Careers in the Cloud - E29: Cem Yöndem: The Global Impact of AI and Data

By Cem Yöndem

For our first episode of 2024, we had Cem Yöndem, VP of Digital Customer Relationships at Schneider Electric, join us to discuss one of the biggest talking points in the last year - AI.  

Leading the team at the Schneider Innovation Center, we talk about all their insider knowledge on the use of AI, how businesses can adopt it successfully, and how it will impact the workforce in the future.  


  • 02:09 Speaking at Dreamforce
  • 03:58 Being a speaker vs attending events
  • 05:13 First use of Einstein Analytics at Schneider Electric
  • 07:14 How do you choose the speaking topic for Dreamforce
  • 09:58 How will AI be the most impactful in the CRM space
  • 14:24 How to be successful at adopting AI in a business
  • 18:46 AI Data compliance and storage19:30 What's happening at the Schnieder Innovation Center
  • 23:16 The strategies for retaining tech talent
  • 26:03 Stand out locations to live and projects worked on
  • 28:34 How will AI impact jobs and areas of new demand 
  • 32:04 Where is the workforce least ready to meet the demands driven by AI
  • 38:13 The future pipeline of AI use in Schneider Electric

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How is your Spanish, by the way?

Uh, not that good as I wanted to be, but I think I'm getting a bit old.



Come on.

I mean.

I use all my energy for, uh, a bit Arabic and for Russian, so.

Because, uh.

Yeah, I did, uh, almost. It's been already 15 years on expatriation. So different geographies, interesting places.


Dubai, Russia, Cairo, then a bit in Europe. Paris.

Now I'm settled in Spain. It's been already five years.

Five years. Okay.


I still can't learn Spanish, though. Uh, yeah, I cannot.

Honestly, I feel like even when I was here for Barcelona two, especially because you speak like other languages as well. And Barcelona, a lot of people speak English.

It's hard to really focus on learning. 

You want to go, you want to go order something. They speak English. It's not like you're forced.


Well, I'm good at restaurant.


That, that that part I can, I can pull.

But in general I think the problem I mean in the office.

 Okay, uh, ten hours a day, I spend in the offices, usually. International Company and more than 25 nationalities. It's the pure English language in the offices.

I mean, that's why maybe I'm also a bit lazy now with, uh, I mean, no, I hear you. Complicated.

Yeah. No, no, I hear you, I hear you, I hear you, but it's good to it's good to finally sit down and meet you.

We were just saying this before. I think when I first came to Barcelona, it was that first year, 2019. 

We started talking. And it's funny too. The first topic that we spoke about also had everything to do with data.

It was Einstein Analytics, right?



That was our first real thing that we sort of kicked off and gave a shot to help you out with. And lots has changed. I moved back and we still are in touch. And here we are today.

Like, I guess I've been at Montreal now four and a half years.


Hey, first of all, thank you. I mean, when I heard that you're coming, so, uh, I will. I wouldn't miss it for the world.

I've been asking for, like, for so long.

But we keep missing each other every time I come back.

So nice meeting. You in person.

Yeah. Of course, man. Thanks for having me.

Of course.

No, really, really happy to have you here. And finally dig into a lot of this that that we've been, uh, we've been thinking about and seeing a lot about you, especially this year.

So I know you spoke at Dreamforce before this year. You spoke again, um, having the opportunity or having had the opportunity to speak at so many different events or we'll even we'll just say like high tiered events, like something like a Dreamforce.

Um, what is that experience been like for you and did your experience, uh, match like you did? Did your expectations match the reality basically before you, before you did the speaking?


We always say in United, the best kept secret of Schneider Electric is the Schneider Electric itself.

So what does it mean?

All these years we were doing a lot of good things on the in the IT area, exploring different projects, products. But we we were not that much on the outside and the, uh, showing this competency to the external groups. So that's why three, four years ago we made a decision, okay. We would like to participate these events. I mean, these invitations were always there, but we were not that much leveraging earlier, especially in the IT domain.

Mainly we were putting on the business activities and the Schneider marketing concept on the front line. But for the last three, four years, we are also participating in these events.

For me, it's a great opportunity because I don't see them as just a presentation opportunity of what we do in Schneider Electric, as well as to learn from the other participants, the other customers of the products that we are attending seminars to.

So that's why it's a great opportunity. I really enjoy those periods and these times.

And also you've been you've been there. I mean, uh, San Francisco, Dreamforce, that week is a special week. I mean, the massive number of brains flowing into town. So not only Salesforce ecosystem, also the rest of the other ISVs and the other uh, university representatives all around that you ca really have a good tech talk.

Yeah, talk about future mainly.

Do you do you feel like you get more out of the events when you're a speaker compared to when you're not like the networking opportunities that just come up because you're now speaking as opposed to just attending?

Or I will say it's easier because, uh, when you present something definitely after the presentation, I mean, before and after you are getting in touch with so many people through LinkedIn, through even in-person connections.

Two reasons, okay, there are always commercial ways of, okay, people are trying to sell you something. The second part is mainly about they want to learn more in detail because, you know, it's a limited time, limited, let's say, opportunity to tell in detail what you are doing. And especially I prefer to talk about the real things I do, not the things that I want to do or might do. I really we delivered so basically actual projects on results that we have achieved with that product.

So that's why people really value that.

And then afterwards we I have so many connections after those events that I follow and try to explain a bit more in people, of course, in the confidentiality constraints, what I can share, what I can add.


But that's the that's the most valuable piece of these events for me.

To be honest. A lot of people even reach out, probably for help to. And just some guidance. Or we were trying to do exactly that, or I had no idea Schneider was was doing this right.

Because I hear that a lot, even. Right. When were you guys the biggest, uh, user of Einstein? 

Back in the day at one point.

Well, we were one of the early adopters of Einstein.

Even in the beta stage. I mean, we took the product. We wanted to try it out. I mean, test it out. So that's why we accumulated a lot of experience. And the product is also evolved with Schneider inputs and our tests and the fails, let's say. So that's why it was an interesting domain.

But not only the Einstein I mean for currently, for example, we are working on the marketing cloud as well for the new activities. So we always try to provide this feedback to the product teams as well.

And Salesforce.


Luckily, thanks to our, let's say, uh, ten year inside inside Salesforce ecosystem, we have a lot of doors open to us that we can directly discuss very senior level product managers in the organization to make things happen in a way that Schneider wants. So basically impacting their roadmap or creating some specific solutions for us that we can test.

This has worked for you, right? At the end of the day, they're going to want to take that feedback seriously and impact the product. And that impacts their sales, and other companies.


And that also puts you on a bit of a pedestal when you get to speak, because those stories are the driver for what's coming.

And it's not only the success stories. That's what I like with the Salesforce as well. I mean, they would like to hear also with the not maybe, uh, open discussions, but also some customer interactions, 1 to 1 organized by Salesforce teams. We also share about our failures or our learnings because I don't call any failure as a failure. It's a learning to next step.

Right. So these are also the valuable inputs for people, especially if they are a bit later stages in their journey comparing to us. So basically imagine somebody is coming and telling you what you will face in two years of time now so you can leap, you can mitigate.


You can leap a lot of steps in this journey for that.

So this is also another thing that we have the opportunity to share with the other people attending those events.



You got to start charging for the Dreamforce talks.

How uh, I bet a lot of people even say like just in the last session, right. You heard someone saying that I really would love to speakat Dreamforce. I was on the phone

with someone the other day with, like, all that's it's my dream, right? Like, I really want to be able to do that. I assume it's a lot of people's, uh, dreams. What's the process exactly is you you put your, your proposal in for your talk and sort of luck of the draw for those who don't know, how does it work?

Well, to be frank, we are a bit lucky on that because, uh, Schneider Electric is part of Salesforce ecosystem for a long time.

And if you realize the Mark Benioff keynote was having Schneider Electric example and the opening day. So that's that's that makes things a bit easier for us.


So what we do, uh, along the year, we develop several projects with Salesforce, and we line up for the key success stories that we would like to share with the people.


So, uh, this year it was mainly around AI, but mainly predictive AI, because gen AI is a new topic that we are exploring that we will we can talk about that later on as well.


Uh, mainly AI stories. I mean, predictive AI, what we have done with Einstein, uh, discovery tool, I mean, all this transition because we have the early stages of the product.

So, uh, we share the topics that we can talk then depending on the, uh, interest and the availability of the slots, uh, which was our sessions and we participate, uh, this is one dimension. Second dimension. Also, we work several ISVs around the Salesforce ecosystem and partner ecosystem. Right. Also, we do good things about, uh, those products as well. And they also invite us to be part of their confession sessions or sharing sessions. So it's not only the Salesforce agenda, also the Salesforce partner Ecosystem agenda that we also participate myself, my technical teams, etc.. 

And also, you know, there is a technical track in Dreamforce that we also put, uh, our architects in the uh, specific sharing sessions with them. So there are different opportunities to take part in that. Uh, definitely. 

If you have something good delivered, your key account manager will be aware of that. And with his connections with the event and the marketing teams, you can get your seat guaranteed, right? 

So I think that that's one thing too, that I mean, there's a lot of great success stories that you can find, whether it's at one of the Dreamforce events, like being presented brand new or even on the Salesforce success story part of their site, there's tons of really good ones, but I think there I'm sure there are some companies that are sort of missing the trick on that, because there's a lot you get in return by offering your. This is where we did really well, this is where we were challenged. 

And you and you create that for them. There's a lot that comes back, like you said, right? Where maybe they're more willing to say, hey, we can maybe fix this for you now that you've given us all of that and make it better out of the box as opposed to all the customizations. But on the topic of AI, it's been all over the internet. I mean, from from my perspective, because I work in recruitment, I'm on LinkedIn. Quite. A bit all over people's LinkedIn timelines as well, posting about, you know, different products and whatnot. If you, um, if you think about the CRM space specifically, what do you think or what aspect do you think is most or will be most impactful? 

Right. We talked a little bit before about omnichannel integration, data accessibility, IT integration capabilities, like how does AI sort of bring all of this together and what is the most exciting for you at this point? 

For me, really, we are entering into CRM 2.0 ERA. In what sense? I mean, I don't see this one as an AI revolution. It's for me more than a CRM revolution because as we said, all this integrity or connectivity that we had along the years is taking it to the next level with the generative context. Right? Which means. A technology is helping humans to create better content, better data, better interaction with the customers. So that's the main difference. So basically so far I mean all the examples, if you look at the areas where the CRM is, uh, focusing customer support, sales, uh, field services or marketing area. So the use cases that we are exploring today is really game changing, ones that can contribute to the efficiency of the internal employees as well as the productivity on what you can do more with the limited resources. Sure. So that's that's why I always see this as an opportunity, as a kind of assistant that we are developing ourselves for each and every business function that we have. 

Um, and also another thing everybody is expecting, like a super revolutionary results, right? It doesn't need to be that because imagine a big companies like Schneider Electric, even 5 to 10% of efficiency is already a big number that we can reallocate our energy time efficiency for growth purposes or other areas. So that's why it shouldn't be like a revolutionary change, in my opinion, because at the end, it's a technology evolution, right? Everybody will catch up with it two, three years. There are some early adopters, there are some late ones, etc. coming into the game. But the difference is how you welcome this into your organization. 


That's also another parameter that we, we miss. Somehow everybody thinks that it's a technology game. In fact it's not. Because technology will help and technology will mature so fast, faster than the other life cycle of the other software products. But the problem is, if you don't anticipate, if you don't. Internally accept that it's coming. You will have difficulties. 

What does it mean? For example? Gen AI capabilities is bringing some help to your customer support agents. Right? Mhm. But customer support sales, they are very heavily process driven activities. If you don't adjust your processes internally, if you don't check your data quality, that which can welcome these capabilities from Genii, you will not be able to leverage it. Product will be there. You'll buy it, you will plug it, but it will not work, or you will not get the results that the others are getting. Like half the other, uh, products, these companies. So that's the major difference I see with AI, because there are a lot of dependencies or a lot of, let's say, uh, preconditions or kind of things that you have to mature before you get the full value out of it. Right? That's the point that most of the people are still missing it. They are still looking at, oh, there's a perfect product. I give another two years, it will mature. Then I'll buy it. I plug it, it will work. It will not be the case. 

So this is one of the advantages in Schneider Electric because we really adopt this kind of products very early stages. When when I was not that popular, we were doing our first AI use cases 1918 period. So we started with very simple, okay. Let's say uh, prescriptive. Then it moved into predictive area. Now we are moving with the generative piece. So that's why if you don't follow the cycle, if you don't mature your organization for that AI will be there. Good toy shiny. But you will not be able to leverage and get the value out of it in your organization. That's my personal opinion. 

I feel like that's like used a good example with AI, but I feel like that's with any technology we talk. 

I went to a, um, it was specifically for manufacturing in the energy space. I went to an event in, in, uh, Western Canada. And for the most simple CRM things, the salespeople I was speaking to were like, oh, no, I never want to use that. Yeah, we have that. But I like my pen, I like my paper, and this is how I do my thing, right? Like it's nothing compared to what we're talking about with AI. But I'm just saying a general, very simple CRM technology couldn't even adopt that. So if you look at and those are smaller companies, right. Not to the size of Schneider Electric at all. How have you guys become so successful in. 

Yeah okay. Early adopting. Great. Right. But how do you get everyone on board to actually change and adapt their processes to be honest. 

So, so often because to innovate that much over whatever period of time you have to keep changing, I guess. Right. It's not true. Always one change and you can do all this, all this tech, uh, implementation. You're constantly changing a huge workforce. What's the key to success there? To be honest, in in AI domain, uh, I can say four major lines as a learned lesson, right? 

For us. One of them, as I said, early adoption is key. 

The second one is definitely the people element, the competencies internal, not only the technical ones. Also, on the business side, people who can talk data can think data and who can act with data, right. So this is a big transformation. 

Even a. Non-technical person. Sorry to interrupt like a sales person exactly. Has a more of a data mindset and knows what to do if it's if it's provided to them. 

Exactly, exactly. This is the second important takeaway for us for for this journey along the development cycle. Definitely. I mean, the pivot thing, okay. Whenever you don't see the value, you drop it. 

So that's why it's the third point, the value realization. So how you can engage a sales guy to follow your opportunity scoring or rating results coming from the system. Right. Versus his 1015 years of experience. Right. That's not an easy thing to change. So the only way to do it, proven data and the value realization coming out of your solution. It takes time. It's not easy. You have to catch the early adopters or the ones who is having this excitement sure to it to use this as a kind of motivation group. And then afterwards, if your solution really works, then you get the buying of the rest of the company. But it's not for the old use cases. I mean, there are so many use cases we dropped or we pivoted in original use case into a new one. I mean, that's that's the constant part. 

In order to do that in a big corporates, sometimes it's not so easy because you don't have that much flexibility or understanding because it's a guy always a success oriented organizations. Right? I mean, when you start the project, you finish with success, okay? Right. AI is not that type of projects. In fact, AI is a constant learning, constant changing. I mean, the initial scope that you started and what you ended at the end could be different. But the idea is the value creation, if you can reach that with a different ways, can be technology can be, process can be I don't know, organization wise. Still it works. So we have this spirit. We have this spirit. I kind of kind of entrepreneurship I mean, inside the organization, uh, which, which I really value. And that's why this is also another things that we also open this kind of, uh, early adoption possibilities whenever even there is no mature product to do that. 

Okay. The third point, I think this is very specific to AI is the compliance and the regulations in the countries, because whatever works in US might not work in other geographies. So that's why. And the most of the AI products are originated from there. So that's why that's also another dimension that we are trying to see. Because if you want to really if you have a multinational workforce that you want to use these cases and scale up, you need to consider those elements as well. So that's also taking a lot of time because, you know, there's no logic in, uh, legal rules. Right? Right. You have to comply with them. You can challenge them. You can put a funny comments on them. You don't accept them, but you have to comply with them. So whenever you have a big company in the corporate level with a different geographies, then this takes really too much time for you to align and say that, okay, yes, this data is safe to use with this use cases and this geographies. And then we move on. 

So that's also another dimension, especially for the multinational companies to consider the data compliance and the local regulations. I'm not talking about the GDPR right. Beyond GDPR, there are so many different expectations on how do you keep the data. Where do you keep the data? I mean, which type of, uh, corrections or masking you have to apply or where do you take your data for the AI analysis, besides your data storage locations and how do you manage this data there? I mean, these are all kind of basics that you need to deal with, even, uh, for simple scale up ambitions that you have in your, uh, use cases. Right. And the difficulty of doing that is, is multiplied. Multiplied. 

When you look at the size of the Schneider Innovation Innovation Center, right. Is most of the AI work that you guys are doing? If you can share happening right here in Barcelona at the Shared Service. Center, uh. 

Different locations, in fact, we have several hubs. Uh, we have a great IT hub in Barcelona, almost 500 people now, uh, 25 nationalities. Uh, and I know this is a keyboard, but almost 27% with the women in tech, uh, ratio that we are proud of. And it's increasing. Nice. So, uh, and covering different, uh, practices from AI to network to security to Salesforce ecosystem, etc.. So basically we develop AI solutions in Barcelona, uh, mainly for our internal usages, basically for the frontline employees, what we call customer support, sales community, etc. but also we have in Schneider Electric, which we are also proud of. We have an organization called AI hub. It is an organization, Transversally, covering all Schneider functions business as well as it.

So basically we are also it I mean, uh, AI solutions that we sell our customers. Right, for the electrical chargers and the electric vehicle chargers, uh, example, which has been shared by Gartner, by the way, symposium, uh, two weeks ago in Barcelona that we were proud. So basically AI organization is having different locations. So we have us based we have uh Bangalore also office. Uh, we have in, uh, France people, uh, basically 4 or 5 locations. We develop our AI activities today. 

But when you when you look at the foundation, I guess, that you guys have built here in Barcelona, we can use that as an example to sort of enable the organization not just to maintain, but continue to sort of innovate with, with respects to AI digital capabilities. Um, it's that's where it really is. Right? Like the the core of it, I guess, is, is what you've been able to grow here because I think you've said you've been here for five years, right? 

Yeah. So that's what I mean. Just when I was getting here I think is when you were, were moving over. So if you look at what you've done in such a small period of time to create a good base, now that I know the other locations have been there, but you can now branch from what you've done here. 500 people is a lot to to innovate fast and and and that or to innovate that quickly. We'll say. 

True. Right. So basically uh, that's one of the things also I'm personally proud. I mean, it's a group work. But when I moved here, we were around uh, close to 85-90 IT competencies working in Barcelona at that time, our biggest, uh, ambition was to convert Barcelona into an IT hub. Right. Because we were aware of the dynamics of the region, we were aware of the popularity of the region not only for the Spanish workforce or southern European workforce, also multinational talents to to be here. And then with our management support, we expanded our hub capability for the different practices. As I said, uh, security came, data came Salesforce delivery came, network came. I mean, uh, ERP was the starting point, which was the base setup. But even for the new projects that we are onboarding ourselves, we are also. Averaging a Barcelona hub location to extend our competencies. So that's why that's definitely true. Uh, we like to be here, we like to innovate here, we like to deliver from here. And I believe the last, uh, couple of years of delivery of this center is proving that we are in the right track. 

Yeah, the the retention, too, on the people I personally can attest to just from speaking to people in the market. I'm not just saying this because 2%. Yeah, that's what I mean. The numbers are there. 

We can we can cut it at that point. Right. Like you guys have done really, really well. And I know the employees are treated really well. And it's not just the ones who live in Spain. Like, I personally know people who've moved from from Bangalore to a different location, right? Whether it's Spain or France or wherever. And I mean, it's to a point where it's like, okay, where do you want to live? Okay. You want to live there? Done. Snap of a finger. They're sorted out completely. I don't know if it's still like that, but what are the secrets or not? Secrets, but, um, strategies to make sure you retain your people as best you can for, for years to come. 

Well, first of all, uh, Schneider Electric employee value proposal. I think that's that's very strong. Now, I mean, uh, I'm with this company 22 years now. Incredible. I mean. That's unheard of. I mean. My journey, my journey, different geographies, different projects, different era. I mean, whenever an it. Area or domain was popular. We were investing in Schneider Electric, so I never had the chance to. I need to go another company to learn about this technology. No company has provided this opportunities for me, but this is my personal story. When we look around, uh, the value proposal, as I say, Schneider Electric, because we do things for the good of the society and the community in general, especially our sustainability agenda. 

Right, is proving that it's not greenwashing. I mean, a lot of companies talk about that doing really greenwashing around that. But Schneider strategy, it's been already, uh, close to ten years building around how we can be energy management and industrial automation leader in the market, supporting sustainability driven agenda for the companies and the society. So that's very important, especially for the new generations. Uh, because the the people profile also changing, especially after covet has changed a lot as well. People care. About different things as. A HR company. Also, you're you're following that, I'm sure very closely. I mean, people like to spend time more on meaningful things, you know, salary. Yes. Important we have to pay the bills. Right. But on top of that, the meaningful delivery, what I do for myself as well as my society, I mean short distance, wider communities and what's my impact in general. 

So the companies like Schneider Electric, who is giving this or which is giving this opportunities to the people, is always having a bit more, let's say attraction from the new talents or new generations. I will say. 

I had this conversation earlier this year with someone who also it wasn't 22 years, but it happened to, I think, be 11 years at the same company, which again these days is is unheard of. Right? It's just it's not common. Like you said, people, they want something different, whether it's the mission or whether it's just simply working with the new tech. They want to do something new in a new place. Having had the opportunity to work in so many different locations with so many different technologies, like you said, different missions, that's really, I guess, what's what's kept you doing what you're doing. Are there any, we'll say highlights and maybe places that you've lived or besides what you're doing right now with AI that's, you know, that's got you to this point anything, uh, whether it's somewhere you've lived or projects that you've completed that really stand out where it's like that was that was a highlight for me. 

That was a big well. Again, my my journey is a long in Schneider Electric. I mean, back in 2000, 2005 period, it was the ERP era, right? So we were moving from, uh, dumb terminals. Okay. Uh, DOS based screens where you run some of applications into ERP systems, right? You have a new GIS. And it was it was a change. Yeah. So then afterwards it's followed by, uh, CRM revolution in, uh, CRM in cloud, right, with the Salesforce, uh, introduction to our ecosystem. And what year was it, um, 2008, 2009. We were we were, uh, deploying our first projects.

 Wow. So at that time, uh, I was lucky to be part of that initiatives as well. Then afterwards, it has followed with the, uh. Marketing and, uh, e-commerce and all this CDP connecting the dots how we can reach your customers web mobile technology era. So that was also the next step. 

The currently it's AI, right? Everything is AI is like on top of all this commodity and the new platforms, a kind of a new flavor on top of that, how we can make them more efficient and more productive for our users. So basically, uh, as I said, I was lucky to be, uh, to be in the right places where also the Schneider business was booming, different geographies. We were opening factories, we were opening new offices, we were deploying new countries. So that that drove me, uh, along this journey with always with the passion and the, uh, learnings, learnings, opportunities. I mean, uh, this was the biggest, uh, take I would say. 

That's. The one commonality in people who have had, like, that type of career path, right, where they've stayed in one place for, for a very long time in tech specifically, is that it's there's there's so much it's kind of what you just said, actually. Right. You, uh, you iterate really quick and often. Right? And you end up getting a new flavor every time that you do it. And that's what helps you develop and grow that switching another company won't accelerate you as far right as as staying where you're put. And that I think is is a real cool thing. 

Looking at 2024, a lot of people with respect to to tech delivery, right? Not just from a recruitment perspective, but in general, even delivering more with less right is a big theme for a lot of companies going into next year. And we're hearing certain companies reevaluating their shared service model, their insourcing, their outsourcing. 

You know, many companies are taking either experience in 2023 and trying to make a better change for 2024, where they some companies just sat on it this year and didn't do much right, and they can't afford to do that next year. And that's why they're sort of making these changes. 

Um, do you think while AI will impact, you know, all of our jobs in the space, are there new areas of demand that you see, maybe an increase coming that's going to bring some life to, to these organizations? 

Um. Well, first of all, I think I, I mean, I don't see this one as a threat now in terms of job continuation of our people. Uh, if you look at it for it, I mean, where we use AI capabilities, for example, code generation or, uh, we read articles. Okay. Softwares will not exist in the future, right? It will be replaced by AI. So I think we are still far away from there because there are, as I said, a lot of, uh, tiny details in this about the compliance intellectual property rights on where did you find this code? 

Is it code that you can use or you can use. As fast as people think, even. Even that's the case? Uh, I think technology will move faster. As we discussed earlier, however, I always see those ones for a kind of assistant capability that you can do more with existing capabilities. It's not this will replace your job. I don't see like that. I mean, this was the thing of every technological advancement investment, right? I mean, it's just impacted our output. So basically we were able to deliver more with the same amount of people. Right. 

And that's our strategy as well in Schneider Electric, I'm proud to say that even during the Covid times, we were able to maintain our workforces. Any locations without following this layoff, uh, trends in the uh, Valley or in the other locations to support the growth of Schneider Electric at this time. Because, as I said, our agenda is the right one. We are serving for efficiency purposes. That's why all what we do with it is the instrumental piece of our success today. So that's why even if we cannot or we will not double what we invested this year, in next year at least, we will continue to, uh, maintain the same level of deliverables or same level of bandwidth on our side for the IT versus this is how we see at the moment. 

But of course, the market is having millions of different parameters now that we depend on, uh, at the moment I'm not so pessimistic about 24. It's more I mean, uh, maybe wait and see type of year. Maybe we will not go into massive investments like we were doing last three, four years. However, we will not also going to drastically cut what we have today. So we don't have this projection. 

I think it makes sense if what you're if what you're saying is working right where you're not trying to. I just said companies are trying to do this, but in your case, you're not trying to do more with less. You're just trying to be more efficient with what you already have. Technically, that should allow you to keep the people that you already have doing meaningful work and innovate for the for the better. So the future people that you hire can also reap those same benefits, I would say. Right. Definitely. 

Uh, what where do you think the workforce is least ready to meet upcoming demands of AI technology? 

Um, well. Definitely. I think. Uh. The definition of all this new data roles like data analysts, they are becoming now data scientists. I think there's a lot of gray areas, and there are a lot of gray areas in this transition, in my opinion, because it's moving too fast. Yes, data was important, but data architect, data analyst now data scientist or data quality, uh, roles. I think this will require a bit more time to mature. Exactly. 

What do you do within the definition of that? Then afterwards, of course, when we go to the AI specific. Yes, there are very specific areas. I mean, you should have some, uh, basic knowledge around LLM stuff, okay. Modeling, training the models, the coding around or the layers to prevent, uh, provide your solutions, etc. those things will not change that much. It's a specific competency, but especially around the data piece, I think I see the demand that this should be a bit more mature in the following period. I mean, we should we should see that the positions will be clarified. 

Exactly. Okay. 

Who does what. Because there are also a lot of transformations happening on the profiles itself. Right? Earlier you were working on the basic data quality stuff. Now you became a data scientist where you deal with the unstructured data. Well, the data structure is still important, but not as much as 2 or 3 years back. Right. So these are different, uh, kind of competencies and technology also supporting you on that. So basically I area I believe, uh, we still need some competencies, uh, to support the growth that we are having, not only Schneider Electric, also the market itself. Okay. 

Then afterwards, another important role is the, uh, I call it a kind of generalists, because when you, you can have very high tech profiles, but then afterwards you need to explain, you need to tell your story to your business. Right. So this is becoming a little bit gap now because when you go very high tech which is happening with the AI etc., for example, you are having difficulties to explain people to convince people because imagine in AI case, you give a data in a black box and it comes in output and you said, okay, now tomorrow you will operate with this data, okay. Where this data is coming from, how we can explain, right? I mean, all this kind of things, all these models, we spend a lot of time today also try to socialize the basics of what is AI. I mean, prescriptive, predictive then generative. Now in the in the future, maybe autonomous. I'm following the Salesforce terminology which which I liked. I think that's the journey that they define in this journey.

What does it mean? What is different from past and today right between the past and today. So these are, I think, the key things. I mean, I call it a kind of generalist roles on top of the expert layer, usually the mid-level managers or somehow even the senior managers are playing this role to explain the X-Com level. Because believe me, in each and every corporate now AI is the Xcom agenda. 

Yeah, I believe obviously. Yeah, obviously. 

But they need clarification. I mean, and we are asking them to invest what we do in here. We really spend time to explain okay, what is this. What's the value behind that's going to provide how we will do that. Why this use case or what not this use case I mean how we choose our priorities etc.. So that's why this is also giving a lot of, uh, opportunities to write profiles to translate this technology, language and ambitions into business language to explain to the sum. They can harvest at the end. Right, exactly. 

So I feel like there's a because I've been a few I events myself, I feel like there's a lot of companies that maybe aren't doing what you just said, where they're not focusing so much on the end value, like, what is this really going to bring your approach to me? 

Again, I'm not super technical at all myself, but it seems very practical and it's very actionable, right? And I'm not saying it's it's an easy thing to do, but I mean, I've, I've been at events where the person speaking about AI reminds me of like a marvel supervillain. You know what I mean? 

Like, that's the kind of show that they're putting on where it's a lot less practical. Yeah, that's what I get from them, which is like the opposite of what I see from you, which I feel like is part of why you guys have had so much success. Practical. Well, definitely. 

And also there are really good practices, which I discovered one of them during the, uh, one of the roundtables I attended, uh, in the Gartner symposium, companies now organizing, for example, one day. ChatGPT kind of, uh, try workout or how you call it, I don't know. Yeah. Like, uh, like. A, l

ike a lab day. Sort of. 

Exactly. Lab day. Perfect. 

So they let, for example, team three, four hours during the day go to ChatGPT external or internal one. For example. We don't use public ChatGPT. We develop our own version, which is the controlling the data. Exactly. And, uh, they are asking their employees to run different use cases to show that it's not a scary thing. It's not going to take your job. It's going to help you. Use it for something. Like what? What can you play with this and find? 

Basically the the magic behind here is definitely we always go with some fixed use cases. Right. But there are a lot of opportunities that people actually doing the job can emerge. New use cases that we never thought of. So these are very interesting practices today. I think this is a bit, uh, breaking the, the, uh, kind of, uh, prejudice on AI. Okay. This is, uh, supervillain stuff. Okay. Uh, this dystopian scenarios coming into picture, etc.. 

So I think that was a good exercise to break that. So there are a lot of, uh, opportunities to first prepare your people. As I said, it's going to be all about people not developing ones, and also the ones who are going to use that. Right people. And also the creating a bit, uh, kind of know how in the organization to kill this information asymmetry, especially when the high tech topics comes in. I'm excited about it to try things exactly and come up with solutions that are going to they're going to be valuable for the rest of the company. 

The last question I have for you, and again, as much as you can share, we'd love to hear whatever you can't. Totally cool. But when you look at what, what, what Schneider Electric is doing right now with AI. But but what's future in the pipeline? What's coming down in 2024? Is there anything that you can share with us as one of the earlier adopters of this tech? And, uh, yeah, we'd love to hear it. For everyone who maybe hasn't heard what you've said at at. Dreamforce. 


Uh, we have three different agendas which are working in collaboration, definitely, and sharing opportunities between them, amongst them. So one of them is the products that we deliver for our customers, and we have AI solutions on top of them. The example I have given earlier for the electrical vehicles domain, so we have several use cases around that, but not only. 

So this is one the Schneider product portfolio AI group. 

The second block is for the efficiency and productivity purposes for our employees. It can be frontline employees. It can be back office, it can be finance. It can be R&D for different, uh, organization members. We test different use cases to see how AI can help them in their daily operations. 

And the third section, which is very close to my heart, is it for it? Because we know that also, I like the examples we have shared right on the code development. But not only I mean, there are so many different areas in it that you can apply, or I use cases to improve your quality, even reducing your technical debt solution, technical debt. Imagine a machine or code can goes back ten years of legacy and tries. To. Fix your technical debt. I mean code smells or problems there and prepare you for the better. Let's say, uh, future. Imagine the maintenance. Efficiency. Yeah. 

So I mean, there are different and the very promising opportunities there that area as well. But for us, we try to move our agenda among these three different streams for our customers, for our employees in terms of efficiency and productivity, to welcome the business growth with the existing workforce and it for it. This is more our, let's say, internal agenda in order to reduce our TCO or, uh, maintenance cost or as I said, the technical debt that we have on the solution. 

Side for the. Future as a game changer, right? 

It only makes it easier to build on what you already have. Is there anything that you want to share? 

Lastly, before we sign off about Schneider Electric, anything that you want to share in general about anything that you have planned just for the audience? 

Um, well, as I said, uh, we are proud of our, uh, Barcelona hub, not only also different locations that we operate. 

Uh. If you would like to join us, check us on LinkedIn or our website and apply us. We are waiting for you.