Alejandro Martinez of Erudit.ai on Employee Listening

Speaker A: Welcome back to the People Data for Good podcast with me, Al Adamson. In today's discussion, I had the great pleasure of speaking with Alejandro Martinez. Alejandro is the co founder and CEO of Erudite. Erudite, as you may know, is an employee listening platform that leverages AI to better understand the employee experience. I was inspired to hear Alejandro's story as well as his vision for the future, not only of the employee listening and employee experience space, but where we're going with the future of work. So again, thank you for being here and hope you enjoy.

Speaker B: Hi, welcome. I'm here with Alejandro Martinez. Alejandro, how are you doing, sir?

Speaker C: Hey. Hi, Al. Nice to see you again.

Speaker B: Great seeing you. Thank you for making time. Here we are in San Francisco, Salesforce Tower and you are the founder and CEO of, uh, Erudite. Tell us about it.

Speaker C: Yeah. Well, thanks for coming, Al. It's a pleasure as always to see you. And we are lucky today. Sunny day in San Francisco. So, yeah, I'm one of the two founders of Airlit AI artificial intelligence company, siriusa company. Uh, we are pretty much focused on moving cultural and employee experience data to real time, applying artificial intelligence that we have been creating for the last and training for the last three years applying that to open data, text and open language. So basically we are trying to not disrupt because this is a word that I hate is like make, evolve, you know, the industry and how we understand today to our people and the way that managers and decision makers take decisions, uh, business decision with the people in the. In the middle of them.

Speaker B: You know, as I saw your product as we first talked, you know, you have a very noble stance in terms of why you want to do this. Can you explain what were your motivators in founding Erudite?

Speaker C: Yeah, um, well, first of all, I have a super curious person. Extremely curious. This is the kind of person that when someone say, what are you doing today? I'm alone and I'm fine. And it happened always to me. Even when I was a child, I was the kind of the, uh, nerdy in the school. So it was good because I was tall and I was good at sport, so no one bullied me. But I was the nerdy one. So yeah, I have been super curious during my whole life. And the most thing that you can be curious about is about yourself and about people and about humans. So you are here. There is a sea, there is a sky. What is all this about? So I think that that's what triggered me to start studying psychologists also. I also like philosophy and uh, Physics and all the science that you can study. So human and people intrigue, uh, me by default. Okay, so that's the reason because I try to create a connection between science and people understanding and people behavior and trying to do of this my profession and my labor.

Speaker B: Well, I want to talk about your background and your motivations, even into psychology itself and AI and of people analytics and how it's going to be used, the ethics behind that. So we'll get into those topics. I do want to call out because for our listeners who are US or North American based, you have a bit of an accent from our standpoint. I have an accent from your perspective. Where did you grow up? What's your background? You mind sharing?

Speaker C: Yeah, I came from Spain from Madrid. Uh, my parents are both from them. Uh, I have also some family in Miami. So yeah, I came from Europe and I land here. I have been traveling to the United States during my whole life, I think. And also when I was early 20, uh, I spent some time living in Harlem in Manhattan, uh, because I play basketball and I love. Also New York is one of the, uh, the city is one of my favorite cities. But yeah, I came from Europe, I came from Madrid.

Speaker B: So who's your football club? Or do I even have to ask?

Speaker C: As I mentioned, I prefer basketball. Um, but yeah, my father is a huge fan of uh, Real Madrid. So I was a, uh, frequent, uh, person who attended to Real Madrid games when I was a child. But I prefer another sport. I prefer pretty much basketball, NBA Right now I am going pretty hard on baseball. Uh, I love the Giants. I spent some time also seeing the Yankees last week in New York. Um, and the next thing maybe will be NFL. So yeah, I am trying to deal with the rules yet, but thanks God ChatGPT is there. So it can summarize it pretty well in my opinion.

Speaker B: It's very American. There's judges, there's umpires. It's very litigious. So yeah, there's a lot of rules involved. So good luck with that. I'm here to help.

Speaker C: Thank you.

Speaker B: So going back to your educational background and I want to take that into your inspirations and motivations to found erudite given where we are with generative AI and all that. But educationally, you mentioned psychology, you mentioned philosophy. What did you study and did you study in Europe or here in the us?

Speaker C: I studied in Europe and I am engineer. And uh, I studied also a psychology, psychology degree. Uh, so I am engineer and a psychologist. I started studying engineer because I was the kind of guy that is good with numbers. So my teachers, uh, in the school told my parents that yeah, this guy would create a good professional career in engineer. And there in Europe, uh, well, that's like a good reputational thing, you know, to study engineer. On my side was a, uh, civil engineer. So I studied how to build bridges and roads and dumps and all this stuff. Yeah, like that one, but smaller. That will be a tricky one. But uh, yeah, skyscrapers and all these things. Then I remember that in second or third grade I come back home and I say, oh my God, I hate this degree. You know, I don't like it. And I told my father, hey Dad, I think that I'm going to quit and I'm going to start studying psychology. And he answered me with a, a phrase that I will always remember and is, what's the point of living in the moon if you have nothing to eat? And he was like, so, no, right. Um, I understand him. He came from an uh, extremely poor, uh, family from a super small village. Uh, they take care of animals and fields and these kind of things of agriculture mainly. So what I did is to start giving um, particular classes as uh, private tutor. A private teacher of physics, maths. I'm paying by myself. The psychology, uh, degree.

Speaker B: Wow. And so you study psychology, study engineering, then what, after you graduate, where do you go?

Speaker C: Um, I spend a lot of time also as private tutor. So teaching um, students that they were at the last time in the school or uh, first years in the university. And they were usually students with special needs. Sometimes they have outings or sometimes they are gifted but they are really bad with social interactions. And sometimes, well, it was not normal. But uh, yeah, I spent some time doing this. Um, and the father of one of my students, he proposed me. He was the owner of one large software company based in Switzerland and they create software. And um, he liked me and he told me, do you want to do an intern or to come working with me this summer in Switzerland? So you will be in the R and D department and we can create some cool projects. So this is how I started to create R and D projects for the software industry. So that summer we create three. We create one, um, uh, platform where executives in Nestle are able to receive lessons but in a different way with an approach of the multiple intelligence theory from Howard Gardner, not the regular way to teach things. And also we create another two cool projects. So from that is, um, from that person who was the father of one of my students and the owner of that company is the first person from the first person that I heard or I listened, the word startup.

Speaker B: Wow. So you're now applying your engineering background, you're looking and applying your psychology. You had a relationship that got you to the next level. I mean, how many years was it from that point till you founded Erudite?

Speaker C: Um, like four or five years, honestly. Uh, five years, more or less. Uh, I spent some time working in Switzerland with this person and after that, from the RIT department, he proposed me to. Okay, I like your projects. So if you want to make real one of them at large scale, we can create a startup and I can be your first investor. And it was like, and start what, you know, a startup. And I say, okay, what's a startup? You know, I didn't even know that. So he started explaining me the things, how you have to create a company, how the world uh, works. Because he was the owner of this sort of company, but he was a serial entrepreneur. Uh, he was also um, working and trading with tea from China. He was selling this tea in Europe and he was in charge of the distribution and he also was the promoter or he was the investor of some theatre, um, things, uh, in Spain and some cultural acts. So he teached me a lot of things about this. And he was the first person, uh, his name was Paul and he was the first person who introduced me to that. So after that I created one company on top of one of the projects that we developed there, a net tech company. Uh, it didn't went well. Uh, honestly, I look at myself like five years, uh, ago and I'm embarrassed, not embarrassed because of the person. Maybe I was even more, I don't know, pure one. But in terms of business knowledge, I was a complete baby.

Speaker B: Well, that's how we learn. You know, babies learn, they fall and you know, um, as you're sharing that story, I'm like, well that's a checkbox. I know many investors who wants a leader who actually went out and failed and knows what that's like and knows what to do to avoid that outcome again. So in other words, learning which then led you to Erudite. Yeah. Is that your follow on project?

Speaker C: Yeah, totally, uh, erudite start like three and a half years ago, late 2019. Uh, and it starts also thanks to our co founder Ricardo, uh, because we met completely randomly M. He was living in Mexico and he was looking to get came to Spain and I was in Spain and I was looking for a new chief technological partner, uh, to start a new thing and we met randomly in LinkedIn. We started talking, uh, he fly from Mexico to Madrid and we start talking. Uh, he has invested a lot of time of his life in social entrepreneurship, as I did also in a lot of things related with culture and with education and with people behavioral. So we say, okay, how can we make money doing the thing that we want, which is creating things that are related with humans? You know, I mean, people, people behavioral learning, things like that. So we start checking how is how leaders or how business owners, what's the tools that they have for understand better their people. And for. We are obsessed for one thing, which is put the people in the middle of the business decision globally. M. Okay, so that's a thing, uh, I mean that's the thing that we have been trying to improve, uh, in a no naive way, because that's something that everyone wants to put people in the core of every business decision. But what's the right tool, um, for allowing that, for triggering that, for unlocking that. So it was the challenge.

Speaker B: Well, I want to get into that challenge and the core value proposition around erudite, both when you started and now, and how it's evolved. But before we do that, social entrepreneurship, you touched on it a, uh, bit. But I want to know what that means for you and why it's important to you. Can you share?

Speaker C: Yes. Uh, it was my first company, um, an etech company. And my. I was pretty angry with the educational system. Uh, so I didn't like the educational system that I suffered on myself. And I have to use that word because I really suffer in a psychological and physical way. Not because others, just because of that. I don't know, I feel that I didn't fit on that. You know, it's like why I have to spend eight hours here listening someone teaching me something year and years and years, during years and years and years. So social entrepreneurship for me is trying to improve the society at some point. But it's true that it was at that point was just something about me. You know, I was angry about the educational system. So I was trying to change it. Also I spent a lot of years as teacher, uh, okay, as a private teacher. So I also perceive a lot of suffer from students, you know, people that usually they are special or they are different or they are whatever, but they don't fit with their normative, uh, rules and they suffer a lot, you know, and they were brilliant and they were clever and they really were passionate and they have talent and they are close to demolished, you know, uh, because they don't fit with the educational system. So it Was for me, that's the meaning for me about social entrepreneurship. When it comes from suffering, if it doesn't come from suffering, usually it's like a naive thing that you are trying to do. In my opinion, from my standpoint, it came from a deep experience which is mixed with suffering and has a large part of, uh, suffering. So you try to improve that reality when you see or you can perceive that the suffering is common not only to you, it's also common to a part of the society and you try to fix that or to improve that. That's social entrepreneurship. Wow.

Speaker B: Uh, I love it. And I'm hearing that you're trying to alleviate suffering at scale. And if you empower leaders to do that, then they can have a tool and a process that will enable them. Is that the core value proposition of Erudite?

Speaker C: Uh, I was meaning. Or what I was saying was more related with the first company when we were trying to relieve the suffering, related with students that don't fit with the educational system. So we were trying to create an educational path where you can receive qualification based on the different levels of the multiple intelligence that everyone has. So I would try to create something based on the multiple intelligence theory of Howard Gardner. When you or myself are not clever or are jerks, we are only intelligent in a specific level of intelligence, and in another one, we are not. So the suffering that I was trying to alleviate there is like you boy or you girl, you are not clever or smart or you are not. You are only clever on this, this, this, and you are not so clever on that. On that, you know.

Speaker B: Got it.

Speaker C: So it alleviates also and it relieves, uh, it's a pain reliever when you understand that you have your own internal schema, that it's also valuable.

Speaker B: Got it. And so with Erudite, would you also classify it in social entrepreneurship?

Speaker C: Um, a little bit. But Honestly, it's more B2B. Uh, it's focused on companies. But I think that it is because we are doing. We have an expression in Spain, which is Tragar Tel Sapo, which is to swallow the croak, you know, uh, it means that what's the necessary thing that you have to do for have a good impact or for create a good impact? So I think that we have created and we are creating the right system to achieve a good purpose, which is put people in the middle of the business decision. So I think that in some part, obviously, we also are entrepreneur in a social way.

Speaker B: Well, let's then talk about Erudite and its Core value proposition at the outset, what were you trying to achieve? You just touched on it. But from a technological standpoint, what was unique about the solution that you were creating?

Speaker C: Okay, we start from a hypothetical point. When we say at work we are generating a huge amount of information through intercommunication tools that we are using daily, uh, let's say Google Workspace, Microsoft 365, Slack, Zoom, all of them. We are generating tons of information. And in some way this information in an aggregate and in an anonymous way is already being stored. I mean, if I am the C level of a company and I am using Microsoft or I am using Slack in some way, uh, in our account we are already storing aggregate and anonymizing historical information, uh, related with activity or even open text or natural language. So we say, okay, why if we transform this data that their companies already have in a compliance and aggregate way, in real time data about culture and employee experience. Okay, so that's what we did. Right now the system, my opinion is a little bit broken there. Everyone wants to understand better their employees. But what the market is saying to us and what the system is saying to us is that we didn't achieve yet. The point with the people data, uh, takes too serious, you know, uh, we didn't achieve that point yet. You know, it's like nice to have good data. Uh, you can. Soft data, uh, soft skills. No, they are not soft skills. They are not soft data. They are as harder as the other ones. But we were not able to put this hard data to create hard data about people, you know, or groups of people. So once that you convince people that that's not soft data, uh, soft skills or this kind of thing, that this is, uh, so hard as the, uh, business data, then the things change. So that's what we are trying to create, to take advantage from the data lake that the companies already have and transform that in hard data about groups of people and about culture and employee experience. Only culture and employee experience.

Speaker B: So when you talk about transforming that data, that screams AI to me. Are you applying large language models or AI to your solution? What does that look like?

Speaker C: Yeah, we have a house made AI that we start training in 2019. Uh, we are lucky because we have in house we have a team of really great PhD in organizational psychologists. All of them are women. I know why I'm saying that, but they are women and they, what we are taking more advantage from is from the natural language that we create. Okay, so we start creating our own data sets and labeling them in 2019. Because let's try to down toward this a little bit. Imagine that you are using Slack. We have a lot of common groups where over 10 people are there. Uh, this natural language is already being stored in your Slack account. So you are administrator, you can have access to this conversation, anonymize an aggregate, but you are storing them already. So what we do well, what we did is, okay, let's try to measure over 50 metrics that matters for business and people leaders like rewarrant recognition, uh, engagement, burnout, alignment, autonomy, peer relationship, physical environment, diversity and inclusion. These kind of things from a bunch of anonymous and a great conversation. So that's what we did. The way that we trained this is manually. Basically the only thing that we are doing is replying the judgment of a PhD in psychology. It's like training an AI for understanding if this is a cat or this is a dog. Cat, cat, cat, dog, dog, cat, dog. Same thing with burnout. You choose an operational definition for every metric you create the data set. And a PhD in Organizational Psychology, a bunch of them, they label. When the labeling is consistent, you have a good data set, you can train a transformer model. And if you connect this to um, the information that you have already stored in Slack, you can have real time data, both culture and employee experience in a compliance and safe way and without doing nothing.

Speaker B: So for my own processing, I can see, uh, a variety of use cases that would both mitigate risk and increase the probability that the investments that, that I make in people actually deliver the desired return. But from your perspective, what are the priority use cases?

Speaker C: Totally M benefits or initiatives that you have already in place? We are talking. I'm the number one seller in my company, so I am talking with companies whole day. So which initiatives do you have in place? Okay, A.V. why? And how do you measure the impact? And how many times does it take to understand the impact? Okay, we make some survey every six months or someone answers so we can understand what they want. And from that we can put in place an initiative and six months later we ask them again and we check how the results are. So one is for understanding the impact of our initiatives and for budget efficiency. Okay, you have $1 million for people initiatives in 2024. Which ones are you going to implement? And I would love to be flexible. I would love to put one in place and take two weeks later or one month later if this is working in a real way, you know. So benefits, what benefits do they need? The employees, the workforce. They need, uh, salary, for example, that they can receive the Salary in advance or they need strongest learning careers or learning resources or they need well uh, being because the burnout is high. So it's a huge boost for your benefits, uh, recommendation and it's also a huge boost for understanding the impact of your initiatives. Um, also for uh, climate studies, you if you want to do a climate or a cultural assessment, uh, you can do it in literally depends on the size of the company. But you can doing this in 24 hours, for example in a 1000 employee company. So benefits for understanding benefits that employees need and the impact of the initiative. Second cultural assessments in one day, uh, ma, for example, or large hiring process. When you are merging different groups of people, what's happening there would be great to have the real time polls about how this collision is happening and how you can help them. Uh, so those are some of the examples that we have.

Speaker B: You mentioned measuring impact and I'm surmising that it would also provide leading indicators of whether or not you're tracking towards a certain outcome. Is that fair to say as well?

Speaker C: Yeah, exactly. You uh, can have in consideration that if you are using Microsoft or you are using Google or you are using Zoom or Slack, the information is stored there for years. It's also something that you can use for measure initiatives that start in the past. Imagine that you put in place these benefits six months ago. You can connect early today and understand the impact of these benefits since six months ago till today. You can associate also to events, events that happen in the calendar, for example, a conversation or a communication from the CEO or an outside meeting or a layoff. You can also link what's happening to specific events or uh, critical events that are happening. You can do a lot of things around this and you can associate that uh, to a specific event regarding, uh, leading indicators. Um, of course leading indicators finally is a projection of a bunch of points that you have. Imagine the number of data points that you have with this technology. You have a lot. So when you predict finally you are assuming a risk. If you have more points, the prediction is going to be more accurate. If you say that these people is under burnout risk, they really are. It's not the same thing that trying to say that people is burnout risk because they text a lot in Slack or they have a lot of calls, which is what usually you have in the industry.

Speaker B: So just to play that back, because this is exactly where my head was going, is that, correct me if I'm wrong, you're creating a continuous variable where historically, if you're doing employee surveys for example, you might be doing it once or twice a year, maybe quarterly at the high end, but you are grabbing information daily or ongoing. Is that accurate to say?

Speaker C: Yeah, totally. We are moving, uh, there is great companies doing service. I mean Qualtrics, Kulturram Lattice and I even have personal relationship with them and they are great. And I think that it's something that people should still be doing. You know, we coexist with them. We have customers that are using Qualtrics and they are happy with both, with both companies. Um, but the question is always the same one and what's between the service, what's happening? You know, it's like okay, we have very good service on Monday on January and June and December and what's in the middle. And that's something that HR also receives from the C level team. They are really looking for real time pools. So we can talk a little bit also about the continuous listening. Continuous listening and that's a uh, buzzword that we have in the industry today because it's important. The problem of what we have to understand with the current continuous listening industry is that 90% of them is a continuous asking industry. So we need to be aware of that because employees have their workflows, you know, so we need to balance how and we need to be very careful how, how do we create these insights, ensuring that we are not breaking their workflows.

Speaker B: And correct me if I'm wrong too, if you're going to be doing this, you're communicating it to the workforce that this is happening. And I want to get into ethics and privacy and that, that theme I also, before we do that however is if we are doing this at scale and to your point, we don't want to interrupt workflows. There also needs to be continuous action. So totally. And so the governance process, the decision making process to act on this data need to be in place. And I would put forth that in most organizations they're not there, uh, they're not structured because those processes and governance models have not been the historical norm. So I don't want you to just. What's your take on that in terms of the business readiness to take continuous action if you will.

Speaker C: Yeah, totally. Two parts here. The first one about communication to the employees of course, and we usually start from, uh, start our relations in our world with customers in a super, I mean non intrusive way because we uh, start working with common open groups in Slack, for example. So by default we don't um, use the information in one on one channels or the channels that have the locker on top of them. So also the only thing that we do is we transform the open language in cultural metrics. And these cultural metrics are even aggregate. So teams over five members. So you can see burnout levels uh, in Alejandro, but you can see burnout risk levels in a team where Alejandro is working. And this team should have over five members. If the team is under five members, uh, we don't solve data and you have to aggregate another team to create a larger team. So aggregate data, uh, anonymous data. The only names that you can find into the platform are the managers because this is the way that you use to open the conversation with them. It's not anymore not their responsibility because this is not anymore soft data. This is hard data from now. So I want the bridge with you because you are responsible of the culture and the employee experience that you are creating in your team. So those are the only names that you will find in Air lit manager's name. And they will appreciate that because that's also. I mean you are enabling me and you are helping me. Uh, employees also can have access to the platform and even you can share the data with them and you can uh, create an uh, automatic report that is sent to them so they can have visibility also about the culture that it's in their company. And what we measure is public in the website and how we measure that. And what we do with the data is even also public. We are not allowed to storage nothing. We are only allowed to transform the data that you are already storaging as user of Slack, Microsoft or Google or another of these intercommunication tools. The second question was related with actions, right?

Speaker B: Actions and the governance, the people who would take action. Because, um, just to see this, because I as you know, doing a future of work study and what I'm finding is that this is lacking, particularly given the action oftentimes is multifaceted and multifunctional. In other words, HR has a role. It has a role. Uh, operations would have a role. Legal would get involved. So without biasing your response, which arguably already did, what are you finding in terms of the business readiness to take action and related to it, I imagine that is a sales challenge as well. It's like who's the buyer? Because it could be all of the entities that I mentioned. So yeah, governance.

Speaker C: Yeah, totally looks how our sales cycle work. Okay. We usually offer a uh, 30 day free trial wherein we can connect LOD to your intercommunication tools for free so you have access to the product during 30 days. And you can check all the data there. Also we create a cultural assessment for you in PDF format about uh, the last 30 days. So you can use that. We have a call called, uh, trial success meeting that happens three weeks after. We connect the tools there we share the insights that we have found, um, in the company. For example, yesterday with one of our company based here in the Bay Area, 300 employees more or less, we are drilled down, drill down a team of 12 woman in the operational department. It's feeling really bad in terms of recognition and this is triggering how the males in the same team are feeling because it was curious because these women were feeling bad in terms of recognition, but they were highly engaged. But the men, it was triggering that the males were close to leaving the company. And uh, it was having an impact also in the sales and the marketing department. So we appear on that call and say, and now we have a plan for you. And we already have this plan because we already know for improving which metrics for which kind of company, which actions do you have to take and to put in place. But this is not free. Okay? So they really want the actions, you know, but it's true. And this is how we move from trial to payment. You know, we found these insights right now. Okay, uh, it was great, but we need to move to another relationship. If done, we have to turn the lights off again and we can share with you the plan. The next challenge is what happened with the plan. You know, you can't give them too many actions because budget are limited. So these actions also has to be crafted or must to be crafted in terms of a budget, an industry, a company type size, everything. Okay. And um, know that being conscious that they are not going to put in place more than two or three people related initiatives at the same time. So you really need to give them the right things that you know that are going to work for them.

Speaker B: So I'm hearing that you're empowering them and the means in which they apply that is really going to be up to them at the end of the day. And so that's where I have a lot of curiosity because I see a lot of great insight being generated. However, the actions that would align with that insight, there might be a big hard charge at the outset, but then it fades away in large part because there's not the measurement, there's not the governance, there's not that um, energy to keep things moving towards the positive direction. But I'm hearing that that's what you would provide on an Ongoing basis. Is that right?

Speaker C: Yeah, we are providing insights and we are providing what's the action that we think that having in consideration the current map of metrics and scoring metrics that you have in the company, uh, and your situation, which are the ones that we think that are going to work and to move the needle. But every company is a whole world. It depends a lot of the kind of company, some company, they just take a look to the data, they check and they say, okay, I know what I have to do and I know how to do that more, uh, sophisticated companies and other companies, they don't even have HR. You know, we work with CEOs that they are like 100 and they have externalized the payroll and they don't have people head of people. So you have to do everything for them. So it depends a lot of the kind of company. And one thing that we have found valuable here is not putting all the eggs in the nest of they are going to take actions because you must be able to keep providing value even if they don't take actions. So what you can do is to link that to the events that are already happening in the company.

Speaker B: Got it.

Speaker C: Uh, because we are doing this, we are able in any way to link their cultural data to actions that are happening.

Speaker B: Anyway, you know what you're sharing and there's a few more questions I want to ask. Then I have these rapid fire questions, uh, and then we'll wrap. And the question that I have is around employee experience which has gotten a lot of energy over the last five plus years. And what that actually means and what it looks like and how to measure it has been open for debate. There's a lot of solutions that are out there within employee experience. Of course there's diversity, equity, inclusion, there's well being, there's burnout, there's engagement and those themes. So in terms of what initiatives you're plugging into, is it all of the above? Does it depend on the client or prospect? Is it employee experience? Is that something that you're in the ecosystem of solutions of? What are your thoughts? There's.

Speaker C: Yeah, totally. That's a huge theme right now. Um, honestly in our data what we are seeing, what we are seeing more frequently are problems with reward and recognition and organizational support in the last months. So, uh, a lot of companies that we see that Maybe they have 75% of their employees in burnout risk or 80% of their employees under turnover risk. And these scorings are triggered by some causes which are usually related with uh, reward and Recognition and organizational support. So, yeah, those are the two main things that we are seeing in the last months. And reward. And recognition is not only about salary, it's about recognition. It's about to really recognize people and give people recognition about the work that they are doing. Uh, an organizational support is the same thing or meaningful work. For example, people struggle a lot to connect the time that they are spending, the time that they are spending with the final outcome of the company. So those are these tiny things that we are able to show these tiny triggers to our customers and let them know you think that you have a huge problem with Churn. But let's create something about building recognition which is free gift. Recognition is something which is free or, uh, meaningful work. I mean, it's free to explain to people how the eight hours that they are spending today produce an outcome, a final outcome for the company or organizational support. Maybe you have to invest there, uh, some time, but that's something that you can easily tweak in your benefit program also for getting that. So they are micro triggers that we are observing the whole day. And the two biggest ones right now are rebound recognition and organizational support.

Speaker B: Fascinating. I want to come back to that topic in the future, but for today, uh, we've talked about, you talked about it at the outset, privacy, uh, but I want to talk about ethics, uh, before we get into the final rapid fire questions here. Because when you're gathering data that some employees, some workers might not know when they first joined the organization if they were not privy to communication, if the organization did not socialize how their data is used adequately in the onboarding process, which, correct me if I'm wrong, is largely the responsibility of the organization. There could be this wrestling with the ethical position of applying a employee, um, listening solution. And I'm hearing this a lot. However, I don't know if the industry call it employee experience or employee listening, has done a great job in really explaining that the ethical upside, in other words, the responsibility that we have to actually apply these tools and take appropriate action. Because if not, then we're perpetuating some bad habits or suboptimal processes. So without going further, what's your position, not only in privacy, but on ethics and how organizations can deal with it and how you all deal with it.

Speaker C: I think my point here is complete transparency, okay? If one employee in one company using Air Audit want to check our models or our data sets or our architecture or our database or whatever information that we are storing, he or she will be able to do that for me. That's a good thing because I don't have a problem doing that. So that's my standpoint and um, that's how we operate. If you want to understand how the models are working, you should have access to that. If you want to know how architecture is operating, uh, you should be able to understand that and to check that. If you want to understand which data from your teams or from your company is being stored, you should be able to do that. Um, complete transparency. I know that a lot of companies are building large business around this. Uh, that's not the way that we used to, to make money. We are only AI, engineers and psychologists that are able to measure cultural and employee experience metrics from OpenText, the OpenText that we generate daily. And we are trying to do this in the most compliant and friendly way. Um, also I think that companies should be audited. That's something that must be in place and you should be able to call a specific auditor to understand how the company is going. You. We have awesome companies in this country that are uh, in charge of also security and privacy and these things like uh, Banta or Drata or a lot of companies that can give you or can serve a real time dashboard to your customers for understanding how your privacy and your security is working. So I think that there is a lot of you as buyer. The only dark point that I can see is that you don't have enough information about how to understand what do you have to receive from a software company for understanding if they are compliance or not. Once you have the information, just ask for it as much as you want and as much as you can.

Speaker B: And you being a native European, have uh, particular sensitivities and understanding of GDPR and the EU AI Act. How do you feel about those impacting erudites and just the people analytics industry if you will.

Speaker C: Honestly, I think that we have a problem in Europe, uh, with R and D and with technology. I am um, extremely in love with Europe and my soul is European and I love that place. I love the culture and the most happy moment in my life happened there and will it happen there again? Uh, but I am a little bit sad in some way because we have been historically the lighthouse and what's happening now, we are like fighting between us, we are scared, we are only. Maybe that's not the right answer but I feel that we are reactive and that uh, we are scary and we have been an historical lighthouse. M I love the Europe of the big musicians and the great musicians and the great literature and the Great philosophers. And that's the Europe that I love. And also the technological Europe when we create amazing things. Look at Leonardo, look at all these guys that we have there. And right now I see that we are a little bit reactive, which, I mean that's another whole podcast. But uh, I think that it's also good that we are the balanced player. Europe is the player which is trying to balance what's happening with the data. Because it's true that here in the States and also in Asia and in another place sometimes it's like the wild wild west and people traffic with our data. It happens. So I also like Europe that taking this stand up and saying, okay, we are controlling that we. But not controlled by control because of the control. It needs to be sense. So I am okay if you are putting a lot of boundaries, but please don't stop creating things. And that's the problem that we have right now in Europe. If you create innovative things, there is no way that the companies that you have in Europe absorb this innovation. We are getting older and older and older and every time we are more lag and lag and lag and um, it can happen.

Speaker B: And there's this scary language that's coming out around generative AI and how that needs to be regulated. And that can be a whole topic for discussion. But I want to put that out there to tee up. My final question before the rapid fire questions is with generative AI, with globalization, robotic automation, all these things that are disrupting the very nature of work, what's your outlook for not only the business, uh, of people, you know, meaning I shouldn't say that the HR tech industry, but really the work experience, you know, do you think that believe that generative AI is going to be a good thing for the workers? Is it going to augment their capability or is it going to be something that is going to displace workers and the need for human jobs is going to diminished. What are your feelings there?

Speaker C: I think it's not going to be so big as people think. We have seen that uh, they put steroids in a lot of models during a while and finally this is not profitable. So right now the models are not working so well. They are limited. We are going to create agents that are going to replace everyone that's not so easy. And if they are able to replicate something or to do some task is because these tasks maybe should be automatic. So I think that it will stay as uh, something that it's going to help to do in an automatic way. A bunch of Tasks. But I don't see generative AI so huge. I think that it's going to be useful for a lot of things. Also we need to be protective with some roles. For example, I was in LA some weeks ago and there was a huge mess with the people working in the cinema industry because they were scared that the AI companies storage their face recognition and they are able to create movies based on their face, which is extremely creepy. Uh, so we need to be aware for this kind of use case. Uh, apart from that, from a good perspective, I think that is going to be useful in order to doing an automatic thing, uh, repetitive tasks. But I don't think that it's going to change the world. Honestly. I think that we are already saying this technology is going to change the world. This technology is going to change the world. And I don't know, sometimes I feel that that's something that we do when we are boring. But we are going to change the world or we are going to kill the world. That is not going to be done by a technology A or B.

Speaker B: All right, well let's hope we do it consciously and with virtuous intent. Uh, so here I'm going to get into our rapid fire questions and then I'm going to give you a ch for closing comments to wrap. Up. So first one, what's your favorite genre of music? My favorite genre of music. What type of music do you like?

Speaker C: Um, Rumba. Estopa. That's a music ah, group that we have in Spain.

Speaker B: Yeah, what kind of music? I mean what is that?

Speaker C: It's a kind of guitar. No? Yeah, it's a kind of guitar. And this is rumba. So yeah, this is special style that we have there and we really like it.

Speaker B: All right, I'm going to have to look up room book the first time I'm at a loss. So who are uh, some of your inspirations whether it could be historically or currently. Like how do you, who do you learn from or who have you learned from that has inspired you?

Speaker C: Uh, uh, from the big ones. Uh, Julio Cortazar, I love, I love him. He's one of the best writers that we have in Spanish language. Uh, I love, uh, I don't know, I read a lot to Freud, to Nietzsche and these guys, but maybe they are a little bit more boring. I love Sopenhauer for example, the philosophy of the music. So he's an inspiration for me. Uh, the great musicians also Bach, Beethoven, I love them. Charles, um, Bukowski for example here in the United States and all the beat Generation. I love them. Uh, so, yeah, I think that they are one of them.

Speaker B: All right, well, yeah, you're going to give me a reading list. I'm going to have work to do after this. Uh, what do you do for fun?

Speaker C: I love wine.

Speaker B: You're in a good place for that.

Speaker C: Yeah, I love wine. And I love to travel. I love to travel with my camper van and, uh, with my wife and with my dog. Uh, I love to discover new places. That's my favorite thing in the world. To experience new things and to try to change the things that I am doing. So discovering new places, discovering new people. Put myself also in different situations. I think that it's what makes you human and what makes you an adult, you know, trying to test you in different scenarios. So testing myself in some ways.

Speaker B: Love it. So, last question. What is a recommendation or advice or two for young people, say teenagers who are coming up? You know, what would you hope that they have a perspective on or do?

Speaker C: Read and, um, be yourself. Okay, so try to get the perspective from other ones as much as you can, and try to educate yourself. Well, that's a good, uh, sentence from one of my favorite people in the world, which is Getty. He was a great writer from, uh, from Europe and a good poet also. And he said, uh, yes, basically that you need to. You have to follow yourself. And, uh, you have to understand who you are and that you people should be. You can't alone not be well educated. And well educated is not that having the standard education is follow the big ones, you know, from this earth, huge people was here, you know, in every area. So try to learn from them. Um, apart from that, try to follow yourself. So, yeah, get perspective from the right ones and, um, follow yourself. Follow, follow yourself till the end.

Speaker B: I love it. So, as we wrap closing comments, uh,

Speaker C: I think that we are privileged for being here, you and me, for having this conversation. I think that we have a pretty good life and I just expect to give something in return to. To the world. Just enjoying this wonderful and sunny day that we have here in San Francisco.

Speaker B: Well, Alejandro, thank you for sharing. I learned a lot. It was super fun. And, uh, yeah, all the best to you and your team. I'm excited for you all.

Speaker C: Thanks to you, Al. It's always a pleasure.

Speaker B: Likewise. All right, thank you for listening to

Speaker A: this episode of the People Data for Good podcast with me, Al Adamson. To support the ongoing production of this podcast and similar content relating to the future of work. Please, like, subscribe, comment and share this episode and be sure to check out our website at my fow.net that's the myfuture of work website at myfow.net there you will learn about workshops, courses and experiences we offer so you, your friends and your colleagues can better understand the future of work and how you can prepare accordingly. Thank you again for being here and let's continue to make great things happen.