306: From Systems of Record to Systems of Action: Putting AI to Work in Manufacturing with Sanjay Brahmawar

0:00So you start from a problem that the manufacturer is having and then you work backwards, make sure you understand that pain, and you figure out a painkiller for that pain. Our mission is to bring technology and bring innovation to make these manufacturers champions. Your title is not what dictates whether you're a manufacturing leader or not. It's your attitude, you know, in the office, on the shop floor, in the operation, wherever it is that really sets that apart. It's about amplifying the human potential. All right, we're talking about manufacturing champions today and how artificial intelligence and agents are empowering everyone from procurement to the front line to operations.

0:44We're going to be sitting down with Sanjay Brahmawar, CEO of QAD Red Zone. Let's dive in to today's conversation. Well, Sanjay, let's dive on in. All right. Welcome to Manufacturing Happy Hour. Thank you very much. It's awesome. We've been talking for quite some time, Chris, and it's such a pleasure finally to meet you face to face in this lovely location. You know, we're in M Hub. What is this place all about? Do you want to say a few words? Yeah, it's funny. Like a lot of times I'll have my guests pick like their favorite bar or brewery for the episode. But to an extent, I picked this venue today because as we're recording this, we're right in the thick of IMTS.

1:23And I'm like, I know a good spot not too far where we can have this discussion. And yeah, M-Hub, you know, it's an awesome facility here in Chicago's West Loop. But more than that, it's a platform that really brings together the infrastructure for enabling hard tech development and helping young companies thrive. And then it also aligns them with networks of entrepreneurs, advisors, as well as capital and investment that goes along with that. So I think it's a very cool model, especially as hard tech startups become more ubiquitous, more frequent in this space that, you know, I hope other spots are able to replicate as well.

2:02I mean, what have your first impressions been since you've been here? Oh, you know, I love that I've just had a chance to talk to a few people and I was, you know, there's almost like 300 startups out here. It seems like there are some great ideas and a lot of room for innovation. and you know it's exciting for us also because you know when we think software and we think about you know manufacturing and where software can be used in manufacturing particularly now when you think about ai i think these kind of hubs are amazing because this is you know when you think about nations becoming more competitive in manufacturing you need a lot more of these kind of innovation hubs you know so i love it i hope i hope to be able to contribute here you know oh yeah well for an innovator like yourself i think it's a perfect spot for the conversation so we're We're going to get into the ways that you and your organization are innovating, but I want to get a little background, some personal questions out of the way first.

2:55So one of the things we like to explore on the show is people's unique perspectives on the manufacturing space, because there are a lot of common themes that come up on the show, and rightfully so, because there are a lot of issues that are front of mind for manufacturing leaders across the country. But I'm curious, from your perspective, before we start talking artificial intelligence and systems of record and action. I want to hear something that is maybe a truth that a lot of manufacturers seem to accept, but you feel is wrong. Let's start there. Well, you know, I think one of the things is, you know, manufacturing has been, I guess, an industry where it hasn't been the fastest on adoption.

3:39And so therefore, you know, there is a a certain amount of tech debt and certain amount of modernization that has to be done within manufacturing. And so one of the things I think a lot of manufacturers feel burdened with that modernization and are concerned that they would have to invest a lot of capital, a lot of OPEX to be able to, you know, wipe out that tech debt to be able to avail the innovation that's possible now with AI. And I think that's a wrong understanding because today, with the help of AI, it's possible to do both. It's possible to be able to address modernization as well as take AI to be able to drive outcomes, more productivity, more efficiency, more capacity.

4:24And so both can be done in parallel. And I think that we do see some trailblazers where manufacturers are doing that. you know. So the area, if I were to rephrase what you were saying, they feel there are a lot of prerequisites before they can start going down the path of artificial intelligence. But what you've seen is that you can do both in parallel. Absolutely. Absolutely. And I think the other thing, of course, is that, you know, in manufacturing in U.S., for example, there's half a million open jobs today. So one of the misunderstandings, I think, is that AI is leading to job losses in manufacturing.

5:00Well, that's not true. If you look at all the indicators, actually the open positions in manufacturing are just growing. In fact, this is going to be roughly about 2 million by 2030. So in manufacturing, we are struggling to attract and bring more talent in. And therefore, the role of AI is not to replace the human being. The role of AI is to make that human being, that talent, 3 to 4x more powerful. How do we create more capacity? So it's a capacity conversation in manufacturing, not a cost reduction conversation. And we're going to get into that very soon because, you know, I would say one thing the manufacturing happy hour audience generally agrees with across the board is we've been talking about automation on the show for a very long time.

5:43In fact, this podcast really started as an automation focused podcast and automation, artificial intelligence, realizing that these things are job creators versus taking away jobs as you mentioned there are more jobs than available than we can fill right now so maybe let's go into one of your unique areas your focus around artificial intelligence which if I were to summarize it and in the spirit of having a conversation as if we're having a drink with one another I'd love for you to describe this as if you're having a beer with someone what is define like what a traditional system of record is and how where you're focused is on a system of action first i don't want to lose that point about the beer okay because i came to i thought it's happy hours i thought i'm sure so after this we probably will have the beer and and we should celebrate congratulations on your 300 episodes that's that's quite amazing To answer your question, I think when you think about manufacturing and our thought process there, Q80 Red Zone, we've been in the manufacturing space for the last 40 plus years.

6:53So we have a lot of heritage and a lot of deep understanding of the manufacturing domain, particularly when you think about regulated industries like automotive, food and beverage, med tech, industrial. So we understand that space very well. And so when I think, you know, what is the mission of QAD Red Zone? Our mission is to bring technology and bring innovation to make these manufacturers champions. So that's why, you know, our event, our annual event, we call it Champions of Manufacturing because that is the, that everybody in my company is fixated with how do we make these manufacturers champions.

7:33Now the question is how do you do that, right? And the way we have gone about bringing, so roughly about 10 months ago, we introduced our AI capabilities to the world, and we called it Champion AI. And the reason we called it Champion AI is because of the first topic that I addressed, which is in manufacturing, it's about amplifying the human potential. So that's the whole principle of Champion AI, where we are building agents, we are building assist capabilities, we are building orchestration layers, intelligence layers, all with the objective of being able to amplify the human potential. And these intelligence and our intelligence layer, Champion AI, is expanding from the top floor to the shop floor.

8:19So when you think about, of course, all the operations from procurement to planning to execution to then really running the lines on the shop floor and connecting that, that's where we believe our value is. Yeah, what I like about the way you describe it with Champion AI, as it's called, on this show, we describe it as this is a manufacturing podcast disguised as a leadership podcast, right? So we're very much, I think, you know, use the word champion. I use the term leader because we're all focused on how do we amplify the people in this industry. And you talk about champions from the front line to the office.

8:58I say the same thing. Your title is not what dictates whether you're a manufacturing leader or not. It's your attitude, you know, in the office, on the shop floor, in the operation, wherever it is. That really sets that apart. And, you know, it starts from, you know, I guess the talent and the people that you have in your company. Like, my own background, I started my career working at a Honda factory assembling engines. You know, so manufacturing is my passion. I love manufacturing. That's what I love doing. And if you interact with the people in our company, most of them come from manufacturing background.

9:33Because when we interact with manufacturers, we do find manufacturing is a show-me world. It is not a tell-me world, right? So when you don't understand the domain, you don't understand the regulatory processes, etc., you're not able to have an effective conversation with a manufacturer. So I think that's also very important in the talent that you bring into your company. To be able to really have that connect with a manufacturer, you need to understand the domain. That domain expertise is very important. Yeah. You said something that I was going to ask you about that I want to go into a little more detail on, and that is your background on how you started your career assembling engines at Honda.

10:13Paint the picture of what that was like back in the day, what manufacturing on the front line looked like back then. Well, first of all, nobody in my family ever became an engineer. So everybody is a fighter pilot. I'm the odd one out who went to engineering. But look, engineering at that time was very interesting, right? There were no mobile phones. There were no dashboards and fancy. Everything was pretty much nuts and bolts and literally reports that you used to be able to understand. You had Kaizen methodology to be able to really do continuous improvement, quality circles. And so I think there was a lot more emphasis on the engagement part about working in a manufacturing line.

11:02So in my early days, I noticed the challenges that you have on our production line. When you lose minutes, when you lose productivity, when you have quality issues, all of those challenges you see that firsthand when you're on the on the shop floor yeah today I think it's a different environment right we have such great opportunity today with AI and technology to be able to not just plan but then to be able to be proactive in the execution like today when there is a line lead who's standing on a line and running that line they don't only know what the plan was they are actually able to use ai to try to figure out how is that production line going to run what are the top two issues i'm going to experience we have an agent called the line lead agent champion ai agent and that agent is working with the human line lead to help that person think through what are the top two issues you're going to have how how is that going to impact your oee so instead of this individual spending a lot of time collecting data and preparing, et cetera, which is what I remember from my days, now that line lead can actually focus on the issues, focus on making sure that that production line has no disruption.

12:23And that's a big change from the time when we, at least when I started my career. Do you reflect on that often, right, where you think of what you were focused on? Because you were highlighting, right, what happens when you lose production, when you lose quality, right? Has that ultimately shaped how you think about what the future of manufacturing should look like, where the individual on the front line is no longer firefighting necessarily in that same way, but really maybe proactively looking at how they can do what they're doing even better? Would you say that's fair? How would you describe how it's shaped your perspectives moving forward?

13:03Yeah, well, look, you know, I mean, I think some things will not change in manufacturing. You know, if you think about manufacturing, what is manufacturing? Manufacturing is actually managing constraints, right, to the best possible. So you always have inbound constraints, material storage constraints, production line constraints, people constraints, outbound constraints. So it's all about managing those constraints in the best possible way to be able to produce your goods, ship your goods, and get your measures. So that's one thing that doesn't change. The second thing is, in manufacturing, it's all about outcomes.

13:38It's not so much about the fancy technologies or what you're using. So I don't find any of my customers asking me, Sanjay, which LLM should I use? They're not talking about it. They are interested in how does it impact my OEE. How does it impact my OTIF? How can I improve my margins? How do I increase my capacity? So the customers are focused on outcomes. And I think everything and every innovation that we bring into manufacturing environments has to have that thought process. So you start from a problem that the manufacturer is having, and then you work backwards, make sure you understand that pain, and you figure out a painkiller for that pain.

14:21You know, the manufacturers are not interested in vitamins. They are interested in painkillers. And so that's our philosophy. That's the way we work in Q80 Red Zone, really be on the shop floor, understand the manufacturing environment, and then work backwards into what is the innovation you need to bring in. So we've been in a world of manufacturing for a while where, to one of the topics we talked about earlier, I think everyone looks at their system of record, ERP, if you will. They look at that as a holy grail of information with inside of the facility. I'd be curious to hear what the manufacturing world was like, maybe even just like five years or so ago, where people were more in that mindset and how you've been helping it move to more of an action-oriented mindset, a system of action.

15:17Paint the picture of what it was like and then maybe whether it's through an example or a story, paint a picture of what it can look like today. Yeah, I mean I think look, systems of record are always going to be important. Why? Because as I said, you know, manufacturing is a predictable process and it needs to be managed in a structured way and that's how you get an output and then you can manage your outcomes. So those processes are documented in your system of record and then you have your transactions and your data in your system record so that's your one source of truth and you you you have your data in there but systems of record you know traditionally have been ones that are you know capturing data capturing transactions reporting you know kind of it's it's very reactive whereas manufacturing is not reactive right manufacturing is 24 by 7 things are happening on the shop floor and so So how do we take that data and that domain understanding those processes and how do we convert that into a proactive?

16:19So how do we predict? How do we understand challenges before they happen? How do we also provide the leaders on the shop floor or in the offices or the procurement, for example, what do they do with the exceptions and the problems when they hit the problems? So I can give you an example. let's assume there are you know five orders to be shipped on a particular day okay if you have a problem in in supply delivery do you know what happens in a manufacturing shop everybody runs very hard and they move mountains to ship those five orders because otif is very important on time in full and that's the metric and everybody's trying to meet that i lived in that world in the automation space for exactly yeah now with the help of AI what we can do is to use manufacturing intelligence to be able to figure out okay we have supplier issues on these five orders we can check the contracts very quickly to understand which of those orders have contractual penalties to the top two ones have contractual so yeah everything has to be done to make sure that those two are delivered it could be possible that there's inventory somewhere else that that can be used for a third order.

17:36That the system understands because AI is looking across the board. And then the balance too, the system says, well, we can go back to the customer and we can renegotiate the delivery date because we know that customer has enough safety stock and we are assured about the inbound delivery dates. And that way you can strike a balance between your profitability as well as your customer satisfaction and your execution. That is what we call manufacturing intelligence. So that's the way it changes, you know, to become a system of action rather than a system of record. Yeah, and that's a very concrete action I think a lot of people can visualize, right?

18:12I love the five orders example because you talk first, like, how do we prioritize, right? Where are we going to get penalized? Those two move to the top of the list, right? Where's one where there's a plan B that exists, right? A different part of an inventory that can help complete that order and get that out. And then, again, another action. Who do we have to have conversations with to say we need to renegotiate? when this is getting shipped out. So perfect example of the different actions that can occur and when they can occur when you have artificial intelligence to help you prioritize and get visibility to that.

18:46And so that is the shift, right? Moving from a system, not to say that the system of record is not needed. System of record is needed, but now with the help of Champion AI, we can actually convert that to a system of action where the human being is, in fact, spending their time on managing those exceptions, those challenges, and those disruptions. And hence, you know, their capacity is utilized for those and to do that in the most optimal way, make the best and the most optimal decisions. So I have a couple questions based on that then. I'm curious where we've seen this play out in real time so far.

19:23There's another specific example you could share. but also I'm curious to hear what manufacturers need to overcome to be able to implement something like that as well. So can you give me another example of where this has played out in the real world? Yeah, I mean, let's take the inbound side, right? So if you think about all manufacturers have to procure their either direct materials or indirect materials. Now, a buyer spends a lot of time obviously securing a supplier, setting up the contracts, etc. But then after that, a big portion of their time is spent on trying to monitor and understand how the deliveries are actually executed.

20:09Are the materials coming in on time? Because if the materials don't come in on time, we're going to have delays and there's issues. You could have a stock out. You could have issues and disruption in the production line. It's not unusual that almost 40 to 50 percent of the buyer time is actually in trying to manage that communication with the supplier. That doesn't happen to the system. It happens through emails and conversations and messages, et cetera, you know, and then new schedules, new dates, et cetera. So the buyer has to keep on top of it. We built an agent that effectively works on what's called the messy inbox.

20:47It understands all of that unstructured communication, combines that with the structured data in your procurement system, and then literally saves the buyer 40 % of their time. So now the buyer can actually spend their time on literally onboarding more suppliers, creating more capacity in terms of inbound, and also managing those very few exceptions that they really need to manage. So you see, you suddenly increase the capacity of that buyer by 40 % or so. And that's a great example of where, you know, adoption of, you know, an agent, adoption of Champion AI is, you know, creating a significant difference to the way of working for that individual.

21:32Now, to your point about what's the barrier to adoption. So, again, two things. One, there is a myth or at least there's a misunderstanding that you have to do a lot of upgrades and modernization to be able to use AI. Now, that's not true. It is possible to use AI with the existing landscape that manufacturers have. I often tell my customers, our job is to meet you where you are, bring AI to you, where you are not to have you go through six months to a year of transformation for you to be able to use ai so that's kind of one thing and that's basically a mindset and we have to make sure that everybody gets educated around that and breaks that that mindset or changes that mindset and then the second thing is about um you know kind of um starting small working on a particular use case and driving outcomes on that use case.

22:30And I think that's also really important because otherwise, if you start with like six or seven initiatives, often they end up with just proof of concepts and you're not able to really create outcomes. The ROI is not visible. And then there is disillusionment and I guess in some ways, it's a bit of a setback, right? So adopting AI, it's also very important to be maniacally focused and prioritize what is the one or two pain points that you want to go after what are the specific use cases and are there clear agents or orchestration layers that can really help you with those problems so i think those are the two things that i think you know are really relevant for manufacturers today i have a general question in that case you mentioned you meet manufacturers where they are and in turn help identify the use cases of where they can adopt.

23:22So generally, where are manufacturers? Where are you seeing, where are they today? Are they in a spot where they're actually ready? They just don't know it? Are you seeing they're in a spot where there's a little bit of homework to do? What do you typically see, Sanjay? Look, most manufacturers have quite, let's say, quite a diverse landscape of applications, right? They all have an ERP. They're all using some form of an MES. They use a QMS, CMMS, they might have some other execution systems. So it is a diverse landscape and it's very important that they're able to bring information together from all of these different applications or tools that they're using.

24:03And then there might be Excel spreadsheets and other kind of tools that are always there, right? So most manufacturers are in that kind of a situation. They have legacy and they have some modern technologies. And these legacy and modern technologies are working together and they have people that are spanning across that because they understand how all the systems work with how newer systems need to work. And I think AI can actually bridge the gap quite well because it can allow them to have that integration with their landscape as it exists today. So now you're using AI to get a lot more productivity out of your existing systems landscape, which I think is the big advantage.

24:53Because in the past, there's always been the conversation, you have to do a lot of digital transformation. My conversation with my clients is, no, you don't need to do a lot of digital transformation. You have your current landscape. You can continue progressing, modernizing that at a pace that is, you know, relevant for you. But you can immediately, right now, take the advantage of AI. So that gives you the benefit and gives you the productivity uplift. Let's talk a little bit more about use cases as well, because the stories we've been talking about so far, first example was really around scheduling, right, when things aren't necessarily going to plan.

25:29You also talked about getting a 40 % increase in capacity for someone in procurement when they're able to work on the things that are the least transactional and most important to them doing their job in a more strategic, prioritized basis. What are other use cases you're finding these days? You know, I think you have to think about the end-to-end manufacturing process and you can identify areas where we specifically focus on a use case. So, for example, on the shop floor, you have quality agents that are specifically looking at the quality process and being able to really help identify issues way before it becomes a quality issue.

26:19You have maintenance agents that are able to monitor and plot and predict areas where there might be issues within, for example, to avoid downtime, etc. There are, like I gave you an example, line lead agents that are working specifically on that. There are agents that are for supervisors who can look across multiple lines and see how their production floor is operating. You know, what are the differences between the lines? Why are those differences? And how can they make sure that, you know, all of them are stepping up in terms of their OE? So those are kind of, you know, areas and use cases on the shop floor.

26:57And then when you think around the entire manufacturing environment, the procurement, the outbound deliveries, the end-to-end process execution, looking at balance between EBITDA and output delivery schedules, OTIF, those kind of areas are very specific and obviously valuable to manufacturers from an outcome perspective. Yeah. Want to talk specifically about people for a while, right? And they've been a part of this entire conversation so far. But I'm curious, what does this shift mean for people on the front line and also in the back office, if you will? I'd love to understand that because if we are getting these 40 % increases in capacity, like we talked about from a procurement standpoint, what does this mean for them as folks that are powering this operation?

27:55And what does this mean for the overall business? Yeah. You know, I think there is a certain amount of change management, of course, to be done. And I think part of it is education and hands-on experience, right? Theory never cuts it. It's about creating demo environments and creating sandboxes where people on the shop floor or within the office areas can actually experiment with agents and understand what outcomes they can drive. So that's kind of one part of it. But I think the second part of it is improving the quality of work for the individuals in manufacturing. so that the humans in the loop can actually focus on the more value-add areas, the decision-making, the problem-solving, the innovation side, and leave the agents to do part of the automation around collecting the information or analyzing the information to bring it in terms of what are the top two things that we need to spend our time on.

28:56All of those kind of activities can be done by agents, right? Agents don't need to sleep. they can do that 24 by 7. So you can tell, so what we do is we map all the personas in manufacturing and we create these agents that become champions for those personas. So you could have a planner, you could have a line supervisor, you could have a plant manager. Each of these people have their own agents and these agents, they can ask the agents to do some of the mundane tasks, which then allow them to focus on the more interesting and let's say with more value and decision-making jobs. So that's one aspect of more engagement, more excitement in the work that you do.

29:35And I think the third part, which is very relevant, is attracting talent into manufacturing. We talked about the deficit, half a million open jobs. How do we bring the kids, I have two young boys, and how do I excite them about, hey, manufacturing is a great career. Join manufacturing. don't all think about going and building software companies, but manufacturing is a great space. Well, when you create technology with AI, and the engagement is quite a rich engagement, the interface is like a chat interface. It's like a clawed interface. Those kind of excite the younger generation. That kind of ways of working are what they're used to.

30:21And so when we can create that kind of an environment, we're going to be able to excite some of the younger generation and bring them into manufacturing. So I think that's the third part that I also feel is quite important when you think about the people aspect. Yeah, I was actually, I was going to ask you about the next generation, but I think it ties into what we were just talking about with the different type of agents that exist that serve as champions for the people and roles and quality. You talked about your line lead agent, talked about procurement. We've covered quite a range in that regard.

30:50And I'm curious, can you share a story or an example that illustrates where by having this type of artificial intelligence, these agents, where that has increased someone's job satisfaction, the type of work they're able to do that's more fulfilling? I'd love to hear an example about that because I do think that ties directly into what you're talking about. Hey, if we're going to get people excited about manufacturing, particularly the next generation, your young boys, like that's what it's going to take to make it happen. So do you have a story that kind of illustrates that? Well, you know, look, I've been in the company now almost 16 months.

31:27And so one of the things when I joined Q80 Red Zone, I did was really get out and, you know, try to visit as many customers as I can. I think I visited 70 odd customers in my first 100 days. And the whole point was walking the shop floor. And, you know, that's where you learn, right? In manufacturing, when you walk the shop floor, you interact with the line leads or, let's say, the workers on the production line or the supervisor or the plant manager. And you start understanding what are the challenges that they have. And so, you know, we are in 6 ,000 plants globally. And, you know, we measure, you know, the production runs of these plants.

32:09And so on an average, we have statistics that show plants are achieving, on an average, 26 % productivity. They have the plants that are using the technology and AI are achieving 80 % engagement on the shop floor and 35 % better retention of talent. So there is enough evidence to show that if you use innovation and you use AI, you can improve your productivity as well as your worker satisfaction. and let's say now to your point about give me a practical example so you know that we have red zone which is basically software that really empowers the front line you know historically all the technology the ipads the the computers were all given to the top floor right to the to the people in the offices and they were the ones working with technology where the people on the shop floor really had paper and pencil now we flipped that over we said no let's take technology to the point of work.

33:08That worker has an iPad, which has all the information about that production run, OE disruption, everything right at their fingertips, all the documentation, everything. They don't have to go and do, you know, spend any time on paper or pencil or go to any other workstation. They have it there. And I went on the shop floor and I was this lady who was, you know, a lying leech who was taking me around and showing me how they run and how they use Red Zone to know exactly what's happening, when it's happening. They have these TVs all around showing with colors exactly what's the output of a particular line.

33:45And they have a bit of competition going on because several lines and they can see all the production lines. At the end of that entire tour, one thing that was so evident was the pride she had in how enabled and how independent she was, right? She didn't need the supervisor to tell her anything. She knew and she could tell the supervisor exactly how things stand. At the end of that, she told me, if you take this away from me, I'm going to kill you. That kind of showed me, you know, when you bring the right technology and innovation to somebody who is working on the production line, it changes.

34:20It completely changes their demeanor, completely changes their excitement and their passion about what they're doing. And I think we need to do a lot more of that, right? Yes, her blunt response is the exact type of candid story we're looking for on Manufacturing Happy Hour. I mean, one thing that sticks out about that is you're putting the decision-making power into the hands of the folks on the front line. Because in the past, if you don't have a system of action, you need to wait for someone, usually in upper management, to walk out on the plant floor to say, hey, there's this big macro problem we've seen.

34:54And you, Mr. and Mrs. Individual on the front line, now you need to go solve it, right? That's not a fun situation being. All of a sudden, you're under pressure. You're being told. and you know so there's a report you get there you try to do a huddle you're being told what to do etc now suddenly you have a situation where that line lead that operator right knows exactly what the issue is and they are you know proactively planning the actions that are needed to avoid the disruption how empowering is that you know and I think that's what AI can do you know so yeah I mean that's the exact type of scenario exact type of situation where artificial intelligence makes a huge difference.

35:32Like you were saying, you were just going through all the macro examples, the macro stats, I should say, of how this solution, how artificial intelligence has helped improve worker satisfaction, retention, etc. Excellent story as we get to the end of our conversation. I do have a couple more questions. One of those is around US manufacturing specifically. I'm very curious to hear your perspectives on this, and I'll probably add some of mine as well. How do we get folks to find the courage to take on this type of new technology to bring it into their process, right? You said much earlier on in the conversation that you don't necessarily need every single prerequisite to make it happen, right?

36:14There are things you can be doing today. So where do or how do leaders find more of the courage to take on some of this across the U.S.? Yeah, I mean, I think, you know, one, it starts with leadership, you know, and making sure that you can help the leaders experience the technology, see the power, and then, you know, help them understand what it would do to the overall environment that they manage. So one of our own lessons, for example, was we created an agent for the line lead, and we went about and talked to several customers and we were trying to help the line leads. Now the line leads got it.

36:54They were like really excited. But we didn't spend enough time talking to the supervisors and helping the supervisors understand the potential. And so that was a lesson that we learned. You have to start with the leadership to help the leadership understand that this democratization of information knowledge is so important for them to increase their capacity, for them to increase their OE, for them to reduce their problems and disruptions or quality issues. And when they can make that association, when those leaders can make association between the innovation, the AI, and outcomes, which is what I think is very, very important in manufacturing.

37:35AI, for the sake of AI, nobody is interested. Right. You know, nobody in manufacturing is sitting there waiting for somebody to give them AI. Nobody. They only want it if it impacts their outcomes. Right. So therefore, I think there's a very important job to do, which is to go talk to the leader so that they understand how AI can help them drive up better outcomes. And then with that, the second part, of course, is to bring agents or solutions that really hit the use cases. They're not generic. Because think about it. We have limited talent and capacity in manufacturing. I brought something like 30 of our clients in May this year.

38:22We brought them together to do a session on AI. And, you know, I got three messages from them. Message number one was, hey, Sanjay, we don't have any extra money, okay? But we are carving out 5 % to 7 % of our OPEX IT budgets because we want to invest in AI and we want to understand what we can benefit. it. So they are carving out some money to really drive and use innovation. But ROI is important. You know, fast turnaround is very important. The second thing was we're struggling to attract AI talent. It's not easy to attract AI talent in manufacturing because most of the AI talent is being recruited by Anthropic, OpenAI, you name it, you know.

39:02So that's the second thing. So that means that we don't want to build everything ourselves. Don't give us a blank platform and then we have to start building. We need real solutions that drive outcomes. So that was the second message. And then the third, I think, was the most important message, which was trust is very important in manufacturing. We run 24 by 7. We run regulated. We are in regulated industries. We have governance, auditability, traceability, and we want to protect our alpha. So we want very high level of assurance that, you know, all kinds of models are not learning from our alpha and our data and everything is protected.

39:40So trust, governance, therefore we are first and foremost inclined to work with our partners who we've been doing business for quite some time and understand our environments. And so I think these are the kind of messages we got from our customers. And I think those are things that are very relevant in manufacturing. And as we bring innovation, we have to be very conscious about those aspects. Yeah. One question I have for you as a leader as well, because I like how you highlighted, you know, carving out OPEX, struggling to attract AI talent, and then talking about trust as well. This is talking about trust in a general standpoint, because I think that is one of the barriers people have around AI.

40:20You know, as a leader in the industrial technology space, you know, how have you been effective at establishing trust? Because I feel like when there's trust in general within a culture, that allows there to be trust in new technology and new tools. So I'm curious how you've managed that as a leader of your own organization, maybe how you've seen other leaders managing that. Yeah, well, I can tell you, when I started, one of the challenges we had is we had quite a lot of issues around product quality and response times. And so when I was listening to my customers, they told me, look, we love Q80 Red Zone, we love that you have 40 years, but some things are challenging.

41:01And so I brought the entire company together at the end of the, like literally at the end of the first three weeks. And I said, hey, for the next one month, we're going to do 24 by 7 bug fix. We call it Operation Bug Kill. And, you know, it obviously sounds really funny and we made T-shirts, et cetera. But the point really was to make sure that we can, you know, stick up and deliver to the trust that our customers have in us. and make sure that our customers get the service, the support, the quality that they expect from us. And then the second thing is, within the organization, shifting the focus on adoption.

41:43Because in technology, the biggest point is adoption. Selling technology, everybody is maniacally focused on that. But the point is if the customer is not getting value out of that technology, The likelihood of you going and asking for their trust in solving another problem is very low. So getting the organization very focused on adoption, whether it's through implementation, whether it's through coaching. We do a lot of coaching on the shop floor into how to use AI and our red zone and our adaptive ERP capabilities. Through that, building that trust with your client that your focus is on adoption and outcomes and not just on selling the technology.

42:28And I think that is also, you know, we have been working on that in our organization to make sure we stand up to that trust that our clients have with us. Yeah. No, excellent examples. Last question I have for you. What would be the one thing you want folks to take away from this conversation? If you had to pick just one, or maybe we've been talking about systems of action, what's maybe one action you'd love to see them take or they can take to help move the needle forward? Yeah. I mean, I think, first of all, the one message I would give is that, look, this is a watershed moment for manufacturing.

43:02I think this is an absolutely awesome opportunity for manufacturing to leapfrog. And what I mean by that is, and there's always been this challenge around, there's a lot of digital transformation to be done and we need to wipe out all this tech debt before we can really. And I think this is a big opportunity now for manufacturing to do both things in parallel. There will always be some amount of transformation and some tech debt that has to be taken, but a lot of leaping can be done with the help of the AI, which really is focused on driving outcomes. So, you know, my message to all manufacturers is, look, the cost of not acting is far more now than the cost of acting.

43:46So, you know, this is not a matter of like, you know, sitting there and waiting and sitting on the fence and observing. If U.S. manufacturing wants to be competitive, if U.S. manufacturing wants to be the leaders, they have to act and take this opportunity. And that's my very direct message to all the manufacturers. is, you know, don't miss this moment. And the second thing I keep telling everybody is, don't think you are, you've missed the moment also, right? Don't think you're so far behind that, you know, you're lost. No, nobody's won the race, okay? You know, speed, of course, is going to determine your success.

44:26So, you know, take a step, move forward now, break that inertia and avail this moment. That effectively is my message. Yeah, no, great summary. Great conversation from start to finish. Appreciate you taking us through specific examples, not only from your own career, but things you're seeing across the hundreds of manufacturers that you've been able to visit during this time. Sanjay, thanks so much for being on Manufacturing. Happy hour. Thank you so much. I really enjoyed the conversation. I'm looking forward to the beer now. Yes, absolutely. Next time I'm bringing beers to the interview. Awesome.

45:00Cheers. Prost. Prost, exactly. Hey, thanks for tuning in. thank you to Sanjay for jumping on this episode. If you would like to connect with Sanjay, learn more about QAD Red Zone, head over to the show notes page at manufacturinghappyhour.com. Make sure you're subscribed to Manufacturing Happy Hour wherever you get your podcasts. And with that, stay innovative, stay thirsty, and we will catch you again next time.

45:36We'll be back.