Empowering the Frontline: Talent & Transformation in Manufacturing

Jeff Kavanaugh:

Companies are excited up to 40% improvement their thinking from AI and related technologies. And yet our research also showed only 2% feel like they're ready across these five dimensions.

Rajiv Puri:

And for a long time, manufacturing organizations have worked in a very, very siloed manner. But now with generative AI, knowledge is becoming democratized and people can use it to contribute not just within their own batch, but they can contribute to larger enterprise batches.

Jeff Kavanaugh:

We'll jump right in. I’m Jeff. I look after the Knowledge Institute at Infosys, which is our business research arm. And before that, in more decades than I care to admit, in manufacturing, probably 300 plants across every continent but Antarctica. But today, I'm more in a mode of interviewing our other guest here, Rajiv Puri, who is the vice president for manufacturing in our Infosys manufacturing unit and for strategy and looking at the trends and where things are going. So in addition to being out in the field every day, he's also thinking about the bigger picture. So welcome, Rajiv. Well, if you think about recent research, and we've had some on AI, it says that companies are excited. Up to 40% improvement, they're thinking from AI and related technologies. And yet, our research also showed only 2% feel like they're ready across these five dimensions. And that's an interesting dichotomy. And how are people going to overcome that? Well, talent is going to be one big area, including empowering those frontline workers. So over the next few minutes, we want to cover three or four facets of this talent discussion and this learning organization concept. And so we'll dig right in, jump right in. First of all, Rajiv, a lot of people traditionally have thought of learning as upskilling, upskilling, upskilling, which is great. But where are companies falling short? Is that enough?

Rajiv Puri:

Gartner recently said that the world is moving from siloed skills to enterprise-wide skills. Actually, Gartner didn't say that. I said it. But if I say I said it, people don't take me seriously. Now, I’ve worked in manufacturing right from the beginning of my career. And in one of my visits to meet a client, I saw something which really astonished me. So I went to this place, to their headquarters, and they had five tall towers. I asked my host, what are these towers for? They said, this is the procurement tower. So, the entire procurement organization is in one tower. CFO organization, second tower. R&D organization third tower and each of the towers was connected to the other towers through a very very narrow bridge. The bridge had a lock. I would call that a value lock because when the problems occur in the field, if there is a quality issue, it's not just a procurement issue. It's not just a quality department issue. Everybody has to collaborate. And for a long time, manufacturing organizations have worked in a very, very settled manner, but the times are changing. Today is the need for cross-functional collaboration. We're talking about Scrum teams, Agile teams being put together more often than not. And on top of it, we have the power of AI because one of the drivers for silo behavior was knowledge retention, right? Everybody wants to keep the information to themselves, but now with generative AI knowledge is becoming democratized and people can use it to contribute not just within their own batch, but they can contribute to larger enterprise batches. So back to your question, Jeff, upskilling wasn’t about somebody joining as a procurement analyst, procurement manager, procurement editor, just same patch. Now is the time for cross-skilling. But the more well-versed you get in terms of how the whole thing works and how you can contribute to it, to your colleagues across the aisle, the better our business outcome is going to be.

Jeff Kavanaugh:

Well said. And I think you go back to that self-directed work teams as well, swarming around, learning different job to pitch in. Well, that all sounds good being cross-functional. If you think about going beyond the workstation and you think broadly across the whole company because part of this is also top down or it's enterprise wide. There's a concept called the learning organization. That's why you see in the title and it's not just a nice cliche, although it does sound pretty nice rolling off the tongue. The learning organization, who wouldn't want to be part of that? The World Economic Forum has a concept called the Lighthouse Network. Any of you familiar with it? Maybe you've seen some of the case studies. Wonderful way of showing good examples globally for companies.

Siemens has been part of that, Bosch and some others. Well, post Davos this past year, they decided it shouldn't just be a case study, it should be the way companies work. Why can't we systematize this? And so they decided that they would develop the Lighthouse Operating System, an operating system for companies. And it progressed. There were some teams and there were six different streams of principles identified. The work process themselves, across the plant, across supply chain, sustainability, and accelerated digital data. Well, there was a sixth one called the Learning Organization. And they had reached out to me a couple months after they got going, and they didn't really have a leader for it.

We were excited because, we'll talk about the Live Enterprise in a moment, that's something we believe in passionately at Infosys and the Knowledge Institute. So wanted to share that concept with you. And one, put a bookmark mentally that it's going be coming probably by January. Right now we're still in the draft stage. It's a capability maturity model.

For the learning organization part of this, there are three big areas. One is people development. It's frontline skills, so upskilling, cross-skilling. There's leadership development. So leadership as a distinct competency is part of this operating system. And then career architecture. Think about that. When you get all these skills, what do workers do? Is there path for them? Is it built in so you can pay them more? Will they stay with you or did you just train them to go somewhere else where they're paying $1.50 more an hour or whatever else it is? So talent development is one aspect. The other, performance management. How do you identify targets in this new world? How do you identify metrics? How do you monitor them? We have the tools for it, but what do you put in place? And then lastly for this one, performance improvement. Yes, root cause analysis, theme of all kinds of fault analysis, but what kind of formal feedback mechanism do you have for this learning to find what's wrong and then incorporate and then make improvements? And then lastly, we call it system adaption. The organization design, the organization development, the change management. That's not just off in a corner somewhere, change management or adoption is critical because if you don't adopt in a company, nothing changes to the best ideas. And so what, there's a whole concept if you look up micro change management, it was an HBR article that we did a few years ago, the idea of many small changes, carefully orchestrated, actually lead to the adoption because how many times are you asking employees to take a leap of faith? There's a new system. Well, what if you embed those small changes in their actual processes and so it's not a big of a deal, they don't even realize it and it's actually helpful to their work. So that's something to look for in the future. I won't dwell more on it, but very excited about that. You'll see it come out. In fact, the World Economic Forum is embedding that into an AI enabled tool, like a generative AI conversational tool that at some point next year, member organizations will be able to query, conduct assessments, and it should be something as learnings occur, they're shared across the world. So hopefully, a lot of good things will happen there. Switching gears now, or at least getting an example, at Infosys, learning is a big deal. In fact, learnability is a key trait. You might not see that in the dictionary, but we use it in our company a lot. Learnability, the aptitude and the capacity to learn. And we've tried to incorporate that into what we call the live enterprise, and wanted to ask you, Rajiv. What are some examples of what we call the modern operating model for a manufacturer for the Live Enterprise?

Rajiv Puri:

Yeah, I think that's a great question. And I would actually start first with Infosys, our own story about Live Enterprise, and then shortly get to manufacturing. So Live Enterprise is all about actually learning from nature. In nature, everything, every living thing, it senses, it feels, it responds. So we took that inspiration and said how we as an organization can start to better sense the way our operations are running, our customer operations, our internal employee operations, where are they going? And then how do we respond proactively or reactively, so to speak. And we started to put the foundation within, in place within, in terms of digitization of processes, migration to cloud, et cetera. And then we got tested because COVID-19 hit and we are a very large company. Within two weeks, we're able to get 200,000 people to start work remotely to make sure that our customers' business processes are running flawlessly. We are managing the digital highways of our customers' organizations. It's very critical. Our success was their success. And we're able to do it because we had put all the foundation elements together. Now, coming to Jeff, the other part of your question, which is how does this apply to a manufacturing client or client that you work with? Same logic, right? So the whole Live Enterprise is, the foundation is like a digital brain, which is doing all the sensing, the feeling, and then obviously it gives the commands to respond. So we've taken this kind of thought process to our clients, where it comes to supply chain management, where you see there are so many shocks, which have occurred in the industry over the last five to six years. How do we better respond to a shock? Shocks are not in our control, but our responses are. So how do we sense them quickly enough? How do we understand what does this shock really mean?

If a supplier is going to not be able to provide the product that they're supposed to, what are our alternates? It could be an alternate supplier, could be an alternate material. And these things are not easy because a lot of clients, they struggle with the quality of data. So we have worked in terms of correlating, okay, if this is the specification for this material, what could be done alternate material, right? So those kinds of interventions are really allowing our clients to become as resilient as we have been resilient ourselves.

Jeff Kavanaugh:

Yeah, I think one takeaway from that is the importance of foundation. You got to pour that concrete, so to speak, beneath the surface, because otherwise you can't pivot. Beyond that, what about learning itself? Because what are some examples that we've seen at manufacturers where they've used learning to actually get a more competitive advantage?

Rajiv Puri:

The best example I can think of is, it's a public example, it's Siemens. Since we are in knowledge industry, are all knowledge workers, me and Jeff, both of us and all of us. So we had again put a very solid foundation of a digital platform that we built called Lex. It's like Netflix for Infosys. So anything that is relevant to me as a, as a persona, right? In terms of trainings that I need to go through. Trainings that a manager of mine may assign to me. Trainings that my peers are going through. All of that comes to me as a recommended set of trainings which I can go through. We took the same solution to Siemens and they have a 350,000 workforce across the globe in all different functions. And they adopted the solution. They call it Siemens My Learning World. And this solution is allowing them to bring the right training and knowledge to shop floor workers, to people in the field, to people in the back office, everybody. And thereby they are able to become much more resilient and much more responsive to their emerging customer needs.

Jeff Kavanaugh:

Yeah, and I’ll take it a step further. If you think about the ability to learn anything, anytime, anywhere on any device tailored to your needs, that's essentially what it became. And the reason I wanted to highlight that wasn't so much that we helped them. It's that one, they realized that and they adopted that as a competitive weapon for them, as a strategy. It's also that it was all tailored to what the individual needed at that time. So as you're working with your collaborators, manufacturing great, you're working with economic development, what's the ability to empower those workers? Because not only is it more valuable because they're learning what they need, not just in case, like I mentioned earlier, it's also they feel that power. It's not fake. That's trust, the trust. You're credible. And they take some ownership. What's that word? Agency for their careers. And it's a career, not just a job. And so the combination of that, they found retention went up. The resilience is there because they could also jump over to micro credentialing and other skills. And so ask yourself whenever you assess others, assess companies in your state as you're working with companies, how resilient are they? How good are their educational systems or training systems? How well is that feedback incorporated into those careers? Because I think the young woman who had mentioned what the next generation is looking for? Well, if you get that evidence that they're valuing your career, they're valuing you, they're giving you the sense that then you're want to stay with that company, even if they are near Milwaukee or wherever it is. I like the fact you have the extra faucets laying around. Beyond the learning aspect, what about career and architecture? Can you mention Infosys, some work that our company has done with thinking about this digital quotient for example?

Rajiv Puri:

So we are looking at, for example, the era of AI is closest to what we can think today, where we stand from a digital question perspective, right? We are expecting everyone to be AI aware. Everyone. We are expecting at the next higher level a lot of us to be AI builders, right? People who can actually take a business problem, build out a solution, demonstrate a solution. And we are also expecting a set of people at the very pinnacle to be AI masters, who can actually build small language models, right? So that's just an example of how we look at digital and now AI quotient in terms of every individual's capacity and capability to be able to solution for our customers because that's what our job is, to solution and solve difficult problem statements that our customers provide. So Jeff, does that address your question?

Jeff Kavanaugh:

It does, there's one other aspect. We're all familiar with IQ, NEQ, and our talent organization came up with truly DQ and I'm from Texas, not Dairy Queen, but it's pretty enticing. It was digital quotient. And it wasn't just a buzzword. Because when you first see that, you roll your eyes and think, okay, it's an HR initiative and they're putting, you know, but it wasn't. They thought through very intentionally and purposefully what are the new skills and new ways of thinking and working and how to measure those and reward those. And so the digital quotient, was something, it is something, that on everyone's performance evaluation, there is a number, there's a level, and you're on a path, and it's incorporated into people's feedback. And that led to the AI also knowing that not everyone's going to be a model builder, but everyone needs to have some base level. And to get 330,000 employees all to go through some set of training, all to demonstrate proficiency. It's not easy, but at the same time, that also once you've done that, you're halfway to adoption because people are part of it. It wasn't just a MarCom's thing or internal communications. I think that's what's also a takeaway is people need to know you're serious. Employees need to know that it's serious. That's why the chairman of our board, that's why our president and leaders are regularly communicating this importance. It's included in their performance feedback and not in a stick kind of way. It's very much a carrot because achieving these competencies and called micro certifications in the company is actually a path now and required for the next level of promotion advancement. So with that, there's a lot of other things we could cover. What I wanted to do was make this as valuable as possible for this group and so open it up for questions. You've got a research organization and a technology firm. We think a lot about this area. Anything's on the table here if you have questions.

Question:

I'm interested in talking a little bit more about how you mentioned there's going to be an increase in demand of individuals who can develop those AI agents, who can understand and take a problem and be able to put together a scalable solution with utilizing the AI technology. And since it is such a new technology, and we're all having trouble finding those technical experts or those individuals who are coming out of school with that technical capacity, do you foresee a way that we can tap into just the general employee base and having maybe the editor or the receptionist or other individuals develop these skills so that a company can transform their backend operations and have it be more AI adoptable.

Rajiv Puri:

We have seen this actually already happen in two ways. One is a lot of manufacturing customers, what they're doing is they're setting up these, I might just call it like an AI playground or bring your own document to, bring your own content kind of a space where if you're a secretary, you have to build out like a minutes of meeting or you to plan a meeting, right? You can bring in information from your email repository, your SharePoint, et cetera, and use the tool to become more effective at what you're doing. Similarly, if you're a procurement manager and so on and so forth. So that's just one category of use cases that we're seeing. The second thing is AI itself is becoming very democratized. So there is this whole concept of wipe coding, VIB wipe coding, right? Which is basically you can start to build agents using natural language, just like you and I are talking, right? So this is something that even business can get a very quick grasp at. And I've seen it actually anecdotally, one of my clients, they had hired this someone fresh off of college, that person was working in the finance organization. And there was a business problem that they were contending with last like 18 months, they're not being able to crack it. And this person was able to use wipe coding and demonstrate how it could be solved just by himself without using IT or anybody else. The CEO was so impressed. The CEO put a chair next himself in the cabin. He said, yeah, I want you to sit here and teach me how to do wipe coding. So just an example I thought would be pertinent in the context of your question.

Jeff Kavanaugh:

If I can pile onto that… two parts. One is we're seeing, we do this in our company, we've seen it a lot also. Isn’t just Shark Tank? Isn’t just a hackathon, but it's kind of a combination between business ideas in the departments that are across the company. In fact, I saw an email today in our company where i think the top eight out of many hundreds and thousands were bubbled up they were given some money and encouraged. And some of those people weren't programmers, they were coming from wherever. It was very very open. But programmers of course weren't dissuaded from doing it and in some cases in many cases it's a team effort so you might have two or three individuals, Some with good, maybe written skills, maybe some that are good coders. Point is, on a regular basis, the opportunities are there, there are rewards for it, and as people demonstrate it, they're advancing fairly rapidly. So that's probably the big part. The other is, I think our limit here is our imagination. I kick myself periodically because I'm struggling with something and I realize, what if I just ask Perplexity? And then 25 different prompts later and 10 different versions later, this thing that came out is just amazing or it's something I can then take to someone and they can visualize it. So maybe it's because, you know, I'm an older fellow, but if you, I challenge any of you, the problem that you're struggling with today, that you're trying to solve or address, deconstruct it, start putting it into an engine and see what comes up and keep refining it. The power, if nothing else, it will improve the clarity of your thinking. And that's really, I think, some of the value. Yes, the analysis tools are great. It forces you to be a clearer thinker and logically have more integrity. And as a result, this whole thing is mutually beneficial.

Question:

Could I just ask a little bit of a follow up here? What I'm hearing, so I'm just gonna repeat it back, is that you're suggesting that to get more involved in this vibe coding is a cultural shift in setting that stage for anyone to tackle the problems. Is that what you're kind of suggesting?

Jeff Kavanaugh:

It is, and then the cherry on top, or the other part of it is that by leaders demonstrating that insatiable curiosity and that just dogged persistence and then the imagination, people know that it isn't just permissible, but that's the way we work here. I want to work at a place like that. And then all kinds of other good behaviors happen. People are sharing, they're collaborating, and it's amazing how culture begins with the acts of not just leaders, but people then following up on it.

Question:

One of the biggest challenges that companies go through transformation is, is it top down? Is it bottom up? Is it a combination of the two? You mentioned a word in that last bit of reflections about the word collaboration. So I'm wondering if you could speak a little bit more on how collaboration in terms of using these new tools and really sort of doing that sort of double ended spectrum of both sides coming together. You gave a few examples, but I was wondering if you'd give a few more.

Rajiv Puri:

No, I can just start. So I'll just take a real example. I'll mask out who the client was basically, they were looking to the CEO said that I want to reduce the cost of poor quality, something that any manufacturing organization understands. 16% to 17% of total revenue is the cost of poor quality. And once they had given this statement, it went down the ranks. Then it came down to operating level. And people very quickly realized that to reduce cost of poor quality, you cannot just look at design. You can't just look at manufacturing. You can't just look at aftermarket and warranty. You'd look at it all together. If something is failing in warranty, maybe it was not serviced well last time. But do we know the information? Let's get the data. If it was a manufacturing, the root cause was in manufacturing, let's look at the quality information. So I think in this instance, the objective statement that was given to them to solve for, it drove collaboration and with AI becoming like a catalyst because it was not possible before all this collaboration and being able to exchange information, access information, et cetera. It became a catalyst to get it all together. So just as an example.

Jeff Kavanaugh:

To compliment that, not to just be redundant, something else. Think about the duality. You're holding two thoughts simultaneously. On the one hand, you need to have lots and lots of small teams, many of them, swarms out there. That's good, but you can't scale. On the other hand, you need to have scalable systems. And so what we found is working out there, and we've adopted ourselves, is a relatively small number of large platforms that you can tap into. And then a loose organization where you have lots and lots of small teams. They could be customer facing, they could be on a plant, could be in HR, whatever it is. And the ability to morph, and the ones that are successful and they're onto something, they can tap into these platforms and grow like crazy. That's why it's good to have certain skills that are broad and horizontal. It's the combination of those two things that is the organization of today and the future. It's the modern art, because you can't go either extreme. That's what we found and it took some time, because we all like structure, and you can measure it, but because things are unpredictable, that is the maximum response with also the maximum ability to scale. We've seen that with a lot of clients as well.

Question:

Earlier you mentioned DQ, not Dairy Queen unfortunately. Can you expand on that a little bit? What were those five aspects you said of what constitutes DQ?

Jeff Kavanaugh:

Well, it's several. Some are the soft skills. Some are the ability to know basic tech. In other words, did you go through training on what AI is? Do you know what cloud is? Do you understand data? Also, this is important because no one has mentioned what might be one of the most important terms, responsible and ethical AI. I mentioned ethics earlier. Responsible AI, in fact, last November in this very room, that half of it, we had 80 of the leading legal minds in New York City and head councils, including Microsoft and people across the pond, debating the different aspects, legal and ethical, of where AI is going. Because that in itself is something, if you want go down the path of AI and accelerate, how do you balance the innovation opportunity with the very real risks? But that was one area as well, because responsible and ethical. If you want to have an empowered workforce, guess what? That's a risky proposition, so they better have a good understanding and ethical and values-based grounding. So those are some of the areas. And then, of course, the technical skills, because we are a technology company. But I think for any company, find out what are those four or five major competencies, not just skills, competency areas, and critical thinking, of course, and developing of insights. There's a whole area of research that we're doing and I'm doing for the Knowledge Institute that if any of you are interested in, happy to share that. If you want to participate in it, there'll be some surveys. But it's also something you could take those criteria and adopt them yourself. Because whatever we create, we want to share, definitely democratize the knowledge.

Question:

One of the questions and things that we've really been contemplating for our learning for our students is how to work through disruption, overcome resistance to drive transformation. And some of the ways in which we've been doing that is trying to find parallel examples through storytelling. Because we know when the emotional connection happens, people are more likely to step in and be less resistant to change. So one example that we were talking about recently is the music industry. The music industry has had a rapid transformation over the last 20 years, going from very manual to highly technology-based. And we find that AI and other technology can really be a creative partner, eliminating a lot of the tactical steps that musicians used to have to take. So I wondered if you could tell us a little bit about how you tell this story and culturally helped your very large organization adopt. You mentioned 200,000 people needing to go remote in two weeks, which is no small feat. And what types of examples might you help us share with others who are looking to lead disruption through sort of some examples that might make them feel more comfortable?

Rajiv Puri:

So there is a story I used just two weeks back at an industry event. The story of how, why you should not miss a disruptive window. And interestingly, the story was around another revolution technology. The event was for AI, but I talked about different technology and the technology is electricity. So today we don't think about electricity as a revolutionary technology. It's in the ceiling. It's in the projector. It's everywhere, right? But there was a time for decades and centuries, people were looking to harness electricity, starting with electricity generator. Then we had storage. Then we had the bulb. Then we had distribution. So by 1892, after decades of work, everything was in place for electricity adoption. But for the next 30 years, it did not happen. Because first and foremost, people were actually asking, what is this technology about? What's the use of this technology? So there were inventors who were very clear, but the masses were not clear. So that is one lesson that I used. The second was, even when electricity was adopted, initially, I'll just give you an example. In factories, there used to be a steam engine which used to be kept outside and used to convey power inside. Why? Because the steam engine was hot, it was noisy, it would explode. It was outside. When electric motors came, people just swiped out that steam engine and put the motor in its place. That means every other inefficiency was still there. The belts will come off the propeller, the gears will break, which are conveying power, et cetera, et cetera. So it's an example of you don't want to just apply a technology from outside the process. You want to bring the technology as close to the process as possible. And second lesson was, you better go and ask the end users. If they ask the maintenance staff, is this a good idea to just swap the engine with a motor? They will say, no, boss, it's not a good idea, because a lot of other things are going wrong. You need to fix that also, right? So that's just an example of a storytelling technique I use in that conference.

Question:

You say, we do not want to lose a 30-year window. I mean, AI, an AI year is just three months. It's not even one year, right? So we ought to really, really move very fast.

Rajiv Puri:

Where they give an example like that, since it's brilliant. Can't really top that. Drop the mic there. Drop it. Maybe on the how part. The word empathy sometimes gets glossed over like it's a fuzzy word. If you switch that out for service. So if you want transformation, if you want to change, can you convince the other that you're in service of them, of some greater good? You're behind a movement of some kind. Maybe it's to save the company, maybe it's for their career, maybe it's for the locale, whatever it is. Position and frame the transformation or the change of the adoption in terms people can relate to personally. Head and the heart, and of course the abacus, I guess, of these are the bean counters. you need to speak the different languages, the language of the heart, the language of the head, the language of finance, and then technology, demystify it. So it takes more work. You work hard so they don't have to. You work harder so it's an easy message. And it is persistent. You have to be a little stubborn. Because if that idea, even if it is burning brightly in your mind, it isn't in their mind yet. And so you have to show that because everyone is looking out, call it a cheerleader, maybe an ambassador, a motivator, someone who’s a leader. Now we were fortunate, chairman of our board is still motivating us, you know, in our company, Nandan Nilekani, and others as well. At the same time, we can all be, you say be the change you want to be, well be that ambassador, you know, have enough energy or maybe passion. You don't have to be an extrovert, but just enough where naturally somebody wants to be part of it. We all want to be part of something help people be part of that thing you want them to on their behalf and then the transformation can happen.

Question:

We invest in early-stage technologies, refashioning value chains to be better for people, planet, and profits. I'm wondering if you have seen any use of AI to get sentiment from your workforce. I hear a lot of executives talking about using AI to assess things, but data is oil. It's the new oil. So you have all these workers. How do we tap into them, to train them, to share their wins, pains, frustrations into AI daily so you have the best sentiment and analysis of what is working, what's not? Who is a real change maker who has good ideas, who's just bitching and complaining? You know, really, we have for the first time in history this opportunity. Who is using that data in that way? Have you seen anyone?

Jeff Kavanaugh:

Well, first of all, the answer is yes. It isn't just AI. There are some great tools that not called AI for collecting information. And our company does look at information. We call it a lot of pulse surveys and directionally. Maybe it's not minute by minute. But I don't think you want that anyway. But it's certainly frequent. We can catch the vibe. The other aspect, though, is to make sure people understand it's not a surveillance state. That's a whole different vibe and it's coming. So I think by positioning it as it's an opportunity for people to contribute, to shape the future of the company, and that's the way our company does it. I'm not saying it's perfect, but the ability to shape what leaders or input people, leaders are getting, that's how it's used. And sure, there's efficiencies, and some cases, I think for programmers, either keystrokes or hours saved because in a technology business you've got a lot of that and you can replace and repurpose. You replace a certain role and then repurpose the folks that do higher work. It's a combination. So I think one, you do the pulse surveys and you get good information but do make sure that it's in service of positive change for them. And then for the other, if it is efficiency, then do it with a mind that it's good for the employees overall or at least be upfront about some of those things.