverages the Delta format, but I like their approach to it and that is versioning these datasets.00:57:00.092 --> 00:57:11.344


So, just like you, version code, when we're building these datasets, we build them, we test them, we do some preliminary modeling, we run some algorithms on them to determine are they predictive, Are there gaps in the data?00:57:11.344 --> 00:57:26.193


We run some heuristics and things like that, and then we'll go and we'll modify, we'll do some more data science and we'll modify that dataset, that history, that evolution of that set over time.00:57:26.512 --> 00:57:28.681


You want to version that, right.00:57:28.681 --> 00:57:42.771


You don't want to get back to the old days, like we used to do with versioning source code, where you're like putting the date and then the time of that source code file and you know, as you're changing that source code file, you know you're losing track of which one was which.00:57:42.771 --> 00:57:43.300


Like.00:57:43.300 --> 00:57:53.452


You want to version that data set, and so tools like Databricks, tools like Fabric, have a really nice method of actually versioning those data sets over time as they evolve.00:57:53.452 --> 00:58:07.880


And that's what you want to do, Because if you find out that you introduced bad data or you somehow, you know, in your effort to cleanse this data, you screwed up the data, you need to be able to go back a couple of versions and say, oh, here's what we did wrong and then be able to play forward from there.00:58:07.880 --> 00:58:10.286


So this again, this is all.00:58:10.286 --> 00:58:11.911


We haven't even gotten to AI, right?00:58:11.911 --> 00:58:15.606


This is just all the data science stuff that it takes to get to a good ML model.00:58:17.489 --> 00:58:24.643


See, this is what I was saying is people think of AI.00:58:24.643 --> 00:58:32.784


They just think of like just said is oh, write me an email to say I'm sorry for missing your party on Friday and it will generate a response for you A nicer response.00:58:33.561 --> 00:58:35.389


They have not gone to your stupid party Sometimes.00:58:36.460 --> 00:58:37.766


Listen, I've been doing this for a while.00:58:37.766 --> 00:58:41.090


I told you I've moved to use Grok.00:58:41.090 --> 00:58:49.505


I think Grok is my friend now, and AI with Grammarly have seemed to solve a lot of my problems.00:58:49.706 --> 00:58:53.360


Well, and this is the blessing and the curse for me of generative AI, right?00:58:53.360 --> 00:58:56.927


So we started this industrial AI division in 2019.00:58:56.927 --> 00:59:00.222


Now, no one had even heard of LLMs in 2019, right.00:59:00.222 --> 00:59:05.353


And so we were doing this AI stuff with customers and we were talking about building big data sets and building models and whatever.00:59:05.353 --> 00:59:11.610


Then, november of 2022, suddenly the world gets introduced to ChatGPT, right.00:59:11.610 --> 00:59:17.865


And now my phone's ringing off the hook because everyone wants to talk about AI, but it's the wrong AI.00:59:17.865 --> 00:59:26.815


They want to talk about this generative AI, and I'm like that's great, but let's talk about AI that we can actually implement today on the plant floor and have a big impact.00:59:26.815 --> 00:59:32.170


So it's my blessing and my curse it gets me in the door, but then I typically pivot pretty quickly to other types of AI.00:59:33.719 --> 00:59:35.503


Yeah, there's a lot to it.00:59:35.503 --> 00:59:56.246


One of the things that I don't want to take away from the topic that we're having, but I think of this and I think from the business point of view I don't want to take away from the topic that we're having, but I think of this and I think from the business point of view, when we deal with AI and it's even myself it's sometimes is AI can do so much.00:59:56.246 --> 00:59:57.829


As Chris started talking about the agents, you talked about autonomous.00:59:57.829 --> 00:59:58.990


You talked about the predictive.00:59:58.990 --> 01:00:05.826


We talked about all these different things and sometimes how does someone come up with where they can apply this?01:00:05.826 --> 01:00:09.742


It's more of so.01:00:09.802 --> 01:00:13.735


What are some things that somebody could do to make their business a little more efficient?01:00:13.735 --> 01:00:14.641


We talked about it in.01:00:14.641 --> 01:00:17.237


You talked about industrial manufacturing, manufacturing.01:00:17.237 --> 01:00:18.280


You have the tools to help that.01:00:18.280 --> 01:00:20.286


They can see predictive maintenance.01:00:20.286 --> 01:00:22.954


The other example that you had was a really good example.01:00:22.954 --> 01:00:29.317


You could see predictive failures of output for due to external conditions and such.01:00:29.317 --> 01:00:34.045


But how does, how does that journey even begin?01:00:34.045 --> 01:00:37.577


Because there's so much to this.01:00:37.577 --> 01:00:44.050


To be honest, it sounds overwhelming to me, and it's not this conversation, it's just all this.01:00:44.050 --> 01:00:51.304


Oh, you can use agents that can do all this, and then you have an agent that manages all the other agents and it's like what do I do?01:00:52.106 --> 01:00:55.505


Yeah Well, so we'll bring it full circle back to the beginning of our conversation.01:00:55.505 --> 01:00:59.117


We talked about use cases, so it's still the same answer Use cases.01:00:59.117 --> 01:01:01.641


Lead, technology follows, so it's still the same thing with AI.01:01:01.641 --> 01:01:10.371


So what we do is we typically sit down with the customer and we talk about where can they leverage AI and what are the use cases that we can use.01:01:10.371 --> 01:01:14.766


Now, it's kind of a chicken and egg thing, because they don't always know what the state of the art is.01:01:14.766 --> 01:01:20.063


So we prime the pump, we teach them just enough to be dangerous about AI.01:01:20.063 --> 01:01:28.182


We typically it's a couple hour long workshop that we do, and then now okay, now they can start to.01:01:28.182 --> 01:01:35.260


Now the wheels are turning and they're starting to say, oh, I get where that could apply, and we're showing them use cases that other customers have done.01:01:35.260 --> 01:01:37.742


Here's where other customers have found success with AI.01:01:37.742 --> 01:01:40.882


So now they're starting to say, okay, now I see how that could apply here.01:01:40.882 --> 01:01:45.119


And so they start saying, you know, what about if we used AI here?01:01:45.119 --> 01:01:46.442


What about if we use AI there?01:01:46.442 --> 01:01:57.684


And then they're sitting down with experts you know in this, and so we can very quickly say, yes, that would be a perfect use of AI, or that's more like Skynet, and we're not there.01:01:57.684 --> 01:02:13.972


So we can very quickly, you know, separate the wheat from the chaff, and we can get to here's some you know, here's 5, 10, 15, really high value projects that we could tackle right now leveraging AI that exists today.01:02:13.972 --> 01:02:15.978


That would have a huge impact.01:02:15.978 --> 01:02:27.715


And then we work with them to figure out what's the ROI of those projects, right, and so then we and then, of course, we got to get justification, all that, and so finally, then we actually can attack that project and we can make it a success.01:02:27.715 --> 01:02:29.179


And then it's just rinse and repeat.01:02:29.179 --> 01:02:33.518


You grab the next project off the list and you, you go through that process again.01:02:33.518 --> 01:02:35.764


So that's, you know.01:02:35.864 --> 01:02:45.202


One of the things that I always tell you know customers this is actually how I wrap up when I'm speaking at trade shows is take that first step right.01:02:45.202 --> 01:02:55.623


I know that not everyone feels like they're ready to take on an AI project, but whether or not you feel like you're ready or not, one of your competitors is probably taking that first step.01:02:55.623 --> 01:03:01.603


So you need to take that first step and even if you don't want to go full AI, then at least do some AI readiness right.01:03:01.603 --> 01:03:02.920


How does your infrastructure look?01:03:02.920 --> 01:03:04.063


How's your data infrastructure?01:03:04.063 --> 01:03:05.501


How's your networking infrastructure look?01:03:05.501 --> 01:03:06.141


What's your infrastructure look?01:03:06.141 --> 01:03:06.902


How's your data infrastructure?01:03:06.902 --> 01:03:07.882


How's your networking infrastructure look?01:03:07.882 --> 01:03:08.344


What's your data story?01:03:08.344 --> 01:03:15.030


Do you have all of these things in place so that when your organization is ready to take on AI, that you've got all the building blocks ready?01:03:15.030 --> 01:03:18.862


So even if you just want to take on AI readiness, that's fine, but take that first step.01:03:19.143 --> 01:03:33.146


But the second thing I tell them, and I said I always say I know it sounds self-serving, but get an independent system integrator involved, because it is complicated and there are so many ways to skin this cat and there's so many different paths you can go down.01:03:33.146 --> 01:03:42.643


And if you just go to the AI vendor, they have a hammer and everything's got to look like a nail right, so they've got one tool that they can use to solve every problem.01:03:42.643 --> 01:03:54.849


So, whereas with an independent SI like I've got all the tools in the world right, and sometimes we have these conversations and we identify a use case and it turns out like maybe that's not even really an AI use case.01:03:54.849 --> 01:04:03.217


Maybe we can solve that with just some visualization and some existing analytics off the shelf analytics without having to go full AI to solve that problem.01:04:03.217 --> 01:04:06.483


So we can go anywhere where the conversation needs to.01:04:06.483 --> 01:04:08.547


We can go anywhere where those use cases lead us.01:04:08.547 --> 01:04:18.400


And so, yeah, have that initial conversation with someone who's an independent expert in this field and have them help you build a roadmap on how to get to where you wanna go.01:04:18.695 --> 01:04:23.501


The other thing is is that once we've built that roadmap, now we can start to bring vendors in.01:04:23.501 --> 01:04:27.721


And now we can start to, because ultimately it is gonna be running on some platform.01:04:27.721 --> 01:04:36.864


So now we can start to bring vendors in and look at what the options are for tools like that that can solve that problem.01:04:36.864 --> 01:04:48.750


And in our world we've got tools like there's a company called Cognite and so they do IT, ot, building those data sets of combining IT and OT data together.01:04:48.750 --> 01:04:52.423


We've got tools like Composable that does autonomous AI.01:04:52.503 --> 01:04:56.034


That you know, that's all they do Autonomous AI in the industrial world right Now.01:04:56.034 --> 01:05:02.023


These are not vendors that you're going to stumble on, but these are all vendors in my toolbox that I can pull from to be able to.01:05:02.023 --> 01:05:06.568


You know, but maybe it's you know, we work with Rockwell, which is a big player in this space.01:05:06.568 --> 01:05:08.570


We work with Rockwell, which is a big player in this space.01:05:08.570 --> 01:05:10.474


We work with folks like Ignition in this space.01:05:10.474 --> 01:05:18.126


So we work with all these major vendors in this OT space, this operational technology space, the world we live in.01:05:18.126 --> 01:05:20.434


We work with all these different vendors and we can bring them to the table and build those solutions as necessary.01:05:21.536 --> 01:05:31.469


That's good, because it does sound overwhelming, and the perception that many may have is that it sounds complicated.01:05:31.469 --> 01:05:33.702


Some people think it sounds easy, I can just do it.01:05:33.702 --> 01:05:39.108


This is where it's even with what we deal with with the implementations and working with software implementations.01:05:39.108 --> 01:05:43.260


Everybody always thinks it's so easy, I can do it, but then you don't know what you don't know.01:05:43.260 --> 01:05:57.382


You don't know what you don't know, and then sometimes you find yourself, you put yourself into a position where maybe you didn't make the decision because you don't have that experienced reasoning to understand the cause and effect of what you're doing as well.01:05:57.382 --> 01:06:11.226


So what are some other from the manufacturing point of view, what are some other efficiencies that you've seen that organizations have gained by implementing AI in their organization?01:06:11.686 --> 01:06:21.822


Yeah, so, and you know it's as varied as the customers that we work with, and so I'll give you a couple examples.01:06:21.822 --> 01:06:35.030


We worked with a life science manufacturer and they had certain published sustainability goals about how they were trying to reduce their greenhouse gas emissions right by X percentage.01:06:35.030 --> 01:06:41.878


By this, you know target year, and you know that's one of those things where I don't.01:06:41.878 --> 01:06:48.324


When people talk about sustainability, I don't know what other technology you're going to grab other than AI to meet those goals.01:06:48.324 --> 01:06:50.684


Everyone's got these really aggressive sustainability goals.01:06:50.684 --> 01:06:55.266


It's not like there's some technology that's in the wings that's about to come in and revolutionize energy.01:06:55.266 --> 01:06:58.855


We know about all the technologies that exist here.01:06:58.855 --> 01:07:04.367


So AI is the technology that we can leverage to hit those sustainability goals.01:07:04.367 --> 01:07:07.043


So they had these published sustainability goals.01:07:07.043 --> 01:07:14.679


And so in life science if you're not familiar the environmental systems, obviously you're trying to control very tightly.01:07:14.679 --> 01:07:22.646


You're trying to control for humidity and pressure, temperature, of course, but those systems are oftentimes set in and forget it.01:07:22.646 --> 01:07:32.108


So it's nobody's full-time job to sit there and turn the knobs and try to, you know, keep it within spec, but then also to minimize energy usage.01:07:32.108 --> 01:07:35.983


So we trained an autonomous AI agent to actually be able to do that.01:07:35.983 --> 01:07:43.628


So it can actually and it does not do set and forget, it actually sits there and makes micro adjustments 24 hours a day to try to.01:07:43.628 --> 01:07:45.820


So it's always working to keep.01:07:45.820 --> 01:07:55.045


You know the constraints are it's got to stay in spec, but it's always working then to minimize energy usage and it can make intelligent decisions based on past demand.01:07:55.045 --> 01:07:57.034


It can look at all kinds of signals, past demand.01:07:57.034 --> 01:08:00.766


It can look at outside temperature, outside humidity and pressure.01:08:00.766 --> 01:08:03.985


It can look at what the cost of energy is at that moment.01:08:03.985 --> 01:08:11.784


It can, it can do leverage, can leverage all of those signals in the exact same way that a full-time human could if that was their job.01:08:11.784 --> 01:08:14.181


It can do that and control that.01:08:14.181 --> 01:08:24.585


They were able to get double-digit percentage decreases in energy usage, which is more than enough to get them to their sustainability goals that they were trying to get to.01:08:24.585 --> 01:08:26.719


That was really just one site.01:08:26.719 --> 01:08:35.402


We're trying now to work with the customer to expand that out to many, many more sites Now from energy, same technologies.01:08:35.802 --> 01:08:36.845


But let's go to.01:08:36.845 --> 01:08:41.439


We've got a customer that makes glass bottles and the glass bottle process.01:08:41.439 --> 01:08:48.926


If you're not familiar with it there's a gob of molten glass, it falls into a mold, air is blown in and then that's how you make glass bottles.01:08:48.926 --> 01:08:56.363


Well, that process is very finicky, so it takes an operator with a light touch.01:08:56.363 --> 01:09:08.454


They've got to be kind of really tuned in and really the customer told us we've got two expert operators who are really good at this and everyone else is just okay at operating this process and it's a very drifty process.01:09:08.454 --> 01:09:19.961


So once it starts drifting, you're making bad bottles maybe for at least the next 20 minutes, maybe half hour, before you can finally get the knobs turned to bring it back to where you're making on-spec bottles again.01:09:19.961 --> 01:09:27.082


So we were able to train an autonomous AI agent to get back to making on-spec bottles in less than five minutes.01:09:27.082 --> 01:09:32.960


Consistently it never took longer than five minutes and typically it took like two minutes to get back to making on-spec bottles.01:09:33.341 --> 01:09:39.921


And one of the challenges with this and why autonomous AI works so well for this, is there's a lot of compensating moves.01:09:40.283 --> 01:09:54.534


So if I increase what's called the orifice the size that the molten glass goes through, if I increase the orifice size or I decrease the temperature on the orifice the size that the molten glass goes through, if I increase the orifice size or I decrease the temperature on the orifice or I increase the pressure on the plunger behind it.01:09:55.114 --> 01:09:57.319


When I'm making these moves, I've got to make a compensating move somewhere else, right?01:09:57.319 --> 01:10:03.640


So there's just a lot of knobs to turn, and it's one of those things where it's almost too much for a human to try to keep track of all that.01:10:03.640 --> 01:10:11.706


So what humans end up doing is they do a lot of test and check, right, so they'll make a change and then they'll see if it makes an improvement, then they'll make another change.01:10:11.706 --> 01:10:13.180


The autonomous AI doesn't do that.01:10:13.180 --> 01:10:20.845


It's got in mind where it's going to go and it can turn all of those knobs all at the same time to coalesce to making on-spec bottles again.01:10:20.845 --> 01:10:23.141


And so that's another example.01:10:23.141 --> 01:10:37.755


You know, when you look at being able to get from making on-spec bottles, getting back to making on-spec bottles in less than five minutes versus 20, 30 minutes, I mean that's huge savings, that's huge throughput, that's millions of dollars.01:10:38.438 --> 01:10:40.243


You eliminate waste as well.01:10:40.243 --> 01:10:46.846


It all comes down to, I think AI can help you increase accuracy to eliminate waste.01:10:46.846 --> 01:10:51.242


And that autonomous AI of turning all those knobs sounds like a person to me.01:10:51.242 --> 01:10:57.885


And almost in some cases I wonder if they could do it more reliably than a person.01:10:57.885 --> 01:10:58.225


I think.01:10:58.546 --> 01:11:02.458


Well, I mean, it never calls off, it never takes a break, it never goes on vacation.01:11:02.458 --> 01:11:06.944


But the reality of it is is that, with most of these, we're not building this to replace a person.01:11:06.944 --> 01:11:08.621


The it is is that, with most of these, we're not building this to replace a person.01:11:08.621 --> 01:11:16.596


The problem is is that in the manufacturing world, they have had massive losses of expertise.01:11:16.596 --> 01:11:22.349


So as the baby boomers retire, they have lost decades and decades of experience.01:11:22.349 --> 01:11:25.444


There was a report from LNS Research in 2019.01:11:25.444 --> 01:11:35.259


This is for US In 2019, the manufacturing workforce the average years, the average tenure in a certain position in the manufacturing workforce was 20 years in that position.01:11:35.899 --> 01:11:38.746


By 2023, it dropped to three years.01:11:38.746 --> 01:11:41.636


Three years tenure Like that's.01:11:41.636 --> 01:11:42.899


That's insane.01:11:42.899 --> 01:11:46.046


And so and when I talk to my clients, they're all seeing this.01:11:46.046 --> 01:11:49.582


They're saying I've got high turnover, it's a generational thing.01:11:49.582 --> 01:12:10.570


Like nobody wants to do these jobs, nobody wants to work these factory jobs, and we can wish that it wasn't the case and we can certainly hope that it changes or we can just live with the fact that this is the reality that my clients are facing, and so they're not looking to replace people, they're looking to try to get that person with two weeks of training who's probably going to quit in six months, to at least get them to where they can make good bottles.01:12:11.574 --> 01:12:12.818


Yes, see, this is.01:12:12.818 --> 01:12:18.338


This goes back to something I always say and I've been saying AI is not going to replace you.01:12:18.338 --> 01:12:19.882


Someone using AI will.01:12:19.882 --> 01:12:21.907


That's right, because that's it is.01:12:21.907 --> 01:12:24.038


It's it's another tool that you're using.01:12:24.038 --> 01:12:37.715


And if you look at the industrial revolution, the advances in civilization, we've always done things to make it easier for people to do more, in a sense.01:12:37.715 --> 01:12:46.899


So again you go back to where someone may not want to do those specific positions or there may not be enough within the talent pool for those positions.01:12:46.899 --> 01:12:53.077


If you can have some something reliable to help, then you can still continue to prosper, be successful.01:12:53.599 --> 01:12:54.882


And that's how we stay competitive.01:12:54.882 --> 01:12:58.215


And when I say we US, you know I'm a US citizen.01:12:58.215 --> 01:12:59.979


I was born and raised in Ohio.01:12:59.979 --> 01:13:02.404


Love this country, love manufacturing.01:13:02.404 --> 01:13:03.707


This is how we stay competitive.01:13:03.707 --> 01:13:15.962


This is how we stay ahead is leveraging these technologies to look over the shoulder of our least experienced operators and get them to where they can run these lines in an expert fashion.01:13:15.962 --> 01:13:18.591


That's how we're going to do it.01:13:18.591 --> 01:13:24.806


And AI it's not the only way to do it, but it is one mechanism that we're looking at to try to do that.01:13:24.806 --> 01:13:32.300


And again, in the role that I'm in, director of industrial AI, obviously it's one of the most common questions I get Are you putting people out of work?01:13:32.300 --> 01:13:33.640


And again, the answer is no.01:13:33.640 --> 01:13:36.323


My clients don't have enough people to do this.01:13:36.895 --> 01:13:42.463


But there is a certain aspect of embracing automation and realizing that the jobs aren't going.01:13:42.463 --> 01:13:48.318


Certain job titles may go away, but it's just gonna create new job titles in the future.01:13:48.318 --> 01:13:50.202


And you know, I got a couple examples of that.01:13:50.202 --> 01:13:52.326


You know, lamplighter was a job.01:13:52.326 --> 01:13:57.063


Someone's full-time job was to light the lamps in the town at the end of the day.01:13:57.063 --> 01:14:02.362


And you know, of course, with Edison and the adoption of electricity, that went away.01:14:02.362 --> 01:14:05.047


Elevator operator was a job.01:14:05.047 --> 01:14:12.703


That was a real job, where someone went to work every day and they were an elevator operator and no one's lamenting the loss of elevator operating.01:14:12.703 --> 01:14:19.488


We're not walking down the street and seeing homeless people elevator operators sitting there on the streets because they're out of work.01:14:19.996 --> 01:14:35.542


The jobs change, and so that's what's happening and that's what's going to continue to happen, and so that's what's happening and that's what's going to continue to happen, as my clients, as these manufacturers continue to, you know, find challenges in finding this talent and they can't find these folks.01:14:35.542 --> 01:14:38.283


As that continues to happen, the jobs will change.01:14:38.283 --> 01:14:45.068


They'll start to adopt more automation, and these are, I mean, you know, look, you know, I've been out in these plants for my whole career.01:14:45.068 --> 01:14:47.270


They're not the sexiest jobs.01:14:47.270 --> 01:14:49.890


Some of these plants are very dirty, you know.01:14:49.890 --> 01:14:51.752


They're not the jobs.01:14:51.752 --> 01:14:52.832


They're very repetitive.01:14:52.832 --> 01:14:58.400


They're not the jobs that humans want to do anyway.01:14:58.400 --> 01:15:06.280


So let's get those humans into jobs that are rewarding, that are jobs they're excited to come to every day, and let's let AI do some of these jobs that are, that are menial and that are dangerous, like that's the other thing.01:15:06.301 --> 01:15:18.047


There's a lot of these jobs that are very dangerous, that we don't want humans doing in the future anyway, yeah, for sure, and I think a lot of the AI is filling those gaps where you are lacking the skill.01:15:18.047 --> 01:15:19.965


So, you know, fill those in.01:15:19.965 --> 01:15:39.766


So, like you're right, you have to have a positive outlook of the tools that we're creating and it's just like Brad mentioned it's part of human civilization, of improving our lives and it doesn't mean it's going to eliminate a person's value.01:15:39.766 --> 01:15:56.279


That value can shift to other areas that are much more productive and maybe more creative and more strategy around that, and then not worry about those, like you said, dangerous, tedious work and have a system do that for you or have a robot do that for you.01:15:56.279 --> 01:15:59.667


You know we'll get just like the horse right.01:15:59.667 --> 01:16:01.841


Not the horse is used for some other things.01:16:02.061 --> 01:16:06.337


Yeah, and that's been the story of industrial progress.01:16:06.337 --> 01:16:06.979


So we're not.01:16:06.979 --> 01:16:08.877


Yes, it was not new, that's why I keep going.01:16:08.877 --> 01:16:11.221


Coming back to ai is not new in that regard.01:16:11.221 --> 01:16:16.261


It's just the next tool, it's the next evolution, it's the next step, uh and and.01:16:16.261 --> 01:16:29.220


But this has been the story, you know, as we continue to move from manual processes where you remember, you've seen the old pictures during the industrial evolution of people lined up and working with their hands and making stuff, to where we are now, where all of that is done with a machine the loom.01:16:30.975 --> 01:16:35.346


I can think of so many jobs that they had children in the mills.01:16:35.346 --> 01:16:44.444


They used to run the looms, they used to run the yarn quickly through the wires, yeah, and they used to get injured and hurt, oh my gosh.01:16:44.444 --> 01:16:48.516


So there are some benefits, I think, with AI.01:16:49.318 --> 01:16:55.206


I think more of it's the mystical, magical black box and the magic that it does.01:16:55.206 --> 01:17:01.342


When you're sitting there creating code or you're doing some processes, it just seems to know what you're thinking.01:17:01.342 --> 01:17:11.725


So I think some of the apprehension is the fear of it, and I think you've had that with any tech, the adoption of it, and I think you've had that with any advancement that was made.01:17:11.725 --> 01:17:18.307


You know, you look back to the automobile, some of these larger advancements and even some other tools that were created.01:17:18.307 --> 01:17:28.047


There's so much to this it's I could talk about AI for hours and days, days and hours, hours and days.01:17:28.047 --> 01:17:29.270


I don't even know anymore.01:17:30.036 --> 01:17:34.527


Now you have more time, brad, to talk about it, because AI will do the rest of your tedious work.01:17:34.855 --> 01:17:37.761


I have so many things that I wish AI could do for me.01:17:37.761 --> 01:17:40.676


I just need to figure out how to apply it to get it done.01:17:40.676 --> 01:17:53.390


And the one thing I keep saying I'm still looking for the ability to manage multiple calendars in one place easily, without having to pull everything into one calendar.01:17:53.390 --> 01:17:57.042


Yeah, yeah, that's such a simple thing too.01:17:58.077 --> 01:17:59.842


You talked about enterprise search.01:18:00.175 --> 01:18:00.756


I was thinking.01:18:00.756 --> 01:18:05.086


The first thing that came to my mind was do you know how difficult it is to find an email?01:18:05.086 --> 01:18:07.420


Oh my God, yeah, yeah, and you go back to.01:18:07.420 --> 01:18:09.712


We have all these tools and all these wonderful things and it's.01:18:09.712 --> 01:18:11.180


How do I find this email?01:18:11.180 --> 01:18:15.537


And I'm with you on that, that's something so simple.01:18:15.537 --> 01:18:30.055


With my own inbox or not my inbox, but my old email box that is very difficult for me to find an email without having the exact match that I'm looking for an email without having the exact match that I'm looking for.01:18:32.595 --> 01:18:42.341


But you know, I got to tell you I think that's one of the use case of how people can get into utilizing these tools, because I get that all the time Like I'm coming up to a meeting and I don't remember the conversation or maybe don't have an idea of what the meeting was about.01:18:42.341 --> 01:18:48.555


Maybe I got pulled in because I got to make a decision was about.01:18:48.555 --> 01:18:49.680


Maybe I got pulled in because I got to make a decision.01:18:49.680 --> 01:18:56.329


So, and I asked AI, I asked co-pilot for the Microsoft product where, like, hey, I'm coming up to this meeting, I'm going to be talking to these people and here's a topic that we're going to talk about.01:18:56.329 --> 01:19:02.520


Give me everything I need to know and all the communication about this, and it does a wonderful job.01:19:02.520 --> 01:19:12.291


So I come in and I don't look very you know, I don't look like I was not organized co-pilot organized for me, and it saves you time, right, even as simple as that Start with that?01:19:12.755 --> 01:19:14.519


Oh for sure, yeah, and you know.01:19:14.519 --> 01:19:35.323


Back to this idea of time, you know one of the things that we will see with there's a lot so much fear and uncertainty and doubt around AI, One of the things that we're going to see is an increase in leisure time, and one of the things like we just take for granted that there's a 40-hour work week, but that was not always the case, right?01:19:35.323 --> 01:19:49.060


I mean, the reason why we're able to have a 40-hour work week is because of the advancements in technology and automation that made that possible, where you could be more productive with less actual time.01:19:49.060 --> 01:19:53.208


We're going to continue to see that, and AI is going to accelerate that.01:19:53.208 --> 01:19:57.646


So we're already starting to see some rumblings in Europe about moving to a 32-hour workweek.01:19:57.835 --> 01:19:58.536


I'm all for it.01:19:58.536 --> 01:20:00.779


That's important.01:20:00.779 --> 01:20:03.104


That leisure time.01:20:03.104 --> 01:20:14.930


That's what makes life worthwhile, when you can spend that time with your friends and your family and be creative and pursue hobbies and pursue things that you're passionate about and not just spend your entire life working.01:20:14.930 --> 01:20:19.882


So I am all for that and I'm ready for that, and AI is going to be one of those tools that's going to bring that.01:20:20.315 --> 01:20:23.685


It will be helpful to go with that, just to add to it a little bit more.01:20:23.685 --> 01:20:25.201


We've spoken about it before.01:20:25.201 --> 01:20:29.485


We need to change the time value that we have.01:20:29.485 --> 01:20:34.819


We need to value productivity and output, not time, because some are fearful.01:20:34.819 --> 01:20:44.854


Well, with AI, I could do something twice as fast if I have to do twice as much where we have to come up with a fair way to measure productivity and output.01:20:44.854 --> 01:20:54.359


To get back to where you were talking about, to where maybe you don't have to work the 40, 50, 60, 70 hours a week with 32 hours of solid time is enough.01:20:54.458 --> 01:20:57.425


And then also, I've read studies.01:20:57.425 --> 01:21:00.497


I'm not a scientist, doctor or any of those, but I read.01:21:00.497 --> 01:21:03.628


I do a lot of reading where the in it.01:21:03.628 --> 01:21:08.122


But I've also experienced it myself where sometimes you put something down, forget about it, forget about it for a little while.01:21:08.122 --> 01:21:11.219


You come back, you're more creative, you're more energized and you're more productive.01:21:12.141 --> 01:21:17.755


I worked at a place that they used to force you to go out for lunch, and the reason why is because the owner of the place and he would buy people lunch and stuff.01:21:17.755 --> 01:21:24.521


He'd want you to go outside because he said that he realized that the individuals were more productive if they got up from the desk and didn't continue working through lunch.01:21:24.521 --> 01:21:26.887


It wasn't forcing people to go out to eat.01:21:26.887 --> 01:21:35.662


His concept was is we want you to take a break during the day, go out, walk around the building, do something so that you're not sitting at your desk all day and you can be a little more productive?01:21:35.662 --> 01:21:37.481


And he was right.01:21:37.481 --> 01:21:42.997


A little fresh air did some wonders, because you come back after lunch and you don't have that afternoon need for a nap.01:21:42.997 --> 01:21:43.797


I guess you could say so.01:21:43.797 --> 01:21:47.100


That afternoon need for a nap, I guess you could say so.01:21:47.921 --> 01:21:49.782


Brian, we appreciate you taking the time to speak with us today.01:21:49.782 --> 01:21:51.823


As I always say, time truly is the currency of life.01:21:51.823 --> 01:21:52.845


Once you spend it, you can't get back.01:21:52.845 --> 01:21:55.846


So anybody who spends time with us, we greatly appreciate.01:21:55.846 --> 01:21:58.309


We enjoy hearing your insights.01:21:58.309 --> 01:22:02.332


I'd love to maybe talk to you again in the future, get a little bit deeper in some of these areas.01:22:02.332 --> 01:22:12.640


But if someone would like to talk with you more about AI and learn a little bit more about what you do to help manufacturing organizations gain some efficiency from AI, what's the best way to contact you?01:22:13.422 --> 01:22:19.358


Yeah, so easiest way is to go on my LinkedIn, so you'll find me there Brian B-R-Y-A-N DuBois, D-E-B-O-I-S.01:22:19.358 --> 01:22:20.720


If you just search for that on LinkedIn.01:22:20.720 --> 01:22:23.242


Yeah, and reach out to me.01:22:23.242 --> 01:22:27.247


Or the Rovisis website is a good way to get in contact with me as well.01:22:27.247 --> 01:22:28.467


It's Rovisis R-O-V.01:22:28.467 --> 01:22:31.471


As in Victor I-S-Y-S dot com, slash A-I.01:22:31.471 --> 01:22:35.536


We'll take you right to my landing page on the website.01:22:35.536 --> 01:22:36.997


But, yeah, happy to talk to anyone about this.01:22:36.997 --> 01:22:39.158


Please reach out and, yeah, appreciate the time.01:22:39.819 --> 01:22:40.458


Great, thank you.01:22:40.458 --> 01:22:44.121


Look forward to talking with you soon All right, sounds good, thanks guys.01:22:44.121 --> 01:22:52.527


Thank you, chris, for your time for another episode of In the Dynamics Corner Chair, and thank you to our guests for participating.01:22:52.966 --> 01:22:54.507


Thank you, brad, for your time.01:22:54.507 --> 01:22:58.011


It is a wonderful episode of Dynamics Corner Chair.01:22:58.011 --> 01:23:01.453


I would also like to thank our guests for joining us.01:23:01.453 --> 01:23:07.185


Thank you for all of our listeners tuning in as well.01:23:07.185 --> 01:23:19.007


You can find Brad at developerlifecom, that is D-V-L-P-R-L-I-F-E dot com, and you can interact with them via Twitter D-V-L-P-R-L-I-F-E.01:23:19.007 --> 01:23:32.365


You can also find me at matalinoio, m-a-t-a-l-i-n-o dot I-O, and my Twitter handle is matalino16.01:23:32.365 --> 01:23:36.063


And you can see those links down below in the show notes.01:23:36.063 --> 01:23:37.407


Again, thank you everyone.01:23:37.407 --> 01:23:38.980


Thank you and take care.