The State of Agentic AI and Prospects for Industrial Automation

Agentic AI has been a hot topic since the beginning of 2025 fueled by the promise of autonomous automation of operations and business for all industries. Yet, much like generative AI overall, agentic AI solutions have turned out to more challenging than anticipated and forecasted. What's the deal? 

Join Leonard Lee, Executive Analyst, neXt Curve, as he shares his insights into the impact of Agentic AI for manufacturing and industrial applications. 

Leonard will be expanding on this topic at MD&M West in Anaheim, CA on Feb 3rd. For this broadcast, he will be joined by Industry 4.0 Club Co-Founder Tim Stuart and CEO Mike Ungar.

Transcript

Mike Ungar: 0:00

Welcome to the Industry 4.0 club's, fireside chat. The club's purpose is to engage the power of diverse worldwide talent to accelerate the global evolution to industry 4.0. We accomplish this by hosting and supporting respectful, thoughtful, and interdisciplinary open conversations, and creating quality content on wide ranging industry 4.0 topics to expand the reach of our community. My name is Mike Unger. I'm the CEO of the Industry 4.0 Club. I have over 40 years of leadership and experience with most of that being at Michelin in manufacturing. I'm joined today by my co-host Tim Stewart, who's co-founder of the Industry 4.0 Club as well. Tim, would you mind introducing yourself further?

Tim Stewart: 0:43

Sure. Tim Stewart. I'm also, the president of Visual Decisions. I founded that company 24 years ago, and we seek to help manufacturers, utilize information from the shop floor to improve their operations. today we have the pleasure of speaking with Leonard Lee, executive Analyst with Next Curve. Leonard will be presenting at MD and M West in Anaheim, California on February 3rd, 2026. The title of his presentation is the State of Ag AI and Prospects for Industrial Automation. Leonard is the founder and managing director, of Ncur, an independent research advisory firm based in San Diego with the mission of helping technology, media and telecommuting. Telecommunications industry pioneers explore and pursue opportunities forming at the intersection of transformative technology and industry trends. Leonard's cross domain research covers mobile computing and wireless infrastructure, enterprise computing and networking. AI infrastructure and data center, media and broadcast technologies, enterprise software and analytics. consumer and industrial IO ot, cybersecurity and trust and semiconductor technology. Leonard, that is lot.

Leonard Lee: 2:06

Welcome.

Tim Stewart: 2:08

Welcome.

Leonard Lee: 2:09

Did you get that off of chat pt? No, that's really inclusive. I'm really impressed by myself.

Mike Ungar: 2:17

Who is that guy, right?

Leonard Lee: 2:18

Who's that guy? Yeah. Really

Mike Ungar: 2:22

dive in.

Leonard Lee: 2:25

sorry, it looks like we're having a little bit of collision here.

Mike Ungar: 2:28

Let's jump in. You've been involved as an analyst and consultant for over 30 years.

Leonard Lee: 2:33

Yeah.

Mike Ungar: 2:33

When you think back on your career, can you talk about pivotal moments that shaped what you do today?

Leonard Lee: 2:39

Oh yeah. I'd have to say it was a moment with, one of my managers very early on in my consulting career who told me that, Hey, you wanna be a partner? You need to focus on one thing and be really good at it. And, I took that coaching session and completely disregarded it. And, went on a path to, pursue my interests, which were really diverse. And so at the time I was doing, database application development work, which was the hot thing at the time, and, decided, Hey, look, you know, I want to expand beyond this. I want to get, I wanna understand businesses, I wanna understand processes, I wanna understand how businesses work. I wanna understand how industries work. I wanna understand how technology. This thing at the time called information technology, can revolutionize, businesses and, economies. And so, and prior to that, I actually was gonna get into iBanking. I bumped into this thing called consulting. And so that's where I started to focus and eventually just kicked off, a long career in. Consulting where I became a partner, principal at I-B-M-P-W-C and then, EY and, did a three year stint at, Gartner and started my own firm next curve, to really pursue my diverse interest, leveraging my diverse background. So when you went through the description, Tim and I was going, who is this guy? Uh, sort of, um, that pivotal moment that. Brought me to where I am today and the kind of research and advisory that I do at ncur.

Tim Stewart: 4:16

Excellent. so for your presentation, I know that you're going to be talking about agent AI and, industrial. A little bit more about what you're gonna be speaking about at mdm.

Leonard Lee: 4:32

Quite honestly, what I really want to do with this presentation is ground a conversation around agent ai. agent AI is one of those pivots in this long stream of generative AI pivots that have occurred since the introduction of, chat GPT and OMS to the public. I want to help, whoever's attending, whoever's listening to have a better understanding of how we arrived at agentic ai, and, what we're really looking at here. And then as we look toward, leveraging this paradigm, if you will, or this next generative AI pivot, how does that apply to industry and what is it that next curve is seeing, Researched and followed, this, continuum of, pivots, for better part of five years. And a lot of this is rebred in the work that I do in the semiconductor industry because, you probably hear Jensen won, talk a lot about, Alex net. These, I've been engaged in this conversation for a really long time. Also, with my background in IBM, working in pre-commercialization work with IBM research in AI and in particular, natural language, processing, I have this background in this stuff, understand, what it takes to actually, realize solutions, leveraging these technologies in its different forms.

Mike Ungar: 6:00

So, jumping off what Tim had alluded to. Agent AI is really a relatively new term. I mean, I think only a couple years from now, or at least maybe I'm not up to speed yet, I'm thinking about a couple years. It's changed a lot, in the last. Two years. Can you talk about just that evolution?

Leonard Lee: 6:18

Yeah. And that's one of the things that I'm gonna be addressing in the talk, in a couple of weeks, is really the genealogy of, agentic ai and how it really is a representation of a confluence of a number of things. Number one, the evolution of models themselves, right? So going from. Large language models, right? To multimodal, to reasoning, to now, there's a term long thinking. and then there's this concept of, chain of action. Okay. You might have heard of, chain of thought. There's chain of action, and then you have AgTech and, ai. And that is also being fed in with a thread of what we've seen in process automation where there's RPA or workflow management, stuff that we've been doing for years, right? And so that being sort of the scaffolding. To allow agents, these agent AI agents to actually do something, in terms of a set of actions, right? And that's really what's being different versus, AI being more of a query tool or something that, provides you with a conversational interface, moving towards something where there's actuation and what one might think is, a closed loop control, autonomous or, paradigm of intelligent automation, if you'll,

Tim Stewart: 7:43

in terms of, manufacturing use cases

Mike Ungar: 7:47

mm-hmm.

Tim Stewart: 7:48

where do you see, the easy places to apply something like this within manufacturing?

Leonard Lee: 7:58

Oh man, you're gonna make me disclose all of the nuggets from my presentation? Is that the thing?

Tim Stewart: 8:05

At a high level?

Leonard Lee: 8:06

Oh,

Speaker 4: 8:07

high level.

Leonard Lee: 8:08

lemme see if I can start with like the high level thing and then we'll figure out whether or not I'll disclose any of the nuggets in my presentation. Because we do want people to come, right? We want fly to Anaheim.

Tim Stewart: 8:19

They'll hear much more detail if they go to the presentation.

Leonard Lee: 8:22

Exactly. A lot more detail. you know, where people really need to focus is foundationally, on, not just data. Everyone talks about data, their knowledge, graph and their knowledge base. This is more important than data itself. I had a recent conversation actually with a telco client of mine. there is this. Trope that data is the gold. no knowledge is the gold. And if you have a really horrible knowledge base and you don't have the systems and the capability to manage knowledge, you are not going to do well with any of this stuff. what AI does is especially in the context of manufacturing, it reacts to data like telemetry. Right, like the structured stuff or, I mean, you know, some people call it unstructured, but usually you have a structured, you know, what kind of data is coming in.

Tim Stewart: 9:21

Right,

Leonard Lee: 9:22

right. I mean, you have a schema. you're contextualizing data at a very low level, but you know what kind of data is coming in, even though, there is this talking point that you, you, oh, it's unstructured and you need to make sense of it. Well, the thing is, is that the AI reacts to data, but it needs to be based on knowledge. Because what we expect the AI to do is make decisions on its own. It doesn't make decisions based on data. It. To data, and this is what we see in ML as well. But this is that mindset that you have to have in order to properly think about, intelligent automation in manufacturing environments. And, you know, all those, technical guys out there, they know this stuff. They're just being impressed that, somehow there's this magic out there that Elon's, bring to table. And that's not the case.

Tim Stewart: 10:13

Let me, probe a little bit further and see if I steal some of your thunder from, the actual presentation. for a long time, I've been working with IOT systems and doing that data collection from the shop floor, and one of the things that I caution my customers about. Is that, an IOT system is a lot like a Fitbit just collecting the information, putting it in pretty charts and graphs doesn't actually make anything better. You still have to do the hard work, right.

Speaker 4: 10:41

And,

Tim Stewart: 10:42

if you still run the same process in the same way tomorrow that you run it today, you're not making any improvements and you're just spending money, on something that's not gonna help.

Speaker 4: 10:52

Yeah.

Tim Stewart: 10:55

Correct me if I'm wrong, but part of the promise of the agent AI is that, you can actually have it now take action on that data that's coming in because it's doing that intelligent interpretation and has the capability of tying back into actually doing things with that data and knowledge, that you're building up. Is that a correct assessment?

Leonard Lee: 11:20

It is an assessment. and it all entirely depends. it's more complicated than that, I think. I think we're hearing more about, the challenges of determinism versus, probabilistic or probability, right? And intrinsically, and this is something that we saw in previous hype cycles with ai. AI is probabilistic. It has issues. It's not accurate. It doesn't give you x number of nines that are required to meet many of the industrial requirements where the processes actually have been designed, to be reliable and of high quality, I recently had a conversation with, a client about K G's, book, and that era of business management there's a huge focus on quality, right? And so there's this legacy of quality that's already been. put in place and now we're introducing the Silicon Valley mentality of break things fast and run away. Right? And I don't know, you ask anybody who runs a manufacturing operations, is that the right kind of mindset to bring into an environment? Now, that's not to say that. AI doesn't have a role in manufacturing. The thing is, is that you have to have that, grounded perspective on how to leverage its capabilities, completely cognizant of its limitations, And so this is the same thing we saw at IOT as well. and because there was a hype cycle there and We're barely coming out of the trough of disillusionment, which I think is a good thing, because people are a little bit more sober. We're not hype drunk, and, there's opportunity. But again, the purpose of my presentation is going to be to help the audience ground themselves and get to that better place that you're describing.

Mike Ungar: 13:27

Well, I have to admit, When I use something as simple as chat, GBT, since we're talking about it seems simple when you talk about agenda AI to talk about chat GBT. Right. But you know, it doesn't always tell me the truth.

Speaker 4: 13:39

Yeah.

Mike Ungar: 13:40

You know, and you mentioned the knowledge base. is that because the knowledge base is it sufficient or is it, tell me why do I need to worry about that With agen AI as well?

Speaker 4: 13:52

Yes. No. And yes. Do you guys wanna kick me off yet?

Mike Ungar: 13:59

It's interesting, but that's what we want to talk. it's an interesting question because I think ultimately, you know, Tim's question about letting'em make decisions. what kind of decisions do you wanna have them make? Needs to be very yeah. Defined, doesn't it?

Leonard Lee: 14:11

which is why you see a lot of, ISVs in particular, actually a lot of tech companies on. Train these, domain specific models, right? And then we saw rag architectures where the whole thesis was, if you plug it into your corporate information and your knowledge bases, then you can rag this stuff, which is retrieval augmented generation to ground the responses. Well, that didn't quite work out the way people thought because people didn't think through how reliable are vector database is. Right. How do you secure these things? How do you, how do you really architect trust into these applications? People didn't think about this stuff. and so there's a lot of unlearning happening. It's happening through these POCs where people are realizing, Hey, this is not what PE we were sold, at the beginning of this project. By this, fast talking sales guy or evangelist, it, there's some real ground truth that you have to deal with. Otherwise, you are definitely not gonna get anywhere close to what that. Strategy PowerPoint suggested, right? And you're absolutely right, the hallucinations have not been solved. And I remember seeing some companies claim that they solved the hallucination problem. It's like, okay, really? if the AI researchers haven't figured it out, what makes you think any ISV out there has figured it? They haven. And so, there again, I'm gonna emphasize there is a lot of unlearning happening and a lot more that has to happen. And the problem and the challenge for manufacturing companies in the industry overall as they're dealing with this hype cycle is figuring out what's real and what's not. And there's a lot of, disconnected narratives out there at the moment that need to be dispelled.

Tim Stewart: 16:11

Well, With all those caveats, are there any well-established use cases where this has been applied successfully within manufacturing?

Leonard Lee: 16:20

Yeah, I think, in the treatment of data. there are these weird back office type of, applications, whether, you know, some of the stuff that's really interesting is code remediation or, you know, like remediating, older system or modernizing, older system. Maybe it's based on like, COBOL or another proprietary language that, that, you know, knows how to code in anymore. I mean, there are these really interesting use cases in the backend that can support the modernization of a, manufacturing environment. And one of the problems that has, and Tim, you're gonna know about this, right? One of the important things about AI is that whatever it is that you're trying to do, X, Y, Z, and it needs to be sort of software defined. And this is why you had this big excitement like three, four years ago about digital twins. Well, digital twins are not trivial. They're very important as manufacturers look at how do we modernize in a unquote AI automation era? How do we ride that curve? And, that is often missing or is in a low level of maturity within many manufacturing, environments. And it's also expensive. It's very, very difficult to do, especially at these, advanced levels, right? Maybe at a lower level. but you're gonna be prioritizing what you, create a digital twin of, right? There's this. cost benefit curve that you have to ride, right? Anyway, I'm gonna try to keep my responses short.

Tim Stewart: 17:51

Yeah. I haven't seen too many true digital twins out there. It's more like digital caricatures.

Leonard Lee: 18:02

Ooh, that's mean. Tim, come on, you must have gotten bit by the metaverse, industrial Metaverse. You know what, but you see, here's the thing. If we look at the stuff in a more grounded fashion, then we can actually start to ride that. Maturity curve. Identify what are those value opportunities that manufacturers can actually leverage to modernize and then translate that into operational benefit. You know what I'm saying? we don't do that. And when you listen to how people are talking about this stuff. They don't talk like this, and that shift has to happen. Otherwise, you're gonna be wallowing in POC hell, which is one of the objectives that I'm trying to, help. one of the objectives when I get up on stage is to help companies, end users avoid dropping it to POC health. Yeah. Yeah. but these are the mindset shifts that have to happen. a lot of, the problems, that I hear practitioners suffer is board pressure. These board members and executives who don't really know the technology, putting out agendas that are, high driven and unrealistic. we have to reverse that. And then the companies that do that are gonna find, they're gonna get outta the POC hell. Much faster than everyone else. And guess what? You will win the other way. You lose, you're stuck in just in hell losing money, right? We see this all the time. This isn't, it's not new, right?

Mike Ungar: 19:34

There you go.

Leonard Lee: 19:35

The same mistake that everyone just seems to make over and over again.

Speaker 4: 19:39

yeah.

Mike Ungar: 19:39

As we, as you, as we think about the future

Speaker 4: 19:42

mm-hmm.

Mike Ungar: 19:42

what's one thing that. If you close your eyes to think three to five years from now, what would be one thing that you would think might be the case with respect to genetic AI and manufacturing that might just sort of challenge our folks on the thinking that to our listeners?

Leonard Lee: 19:58

Yeah. I think there's gonna be some really great, contextual computing, happening at the industrial edge, right? at the moment, as we see this transition. in the AI conversation from all the model training and these Uber neo clouds to, this diffusion of, generative AI and specifically generative AI to the, industrial Edge. I'm seeing some really interesting work being done by silicon players in, providing these landing sites for robust generative ai. whether it's a vision model, whether it's a small language model. these emerging architectures are gonna allow, industrial players to augment, their perception capabilities, right? So going back to iot, there will be, a step change where you can get better contextual intelligence, hopefully at a lower latency'cause latency is a big problem right now. And, um, it will create a lot of possibilities. There's gonna be a lot of boring technical work that should yield a lot of benefit, for, the manufacturing industry. so one of the things that I'm keeping an eye out on, in my research.

Mike Ungar: 21:16

Fantastic. Well, for those that attend your presentation, you shared a little bit of what you hope they should learn there. Anything else you would share that, Hey, come and hear me. here's what I've got to tell you that you'll find interesting. What would you say?

Leonard Lee: 21:29

Everything. I think I already, um, I, I, I would encourage everyone to buy a ticket. Fly out. How's that?

Mike Ungar: 21:44

That's good. Well, I think it's, you know, what's clear to me from listening to you that you really enjoy the topic. you like to have fun with it as well. And so I think folks will really enjoy coming to interact with you, no, I appreciate that a lot.

Leonard Lee: 21:57

Yeah. And I look forward to meeting, anyone who's interested in this topic, I think is an important one, and especially any, end user or, organizations that, want grounding because I think it's important. I want them to focus, ask better questions, number one and number two, to be able to engage with companies at the moment who don't get attention because of the height, who should get attention because they're the ones that can, they can help. organizations, move forward, with AI versus, wallowing in expensive experimentation, right? I mean, this is what we need to do at this point. Otherwise, AI is not gonna mean anything and we will see another winter.

Mike Ungar: 22:39

Thank you. Thank you for that. So we're getting ready to wrap up. maybe Tim, a final thought letter to final thought and then we'll wrap up. Tim,

Tim Stewart: 22:47

I'm anxious to hear the rest of the presentation so I can hear all the details. I think that there's tremendous. Opportunity. I do think, as you mentioned, we have to be conscious of getting, too far out ahead, based on the hype and so forth.

Speaker 4: 23:06

Mm-hmm.

Tim Stewart: 23:07

And, think through some of that, you know, probabilistic versus deterministic, decision making, where do you have humans involved, in the decision making because, in those cases, you already have some probabilistic things in there. And so those may be, better targets for utilizing the agentic AI and so forth. but yeah, I'm excited to hear the presentation and, learn a lot more.

Speaker 4: 23:35

Awesome. you wanna on stage

Leonard Lee: 23:38

that's.

Mike Ungar: 23:40

We'll see you there.

Leonard Lee: 23:42

Yeah.

Mike Ungar: 23:42

Another thought for the audience before we wrap up.

Leonard Lee: 23:44

buy a ticket to Anaheim. D and m West 2026 gonna be cool. You're gonna learn a lot, especially if you're, in the manufacturing sector. And I'm excited. this is my first time, and I'm expecting to learn a lot from everyone who attends, as well as just, wandering around the exhibition floor, bumping into something really cool. Look forward to seeing everyone there,

Mike Ungar: 24:13

So we wrap up. Just wanted to remind folks at the Industry 4.0 Club. Would like to encourage you to take action and attend an event like this, a talk from Leonard or someone also to share something very interesting about Industry 4.0. Read an article and encourage your company, to take action, whether that's, To educate its workforce or develop a smart manufacturing roadmap, there's a lot of opportunity, but we need to move forward. We need to take advantage of the technology, that will help us be more competitive, as we move into the future. we'd like to know what action you decide to take. So please drop us a note on our website at www industry four l club.com or on our LinkedIn page. We, routinely host conversations like these. And if you'd like to be on one of our fireside chats and share your own insights, we'd be happy to do that as well. So drop us a note if you're interested. And with that, I want to thank, Leonard. Tim, it's been a great conversation. I look forward to seeing you in Anaheim. Leonard,

Speaker 4: 25:13

same here. Safe travels.

Mike Ungar: 25:16

Yeah, have a great week, everybody.

Speaker 4: 25:17

Stay warm.

Mike Ungar: 25:19

Thanks.