Ep. 195. First Simplify, Then Accelerate: How AI Drives Measurable Business Outcomes

Speaker A: Sa.

Speaker B: Mhm.

Speaker A: Hello everyone. Welcome to the next episode of Next in Commerce. I'm Mary and I lead content and brand at NetGuru and it is my greatest pleasure to talk to Rodrigo today. To Rodrigo Alvarez Bandres from Siemens Gamesa, uh, in Spain. Hello Rodrigo.

Speaker B: Hello Mary. Thanks a lot for having me. M here today and um, my uh, pleasure to speak with you today.

Speaker A: Fantastic that we have an opportunity to meet because what you're doing uh, at uh, Siemens, and we haven't said that yet, uh, is very much what we want to hear about more, a lot more. So you're dealing with innovation. Um, we're going to be talking about AI, about how AI can boost innovation in large organizations such as yours. We're going to be talking about the best ways to, to weave people in or get people on board while trying to push innovation forward. But before we start, just a word on Siemens Gamesa basically for our audience though, who those who are not familiar with the company. Uh, so basically Siemens Gamesa is a Spanish, um, German wind engineering company, um, renewable energy company based in Spain. So Rodrigo, what is it that you do on a daily basis as. And let me state your title as uh, Enterprise Process Innovation Program Manager.

Speaker B: Yes. So in simple words what I do is I work on improving end to end processes, uh especially the ones that are across supply chain management, but not only limited to that, but to all our process landscape. So one of the things that um, and the pattern that we observe in large organizations is that sometimes we lose the focus on the end to end perspective so we don't balance the trade offs across the different stakeholders. And that is specifically what I do. So looking into opportunities that connect the company end to end and then drive innovative solutions to drive it. Innovative or not innovative, we'll talk about it later because sometimes you don't need very fancy solutions. Um, but yeah, that's what I do and in practice what it means is that I need to speak with uh, and agree and reach agreements, negotiate with a lot of stakeholders from the colleagues that engineer the wind turbines to the ones that manufacture them, that install them, that service them end to end at the end as I was mentioning. And for that uh, I use a specific set of tools. That's where AI comes into play. But it's one of them. So to me it's more about how we uh, problem solving, how we identify problems, what are the right solutions for those problems in the process arena, um, uh, do we use lean methodologies, do we standardize the way we work and so on. So at the end, AI is gaining more and more importance in my role because definitely it's a great tool to accelerate many of the problem resolutions that we have on hand. Um, and yeah, and to me it's a very interesting topic and definitely I'm pushing hard to get it done. And uh, on a side note, Mary, just to mention as well, uh, I'm a very purpose driven person and on top of what I do in my Siemens Games role, uh, I support my local community companies to kind of uh, raise like little startups. I mentor them, advise them how to basically launch their first product, how to reach the market and so on and invest them and invest in them. So that is as well something that I do. Everything that I have learned and everything that I keep learning I like uh, to share as I'm doing in the podcast. So that is something as well I do for my local community.

Speaker A: Uh, that's beautiful. I mean giving back what we've learned is one of the most valuable things we can do. So total kudos for that. You're based in Northern Spain, right? So that's where you focused.

Speaker B: Exactly, exactly. I'm based in La Rono, La Rioja. That is a wine region, famous wine region in Spain. Uh, yeah. Uh, and it's a nice place to be.

Speaker A: Listen, you're coming from a large company now to speak to us. A large, large company, part of an even larger enterprise. Um, what would you say are the top three or a couple of challenges when it comes to um, AI adoption? Let's just focus on AI adoption and innovation meant as AI adoption for the sake of this conversation.

Speaker B: Yeah, to me the barriers that we face are not technical. It's, it's more about organizational barriers. And from here, what I mean is in every company there's kind of an organizational immune system as I like to call it. And this is about resistance to change. So this, and now if we talk about AI, AI is a big disruption at the end. That's what keep hearing everywhere. This is going to change. You're going to lose your job and so on. There are many question marks around it and this is kind of um, the first thing to break and the first barrier that we have that it's about change management and how can we change the way of working. So this will be kind of, uh, definitely the first, the first, the second one, um, it's what I like to call the firefighting. So to create the future, you need time to invest in the future. And when you are so busy dealing with the uh, day to day urgencies and uh, looking to firefight what is coming tomorrow or today and not what is coming ahead in the next quarter or in the next year or in the next five years, then that is something as well that um, it's about investing the right time to create the future. And that balance is not always easy and that is always something. But yeah, this is something that is not going to impact me tomorrow. So why, why should I dedicate time to it? So this is to me like the second pattern that I see and the third one, it's about as well our workforce, the competences. So are we training enough? Are we providing the right, uh, uh, insights that they need to kind of understand, uh, what is the technology and what is behind it. And this is as well a third topic that we invest highly and that I believe every company should do. If you want to create a future, then you need to grow your people. And yeah, this is one of the aspects as well that um, we need to overcome.

Speaker A: And how do you convince people, like super busy people, uh, to find time?

Speaker B: To me it's always, as I was mentioning at the beginning, there's a set of tools in what I do and one of the set of tools that I, one that I like a lot is the problem solving. So definitely it's first starting identifying problems and a problem that really hurts. So when we are capable of conveying and um, structuring the problem in a way that everybody can agree and um, all the stakeholders, big chain at the end, all of them can agree on the same problem and accept the importance of solving that problem, then you have already win a lot. Albert Einstein used to say that 50% of a problem resolution is defining a good problem. So that is a very good trick at least works for me. That is, let's freeze the problem, let's agree the problem. So then we agree this is a priority and then we move forward.

Speaker A: Makes sense, uh, actual tangible examples of what can be achieved. Um, we talked a bit while prepping, uh, for today's conversation. We talked about um, organization charts and processes. On the one hand, um, organizations tend to fall in this like structured trap, like focusing on org charts. I uh, guess it gives people a certain sense of control. And then, you know, um, I believe that you, or I'm guessing you are the kind of person who would uh, wave his hand and hello, hello. Process. Okay. Process first, process optimization first. Like do we say businesses pay enough attention to process optimization versus org charts?

Speaker B: Uh, I've been in both sides, uh, both restructuring organizations and then as well, uh, Improving processes. And to me, yeah, organization at the end is going to tell you who is executing the task. If you look from a process perspective, it's going to answer the who is doing that. But the how we do it and what we do is kind of part of a process. And if you want to optimize an organization, then this is of course my personal belief that first you define what to do and how to do it. So then you can define what are the best cuts in your cake in your organization to really match. Because at the end, the end goal is that when you create an organization that you have the less interfaces, less interfaces across teams and less interfaces within the processes. So that's why to me, makes sense. Of course you need to do both. And especially with AI, uh, I believe many of the things are going to change because our processes will change as well. So this means there will be an impact in organizations as well. But to me the logic is first define what and how you do it and then later who does it. Yeah, and that's why to me it's worth the investment in the process improvement side rather than focus just on the, on the organizational chart side. But it's um, two sides of the same coin.

Speaker A: Yeah, totally, totally. Um, let's get concrete now. So time for actual um, optimizations that you yourself have witnessed or driven. Uh, basically, uh, give us some processes, uh, the ones that you can talk about obviously that you wanted to automate or manage to automate so far at Siemens Gamesa, some cool tangible examples that we can take a look at.

Speaker B: Yes, um, um, again, let me rephrase that. I don't look for automation per se. What I like is to solve a problem. Sometimes the problem doesn't need automation and sometimes it does. But in this specific case of automation, um, at least the way that we work and the pattern that we follow is always there are different processes that we can automate. I like to think about what is the outcome that is going to deliver. Uh, for the business, first processes that we automate, that we automate are the ones that are about preparing information. So we have tons of documentation, tons of um, formats that are not digestible for the human eye or for the robot eye. So all these automations that is about transforming that documentation into something that later can be leveraged by the robot. That is the first thing that we automate. One specific example, you can convert PDFs to something that is much more readable than a structured or for a robot. Yeah, that simple example in that we are investing a lot because what we want is to have a structured data to later make sure that that data is leveraged um, for the next process automations. Second one would be about all those processes that support decision making so that we accelerate the decision making. We have a specific cases. So for our uh, there are many of them, many cases that we improve our decision making. But to that um, normally we work at is about customer satisfaction. So we receive hell of input from customer satisfaction standpoint. Uh, our customers are sending us emails every day, are sending us reports, uh, from where are we performing well and what are not performing are getting us surveys regarding how did we perform at the end of every single project and so on. So we have. So it's a funnel of information that it's flowing to us but at the end it's so much that it's not always, you know, we don't always have the time to stop and read all of it. Yeah. And of course there's a lot of noise in all of that information. So what we do is, and this goes more into the Agentix solutions, this is part again of the toolbox different ways. But with Agentix solutions what we do is digest all that information and process it into actionable insights. Definitely go from um, use the automation to filter the sign out from the noise and then use that sign out to derive meaningful actions that are going to improve our processes or are going to improve our organization or whatever meaningful uh lever that we have in the company that will be the second one. So accelerate decision making. Okay, so what do we really invest the time. Third one once we make the decisions thanks to those workflows then okay, now let's move forward. So let's automate everything that we can so we look into specific um, uh execution processes and then in there it's more kind of um, I would say looking into the, into the solutions it's more about business process automation. Something more traditional. It's what it's called BPAs. But now in BPA you can add AI layers as well and that is something as well that we use a lot um, to definitely release our workforce from that repetitive task in execution that simply we can remove.

Speaker A: Are we talking just so I understand like are we talking about agentic AI specifically or. Not necessarily.

Speaker B: This one is not necessarily. It's about um, in process flows you can use any, any, any robot that, it's a business process automation, it's called BPAs. And in the BPA you have kind of a specific steps that you use AI.

Speaker A: When we, when we talked about um, you know, customer, um, sentiment analysis, uh, here's where also you're implementing an agentic solution to help you out. Or uh, this is, this is, this is not agentic.

Speaker B: It's that one is not, is not agentic specifically. So that one is more about specific steps, deterministic steps, that is the bpa. And we use AI for that. So to for example to process better that that the all that information that is flowing for the customers and to eliminate the noise from the signal. As I said for that we use those automations. Those automations have prompts, big prompts, uh inside of it to. Okay, this is how I want you to read this document and this is how I want you to extract the signal out of that document. This is how we do it. For that.

Speaker A: As for agentic AI in terms of agentic solutions, do you have any that you can talk about?

Speaker B: Yes, for decision making that is something as well that um, we provide tools to our colleagues, um, and could be in any area. But uh, um we are now investing high in the, in the sales processes kind of um, so to get prepared before we move into an approval of a new business or a new investment, whatever that we give kind of uh, an agent to those colleagues to aspire and to challenge what they are going to present. So that is something that for that we use more agentic solutions. So training that uh, basically virtual person to challenge all those businesses that are going to come or all those investments that we want to do in the future, uh, to definitely support um, the ones that are not just approving the business but also preparing for the business approval. The ones that are presenting the business.

Speaker A: Mhm, mhm. Sort of a roasting agent.

Speaker B: Uh yes, exactly. It's somebody to spar with, uh, uh, to challenge what you are doing. Yes.

Speaker A: Actually you know what, you touched on something that I think is soon going to be probably one of the key ingredients of any team out there. You know, uh, we already do it. Uh, we use AI for roasting content, for roasting materials that we do. And I think this will be, this is like a natural assistance that we're looking for, right?

Speaker B: Yes, yes. And that is what I was meaning before with accelerating the system making. Yeah. Because if you go to kind of um, uh you know a meeting in which you are going to ask for an investment, then if you have a spark about it, if you got somebody you know, kind of uh, with uh, with the CEO mindset in that agent, then definitely you're going to be much more uh, prepared for the meeting. So that's what we are kind of uh, investing heavily to provide those tools, our colleagues to be more effective when they present something.

Speaker A: I believe I sort of broke your train of thought. Uh, maybe there was something else that you wanted to mention.

Speaker B: No, no, it's fine. It's fine. Those are two good examples. Um, that is good.

Speaker A: Um, so tell me. Mhm. What process uh, will soon. Might soon be uh, automated, but is not yet processes. So here we're talking about I guess your um, maybe wishes and dreams.

Speaker B: I mean soon enough. I wouldn't say one that is not automated because in reality all of them are not automated because are so big and so um, long that you cannot automate 100% fully the process yet. And I'm a believer that there will be process steps that you cannot automate. That you still need the human judgment, uh, to make the call and to make the right decision. So that's why fully automated. To me I don't see it. But of course I see that, uh, all those steps in processes that are regarding um, that are very repetitive. That is what basically is the quick win where we are always kind of uh, looking for opportunities right now. So you can look in any angle. You can look into the supply chain management and the procurement side. Uh, purchase to procurement or purchase to pay. Sorry. You can look into CRM, you can look into many of them. But as I said, it's not end to end. It's never end to end. There will be parts of it that we definitely need to automate because the, the flows between tools are broken. And that is when you can plug in with this kind of AI solutions.

Speaker A: Mhm. So on the other hand, what processes do you think should never ever be automated?

Speaker B: Uh, it's, it's the, it's the same answer. Because to me in the image process, end to end, as I said, um, there are going to be steps that we cannot automate because we believe in the human judgment and we believe on the critical thinking. And that is something that definitely we need to rely still on humans to make the call. Of course the process will be very automated to provide us that digestion of information as I was mentioning before, the examples, to be better prepared to make the decision and you get the right insights and the right intelligence to make the call. But there will be steps that will require human intervention or human judgment. And that is to me any kind of decision like approving a new business, as I was saying before, that requires human judgment because there are so many nuances that cannot be captured yet by the technology, and I doubt will be sometimes captured.

Speaker A: Yeah. Okay, so a controversial question. Seeing the way things are going, don't you think that for example your very own job can soon be automated?

Speaker B: No, I believe no. Uh, and I hope no. But of course you can never say never. Uh, the technology is so immature yet that we don't know what are the possibilities and the future will bring. Yeah but um, at least this is not a me. This is a lot that I read a lot of quotes from basically the different uh, CEOs that are out there anthropic uh, OpenAI. They keep speaking about the importance of human judgment and m speaking about um, again critical thinking. So are we asking the right questions? Because uh, and this is maybe you observe it as well when you, when you work with AI that it's very good at generating information. But is it the right information? The right information is generated when you ask the right questions, when you provide the right judgment. And are we capable to translate our human judgment to a machine in a full extent?

Speaker A: Mhm.

Speaker B: That a question. And uh, think about the speed as well that we are taking to uh, it's not as fast as everybody's expecting. I believe at least this is my perception, uh, from a big company perspective. So I'm optimistic. I'm optimistic and thus every industrial revolution, uh, job transforms and I believe uh, there will be room for more jobs. It is true that this time the reskilling and the upskilling that we need to do is much faster. And this is where I believe the fear comes from. I'm a believer that there will be room for human job.

Speaker A: Um, that's a positive note. Uh, there are many different stances out there but uh, we're all just trying to figure out what's coming.

Speaker B: And maybe Mary, if I may add more examples. Yeah because there are many sentences talking about this and uh, as I said because it's not just me who believes that the human judgment will still be required. There are many companies that are willing to invest in. Even if we get the robots doing the traditional ah, junior jobs or anybody's job, it's worth to invest in people's education and with the gains of productivity that we get with AI we will use that money partially to invest in our people, to let them grow, to keep solving problems, to raise their critical thinking, their judgment, all these kind of soft skills that are needed, that will be needed in the future more than ever. So then we can basically keep that knowledge because if we simply stop and we don't keep training our People and we don't keep skilling our people here in this, in these arenas, then AI will do the whole job and um, maybe it fails. So yeah, I know these are basically hypothesis and different assumptions coming from different um, from different experts, uh, and big uh, managers. Uh, but yeah, this is something that I can tell you. There are CEOs willing to invest on this. Yeah, we'll invest those gains in productivity back in people.

Speaker A: Yeah, that's a positive take. Definitely. Um, I wanted to circle back to change management for a second. You did touch on it a bit, but I want to take, take uh out more from you, from your perspective. So, so basically how to excel at it, how to excel at change management.

Speaker B: Yes, change. To me, um, as I said it's not about jumping with something so fancy. Uh, uh, you need to be empathic at the end first of all. So try to understand what is the other one thinking and uh, what is their personal situation and what's the problem? What are the pains that he or she is suffering in their daily job. So that is to me the first thing and I'm coming back to what I mentioned you before. To me I follow always like the same pattern. So first of all I have a model that uh, it's public, it's called the 4s. That it's about problem solving, that it's ah, first you structure, uh, ah, first you um, state the problem, the later you structure it, then later you um, solve it and finally you set it in motion. And that is the part when you communicate for change, how you set it in motion. But first identify clearly the problem. What is the problem that we are trying to solve and m, is that really a problem for the stakeholder that you are approaching for your internal customer? It's like um, uh, lean startup. You need to do an MVP and you need to validate with your customer. Um, basically is this really a problem that it's worth to solve? Uh, I believe Eric Ruiz was here, uh, so this is I think a good quote for him. But uh, this is needed more than ever. First of all, question, is this really a problem for your customer? And if it is, then what is the solution that you're going to bring? If that solution is appealing enough and if the solution for example is an AI solution or an AI related solution and it's appealing enough to resolve the problem and if you state clearly the benefits out of it, you set in motion properly the solution, then you will convince, you will convince. Um, at least my experience with change management in big organizations, it depends on the context, for sure. But in big organizations, I believe it's better to do it, um, bottom up rather than top down. So really finding the right sparks in the organization who is really suffering the problem that you have identified and who is willing to invest part of their time to help you to solve that problem.

Speaker A: That's very intuitive, uh, when you hear it. But then again, more often than not, it's done completely the other way around. Right?

Speaker B: Yes, yes. And to me, the tip here is to find. Start the small. Start very small. Don't push things, just pull. You will get, I'm sure you will hear in the coffee machine, you will hear anywhere you have kind of problems from people. That's part of the office dynamics. Everybody will tell you something. So identify that problem, structure it. Bring a structure to it that everybody can understand and admit the sense of urgency. And start as well. Start as well. Prove the concept. If you are now starting with AI solutions, prove the concept, prove that it works. Prove the outcome, the business outcome that you get out of it and sell it. Sell it. This is like, uh, you know, you're pitching market, just pitching, pitching. That's very important. Yeah, that's very important. So I'm not a salesperson. I've never been. I'm an engineer and I come from. My background is in the past was actually executing construction projects. Uh, that's how I started in this company. So my background is not technical at all. But I needed to learn all these skills to convince, uh, a large organization that it's worth the investment. So, yeah, find your lighthouse project and deliver it. That would be my best advice.

Speaker A: Rodrigo. Uh, and the new leader in this new environment, the AI assisted leader, who are they? How are they different?

Speaker B: So I think they're not different from what is expected from a leader. But there's one key skill or behavior that is needed for my perspective, that is curiosity, willingness to learn. Like continuous learning. This moves so fast and the technology is improving so fast that, you know, every week you get updates, you get things. So you need to have the time, prioritize your time, that you really do the right research, that you are up to date and up to speed with, uh, the news that are coming and with, uh, improvements that are coming. There will be things that are noise, there will be things that work for your company. So focus on that. You know, read a lot, listen a lot. Now in, nowadays, it's, it's. I'm super pleased that we have podcasts, we have um, specialized, um, uh, in papers, um, specialized. The websites, you can find a lot of information out there. So just uh, you know, use your. Use AI if you want for that. You know, to do the research for you. But invest everyday time in learning. Learning may come from many sources. So that to me would be the top skill needed to stay up to date.

Speaker A: Mhm.

Speaker B: And then accept and embrace change. This is be open minded. Be open minded. Curious to learn, curious to change, to create a future. This is to me kind of uh, um, what is needed because uh, adaptation is unchanged, is the only constant right now. So to me that it's um. Those are the skills that are needed. Really not technical.

Speaker A: Uh, to wrap up I have a round of three quick questions for you and I'm looking for really short answers. First things that come to your mind. Ready?

Speaker B: Ready.

Speaker A: Okay. Let's go. Complete bullshit around AI and innovation.

Speaker B: Uh, okay. That is going to take over all our jobs. I'm optimistic that's not going to happen. I think I explained it before and uh, and yeah that the quality of AI is not good enough. I really think that that's bullshit. That uh, that depends on what input you provide and what is the. And how specifically you ask what you want on what you need. So Rachel, don't overestimate or underestimate the quality of what you can get.

Speaker A: An underrated leadership skill in your opinion.

Speaker B: Think I mentioned before critical thinking, asking the right questions. So when you. I was mentioning before, when you present a problem, are you asking the right question? Who is impacted? What's the root cause why this is happening? You know, train your challenging skills. That would be to me the skill to learn and to be very present in the future. Very cool. Thank you.

Speaker A: And now uh, leadership again uh, for the end, your favorite trick for getting your team's attention.

Speaker B: Emotions. It's about finding the right emotion. Yeah. So when. And it always connected to the pain. Yeah. So uh, I was mentioning before, be empathic. Understand what is their problem, what is the pain that they are suffering in the day to day job. And always connect what you are willing to deliver, what you are willing to achieve to that emotion, to that pain, to something that they believe it's worth to do and to listen to you first of all because you don't deserve the time unless you are bringing something valuable for them. So connect to the motion.

Speaker A: Mhm. Thank you. Thank you very much. I do like this uh, positive human note at the end of our uh, conversation on AI. Uh, thank you so much. We've reached the end of this episode. Um, thank you for taking the time to talk, uh, to us and share your experiences.

Speaker B: My pleasure. Thanks for having me. Um, yeah. Looking forward to future exchanges. Thanks for that.

Speaker A: Thank you, Rodrigo. Thank you, everyone.