About Frida Polli
Frida Polli is the CEO and founder of Pymetrics - a talent matching platform, that leverages behavioral science and AI technology to help organizations build high performing teams. Doing her part of making the world a better place, unbiased selections using tech is what Frida believes in. She has been recognized as one of the topmost HR influencers and entrepreneurs by a multitude of global organizations. An ambitious professional, who is a role model for many young girls worldwide, she is also a committed mother of three. We are extremely happy to have her on our interview series.
We have the pleasure of welcoming Frida Polli today to our interview series. I’m Aishwarya Jain from the peopleHum team. Before we begin, just a quick intro of PeopleHum - peopleHum is an end-to-end, one-view, integrated human capital management automation platform, the winner of the 2019 global Codie Award for HCM that is specifically built for crafted employee experiences and the future of work.
We run the peopleHum blog and video channel which receives upwards of 200,000 visitors a year and publish around 2 interviews with well-known names globally, every month.
Welcome Frida we are thrilled to have you
I’m happy to be here. Thank you so much for that kind introduction.
Absolutely. Thank you so much for making the time.
So you know, Frida, I wanted to know and I'm sure pymetrics is something you've given a lot of introductions, but I just love the way you put it. You know, ‘it's democratizing career search and neuroscience and AI kind of the framework of the entire concept. How did you come up with this and what exactly is your vision for the organization?
Absolutely happy to answer that question. So I was a cognitive neuroscientist for about 10 years at Harvard and MIT. I really loved the research we were doing. God just became more interested in how when we apply this science, too, a big problem. I didn't exactly know what was gonna happen, but went to business school.
And it was there that I saw recruiting firsthand for two years because that's what MBA students do. And it was at that point that really the light bulb went off because here all these people, very talented, trying to find their next you know, thing in life and the tools that they were being given were very rudimentary.
And the tools companies were using were very rudimentary. We're still relying on resumes and coffee chats and company presentations. And to my mind, there were, like, three things that were missing that our science could help with.
So one was trying to understand something far more fundamental about people than sort of like the factoids that are on your resume. So, like your personality, your cognitive style, your social, you know, aptitude. So that was one big piece that I thought was missing. And that's something that cognitive neuroscience and behavioral science can help with.
The second piece was any kind of analytics. So, you know, right, we can basically use machine learning & artificial intelligence to understand the aptitudes that make someone successful in a particular role or in a particular company. And then, with that, um, machine learning model, can find anyone anywhere that has those aptitudes. So the ability to cast a much wider net on really democratized access to any opportunity is huge.
The third thing is that no one was treating this as an optimization problem. It was very analog. It was like, I'm gonna submit the same thing over and over and over again to the two different companies and eventually someone will give me a shot.
But why not help both companies and job seekers with an optimization problem of connecting them immediately to their best but opportunities rather than having it take, you know, so much time in her petition. So those were like the three things that really struck me that we could help with. And then, you know, personally, I was also going through research. I was, you know, a former scientist.
I wanted to be a tech entrepreneur. I was at the time a single mom in my thirties. I didn't look like the traditional tech entrepreneur who was, you know, male in his twenties, like, didn't have any kids.So I definitely was struggling with this myself.
And also I think one thing that we underestimate in sort of the career search mashing process is how much a person's self-perception can influence what they choose to apply to write and why more women do not choose entrepreneurship or choose other careers where they're underrepresented? Definitely, it's partly because they think they might have a negative experience because of everything they read.
But it's also just because they don't see the people like them in those careers. And so I think that trying to alter through technology somebody's perception of yes, I actually could be great at this job. I have the aptitudes, I have the right mindset and encouraging them to apply to roles or fields that they're under representative is a huge aspect of what this technology can be helpful with. So that's how I met tricks came to be, and, ah, and it's an exciting future that we're building.
That's a beautiful vision that you have. And I'm pretty sure you're inspiring a lot of women out there because it's just breaking all the orthodox notions and all the traditional notions. You are clearly a believer in AI being upgraded.
So what is your opinion on having a balance between, human intelligence, human experience versus artificial intelligence, fall back position.
Sure. Well, look, I mean, I'm a human. In case you didn't realize that. I think human beings are amazing. I think they're so talented, so creative, so wonderful in every single way. I'm a huge believer in, like the positive aspects of humanity.
However, I'm also cognitive neuroscientist, and I can tell you that unfortunately, removing bias from a human brain is impossible. It is not. It's not hard. It's actually just impossible. Right? The other problem is that if you ask the average person, do you think you're more or less biased than the average person? 80% of people think they're less biased than most people.
So obviously, people's perception of their bias is very poor, Right? Everybody thinks I'm not that biased. It's fine. But the data is very clear that no matter how good a person you are, what kind heart you have all of the rest of it, no matter how much good intention you have? You're biased. I'm biased. You're biased. Okay?
And so that is where I think artificial intelligence could be helpful because unlike a human brain with artificial intelligence, you can crack it open. You can say OK, this is what's causing the bias, and you can take it out. That's just not possible to do with, you know, this thing called the brain.
And so, therefore, I'm not recommending that we remove humans or we dehumanize the process. I'm saying that we use artificial intelligence as a method to reduce, mitigate biased workforce decisions. Because again, I don't think we need another recent study to tell us that the work for forces is not as diverse as it should be. It's, you know, got too many Caucasians. It has too many men. I mean, we don't need another study to prove that you just need to look around, right?
And so, But again, goodwill on the part of humans alone is not gonna get us there. We need to leverage technology and again doesn't mean removing humans. It means helping humans have the tools to let them know when they're making a bias decision and how to improve on that decision.
Absolutely completely supports that. And I understand your perspective. We need to leverage AI technology to help….
Yeah, And the thing is, I mean, I think that where the skepticism comes in right because a lot of skepticism around AI being able to be unbiased and there's a couple things I think one is that there are many examples of AI being biased, right?
So let's just acknowledge that is often the case, and that is a huge problem. I'm not denying that at all. I'm saying we should be aware of that and that is a huge problem. And in those cases, the difference between an AI platform that has that is mitigating bias and one that is perpetuating it is the design.
You know, AI is like anything else. If you design it to operate in a certain faction, it will operate in that fashion. And so the systems, the systems that have been designed that have biases, the problem is they're just mirroring human behavior.
They're just learning from humans with no guard rails in place, right? People are just using machine learning, saying that human recruiter decision or matched that workforce decision without either caring or realizing that that decision making process is biased and therefore then you get all these terrible examples of, you know, resume parcer that are, you know, preferring men or whatever the case may be right. That is a big problem.
We need to mitigate that problem. And actually, Pymetrics has taken the stand that we need more guardrails. We need actual stone policy changes. We need some legislative initiatives to try to fix some of this stuff. So we are 100% on the bandwagon of AI and that’s a wonderful thing so long as their safeguards in place.
The only difference, I would say, is that the difference of the bias algorithm, which exists in the bias human again, is that you can fix the algorithm if you want to. And again. If you want to, you have to want to. You can go in, see what's biased about the algorithm and remove, reduce it until and then you contest the album.
The other thing is that you know it's very hard to test the decision-making process done by humans because you can't get into that. You know, the black box of the human brain versus within AI you can get in there, you can tweet you can change whatever up variables it's looking at, and then you contest and illiterate until you can prove in a statistical way that the algorithm is not biased.
So I just wanted to make that clear. I think, a lot of times of skepticism and it's, you know, I understand where it's coming from. However, I think sometimes that skepticism has taken too far. I mean, I've heard many times. Oh, there's no way you can remove bias from algorithms. Well, that's just not true. I mean that's a false statement, right?
And I think people that are making those statements are prejudicing people against the technology that can be incredibly powerful in terms of reducing our own human biases. And again when we deploy pymatrics, we see in a very clear fashion the reduction of bias, the increase in diversity, right?
So for anyone that says, Oh, it's not possible, I would say, Well, okay, come look at our system, Come, look at the examples that I can show you because then and then tell me it's not possible because I know it's possible. So I think we have to mitigate against this sort of knee jerk skepticism that just because a lot of tools are not checking their AI for bias doesn't mean that it's not possible. It is 100% possible.
Absolutely. And that's very important because a lot of people are not aware of this at all and they're non-technical, right? And then when you think of machine learning algorithms you need a lot of data to actually get these algorithms out. And that's your kind of mimicking human behavior. Then there is a thing that old...there would be some bias integrated that's not right.
Well, well again. I mean, it is right. I mean, if you don't do anything, so let me just crack up because I think they're absolutely correct and thinking If I do nothing for my algorithm, it is gonna be a biases human being That is 100% true. Like I'm sorry. I should have started with that. So that is definitely true.
I think that is a huge problem, right? But again, it's not the algorithm for, say, that's the problem at the end of the day, it's like I always say, If you don't like what an algorithm is doing, you won't like what the humans were doing because the algorithm just learn from the humans.
If you don't like what an algorithm is doing, you won't like what the humans were doing because the algorithm just learn from the humans.
So let's just be aware of where the original sentence comes from.
Again, I think people should be skeptical. They should. But again, hopefully, at least in this day and age, anyone that is producing an AI system is at least aware of the fact that it could well be biased if it hasn't been tested and hopefully is doing something about the bias.
The thing that I think is most problematic now is that it's all put on basically it's a self-monitored system. Meaning I was a technologist, I'm saying, Oh, yes. You know, I could have a problem. I'm gonna look for that problem, and I'm gonna fix it. There is no, um there are no external guardrails.
So if I'm not a good intentions designer of algorithms, or if I just don't care or if whatever the case may be, there's nothing that's going to stop me from producing bias algorithms as I can easily do that and I think that is the problem that we face right now is that unless we have some sort of external guardrails that are going to be guarding against whether it's sort of people that don't care enough to fix their algorithms or people that you might actually not feel some biases is not a bad thing, which again, we don't believe that. But there could be others out there. I think that's really where the problem lies. It is all a self-monitored system.
And do you think that this technology is sort of changing human mindsets, you know, rather than the opposite?
Yeah! I mean, I think it's interesting that you asked that, I think. Well, I think especially now, in the current pandemic situation, I think people are becoming more aware of the positive potential of technology. I mean, think about it, right?
I don't know about you, but I'm working from home. I've been working from home now for a month. We're obviously lucky that our jobs can be done using zoom and everything else. And you know, We're fortunate.
However, I think it's showing people that, technology does have challenges and we should be aware of those challenges. But it's not inherently bad. Like right now it's being an aid. It's helping us all sort of come through this pandemic, hopefully in a better situation than if we haven't had the technology.
So I don't know that AI is fundamentally changing humans. It could Well and I think that once somebody sees the power of a well designed artificial intelligence to remove bias, I think that's a hugely powerful, um, experience.
I'll give you my first experience that I had with it. So we were working with ah, well known CPG company. And they had started using biometrics for campus recruiting. And, you know, prior to that, they were going to maybe, you know, this is in the U.S, They were going to maybe 15 schools and they were all top schools and, you know, elite universities and what not right. And then they started using matrics, and then anyone who went to any university could apply, which was really, you know, revolutionary groundbreaking. And that is really the case,
That is how things should be. But That's not often the way things are, right. And then the thing that was really sort of mind-blowing to me is that, you know, they hired from this elite, they went from going to 15 schools to basically 2500 right overnight, so that in and of itself, is amazing. But then the people that were hired were just as likely to come from, you know, sort of whatever college that an elite one.
And what was really kind of mind-blowing was that the applicants they selected to become their interns were so, so, so economically disadvantaged that they had to they didn't have enough money to fly in a plane.
They didn't have enough money to rent an apartment and, you know, fly cross country. And that was really slow, you know, startling to the company because they had never had that experience.
And then that just shows you the pool that you're picking from, right, Because if historically you've never had a napkin say to you. Hey, I need Thio. Um, you know, have a relocation policy upfront that allows me to get this. You know, these funds to come and live in a different city for the summer.
It just started, tells you that sort of very narrow demographic of people that you're selecting from. And that, to me, was really shattering because it was like, Wow, in literally overnight, we can change the type of person that's having access to a really good job in a really good company.
And that was what you know has kept me going all this time despite all of the controversies around AI and, you know, and all the rest of it and that was it stories like that that I find just you really just really heartwarming.
Well, that is amazing. And I think after the pandemic is over. We're gonna need more of these stools to actually the subtle rules, the biased right, And do you still kind off? You know, I'm sure you do a lot of the research,
Do you still come across biases that you keep discovering again and again?
Well, I mean, I think that the bias I just spoke about their socioeconomic bias is something that people don't even talk about as much, we're very comfortable talking about gender bias and ethnic bias. We don't really talk about socioeconomic bias, right?
We don't talk about how our entire sort of you know civilization or, you know, it is structured to, you know, essentially allow privileged people to get better jobs than people that are not privileged, right? And look, I'm a beneficiary of, you know, like lead schools, yes, they have a great education, And I'm not saying we should throw them out. I'm just saying that right now.
The way we've structured society, it's not. It should come as no surprise that socioeconomic inequality is as bad as it is because again, you know, you know, really prestigious companies continue to select from elite colleges. And again, if you keep having that strategy, of course, this problem is never going to get better, right? and again, yes.
You know, there's certainly merit to getting into a top university, But at least in the United States, you are 77 times more likely to go to a top school if you come from the top 1% of the population promoting socio-economic perspectives. And if you come from the bottom 20%. That's just outrageous, right?
Yes, SAT scores and grades have something to do with it. But obviously, there's the question of, you know, the privilege of your background. Right? So I think it's these types of things that I think are exposed by AI, um that in a way that I think hopefully will help us change the future in ways that will guard against and fight against these biases.
Absolutely. I think it will bring about a new revolution for sure.
The other thing I just want to mention is one of the things that I think is really critical in realizing the potential of artificial intelligence on technology, in general, is that you know, right now, right now, because of COVID, we're going through one of the biggest reallocations of labor that's ever happened, you know, since sort of World War Two.
And we can't rely on these manuals, slow non-tech enable processes to help. All of these people who literally overnight, overnight, right, became unemployed. I had nothing to do with how, while they were doing their job had nothing to do with their skill or and they had just literally had to do with Hey, your operation has been shut and, you know, no longer up top.
What we see technologists doing and were one of those technologies we actually are building a redeployment platform that can take all of these people that have suddenly been put out of work capture both their skill, their hard skills as well as the soft skills of their social, emotional and cognitive attributes, and help them immediately understand what are the jobs that I could be well suited for and then connect them directly to companies that don't exist right now, you know, I mean, yes, their platforms like indeed and LinkedIn that are sort of job boards. But there's no platform right now that can really efficiently match talent in that way.
And I'm hopeful that we can take a very crappy situation like COVID and take a technology solution like that and really showcase what could be done at scale. I mean, this should be happening everywhere, and it should be happening now, and that's what we're trying to show is that you know this resistance to using technology and hiring is really hurting a lot of people, you know? I mean, and it's not necessary. It's really our own human resistance to it that is getting in the way. I think.
Absolutely. And let's be honest, that recruitment system is just not up there.
Yeah, right. I mean, like, I think it's, you know, 83% of people have a bad experience. Uh, 50% of people never hear back from a company. I mean, people that think it's working haven't applied for a job in the last five or 10 years. I mean, that's what you know. It's like anyone who thinks it's a humane process that's being done by humans who have never experienced the resume black hole.
And I think it's unfortunately I think sometimes, like oh, you know, some heads of recruiting are, you know, like they're my age, you know? I'm 47 but they haven't been through ah, recruiting process, you know, in the last 20 years, and so there may not be aware of just how demoralizing and discouraging it can be.
And I think that's where I benefited from, you know, sort of being a crew changer in my thirties and actually witnessing. Wow, this is really bad. And it hasn't changed at all since I was in college. And what could we do about this? Because I think it was a very eye-opening experience to how bad it was. And again, this was at Harvard Business School, where people have a tremendous number of options. So if it's bad there, I can only imagine what it's like in the rest of the world. You know what I mean!
Absolutely, And I think they also have data that you know they say candidate’s feedback is much better if they actually interact with technology like, let's say, chatbots that's so we have data to support what you’re, I think for, you know, people just start hoping that have a broader mindset.
And I think at the end of the day, what I would say is like a person wants a job, that is what they want. When they're you know, it's just like dating. When you go on a dating app, The main thing you want to do is find your personal many people, not all but most people are there to find a partner, right, and it's the same with jobs like you are there to find your next best for a job.
There are many things that you'll forgive so long as the end outcome is that I am matched with the job that I like as quickly as possible, right? And I think that's what we always need to keep in mind when we are building the systems and actually mean it.
Getting just a little from pymetrics, if someone has asked to go through pymetrics for recruitment experience, and they are for whatever reason, not matched to the job they applied for, we will first evaluate them from up for other jobs at the company. And then we will evaluate them for other jobs outside of the company, right? So the whole system is built around.
How could we maximize the chance that someone who's using pymetrics, going through pymetrics actually gets a job? And I think all systems should be built that way, you know? I mean, right now they're not built with the career seeker’s mind.
They're built with other people’s minds, which is fine. I get it like, you know, companies have to use them, but there's no downside. In fact, there's a lot of upsides, I think, to putting the candidate first and saying, What would I like to see in the system is a candidate. How can I make it be candidate friendly, empowering? I think you know, that's how we should be building these systems.
Yeah, now that you’ve mentioned it, I think we never really go to candidates to ask them what is it that they want?
Right, Well, pymetrics does actually. We have a candidate advisory council, which is fantastic, and we've actually gotten direct feedback and look, I mean, everybody, you know, it's like dating is a good example, dating there, lots of not fun parts of dating. You have to go on a lot of dates before you find your person right? So it's not that every aspect of my job would be fun or seen enjoyable or, you know, there's obviously a lot of anxiety, but that's even given that background. What can we do to improve things for candidates?
Absolutely. It's like how they say it's matchmaking. Pymetrics have.
You talked about cognitive science and for your resumes like the cover of a book, right? Uh, you know, how exactly do you go about using cognitive science
Yeah, so we use I like to think of it broadly. Is behavioral science because it's not just cognitive science, but basically what we tried to do is used we call them games. But essentially, there are computer activities that were designed by scientists. All of the tools that we use are well known scientific exercises that look at things like memory finding, attention sequencing, altruism, reward profile, reciprocal, and so on. Um, they were all developed by scientists.
We just took them and use them to understand things that are more fundamental about people, right, because I mean a resume, it's fine. There are potentially a few useful things on there, more useful, potentially as you go along.
But even then, I think it's very limited because all it tells you is the very narrow slice of life that I have done right? I mean, you're a human being. You chose a particular career. You could have chosen many other careers. You might have been just successful all those years.
All the resume tells you is like this very narrow sliver of experience that I have. It tells you nothing about the potential that I do have for other jobs. It certainly doesn't tell you anything about a person's soft skills, right? And that's what we learned at HBS was when we were interviewing recruiters and hiring managers.
We would say, What are you trying to get from a resume and an interview? What is it that you're trying to understand? And they would tell us all these things that they wanted to understand about a person. And they're all essentially soft skills, right? They're not hard skills. They're not like, Can you code in this language, or have you ever done a sales job before? It's all about the soft skills of a person, so those are the types of things that we try to measure.
We think that we measure using the pymetric system, and the other thing I think is really important for us to understand, is that and again, this is the way our system works, and we strongly believe in this.
Or is this what we call a fit based system? That means there's no aptitude that we measure that is always good, and there's no aptitude that was always bad. That is not true in other systems. But what we believe is that everything we measure could be adaptive or not adaptive, just depending on the rule.
Right? And let's take attention to detail. If you are very attentive to detail, that means you make a great accountant. It means you make terrible salesperson sales.
People tend to be a little intended to detail because being intensive to detail makes you more creative and makes you more novelty-seeking, which is probably what you need to be when you're a salesperson, but not an accountant, right. And that's just one example of the many things that we measure that could be good either in the spectrum. I think that's critical because I think again, using a resume.
What we have created is a hiring process that assumes that people that have this on their resume, our old good and then people that don't have that are not as good right. And how many, like, you know, blogs or articles Have you seen out there?
Here are the five things you need to put on your resume to get the job or, like, even really offensive things like how to write in your resume for people that are not Caucasian, which I think is awful, right. But the point is, I think the resume based system has created this notion of hiring that there's the haves and the have nots. Right and again, we want to just blow that out of the water. There is no such thing.
Everybody has a fit. It doesn't matter who you are. You have not only one fit but multiple fits. I think it's so ridiculous frankly to think that everybody doesn't have a role in the place of work.
I think that's absurd, you know, and I think that we need to move away from this resume. The pedigree-based system, where you know Harvard students were wonderful on anyone that didn't go to Harvard is not wonderful. And, just create a fit based system where whoever you are, wherever you come from, you have many things you can do in life that you will do well and really increase the chances that someone fit matches those opportunities.
So it's just trying to read between the lines and finding out your list. His aptitude.
Oh, yeah. And again, I mean, I'm gonna use the dating analogy again, which is, I think, you know, 50 years ago, 70 years ago, maybe the message that was given to people was you had to be a certain profile of a person to find your best person, right? And it was, like, sort of a very cookie-cutter, like, I don't know, for women.
Maybe it was that, you had to I don't know. Do your hair well and cook well I don't know... I'm just making it up. Right. Uh, whereas now? No one would ever hopefully say to anybody that that's what they should do. It would be like, Hey, just be whoever you are, be warts and be whoever your true self is. And that's how you're gonna find your perfect partner. I think there's much more of that message in the area of relationships. Well, I think we need that same message for work.
I think we have to stop thinking that there's one type of person that is gonna be good at many things. I mean, whenever I hear the word talent war, I'm like, why are you all fighting over the same people? That's just silly, right? Um, and instead, it should be really about matching people to the right role. And again, I had seen this firsthand at HBS. Like, you know, there were all these talented Harvard students who, like, thought they wanted to be investment bankers.
Let's say, even though their friends were like But you like, 15 hours of sleep a night? Why do you think you want to be an investment banker? But no, no, no. I want to be an investor banker. And then, yeah, they're smart, and they're Harvard students and they get the internship.
Then three years there you like. I hate my life. This is a terrible job for me. And you're like, Okay, but that's my point, right? Is that you're not good at everything and companies shouldn't assume that just because somebody has a degree from a fancy school that they're gonna be good at everything. So let's forget about that notion. Let's assume that everybody has Fits, and that is all about trying to help people find their best Fits.
Absolutely makes a lot of sense and it’s very bold that you're actually diving into the grey of this area and trying to make sense of it because this is something nobody wants to put their hands in it because this is like probably dirty work and just too much of a headache.
And that's amazing.
You are trying to make an effort there, the question that I have when you're trying to assess a candidate, right? There are a lot of good characteristics and a lot of bad ones so is technology able to, you know, make sense of such characteristics that might actually be harmful or something that's happened in the background that might want to be heard?
Well, can you be more specific? Because be more specific, like, What do you mean?
So let's say there’s a candidate who's done something in the past, right, that they're not really to put it the upfront background is a little flawed or there’s something negative that they've done. When you do assessments and tools they’re all fine and the aptitude is good, attitude is also good. I don't know about background validation, so something trumping around that?
So that is again if you're talking about sort of strict things that would come upon a background check, that's not something that we deal in. Mostly. So I guess for a variety of reasons, I think, yes, those instances do happen. And obviously, you know, someone should be truthful about their background, for sure.
However, I think understanding the potential someone has is more important by frankly than whether they've made a mistake in the past or not. And, I really think that's what we try to focus on. I mean, so again, I'm going to go off in a teeny bit of answer. But I was watching this Ken Burns documentary about prisoners end up getting a bachelor's degree in prison, right? And really, the amazing things that they go on go on to do so even someone who has committed a serious crime is behind bars.
You know, once that time is over, even they, I think, should be given a chance. Now again, we should be honest about our backgrounds, no matter what those are, even if it's hard to be honest. So I'm not advocating or cheating or lying on a thing like that. However, I do think that the potential for human beings to do great things is far greater than their flaws. And I guess I'll just leave it at that.
Interesting. Thank you. Thank you for that answer.
And what's your opinion of having like a human capital management platform to increase employee engagement?
I think they’re amazing. I think there's so many tools out there now that, um, that helped increase engagement, you know, there tons of platforms out there now to do that. And I think it's so wonderful to get real time feedback about what's happening in an organization and how if people are happy if they're not happy, and I think that that's that's fantastic, right?
It doesn't mean again that you're going to, uh you know, bypass human to human attraction. It's just that there's a quick way, easy way, and we're all online, we’re all using apps all the time. Why wouldn't we use them, too? To find out more about how our workforce is thinking and what our workforce wants? They're fantastic tools. We should avail ourselves of them.
Absolutely. And you know what so many tools like yours. And then it's humor with. And there's some Sony. All right, What do you think? It's the future of work is heading towards?
I don't know that I had an answer for that before the pandemic hit, and now I feel like it's even….., you know, kind of like an unopened question. But I know, for me, I think that works. I think I think so again, the pandemic has been horrible. It has hurt many people that has caused so much chaos and strife and everything.
I think a few of the good things that come out of it is just showing how resilient people are that we literally transition from a digital workforce overnight, and we just didn't like that, right? Like nobody thought twice about like Okay, now I'm gonna my bedroom. You can't see my background, but there's a bed behind it.
My bedroom is now my office. My child might run into my office at any point like you. Just the fact that way we were so resilient. I think the second thing is that it just goes to show that we even don’t need an office to be productive.
We need, you know, all these things. I mean, work is about finding something that you care enough about that you're willing to commit, you know, 40 hours a week or more to this goal, right? And so I think, a lot of the trappings of a job, whether it's an office or a suit or a commute or flying on an airplane all the time. All those things are not really what a job is about.
A job is about the fundamental work that you're doing and honestly, the mission and the purpose of what you're doing in the world, what you think you're achieving in the longer term. That's really what a job should be about not all this other stuff. I think like the desk in an office in the suit and, you know, airplane travel.
Not what a job should be about, in my opinion. And I think the pandemic has helped us realize that to some extent so I don't know if that's gonna be the future work. I hope that that's a direction that we continue to go in because I think that's much, far more truthful and far more optimistic about work, then sort of all these other trappings, I would say of what the job entails.
No, absolutely I love your optimism. And I think if you can be productive even from, you know, being at home, then why not? It's just up Lucinda and good for the environment as well.
It's good for the environment. It's good for people... Honestly, it's good for women because they don't have to, you know? It's good for people that have disabilities because, you know, obviously sometimes more traditional work environments or challenging work good for so many people, you know?
I mean I’m not saying... look, obviously, there’s a downside to everything but I think the more flexibility we can offer people to do the kind of work that they are passionate about wherever they want to do that work. I think I don't know that. I think that's really the way of the future. So hopefully we can all embrace that.
How do you think millennials are, they have a different mindset than, let's say, baby boomers, or Genz, Do you think there is a way to kind of unify them and then mindsets and do think that technology can actually remove that bias? You know, that mindset bias?
Yeah, I definitely think so. And again, I mean, maybe it's because you wouldn't know this about me, but I am half Italian as was born in Italy. Um, and part of the reason they think COVID had hit Italy so badly is that there's a lot of mixing of the generations, right.
And I would say that in Europe in general, that's true, is that there's far more mixing of young people, middle-aged people, people. And there isn't this sort of, you know, I'm only gonna hang out with, like, you know, my generation.
I think you could learn so much from any generation there and again, so long as we recognize as a generation what tendencies are right. So if you're Gen. Z it is or you're a millennial, it's that I think again, it's another type of bias that I think we should try to eliminate. Because, in my opinion, the adversity of all types is really what creates better solutions.
So, yeah, absolutely. I think inclusion is the way to go.
Yeah, totally. Yep, Only a small trip. And I think, you know, inclusion is something that doesn't come naturally because inclusion means I have to be okay with someone being very different from me, right? And again, it's not that we don't.
It's not that we're not good people. It's that if somebody is more similar to you, it's easier to get along with them or, you know, find commonality. But the whole point of inclusion is that no, I have to like The whole point is I need to stretch myself. I need to be. I need to stretch myself, to be comforted, too, develop comfort with people that are very different from me in certain ways. And it doesn't come naturally.
I think we have to just be honest and say it doesn't come naturally. However, I think there's a real benefit that people can see from that. Like, I'm one of these people that I don't personally like a lot of homogeneities like if I go somewhere and it's all a bunch of people who look just like me It feels boring I think, however, with finding a more diverse group of people there is, you know there is friction.
Um, just because not everyone thinks like me and everyone talks like me and that everyone has the same background, so I think just realizing that and just realizing the value and the beauty of it is super important.
Absolutely. I think it just takes a little compromise and then you….
I'm just gonna end this if you have any other important sound bites that you'd like to leave for our viewers?
No, I don't. I don't have any important sound bites. I would encourage everyone to just think of... I guess I am definitely a glass half full kind of person. And I think that, you know, even in really troubled times, it really is incumbent upon us to retain hope and see what is positive. What can I do? What's the contribution I can make?
I'm to make the world a better place, even when it's not easy to think that way. So that's the thing that I would leave everyone within these times of trouble. And, you know, just always think they're people out there that are far worse off than we are, and that those are the people that we should be striving to make a better world for.
That's wonderful. It was a pleasure talking to you Frida. I had a lot of fun and it was really high energy and I'd really appreciate your time.
Thank you for having me. I really appreciate it.
Thank you so much. Take care.
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October 25, 2021
A very inspiring list. Thanks for putting this together
October 25, 2021
This is such a great list of women leaders! More power to you all