EP 225 - How are AI and IoT solving manufacturing labor shortages?

Speaker A: What we typically do then for those customers. Because for me the question is not whether AI ML is significant or not, whether it's relevant. It's more than, for me, a timing question with those customers. What we're trying to do is when we talk about new designs, particularly also we try to make sure that those customers actually have, uh, systems in place, songs in place, for example, that have capabilities that would support more sophisticated AI ML applications in the future. And what sophisticated means can vary a lot.

Speaker B: Welcome back to the Industrial IoT Spotlight podcast. I'm, um, your host, Eric Walenza, CEO of AGP. And our guest today is Mike Rohrmoser, VP of Product Management for OEM Solutions at Digi. DIGI is a leading global provider of mission critical and business critical, machine to machine and Internet of things connectivity products and services. And in this talk we explored how IoT AI, edge computing and automation can help solve the massive labor shortage that the manufacturing sector is facing in America and around the world. We looked at discrete versus process manufacturing, the differences between brownfield and greenfield deployments, technology and cost developments across the smart factory tech stack, and how to minimize the risk of failure when scaling a pilot. If you find these conversations valuable, please leave us a, uh, comment and a five star review. And if you'd like to share your company's story or recommend a speaker, you can email us@teamsiagrowthpartners.com finally, if you'd like to discuss technology, innovation and growth challenges in Asia, please email me directly at Erik wallenza m@asiagrowthpartners.com thank you. Welcome to the industrial IoT spotlight, your number one spot for insight from industrial IoT thought leaders who are transforming businesses today with your host, Eric Walenza. Mike, thanks for joining me on the podcast today.

Speaker A: Hi Eric, glad to be here.

Speaker B: Yeah, well, I'm really looking forward to this conversation. This is a topic that is quite pressing, I would say for companies in America, where you're sitting, but really around the world, uh, as manufacturing has become more of a strategic priority for many governments, this is actually becoming a more pressing, uh, issue in many countries. When for you, did this topic just start? I think for many people this topic of kind of massive labor shortages in the manufacturing sector became kind of a political priority just probably over Covid or a few years ago. From your perspective, maybe this started earlier. When did Digi start to identify that this was a pain point for their customers?

Speaker A: Well, I think we have noticed that we're facing somewhat of a perfect storm with, you know, three converging forces. I would say we have, we have this demographic cliff. So about I think one third of our manufacturing workforce is over 55 I believe. And I think generally speaking as of today, um, not even talking about the past, we're losing about I think 11,000 or over 11,000 baby boomers to retirement daily all the way through 2027. And so I think we have noticed also again that demographic cliff, that great retirement cliff a while back already, you know, where we aging customers that we have also in many cases worked with for a long time, um, including new customers where we engage with um, subject matter experts that are uh, in that age range and then obviously all of that. What contributes to that is the, the whole skills evolution. You know, there has be more demand for simulation and software skills over the last, I would say five years. But we also noticed that a lot of the younger workers, younger employees, I don't necessarily always have the background because only a few schools actually offer manufacturing relevant, relevant curricula. And then um, even the whole perception gap of only a third of Gen Z, um, I believe is one of the numbers that consider manufacturing careers. You know. So we have certainly seen the aging, I would say, um, as part of our daily work. And that's obviously underpinned with uh, some of the research, uh, some of the market data we see. And then again that is augmented by the old skills related aspects and what younger people actually do from a um, manufacturing career decision which of course then contributes to this massive uh, shortage in I think about almost 2 million unfilled um, manufacturing jobs in the next 10 years.

Speaker B: Yeah, it's interesting how other geographies have arrived at kind of a similar pain point but from different perspectives. So I'm sitting here in China and of course there's a lot of people graduating so there's at least in the short term not such the labor crunch. But they don't want to work in manufacturing. Right. So um, so then it's more of you know, convincing. In India, you know there's a lot of young people. But what I hear from companies that are setting up factories there is they don't have the skill sets, they don't have the uh, the capabilities that they need. And so then they're looking at digital solutions as a way to you know, you have low cost labor but the labor is not able to meet your quality standards to run standard processes and so forth in a stable way. And so you're looking for technology to help address that. So you know, many different routes end up at this same Pain point of we don't have the right workers, let's say. But I guess we can drill into the US because the US is, um, you know, of course, where you're coming from. Um, it's probably the country now that also has the biggest, uh, immediate need to address this problem. If we drill in on the American market, where do you see the roles that are most challenging? Are we talking about the operators, maintenance techs, the specialists who are managing digital solutions? Where do you see the biggest bottleneck?

Speaker A: Well, I would say it probably depends a little bit on the industry. Um, I think there's differences when it comes to, let's say, discrete manufacturing, automotive, electronics. I would say that they struggle most with automation specialists and maybe robotics technicians. Um, if you look at process industries like um, chemicals or uh, food and beverage, they face bigger challenges, more with regulatory compliance specialists and safety certified operators, um, that are not very easily replaced. Um, we talked about the skills gap also and the experience that we talked, that we referenced before. But I do think in the context of what you were saying, that both are solving it in the same way across the industry. Meaning that you can use IoT or industrial IoT to actually almost amplify human capability rather than replace the workers. Um, I think that is certainly also the opportunity when we talk about IoT and we talk even about AI or generative AI, but I'm sure we're going to touch on that later as well, where those technologies can actually help, not necessarily replace the people, but put, perhaps augment the human workforce, where we have those losses that we talked about and help the people that are actually working in those roles to do their job in a more effective way than before. And that will help also then to maybe at least partly overcome the uh, shortage that we are currently experiencing, in my opinion.

Speaker B: And if we look at. So one perspective would be different industries, or at least discrete versus process, having, uh, different skills gaps. Another perspective could be brownfield versus Greenfield. Of course, America has a lot of brownfield, right? So we have a lot of factories that were built in 60s perhaps. And so they have a unique set of challenges. And then more recently there's a few industries where there's uh, a lot of Greenfield that's been kind of penciled in or maybe even already begun construction or come online. Those are different challenges. Let's take those one at a time, maybe first at Brownfield. And when you're working with uh, a manufacturer that has a brownfield facility, how do you work with them to start thinking through what makes sense given the state of our legacy infrastructure, uh, as kind of a thought process or a planning process to begin determining what use cases should we be looking at. What type of technology backbone do we need to invest in in order to get them to where they want to be?

Speaker A: Well, I would say that often. I would say well also because we have existing infrastructure and existing plans. I would say that the economics often strongly fav uh brownfield upgrades at least strategic brownfield upgrades compared to greenfield. So they cost about 40 to 60% less than a greenfield buildup with typically faster RI, even though maybe a little more limited UM versus new con versus new greenfield projects. So for brownfield assessment in general, depending on the customer and the product needs, we typically evaluate a few key areas and again it depends on the project and the customer. The connectivity infrastructure because obviously DG is focusing on connected products and IoT overall industrial IoT existing protocols that are in place mechanisms secure the security posture of the customer, um, operational workflows and then the last but not least also the workforce readiness which is an important aspect. And I think the key insight is generally to understand what you can retrofit versus what needs replacement also. And that's where our products can also bring decades old, let's say serial equipment, old machinery into the digital age without ripping and replacing. Um, and that's really for me the magic of modern industrial iot. So but here's what I've learned from hundreds of those deployments, that the success isn't necessarily always about the age of your facility. It's also it's very much about the clarity of the digital strategy and starting with the actual high impact use cases. So the plans on the customers that focus on like I mentioned before, the worker augmentation rather than worker replacement, I would say they see higher success rates in scaling pilots regardless of how olden facilities or infrastructure is a good example. It's not DIGI specific, but Siemens has a showcase plant. Essentially it's the Siemens electronics works in amber that actually has experienced I think a 13 times production increase in this very same brownfield footprint that is around since 1990. So the Ambrac plant actually produces programmable logic controllers or PLCs and HMIs. And they have achieved that really remarkable increase without expanding the original footprint, which is about 100,000 square footage foot I believe of production area or significantly increasing their staff levels. So means they have achieved a level of growth through increased efficiency uh, retrofitting brownfield rather than simply having a larger factory build with more employees and um, more modern technology as well.

Speaker B: Yeah, no, it's a useful perspective. I mean I hear among um, my clients quite a bit of interest in kind of factory in the dark lines. And I'm always curious, is that more kind of an intellectual like uh, pushing the boundaries interest or is there really a business case there? I guess these are more typically for things like solar panels and kind of somewhat process oriented manufacturing. But do you see scenarios where really pushing the boundaries towards a factory in the dark makes sense given the state of technology today? Or is uh, it very rare where that makes sense? And it's more likely that that might be uh, a useful lighthouse factory, for example, a company that wants to test out new technologies to prove uh, you know, viability, um, as opposed to really having the stronger business case versus more of a augmentation of the existing workforce.

Speaker A: Well, I think over time probably, I mean that concept of having a fully automated manufacturing facility that in its ideal quote unquote incarnation would actually operate without human presence, that's probably very rare. Maybe you see a little bit of that in the automotive space, but even there I would say it's also not the typical customers that I am directly engaging with or DIGI is engaging with. Historically I think it's mostly customers and organizations that are actually, like I said, more looking at augmenting, becoming more efficient, more effective, increasing OEE through means that are building on the existing infrastructure. I would say that a factory in a dark kind of or dark factory approach is very much greenfieldish in many ways and it's probably only suitable in my opinion to a certain number of industries. And you know, I haven't seen many customers that we are directly engaging with in that kind of context.

Speaker B: Yeah, makes sense. Yeah, I think it's a bit more common here in China and you know, probably most likely because it impresses government officials and therefore you're able to get kind of loans to do investments that don't really have a strong business case. So clear from your perspective in the U.S. uh, brownfield typically has a stronger case. Nonetheless, if we look at the labor situation in the US there does tend to be kind of a shift in manufacturing, uh, capacity from the north where you probably have more uh, brownfield that's not fully utilized towards the south where traditionally you haven't had as much manufacturing, but now you have a lot more labor. So there's this kind of tension in the market. Um, when we look at the south and we look at scenarios where you might have to put more greenfield in just because the existing assets are not there. How does your process to work with a customer differ in those scenarios versus in the more typical brownfield scenario.

Speaker A: So you're talking now we're talking about Greenfield versus the brownfield piece.

Speaker B: Exactly, yeah.

Speaker A: So I would say, well, in the smart factory kind of context, even though it obviously extends beyond the smart factory, I would say that would certainly help where those customers were typically also coming from in my experience are um, designing with Industry 4.0 principles from day one. So modularity, interoperability, edge first architecture and zero trust security, which are all very important aspects of Industry 4.0. And I would say that actually that greenfield design approach prioritizes principles like having an optimal infrastructure in place for robotics and automation, similar to what you were referencing. Maybe not as uh, complete as the factory in the dark that maybe you have seen in China, more prevalent, um, but still having an infrastructure in place for robotics and automation that doesn't have those legacy constraints, the modular and scalable systems, future proofing them actually, and having baked in sustainability rather than retrofitted, which is a very important aspect when we talk about automation as well as the sustainability piece. So I think that idea would then also incorporate that fully autonomous operation potential, real time AI and machine learning as core functionalities and production lines. So I think that would go a little bit more towards that. But again I think you can, you don't necessarily have to apply that to a whole factory so it turns into a dark factory. But using those principles even on a smaller scale, um, like I said, those Industry 4.0 principles, I think that's what we're seeing there to help drive those specific projects, um, on Greenfield versus Brownfield. And quite honestly, I mean some of those would still apply to brownfield, at least in spirit, in my opinion. But of course you can do it from ground up. You would actually add it retrofitted.

Speaker B: Yeah, clear. And then if we drill in at uh, some of the technology elements of uh, a smart factory, whether it's retrofit or greenfield, we have, I'd say some areas of the tech stack, actually quite a few areas that are evolving very rapidly. So if we look at wireless sensors, there's been a fairly steady decline uh, in cost and then um, at the same time an improvement in performance which you know, of course impacts the ROI of um, of business cases. Right. Where something might not make sense five years ago and all of a sudden today, given the cost and performance trends, it's kind of a no brainer from Digi perspective. I think this is an area that you have a lot of expertise in how do you evaluate these trends or kind of monitor these trends? Because I, you know this is always a very difficult part when we're running projects and helping companies to understand what makes sense is that there's often this kind of. Yeah, the payback period is, you know, it's 28 months, does that, you know, kind of. And then maybe there are some qualitative benefits. They want to have a 20 month payback, you know. But then how do we value these qualitative uh, benefits? Do we want to postpone for three years? You know, maybe the sensor cost performance improves and the business case makes sense there, uh, so there's always these kind of borderline scenarios where you're trying to understand the quantitative and qualitative benefits and also the trend line for the technology to evaluate. Do we use the tech today? Do we use it, you know, do we push it down the line? When you're working with customers and looking at these kind of edge cases, how do you work with them to anticipate the trajectory of uh, the technologies in your portfolio and um, determine is this something we want to invest in today versus you know, down the line when the cost and quality uh, performance shifts?

Speaker A: I would say it's certainly true. So yeah, on this whole, on the wireless sensor cost plummeting, I certainly have, I think a specific opinion that it's certainly true that some wireless sensor cost has plummeted significantly in the last five to 10 years, almost, I think 70% depending what kind of sensor technology it is. And that can certainly fundamentally change the uh, ROI equation like you said. However, I would also say though that industrial IoT sensors in my opinion are a little bit of a different animal still. I mean it doesn't mean that they haven't been subjected to um, cost savings, um, or lower costs in general. But I would say the accuracy that you typically need um, for industrial use cases is different than obviously let's say what we have seen in phones and other ah, related or consumer sensors. So I think that's certainly an important aspect. So the price stop can certainly help enable mainstream plans to achieve productivity gains maybe more easily with um, less equipment downtime and then also less cost to implement it. But I would also say that it's maybe not always as significant um, from a sensor cost perspective. In my opinion then it's um, for industrial use cases, but then it is for sensor uh, use cases in consumer applications because that's where I would say the biggest price drop happens. So for example, what I have seen in the past and what I've seen in Digi as well is like if you look at wireless offshoots of established industrial sensor technologies which are not the sensors themselves, but such as IO Link. So wireless IO Link, for example, seems to get more traction and helps to drive use cases such as predictive maintenance as well. So I think it's probably more of a combination of technology, I would say in terms of having the predictive maintenance capabilities having. Also if you look at Brownfield again, retrofitting, let's say predictive maintenance, vibration sensor for rotating equipment. I think it's a combination of the connectivity technology, ie, cellular, if you want to make it relatively easy or sub gigahertz, it's the sensors but also maintaining the quality and the accuracy of the sensor and then all packaging that into something that actually customers can deploy easily also. So you don't have to, um, have huge installation costs that come with it, um, that can be easily installed maybe also by not highly skilled employees. I think the combination of it is, I think makes a difference and then wireless comes into play because obviously again that makes the deployment easier, specifically when we talk about brownfields. So I think it's probably a mix of a number of things, not just the sensor cost.

Speaker B: Yeah, okay, no, well noted. So the sensor, yeah, the cost of the individual sensor might not actually shift as much in um, industrial, but the cost of the entire system when you uh, consider connectivity and the deployment of

Speaker A: it, you know, which is a very important equation. And we have seen, you know, in my previous life also we have seen companies that are looking at, let's say predictive maintenance more like in an OPEX approach versus a CAPEX approach, you know, so it becomes more of a service that you as a customer pay for, almost like a subscription. So you don't have the capital expense of getting the sensor actually installed, buying the equipment, running the cellular connectivity as well to the different, um, sensors, but rather having an OPEX approach versus a CAPEX approach on the deployment side to make it easier as well.

Speaker B: Yeah, um, that's an interesting case that seems to be making more sense as companies become more integrated across the hardware and software stack. Right. Then you have one vendor that can provide that entire system. If you look at that acceptance of a OPEX model, what would you say is kind of the market acceptance or the market preference for that as opposed to a capex? Are we talking about 20% of the market or just kind of rough figure?

Speaker A: Well, it's a good question. I would say, I mean again, this is a little bit more of my direct Experience, uh, in the past, I would say that there is certainly a lot of interest in that kind of model. But if you. Percentage wise, I would say it's probably at this point, based on my experience, there's a little bit of a guess, I have to admit, Eric, maybe a third, um, maybe what they're trying to talk about, third a quarter, maybe in that range of customers that have driven that kind of deployment. I think there's a lot of interest in that as well. And I think the model in itself, especially if it can become very cost effective and from a price point interesting enough so that it's not, let's say $1,000 or $2,000 per machine or per equipment, um, it can be lower than that. I think that there is probably a specific point where this becomes more interesting from an OPEX perspective. Again, interest I've seen quite a bit for that. It's not an easy model to provide even for the company itself, the vendor itself, because as you can imagine, somebody has to finance the equipment as well, um, that the customer is actually leasing essentially, or subs. So I think the concept itself is good. I'm not sure if it hit that specific point from a pricing perspective where we have seen widespread adoption, but I do think the concept is extremely interesting, especially in predictive maintenance.

Speaker B: Yeah, yeah.

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Speaker B: Thank you.

Speaker A: And back to the show.

Speaker B: Right. I remember when Munich really got into this and I guess you need that kind of, uh, financial intermediary to come into the market in order to finance the capex to make this really scalable. What about on the connectivity side? You mentioned this earlier. So there's cellular, I guess, LTEM, uh, 5G are technologies that have, you know, matured recently. Do you see, uh, a significant move towards adoption of these kind of higher capability, uh, on campus networks or what do you find to be more common? Are people more relying on, uh, more lower cost or traditional systems? When we talk about enabling wireless, uh, sensors in a facility, I would say,

Speaker A: well, I Think it's a little bit of a, um, little bit of a mix. It depends a little bit on what kind of. If you stay with the sensor example, the sensor could be for the ease of deployment, could be something that actually is a connected sensor, um, like what we talked about previously, um, as it relates to let's say vibration temperature sensor for rotating equipment or electric motors. It certainly is much easier to deploy if it has a say a cellular interface. Especially when you look at maybe potentially employees or staff that is not necessarily a technician that's not necessarily highly skilled on the digital side. Conceivably you can have a connected sensor that is then easily deployed with cellular on a specific piece of equipment without any need for gateways or any kind of networks that would be actually be within the facility itself. So that's, that's certainly one, one use case where cellular connectivity can be interesting. Um, either because a, it's ease of deployment and or maybe if it's a more remote facilities. Of course that can also be then a little bit problematic because if you're indoor you might have not the cell coverage that you need. You might be around equipment that's not very conducive to connectivity overall. Um, so what we have also seen then is certainly a lot of sub GHz deployments, sub gigahertz networks that come into play there as well. Um, when it comes to sensor connectivity and that applies to predictive maintenance as well. Now that typically would imply that you do have somewhat of a um, gateway that actually then those sensors would connect to or the equipment would connect to. But that's certainly another angle um, from a technology perspective how those could be deployed. When it comes to the cellular connectivity that we mentioned before, I would say certainly still we have seen LTE LTE Cat 1 even you don't need that kind of connectivity um from a speed perspective for a sensor like that. But it's certainly more because it's ubiquitous in terms of coverage which can be a little bit of a problem certainly with LTE as well. Or nviot that's even worse recently. Um, some of those networks also have in sunset. So I would say often it can be LTE Cat 1, um, LTE Cat 1 bis is something that uh, we have seen a lot of traction in recently too. So essentially a little bit of a, of a hybrid between let's say LTE and LTE Cat 1. So you get, you get all the affordability and relatively high speed but still not super high speed that you would need for industrial IoT applications. But you don't have the coverage problems that you have with LTE M or MB IoT because LTE, like I said, LTE Cat 1 coverage is pretty ubiquitous. So we have seen that a lot. I wouldn't say that there was a huge rush towards lte M&MBRT MBRT less so. But LTE Cat1bis I think can be a very interesting technology. And then going forward if you look at 5G, even though it's probably two or three years out, about Redcap can be interesting for those as well. From a deployment perspective.

Speaker B: There's another area of the tech stack where we've seen a lot of very rapid development recently, which is um, edge AI on uh, modules on SOMs for things like vision inference for example, uh, at the machine if you need really low latency for a high throughput production line, something like this. Are you seeing a lot of adoption already of edge AI or is this more something that you see also out in like a two to three year time frame?

Speaker A: Well of course AI ML is in everybody's, on everybody's mind as you can imagine what we have seen so far. So I uh, would say it's probably again a multifaceted answer. I would say. So yes, we do see certainly interest in iml. I would say in general also though that a lot of customers might be very interested in it, but uh, don't necessarily always know how to go about it, sometimes even having some difficulties to believe it or not to find the use cases for it, other than knowing that they probably should be doing something. So what uh, we typically do then for those customers? Because for me the question is not whether uh, AI ML is significant or not, whether it's relevant. It's more than for me a timing question for those customers. So what we're trying to do is when we talk about new designs particularly also we try to make sure that those customers actually have uh, systems in place, soms in place for example that have capabilities that would support more sophisticated AI ML applications in the future. And what sophisticated means can vary a lot. So if you look at a, let's say sensing application, predictive maintenance, you can actually do that without aiml. You can have that, you can do that with thresholds as well. But you can have also sensing applications that might not need a lot of AI ML horsepower, where you might maybe perfectly fine with one and a half tops. You don't have to have 20, 40 tops for the use case. So we make sure that those customers actually have a platform that can grow with their specific Needs even though they might still experiment at this point in time and looking at uh, ways to prove the concept of using iml. So I would say that's a general statement that's true in many cases. Now there's pockets I would say where AIML can be very strong. And one of them you brought up already which is uh, I would say vision. That is something that computer vision is something where uh, we do see use cases which can also scale quite nicely by the way from a, from a requirements perspective, from a horsepower requirements perspective for IAML in terms of what the platform needs to provide there. But that is certainly for quality inspection, um, use cases, anything like uh, traffic management, people counting. So those use cases for image recognition, computer vision, those I think are well uh, defined use cases where we have customers that are interested in it, where you can actually have a platform where software is available also and those customers typically know what they're looking for. So I think those pockets are there. But I would say there's a lot of industrial customers when you don't necessarily have those use cases in mind directly where there's a lot of questions about well what exactly can I do? Should I do, how do I uh, take advantage of AI ML across those other use cases outside of computer vision?

Speaker B: Yeah, yeah, clear. Yeah. Okay. So this is very company specific challenge. There's another, let's say the other AI that has um, uh, been very prominent recently which is Genai. I know this is a bit farther from your traditional business but I'm sure nonetheless it's a topic that you've had to discuss many times uh, in the past years. Are you seeing real adoption cases for this uh, among your customers? Because if we talk about the challenge here of, of labor and enabling labor, Gen AI seems to have a very strong set of use cases around being kind of um, enabling this engineering copilot, this guide, this root cause analysis and helping people to sift through old PDF reports of faults and kind of identify potential hypothesis for uh, root causes for quality issues and so forth. So there seems to be a strong set of use cases around helping engineers to understand the operations. When you have this kind of high turnover and new people coming in who don't have the 15 years in manufacturing that the workforce used to have. Are you seeing already real cases of adoption or people primarily still testing and kind of doing small pilots and just seeing how this technology might fit into their operations?

Speaker A: Well I would say with respect to it certainly is the bus as we all know, but where really genuinely fits in the factory and where it's I would say maybe still hype, I would say we should probably separate a little bit signal from the noise so to speak. If you look at Gartner you probably have know, you probably know as well that Gartner has moved GENI in their Gartner hype curve from the peak uh, of inflated expectations to the uh, trough of disillusionment phase I think as of this year. And I think that certainly has some validity because I would say that Gen has some, certainly deliver some genuine breakthroughs. There's no doubt about it when it comes to for example product design optimization, you know where I can create, generate thousands of design alternatives based on certain constraints and parameters. Root cause analysis of course for faster uh, troubleshooting using Genai and of course then documentation and compliance automation, um, that can deliver very significant productivity gains. But I would still say, and there's some McKinsey research also related to that that ah, it still falls from a use case perspective probably mostly across four functional use cases which I think uh, probably like customer operations marketing and sales R and D software engineering. But I do see at least this point a much lower value potential for manufacturing functions because I think Genai definitely excels at content creation and synthesis but not necessarily at this point the numerical kind of optimization that drives manufacturing value. So like I said, it really works for product design. Through generative design it can help reduce the material cost and optimize for manufacturing. It can certainly help documentation generation and knowledge management and deliver productivity gains to that of course uh, generative AI generate code components can help and then process as I mentioned, process documentation and compliance automation where it's still hype in my opinion is when it comes to of course real time process control and safety critical decision making. So currently GENAI is not fast or reliable enough for manufacturing decisions, millisecond manufacturing decisions. And that goes a little bit back to our autonomous, autonomous factories, the dark factories that we talked about when they become more prevalent in which industries. So I would say the smart play in general for using Genai is again like I said at the very beginning is to augment human expertise, not to replace it and then generate maintenance procedures, for example for predictive maintenance from sensor data like we talked about. I think it's perfectly capable um, to deliver value there on productivity gains, training materials for technicians, uh, from historical incidents, uh, optimizing production schedules based on demand patterns. I think that is where the ROI for generative AI is proven today in my opinion. And in general I would say nail the fundamentals with proven edge computing and standard AI before chasing the whole Genai train. That I think is a very fundamental initial um, approach for me. And as far as our products are concerned also Eric, that is also the use case envelope in which I would see we can use Genai. So we have a remote management platform, device management platform, uh, uh, digital manager where I can definitely see Genai, um, help with pattern detection where can summarization of reports of device behavior capabilities like that. And I can see us deploying it there. But again the use cases are very specific and I think with the manufacturing they're more functional still in my opinion.

Speaker B: Yeah, it makes sense. It feels like Genai at this point still needs to be primarily in the domain of the, you know, the software development companies. And where I've seen the most success has been in companies that have a legacy software system that is uh, using these more traditional data analytics tools and then they're integrating Genai as a function within their system, for example to improve the uh, user experience potentially to grab data that otherwise you know, was challenging to grab and integrate it in from, you know, to access new data sources. But not, you know, not kind of gen AI native uh, solutions but really integrated into an existing solution as a component. Right, that, that seems to be useful but that you know, that's then product development and it takes time in industrial software to make the product really work smoothly and comply with uh, you know, customer expectations. Right. So this would probably be down the road a little bit. Mike, if we look at then this challenge of moving from a concept to us, uh, to scale. You mentioned this a couple of times that with edge AI, for example, companies kind of being in that period of saying yeah, we can understand that this is going to be relevant to us. We're not quite sure what use cases and this is a very natural problem. Right. Because um, you're then you're always in the case of making investments before you know what the actual result of that investment is going to be. Right. And that's a, that's a challenging thing for any management team to approve. What have you found to be most successful as a process for effectively doing pilots or let's say for de risking a use case before you go to scale. So I mean pilots in theory makes a lot of sense, but it's actually often quite difficult to make a pilot that accurately represents the complexity of the system at scale. Right. So you might have a pilot that works very well on one machine. You try to scale that up and then you figure out okay, but actually we've got 400 machines that all look the same, but they all behave a bit differently. And you know, and then this is just, ah, a much more challenging, uh, proposition than just making the system work on this one device. Um, so, you know, what process have you put in place to smooth that journey from initial evaluation through a successful pilot and then to successful scale?

Speaker A: Yeah, I mean, I think depends on how the definition of scale, I would say, I mean the sobering reality when it comes to IoT projects and pilots is still at about 70 to 75%, about never really scale beyond the proof of concept. And I would say in my opinion, um, that the failure pattern is relatively consistent. The companies start with technology instead of business problems, in my opinion. So in my opinion the difference between that pilot purgatory and the production success is prioritizing business outcome and then also the focus on scalability. So in the sense of what you talked about, I mean your example was more like, okay, I have machine A, but I have maybe also machine, machine type B, C, D, E, F, G that are not quite the same. I think that also I would fold that under the umbrella of making sure that you have a business outcome that is defined. And that would also mean that you're not just trying to test a piece of technology on a device, but you actually say, okay, I have these devices, I have this equipment, main shop equipment. And that is what I, these are my specific goals that I want to get out of it. From a business outcome perspective. And when it comes to the focus on scalability, I would say that again that avoiding that pilot purgatory, you can do that by designing for scale from day one. Because if your pilot requires any kind of unique conditions or excessive customization, it won't scale. And so that is why we, and why I personally also like the term proof of value a little more than proof of concept, meaning you're building pilots again with business outcome in mind, but also with ideally production ready components that are actually, or as much as possible production ready components that are designed to scale from day one. So I think that is certainly, um, something I would focus on, um, that we are focusing to make that happen versus just testing. Yeah. Can I theoretically do this? Sure. But then as soon as I say this is a good idea, I need to start building that product and so focusing on that proof of value, focusing on business outcome and scale with production ready components. I do believe that is, that is one way to actually reduce that, the failure rate of IoT pilots, in my opinion.

Speaker B: Yeah, yeah, good perspective. Yeah. I guess the POC concept or uh, MVP works better for consumer scenarios where uh, you can kind of radically change, uh, the solution later on once you figure out that the consumer actually finds value. But in an industrial scenario, you want to find a solution that is actually going to fit with the architecture and um, safety requirements and also the right cost structure, et cetera. Ah, so that proof of value concept makes sense here. My last question. We might have some folks listening in who are either younger and kind of figuring out the direction of their careers or potentially a bit older, uh, but also pivoting in their careers. If you look at the manufacturing environment in the U.S. uh, and you were advising somebody on where would it make sense for you to build skill sets to make sure that you're future proof in terms of your ability to contribute to the development of manufacturing in the US what would be a few areas that you would recommend people consider?

Speaker A: Well, it seems it's a good question. I would say we talked a little bit about in the beginning. I would say that uh, probably one of the hardest roles to backfill are maintenance technicians, technicians with digital skills. Um, I think that's probably where one of the biggest massive, um, skills gaps are. So people essentially that can bridge operational technology, information technology, I mean the age old uh, ITOT gap that we talked about for years now, decades, I think that still is an issue and I think that's accelerating quite a bit. Um, so anything that's related to digital skills in OT environments, simulation software skills, that I do believe is one of um, the ways how even the younger generation, as you go into the job, into the manufacturing environment, can actually make a difference where you actually have a worker that is not just mechanically inclined, technically inclined to, but also has the digital skills required. And maybe then we can finally bridge that OTIT gap.

Speaker B: Yeah, great. Well Mike, thanks for your time. Um, anything that we didn't touch on today that you think is really important, uh, for folks to understand?

Speaker A: I don't think so. I think we had a pretty good, we touched on a lot of different subjects, I would say. I do think maybe one thing, maybe I find interesting as well, when we talked about pilots providing value and what maybe issues are, uh, with respect to adoption, we talked a little bit about adoption in the context of the technology itself, aim itself, when it comes to building products or developing a solution. But what we also have seen, which is interesting, I think, is that operator trust can be an issue. So let's say you have a solution in an industrial setup that actually um, uses AI ML, for example, building operator trust requires a multifaceted approach as well. So AI systems we touched a little bit about on AI in general, but AI systems that actually can provide clear reasoning or recommendations, those are the ones also that can deliver the highest acceptance rates, uh, so that operators do not feel like they're replaced by the system or they don't know what the system is doing. Focusing on more of a do a small thing first kind of setup, I think that is also a very important piece of the puzzle when it comes to deploying IML and actually have human acceptance as well in the workplace including the IT and IoT alignment. Um, which is another challenge as you know where uh, industrial IoT projects often have organizational silos, um, as their biggest obstacle and solving that problem also having something that's more of a top down approach where the entire organization buys into it and understand what it is that is being done, why it is being done and how it actually benefits the organization, every single individual. So the buy in across the organization I think is a very important aspect. And again that goes back to your question about what kind of people do we need in the future. Uh, I do believe it is the people that actually can bridge that IT OT gap from a skills level and, and are able to combine those skills in a single role. Uh, I think that will make a big difference to drive all this forward at a much larger scale.

Speaker B: Yeah, yeah, that's a great point. I mean this is the management challenge of understanding how to make your teams comfortable using AI and other systems. But it's also the challenge of every employee uh, in the company to make sure that they themselves remain relevant and increase their value as systems are digitalized that they're able to use those tools

Speaker A: you need digital translators so to speak. You know, so people can actually talk. Both sides of the equation. You know, in terms of the organization responsibilities, I think that's a, that's a very important role and I think that is certainly one of the main items that we need to somehow solve some problems that we need to solve, challenges that we need to solve.

Speaker B: Yeah, uh, great. Well Mike, thanks for your time again.

Speaker A: Okay, thank you Eric.

Speaker B: Thanks for tuning in to another edition of the industrial IoT spotlight. Don't forget to follow us on Twitter IoT1HQ and to check out our database of case studies on iot1.com. If you have unique insight or a project deployment story to share, we'd love to feature you on a future edition. Write us at eric walenza@iot1.com.