Speaker 1: Anyway, let's start this talk with a couple of questions. Do you think you could ever find a job that was customized for you? For your needs, for your skills, for your personality? A job that would actually satisfy all of your expectations? Do you think companies could get to know their employees so well that they knew when they were not happy and how to gratify them? The answer today is probably no. We just settle for what's out there. But facts speak for themselves. Only 13% of global employees are truly committed to their jobs. Do you imagine that, 13%? Churn rate reaches up to 25% in several industries. And yearly associated cost in the US only exceeds $500 million. Truth is, companies still use outdated practices, old processes, and no tools, no relevant data to respond to today's demanding talent market. Today's talent practices are executed by people like you and I who take high stakes decisions based on their own assumptions. What is called an unconscious bias. And most of the time reacting, rather than proactively and strategically answering to talent needs. Which leads to rushed decisions and to one third of new hires quitting their jobs after only six months. After studying the data of thousands of candidates, I started to develop an algorithm capable of simplifying and quantifying elements that companies believe were impossible to handle. Or were only for mathematicians to know. Let me show you a graph. The first line, the orange one, shows someone's level of authority challenge. This is the personality trait linked to someone's readiness to challenge authority and traditional values. As you may see, the line started quite low, but raised as time passed. The blue line shows someone's level of conscientiousness, which is the personality trait linked to success. And according to several studies, it is the most important factor when it comes to employment retention. What can we read from both? Well, they show the annual data of an employee before he flew away. As you may see, he became more challenging, more aggressive, as he became careless. Now, where does this data come from? From tests applied to these people? No, people can cheat on tests, and you probably know that. So, I figured out that the way we express ourselves usually show who we are and our state of mind. We use language in different ways, and those differences reflect our personality. Studies have shown that even though our choices are unconscious and spontaneous, they do reflect who we are. There is a direct association between keywords and phrases and major aspects of personality. For example, extroverts use lots of fun-related words like music and party. People with lower emotional intelligence use negative words like angry and stressed, and narcissists love to talk about themselves and use lots of I, me, and myself. But it is not only words that I take into consideration. The way we communicate also plays a major role. For example, a poor grammar shows lower academic education. Absence of typos show perfectionism, and emoticons can be a sign of friendliness if the document is informal, or immaturity if it's formal. Long emails reflect energy. Chaotic emails are a sign of creativity. Instance responses show impulsivity, and no responses show a lack of interest. So I took what most of us use most of the day, work-related emails and chats, and I used the algorithm to help me identify keywords and trends behind employee disengagement. Was it possible to predict when someone was becoming detached? And if so, how would it impact a company's retention and recruitment strategy? And ultimately, how could we help the employees? So far, the algorithm uses data coming from the candidate's individual answering trends. So how long is he taking to response compared to before? Is he or she working late as she used to? Is he working over the weekends like last year? It also takes information from the market. So how is the marketplace impacting that particular role? And ultimately, it uses text mining to identify keywords and phrases to then link them to a personality model that uses common language descriptors like the ones I showed you before. Now, when you mix all of this together and over a period of time, the algorithm learns how that person behaved. And it's able to predict with very high accuracy when someone is becoming disengaged based on their behavior and personality. Now, all of this sounds really cool. But my true purpose has been to one, help companies develop their internal business and their external talent attraction. And two, to make the recruitment process candidate friendly by adapting it to each individual personality. Evolving to a data and predictive approach will facilitate through retention and recruitment. And will allow anyone involved in talent acquisition, which honestly should be everyone in the company, not only HR, to know who's gonna be the best performer in the short and long run. And to customize the whole process to each individual role and to each individual candidate. Now, all of these sounds logic and very uplifting, at least to me. But all of these raises questions about accuracy and privacy. Nevertheless, applying artificial intelligence to the recruitment process could ensure more diverse, empathetic, and dynamic workforces. And according to studies, an algorithm could increase the accuracy of selecting job candidates by more than 50%. Unlike how things are done nowadays, elements like your professional background, your social background, your cultural background, your skills, assessments, will be analyzed to know if you're the best candidate for the role today. Because you will be able to address the challenges associated to the job, but also the best candidate for the company in the future. Making sure that the career path also matches your history and aspirations. And it is not only that algorithms are able to probably address things better, according to studies. And that people who dedicate their lives to it usually have lots of information and probably more than the ones we can contain in an algorithm. But the problem is that people are usually distracted by things that are only marginally relevant, could apply unconscious biases, and usually don't have the systems needed to analyze all of the information in order to predict what could work today, but also in five years' time. By getting to know people better, companies will also be able to address retention. Since now they will be able to tell who's becoming disengaged before they leave, they will have the opportunity to react before it's too late. Either to retain that person or to find a replacement on time. Let me give you an example. Let's say Bob has been working for X company for the past three years. But now he's getting bored, cuz he's millennial, and he wants to make a change. But he doesn't have such a good relationship with his boss, so he doesn't feel like talking about it. And honestly, he doesn't see lots of opportunities within the company. So what happens now? Bob feels trapped, and he starts to change. His response time is faster and shorter. He changes his LinkedIn profile, starts connecting with recruiters, starts to actively look for job applications. He's just different. When he finally decides to walk away, he just tells his boss, who panics, and runs to HR and notifies about the urgent vacancy. HR starts to look for a replacement after Bob's gone. It takes them about four to six months to find one, another two months to train the new guy. In the meantime, Bob's team are asked to take more responsibilities and workload. So they get pissed, and they also get disengaged. But once again, no one notices, and this continues on and on. I know it sounds crazy, but I see such things happening on a daily basis in all sort of companies. With this solution, the algorithm would be able to identify the change in Bob's behavior, would send a notification to the boss, suggesting how to proceed based on who Bob is and what he wants. Scary, brilliant, you will decide. But through this, using an algorithm in HR-related processes could allow the possibility of creating predictive Thailand pipelines. Since now companies would be able to tell when someone is ready to leave before they do it, the system could potentially inform the recruitment team about skills that they need to start hiring for, and even suggest a list of global candidates analyzed based on success, job factors, rather than traditional job descriptions and static resumes. In a very short future, things like gender, ethnicity, or age will no longer matter. If you have what the job requires, then there should be no unconscious bias in your way. It won't matter if your boss doesn't like you, if you're a mother of two, or if you believe in the Mayan gods. If you have what it takes, then the job should be yours. So with all of these technology, you might be thinking, why would a company need an HR team after all? Trust me, they will, because this is all about human connections after all. We're talking about our future here, so we'll want to know who's the people behind it. Recruitment, nevertheless, as we know it, will disappear. It will evolve into something that no algorithm will be able to change and to, yeah, to change. To change and to, yeah, to take. It will become a strategic guide for the company and a human element for the employees. So this means that using a statistical model will replace hunches with data, presumptions with models, and intuition with success ratios. But the final decision will be taken by the human and not leave it to an algorithm. This way, in a very close future, companies will be able to retain their talent longer, will be able to identify the best candidates faster. And at the same time, us as candidates will be able to identify and work in jobs that we really want and that are really aligned with who we are. And that way, the question that I first ask you, do you think we could ever find a job that was customized for us? Will turn into a yes. Thank you. Thank you.