Episode Transcript
[00:00:00] Speaker A: Okay, Max, thank you for joining me. Let's just get started. Hi Josh, it's been really exciting and fun to get to know you and your company. Why don't you start with a little intro and tell us about. I'd like. I think it would be really interesting for people to hear your background really briefly how you got into this space.
[00:00:17] Speaker B: Thanks first to Josh for inviting me today and very happy to be there. My name is Max.
I'm actually based in Paris now and will be moving very soon to live in in New York. A few words about myself. I have always been an entrepreneur. I have been building Internet startups since now more than 15 years across Europe, Middle East, East, Asia, Latin America. Very different industries I've sold and some companies and I started a new journey 2022 which is Maki people. And it's very, very exciting time and moment to, to be building a new, a new company at that time.
[00:00:59] Speaker A: What, what encouraged you before we get into the technology and the product and everything, what encouraged you to get into the area of HR and recruiting and people stuff?
[00:01:07] Speaker B: Yeah, it's a very good question. You know, my previous company was into logistics, so not nothing similar to. To hr, but I am very, I would say simple in a way that for me technology should be there to solve concrete business and human outcome. And actually this is maybe the link between logistics and people is using technology in order to help people organization living in a better way, I would say. And what I believe is fascinating in HR and in usage of technology and particularly now AI into hr, it's you can have an impact on million billions of people lives. And if you change organization in a way you can change basically the life of billions of people. You know, when you are an entrepreneur and you need to have a mission in order to wake up for the next 10, 20 years. Every morning I think this is a. You got your. Why every morning when you go out of bed.
[00:02:08] Speaker A: That's true. Great. So as an entrepreneur, you've done a lot of hiring and management and business growth and sold a couple of companies. What is the vision? I know Mackey is currently in the recruiting technology space. We'll talk about that in a minute. But what is your vision for the bigger picture of how you want to automate different areas of management and hr? And then we'll talk specifically about the stuff you're building and delivering now because I think it's very, very exciting.
[00:02:35] Speaker B: I would say there is two things currently, and I think it will last for long that excite me a lot in hr, the number one is in human resources. The majority of, of the action, the signals between humans are made of conversation that can happen face to face over the phone, over zoom, et cetera. And all these signals are either lost as soon as they are produced as they exist, or they are kept in an unstructured way. Everything disappear after my application process to that company or sometimes after my five years as, I don't know, sales, executive enterprise in that company. And I think the power of AI nature, it will keep all this data, it will allow this data to be structured, to be trained. So it will lead organization to take better decision about human capital. If you are an organization and you conducted, I don't know, 1 billion interview last year, AI will help you to make sure that next year your 1 million interview will work better because you will have identified all the insignia that will help help you to take better decision. And this can be applied, you know, in recruiting, but also in other domains. So at the end, you know, it's about for me the power of data that lead to take better decision, that lead organization to perform better. And the second topic that excite me a lot is that in human resources you have a majority at the end of the action of the task of human people that are not about human resources, but that are about admin tasks. And here, you know, we will have time to talk about it. But me, when, whenever I speak with a HR leader, I said how many percent of your time last week was really about adding value and you know, doing human interaction versus sometimes dealing with emails, with workflow, with interview, with screening, et cetera. So I think it's also a perfect way in order to, to lead better efficiency and optimization and higher productivity.
[00:04:40] Speaker A: Beautiful. Well, I feel exactly the same way you do in all respects. So your vision of AI tools in HR or systems is capturing the entire, I guess it's often called a life cycle, but the entire process of employees coming into the company, joining, getting promoted, performing, moving from place to place and optimizing the process, using data in a variety of ways. Is that a fair sort of overview?
[00:05:07] Speaker B: Yeah, exactly. If we can picture, you know, in 10 years, I hope even less. I would love as an HR leader or not even a hr, but you know, anyone in the organization to be able to type. Could you please list me my top 500 employees ranked by our 12 core competencies and tell me who I should promote for that promotion or who I should affect to that new department in the organization. And this, you know, we will go from a world where we Input and store information in database in the era of cloud and software to a world where this new orchestration of data will come to human with already decision and an ability to take an action.
[00:05:52] Speaker A: So in a sense, what you know, you know the history of this. It's been very, very difficult to do things like you just mentioned because we've had all these sideline projects of skills analysis and assessment. But in a sense, the way you're thinking about this, if I understand it, is the.
And the tools of AI will continuously be assessing and coaching people and then be for a variety of use cases and then that data will be available for selection of people, promotion of people, development of people, coaching of people, deciding how to pay people, reward people. I mean, I even had a meeting yesterday with a big company about contingent labor where it turns out for people that are frontline employees, 75% of them never get promoted at all because no one even knows what they do. They just do their stuff and go home. So you're sort of seeing this whole tapestry as an integrated process. Is that fair summary?
[00:06:46] Speaker B: Yeah, it's a fair summary. You know, when I ask HR leaders, how do you simple question and of course you know, I don't have the answer. But how do you assess your workforce? Exactly what you mentioned is there. I have an answer. We do nothing. Either we do self assessment, okay. Either we do two, three times a year, few assessment of few specific skills or we have also some notes between employees and manager in a CRM for the yearly, you know, review, et cetera. And me, my answer is like, what do you do of the 40 hours of work of your employees on your workspace multiplied by your hundred of thousand employees? This is goldmine signals that you know, could lead you to better know and better decision.
[00:07:36] Speaker A: Absolutely. Okay, so let's get into how the system works. Why don't you step us through? I know at this point you're mostly going after big talent acquisition scenarios, but I know it'll go beyond that. Why don't you walk us through the, the Maki product set today and particularly the, the assessment part in particular, which I think is fascinating, but. And just explain how you've decided to solve this.
[00:08:00] Speaker B: Yeah, definitely. So, you know, when we started maki end of 2021, end of 2022, the first version of our product was we want to offer to large international organization a product so they can assess in one platform any type of skills. And today we can have hundreds of skills through Mackie, from soft skills, power skills, cognitive skills, behavioral languages, coding, or Art skills in different fields backed by science. So from day one, I had a strong team of psychometrician based in Cambridge University, leveraging the best technology and creating a singular unique candidate experience branded to our clients. We never exist for the candidates, you know, Mackie, we don't exist. I don't want Mackie to exist in a certain way. And so that product, you know, allow already organization to have one single platform to assess externally and internally a large variety of competencies.
[00:09:01] Speaker A: Can I stop you for a sec?
[00:09:02] Speaker B: Yes.
[00:09:03] Speaker A: Industrial psychology and assessment is at least 30 years old. I mean, it's a lot probably older than me. What is different about the way you do it versus the way everybody's always done it? Yeah, you've got these great PhDs. Well, I think it'd be good to explain how you guys do it because it's quite interesting.
[00:09:21] Speaker B: Yes. So the foundation. Foundation has a scientific competencies called psychometry. We have the engine of psychometrician that are in our team and are able basically to produce all the scientific data that our model shows. Correlation of performance.
They mitigate bias. You know, we have been permanently audited since day one, et cetera. Now what did we do differently in that first version of that product? When we launched first, it was all type of skills under one roof, which was very singular because sometimes across some of our clients, we replace up to 10, 15 different vendors. You know, we had some clients, they were using one platform from video interviewing, one from coding skills, one for psychometry, one for languages. So all skills under one roof also.
[00:10:09] Speaker A: Coding skills, technical skills, industry skills, soft skills, customer service skills, leadership skills, all in one approach. Okay.
[00:10:18] Speaker B: Yeah. At the end, you know, when we will think about it in five years, we will say why I was using 12 different vendors to assess this.
[00:10:25] Speaker A: I'm not arguing against you. I just want to make that point so that people listening to this hear it.
[00:10:30] Speaker B: So, so this is the first one and second point, we really wanted to build conversational experience so candidates would be interacting with the brand they apply. If you are, I don't know, Walmart, you don't want to have a Mackie people experience as soon as you apply for a job, you know, in a supermarket or graduate. So we build from day one Walmart experiences where you can interact with avatars fully branded. And here basically we push to another level to what was existing before the engagement of candidates and the experience they were living and also the format they were interacting. So now, you know, candidates can interact in conversion experience through audio, through video, talk to the avatar, et cetera so.
[00:11:18] Speaker A: Let'S just explain it to the people listening. So it's essentially a conversational experience, a development tool that allows you to build conversational experiences.
How does a company create one of these AI based assessments that's unique to their needs?
[00:11:33] Speaker B: Yeah, they will define basically for any single role they have in the organization, what type of skills basically they want to assess. And of course our engine will recommend they will also be able to fully brand from A to Z the experience from the first touch point, the type of avatar, cloning the voice, cloning the background, et cetera. So the candidates will live basically the experience as if she were in the organization. And this, Josh, was the product we released three years ago, you know, as a V0 call it. And then we started to understand that AI and more specifically large language models were allowing us to completely augment the possibilities of our product. And here we started to keep, of course, the foundation of what we have built. And we have started basically to understand how we can push this experience to something better and that could answer different use case for our customers. And here we started to build different type of AI agents that will answer to these different needs. So now if I give you in one minute an overview of the type of product we have. We have a product that can screen pre screen application. This is the top of funnel. You are an organization, you receive millions of applications. Today we don't believe at Macheon CV and we are of course not the first and the only one, but CV today, you know, the last survey, 76% of CV are faked by AI in Europe today. It's what creates the largest bias and unfairness. And if you ask a Gen Z, the alpha or whatever, if they have one, they say no. They just, you know, it's not something from our age.
[00:13:20] Speaker A: So a candidate. So at this point a candidate coming into your system gets assessed irregardless of their cv.
[00:13:28] Speaker B: Exactly. Regardless of cv, no CV anymore. You apply to your dream job and you get the agents that will screen you. And here we have two type of agents. One that will be a phone, AI agents, we can recreate a phone conversation and we can basically customize very deeply that conversation. So if you are, I don't know, H and M in retail, we can clone the voice of the real recruiters, we can feed the agents with H and M culture, branding, tone of voice, and the candidates will interact with an agent as it was honestly a natural human LED conversation. And that experience can be done by phone or it can be done basically, as I mentioned, with an avatar visual and he can use your desktop or your phone. So this, you know, will be first agents for pre screening. And here I give you an example. I was speaking about H and M. We work with them in more than 60 markets now. They saved last year 250,000 hours of screening. This application, they automated 80 to 90% of the whole process. And this is completely game changer for recruiters, but also for candidates.
[00:14:42] Speaker A: Fantastic. Yeah. And I'm talking to them. So in order for this to get set up though, sounds like, correct me if I'm wrong, you, you have a little consulting engagement of some kind to help the customer define the agent. Because a lot of people don't really know what the capabilities are that are needed in different roles, to be honest. They just write down a job description and that's it. Is that correct? Yeah. So you're helping them build this custom agent that either visually or over the phone assesses candidates. How long does it typically take a candidate to go through the screening, pre hire assessment process? A couple minutes, five minutes, 10 minutes? Up to the client?
[00:15:21] Speaker B: It's up to clients and up to different roles. We have very different use cases. You know, we, we do, you know, cabin crew for airlines or you know, we do as well volunteers in stadium, you know, for the World cup. And we also do it consultant or audit and accounting role at PwC Deloitte or EY. So we have very different type of use case in industries and this experience can go from five to 15, 20 minutes.
[00:15:51] Speaker A: I would have liked to have taken the Deloitte assessment before I went to work there, but it's too late now.
So it also does technical assessment. Right. It can ask people technical questions and coding stuff.
[00:16:03] Speaker B: Definitely, definitely. You can have basically through that experience, few questions regarding SQL. You can get basically assess your level of English and your leadership capabilities.
[00:16:15] Speaker A: To me this is a huge innovation. If I think about the other products I've seen, I won't mention the company's names, but there's conversational interviewing tools. But their level of assessment depth is very light at this point. So this is a big part of your, to me, your innovation here and creativity is immediately you get something that can assess candidates extremely quickly and extremely successfully. And I assume there's a feedback process so the system gets smarter over time or how does that work exactly?
[00:16:43] Speaker B: It gets smarter, it learns, it's give straight feedback personalized to candidates with some content in order to improve.
And first candidates loves it. You know, I was just before our call with one of our clients, BNP Paribas, they have a metrics of 98% of candidates that says it's the best experience application process in their life and it's strongly increase their perception of the bank.
Candidates really really likes it. And I think, you know, it's one of our key North Star metrics. And on the other end, you know, for the organization, it's clearly streamlines the process because at the end in few years people will believe we were completely crazy to go through hundreds, thousands of resume one by one that look like a bit the same. So we can clearly automate that process and free human from this admin task. And it also help to clearly accelerate the time to answer to candidates in the time to hire and as well to don't lose candidate in the top of funnel and don't lose the best candidate on the top of funnel because now all our clients can come back to candidates within 24 hours.
[00:18:00] Speaker A: I'm just curious, as a screening tool, does it tell the candidate very quickly that they're not a good fit or how do you deal with that sort of screening? Part of it.
[00:18:10] Speaker B: As soon as the experience basically is done, all the answer I put into the system of our clients. So we are integrated to all the applicant tracking system. And on the other side, recruiters, humans, we look at it to be able to filter and sort and take decision. So which is very important. We, we yes, the answer is yes, there is still a human behind the scene. It's also a strong, you know, requirement in the usage today of AI to make sure there is a human in the loop. Even if we have been, you know, audited several times by different organization, different states in the US as of today, human are on the loop and they use AI as say copilot in order to take their decision.
But you know, if you go sometimes I love when I speak to some of our clients and they say, you know, on Monday morning I used to be a bit stressed because I knew I will have Hundreds of applications, CV or multiple interviews, etc. Now in a few minutes basically I can take decision and focus my time on what's matter. This means a lot on how also the role of recruiters will evolve. And at the end, you know, we put at Macchi, our mission is give human resources more than human power. We don't believe, you know, recruiters will be replaced. We believe their life will get better and their task will change.
[00:19:34] Speaker A: I completely agree. Okay, so for talent acquisition this is just sort of a, in some sense miraculous. But there have been conversational recruiting tools For a long time, at least for six, seven, eight years, that they're not super intelligent, but they're good for answering questions like what are the shift hours and how far away do you live from the facility? And things like that. And what happened with a lot of those vendors is they either went out of business when the AI stuff became a commodity or they became almost entire. Atss, is your system becoming or already becoming have become a whole ats, or do you look at that as a, an integration? What's your longer term process roadmap for the agent?
[00:20:21] Speaker B: I didn't want that question, Josh.
I'm joking.
I'm joking.
[00:20:26] Speaker A: Well, because I think it's. Because ultimately, here's the reason I'm asking. I think what you're building has a whole bunch of applications inside of companies. Internal mobility, leadership development, succession, et cetera, performance improvement. So I'm curious if you want to explain a little bit where you're trying to go, because I know you're a very ambitious guy.
[00:20:46] Speaker B: Yeah, no, no, let me answer. You know, fourth step. Step number one, we are building AI agents for talent acquisition, recruiting. I want to have an agent for sourcing, help me to go source candidates externally or internally, improve over time, source me the best candidate, engage them in the process. Then I have another agent that can help me to screen candidates. You know, all this application I receive and shortlist the candidates. Then I have another agent that can help me to schedule interviews because this is should not be done by human. And then I have another agent that can deeply in depth assess skills in a longer time frame, et cetera. And I have another agent as a copilot during my interview with me between two humans. This is a suite of agents that, you know, has a system of agents working together and improving the life and the role of recruiters. Then we have step two of our mission, and we started at the beginning of the conversation to mention it, how we can capture the signals in the workspace of employees and make talent management dynamic. So all these signals you produce every day, they get used for you as an employee to get to know you better. But for organization to also have a better view of the skills of the workforce and how to take better decision.
And now if you look at this and you are in a world where you have agents that under this workflow and capture this data, you are moving from an era where you add traditional software where data were inputted by human, where data were static and very hard to use. I call it, you know, ATS in hr, but it exists as a CRM in sales or in all other domain, you know, if you ask a larger condition, is it easy in your ATS to extract data? And can you tell me from the last seven days the time to hire on the drop rate between screen to interview? I will never get an answer. But this question will get an answer in three, four, five years, as we are now to the use to get an answer by asking a complex question to ChatGPT.
So I believe we will move in a world where this applicant tracking system will disappear in the way they are today. So they will evolve. And they are already evolving, of course, because they understand that just having that type of database and workflow to move the candidate from one state to another was not anymore.
[00:23:22] Speaker A: I completely agree with you. The whole concept of an ATS or an LMS or an HRMS for that matter is rooted in the transactional world of 25, 30 years ago, right before we had AI. So I mean, at some point we're not going to be calling them ATSs anymore. That phrase will disappear. But I have to say, in the learning space, the LMS idea never seems to go away. But so let me ask you a question that comes up a lot when you say agent in that world.
[00:23:51] Speaker B: Josh, just one comment. In that future world, I believe that all this data point will be pushed directly to the data warehouse of the clients and we might not need to have the intermediate layer that we call applicant tracking system.
[00:24:11] Speaker A: Right? Yeah. Which gets me to another question about AI. And you know, I'm doing a lot of work because we have all of our AI stuff on architectures and when you say agents, so you're going to have all these agents that do these wonderful assessment and coaching and selection things and screening and so forth. And by the way, end development. I assume that, you know, this thing that's smart enough to assess your skills could also coach you eventually. What architecturally is an agent sort of as a system versus the infrastructure somebody already has? Because every customer you sell to has Oracle, SAP workday or some other payroll system, and they're getting a pitch from those vendors. Well, we have agents too, and everybody has agents. And all of a sudden the word agent means nothing. So how do you define an agent? Because I, I, I, I think there's probably a good way to define this. I'm not sure I know exactly how to say it, but you're thinking about it from scratch. So I think opportunity to explain it to people.
[00:25:08] Speaker B: For me, it's how we can build a system where an intelligence can take an action decision in a system with constraints and with the ability to reasoning, but also to improve.
And I think this also the big difference between your workflow automation and agents is your ability to take action and to train and improve over time.
[00:25:36] Speaker A: So we see the agent is intelligent enough to improve and to learn.
[00:25:42] Speaker B: It should be. And as from my understanding, where we are today, we are at the very beginning of these possibilities.
[00:25:51] Speaker A: Let me ask you a technical question that I've talked to Sana about this and I'm curious if you have an answer. I think in most people's minds, the way HR technology works is there's a quote unquote system of record, which is basically a relational database or an object oriented database, but usually it's just a relational database. It's got a bunch of data about people when they started, their name, their job title, where they worked, how much they make, all that stuff. When you do these AI things and you assess people with these intelligent agents, there's some data set about a person sitting in the AI in some form. I have no idea what form that is. How transportable is that data from place to place?
Or are these proprietary systems? Like if I buy Mackie and I use it and I create all this intelligence and then I decide, you know, you get bought or you know, something else happens, we don't want to, can we move this data around? Can we share it?
[00:26:47] Speaker B: I think there were a nice article last week from Tenorovitz that says this new AI company might not and should not become the system of record, but should become the system of work. And at the end, which is essential is like to produce that data, but also to capture data from external sources and be able basically to produce a smart intelligence around that data and put it somewhere. And it can be in the system of record.
And I think this, this type of what we call, what we can call.
[00:27:22] Speaker A: So the idea is you're going to export out of this into a more structured, formal, traditional data system.
[00:27:28] Speaker B: Yeah, at the end, you know, I don't intend to replace a top database of Fortune 500 companies, whatever they use, you know, work there.
[00:27:37] Speaker A: While we're talking about theory and concepts here, let's suppose Maki is used. You know, I'm working in Deloitte, I'm a partner and I'm doing all sorts of cool stuff. And Maki assesses me and says, you know, you're capable of being a much more senior partner based on all the assessment that say, Deloitte or somebody has. So we want to give you a larger Bonus or something. So I. So the system knows my assessment versus everybody else's and you could tell the system what the budget is for salaries this period. So the system could recommend my increase versus somebody else's. Doesn't more and more of the data flow into the AI and then I'm not sure what that other system of record is even used for anymore. Or am I just crazy?
[00:28:21] Speaker B: Yeah, no, no, you are asking the right question. No, you are asking the right question. Today. This data that you mentioned produced by Mackie, they are feed, you know, into the ats, but I'm not sure it's after in the ATS it's properly use to the business and I think in five, 10 years it will be able to be used much more easily to the business.
[00:28:44] Speaker A: I just think this is an interesting, I didn't even mean to bring this up on this podcast, but I think this is an interesting thing people are going to start scratching their heads about pretty quickly.
[00:28:53] Speaker B: Yeah, just you know, 30 seconds. I love the vision of Satya Nadella, the CEO of Microsoft. You know that says at the beginning of the year very with being a bit provocative. SAS is dead. And he said I won't need for instance for financial analysis and financial planning to have an army of poor BI data scientists in my organization working days and night on spreadsheets everywhere. In all my department I will have basically an agent. I will say what would be my Q3 forecast in that division and and agents basically will go through all the spreadsheets and find the information and give an answer and a recommendation. And this I believe will apply to HR as well in, in few years when you will say who should I increase? At your Deloitte example, you will get your list of the 10 people rank based on data driven and objective criteria.
[00:29:53] Speaker A: Well that's certainly the way I see it too, Max. That's, that's why the big ERP type companies are passionately working on stuff to kind of get in the middle of that transformation. Okay, let's see. So give us some examples. I mean I've talked to a bunch of your customers, so I know these. But I think for the people listening to the podcast, H and M, give us a couple minutes or a minute or two of what transformational things you guys have been able to do for companies.
[00:30:17] Speaker B: As I mentioned at the beginning, we believe that, you know, technology is here to serve business outcome and for us business outcome is a measure in what we call roi and we aim to transform. You know, we don't see us as a Classical, I would say software, but more as a workforce multiplier. And as we move the needle drastically in some key metrics of the organization, it speaks not only to the chro but also to the CFO and to the CEO. If I give you the example you mentioned from H and M, you know I mentioned we saved 250,000 hours last 12 months by automated 80 to 90% of the screening process. A it's drastic in terms of time saved and what these humans can focus their time.
We have decreased attrition turnover of employees in store after 12 months by 22% globally. This is also, you know, if you look if you're an organization that recruit 10, 20, 30,000 people per year who have a ROI in terms of productivity that is more than 100 million USD. Third, we decrease by 3x the time to hire from 42 days to 15 days. Something like this. And while having 8 or 97% of candidate sales is one of the best experience of my life which at the end today or so if you are.
[00:31:42] Speaker A: Company more, more fun than going to the movies.
[00:31:45] Speaker B: No, but you know, this is key. You know I was with a, with a bank yesterday and they completely get it right. They told me today we are also not afraid only about losing candidates, but also to losing customers. You know, when you add basically the four metrics we have already several use cases in very different industries. Whether it's insurance, in it's, you know, consulting bank, luxury fashion where we have now made to the C level committee as a solution that is becoming a new standard across the organization because it has helped increase the top line or reduce the bottom line of the organization. And this is what we call internally at Maki, we are ROI obsessed with our clients. This is what matters to us is to understand that if the problem of our clients is to recruit someone in 72 hours versus three weeks, we will build and leverage our technology only towards that business goal.
[00:32:43] Speaker A: And I compliment what you just said and add a few things just to give some more context to people listening. I mean those are fantastic stories. I think what's happening with AI and agents and the kinds of things that Max is doing is, you know, the initial use case scenario is we're going to reduce the number of recruiters, we're going to do the amount of advertising cost, we're going to make a better candidate experience, we're going to maybe improve the retention rate a little bit. But ultimately this is a growth engine for the company because if you can recruit people faster or get better fit candidates, you can scale up your product, your service, your business faster.
Like if you're a, if you're a company in the logistics industry and truck drivers don't show up because they're, you know, sleeping late or they're the wrong people, your customers are affected by that. If you're a healthcare company, if the nurses are the wrong fit or they're not hired fast enough, you can't produce patient outcomes. I mean These are huge ROIs, much higher than the ROI we see within HR itself. You know, I think what's happening with you guys and some of the other companies building these agents is you're building business accelerators here, not just hr, employee improvement systems.
[00:33:57] Speaker B: Yeah. And I think this is fascinating for HR leaders and choir because it, it can. At the end we come back to very basics organization. It's about people.
And if technology can lead to better use human capital in the organization, there is nothing more important for me. I'm a big fan now of seeing the, at the table of, of the CEO. You know, last week I was with a very big US retail company.
We don't work with them, but I was speaking with HR technology leader and she told me it's now a mandate from the CEO to create retail hiring excellence. And you know, I love when I hear the statement because you know, it's reality number one, if you can use technology to build better teams more efficiently with less turnover and make happy employees, etc. You know, it's a gold rush. And I think in the next two, three years we are about to see more revolutions that we have seen in the last 20, 30 years. And you know, Joshua, the number one speaking about super worker, et cetera, I think it's very true. You know, it's. We are, we are living a very cool ages.
[00:35:13] Speaker A: It's true. One more question for you, Max, that I think might be kind of fun to answer for you. And I think people might get some interesting information for somebody who's coming into HR relatively new, you know what, three, three, four years. What is the biggest surprise you've had about this space versus the logistics and other businesses you've been in the past?
[00:35:34] Speaker B: Very interesting question.
[00:35:35] Speaker A: You can think about it. We can edit it.
[00:35:37] Speaker B: Yeah.
[00:35:38] Speaker A: Or what? Maybe, maybe the better question would be what would you like to tell people who've been in HR a long time that you may have learned coming in from the outside?
[00:35:49] Speaker B: I think what I find interesting and challenging at the same time is sometimes to.
When we have discussion about the possibility and the outcome of our product at the End, the impact and outcome can be very huge. As I mentioned, it can move the needle across the top line or the bottom line of an organization. But to achieve this, you know, it's a long, long, long way. It's very complex. And, you know, we struggle every day as a new company and we have really to sit with HR leaders and also to finance leaders, to operation leaders to build a strong business cases to really prove that, you know, our technology, not only our technology, but new type of technology can already have a big impact.
And sometimes it's difficult because we have seen in HR people buying lot of software. Sometimes I have clients, they have 10, 15, 20 HR software. But when I ask what, what is the value at the end of the year? Can you describe for each of them one or two metrics proving the value in 80% of the cases? The answer is no. Or it's like something like it's used by my team sometime, et cetera. Some are happy not, some are not. And for me, this is the worst things we can expect from a software. For me, you know, software is here to drive value, if not out, right? And this is a challenge and difficulty we have at the same time to, to prove that value and to make that value something, you know, really concrete.
[00:37:27] Speaker A: Well, it's exactly the way I, I think you're exactly right. And there's, you know, 50 years of experience and probably 30 years of software sitting around in these companies that needs to be upgraded or replaced with some of these new AI things like what you're building. Thank you, Max, for explaining what you're doing. It's really exciting, very transformational, and I'm sure a lot of people listening to this podcast are going to learn a lot about new ideas and how to rethink the way they think about their software stack, but also just about the way they think about their whole architecture of their companies and how they operate. So thank you again for your time today.
[00:38:04] Speaker B: Thank you very much for the invitation. Jos, it was a pleasure.
