Welcome to The Business of Giving. I’m your host, Denver Frederick, and today we have a guest who is transforming the landscape of employment around the globe. Dr. Mona Mourshed is the CEO of Generation.org, a nonprofit that has trained over 120,000 adults into new careers across 17 countries and 40 professions.
Dr. Mourshed shares the powerful inspiration behind Generation:
“Youth across the world were protesting the situation of education not leading to employment, and so that’s really what sparked the thinking: What would it take to not just train people, but train and place them into new careers, and to have that change last over time?”
We’ll explore how Generation.org is breaking down barriers with their unique seven-step methodology, the impact of AI on the future of work, and how they’re empowering individuals of all ages to achieve economic mobility.
Here is my conversation with Dr. Mona Mourshed on The Business of Giving.
Denver: Dr. Mona Mourshed, welcome to the Business of Giving.
Mona: Thank you so much.
“Youth across the world were protesting the situation of education not leading to employment, and so that’s really what sparked the thinking of: What would it take to be able to not just train people, but train and place them into new careers, and to have that change last over time?”
Denver: Mona, could you share the founding story of Generation.org and what the core mission of the organization is?
Mona: Absolutely. So, we actually began our work 10 years ago… we’re going to be celebrating our 10th-year anniversary in November.
Denver: Congratulations.
Mona: Thank you. And so, what we do is to support adults of all ages to be able to achieve economic mobility, and the way that we do that is through a career. So, we train and place adult learners into careers that would otherwise be inaccessible.
So, when we began 10 years ago, we explicitly started with five countries in very diverse contexts, so it was the U.S., India, Mexico, Kenya, and Spain. So, we chose them for their diversity because our goal right from the very beginning was: How can you develop a global solution? Because often, things become very context- specific, and so unless you work in very different geographies, you don’t know what is universal versus what is context-specific.
And so, when we started, it was very much with a focus on youth, and this is part of the origin story of Generation, which is that you will recall, right leading up to 2015, there was the Arab Spring and very high youth unemployment in the Middle East, so 40% plus. That then rolled into the global Occupy Movement, and youth across the world were protesting the situation of education not leading to employment. And so that’s really what sparked the thinking of: What would it take to be able to not just train people, but train and place them into new careers, and to have that change last over time?
So, that’s what sparked Generation, and so where we are now is that we work across 17 countries and 40 professions, and those professions span tech, healthcare, customer service, skilled trades, and green jobs. We have over 120,000 graduates, and half of them happened in the last two years. And so that’s what we call essentially breadth. So that’s our geographic footprint. We have also 18,000 employers.
And then we have depth, which is our employment and income outcomes at three to six months post-program. And so, our graduates have an 84% job placement rate within three to six months of program completion.
And then, there is durability, and durability is: Does the change last over time? And so, we know that two to five years after program completion, 76% of our graduates continue to be employed, 70% can meet their daily financial needs. Of those 76% that continue to be employed, three-quarters of them are in jobs directly linked to Generation, and they’ve now earned over $1.5 billion in wages.
So, that is, in a nutshell, what Generation is.
Denver: Well, that’s a very impressive nutshell. Thanks for that background. Also, you have a unique seven-step methodology. Why don’t you walk us through that approach and explain what makes it so effective, Mona.
Mona: Absolutely. So, let me talk about how we landed on seven steps. So, the very first thing we did was to map, let me call it the Education to Employment Journey and all the things that get in your way along that journey. And so, our seven-step method is designed to blow up each of those barriers and to put in place an intervention.
So, for example, often what happens within our space is that people are trained, and then you look for the job. So, we decided that our very first step would be to find the jobs.
So, that is the very first thing. So, we work with employers to pre-confirm job vacancies, to understand what is the demand that they have, and then we always seek to have 1.5 times the number of job vacancies secured versus the number of people in the program because life happens, and life might change, and we have all witnessed over the past years what exactly happens in terms of life’s twists and turns.
Denver: For sure.
Mona: So, that’s the first step.
The second step is that we recruit our learners, and we are always looking for profiles that are different than what the employer typically hires.
And so, for example, if they’re typically hiring university-degree holders, we’re looking for people with secondary school or vocational backgrounds; if it is a male-dominated profession, we’re looking for females and so on.
And then, the third step is a 6- to 16-week profession-specific training, and this used to all be in person, and now it is either online or blended. We do have some programs that continue to be largely in person. So, for example, solar panel installer, it’s very difficult to do that online.
Denver: Yes, I would say so.
Mona: And that program interweaves technical skills, behavioral skills, and mindsets. And this part is really important because often, training focuses on the technical skills and not the mindset and the behavioral, or they’re teaching them separately. So, technical skills are here; mindset and behavioral things are here, and the reality is that they manifest differently in different professions.
So, what problem solving looks like and how it manifests in one profession is not the same as how it manifests in another. What I have to do to make a plan, to make sure that I do all the tasks in my role, it can look different in a different role.
So, we’ve braided it together and the training is about 70% practicum. In parallel, we offer social support services. So, everything from mentorship, to, in some cases, it’s a stipend. It depends on the circumstances of the learner.
And then once they complete the program, they then interview with our employers. We try very hard for these to be demonstration-based interviews, so that our graduates can show what their capability is.
Denver: They can do, yeah.
Mona: Exactly. And then once they are on the job, we continue the mentorship for the first three to six months on the job.
And then after that, we are tracking the return on investment for employers, for our graduates, and I should say we now have about literally nearly 50 million data points that span the entire spectrum. And then we use that data to make the program better. So, that’s the seven steps.
Denver: Yeah, that’s a nice holistic approach… and I love that about the mindset. The technical skills are going to change. The mindset’s going to last a little bit longer if you get the right one and be able to deal with those changes when they come along because it happens with every profession.
Mona, what have been some of the most surprising insights you’ve gained about the global skills gap? And what are some of the biggest misconceptions that many of us have about youth unemployment?
Mona: So, so many responses to that question, Denver.
Denver: That’s why I doubled up on it.
Mona: So, let me talk about the journey we’ve been through with employment because I will say like, first and foremost, the rate-limiting step is the jobs, right? It’s not figuring out how to design curriculum and getting to mastery; it’s not the recruiting; it is not how to do the data. It’s getting enough jobs. And the journey that the world has been on, I think very much echoes our experience because we are obviously demand driven.
So, first and foremost, when we began, we were very focused on which jobs are experiencing either a high degree of growth, so employers want a hundred of these people, but they can only find 10. Which jobs are experiencing significant variation in productivity and quality on the job? Which jobs are experiencing high degrees of attrition, and so therefore, employers will value those that have retention and so on. And then obviously, it has to be an attractive job to the learner in terms of wage level, career growth, and so on.
So, when we began, we were finding that, for example, tech had a lot of these components. So, tech was high growth; there was a lot of job poaching of employees, and so retention mattered and so on. Then during the pandemic, what happened there was that there was actually a surge in tech jobs because essentially, this was after the initial set of layoffs, but there was a surge because if you remember everyone now needed to be online and so…
Denver: Absolutely right.
Mona: …a lot more cloud developers and so on.
Where we are now, given the economic uncertainty and AI, is that we’re finding that there have been obviously many more layoffs, and we’ve seen that across tech roles. And on top of it, we’re seeing that entry-level vacancies are shrinking, so about 30%,versus what they were two to three years ago. And employers are rarely willing to pay for these roles as well in this current context, and you are finding that people who have let’s say three, four years of work experience are willing to take entry-level roles because of the market environment that we’re in.
And so I’m just sharing one example to show how the pendulum swings so rapidly. And for us, why do tech jobs matter? Because they typically have a higher wage level than what is the case for other entry-level roles.
So, we are now in a world where the entry-level role as we know it has essentially vanished. You know, 94% of entry level roles in tech require six months, one year, two to three years of work experience. So, the entry-level business is changing dramatically, and that matters a lot for an organization like ours.
“And we actually just released research this week about the use of AI by mid-career and older workers, and the reason why that was interesting to us and an important question to ask is because when you ask employers why are they hesitant about mid-career and older workers, it tends to be about technology. It’s about a perception that they won’t use technology as well, that they don’t learn new processes and new things as well as their younger peers”
Denver: Yeah, it certainly does. Generation.org, you initially focused on youth unemployment, but later it included mid-career and older workers. What prompted this shift, Mona, and how has it influenced your approach?
Mona: We started with a focus on youth because of the high degrees of youth unemployment that we were seeing across the world, and as I mentioned, this was, in 2015.
Denver: 2015, yeah, right. I got it yeah.
Mona: Then, in 2018, 2019, we began working in Singapore, and we’re working a lot with SkillsFuture, which is the government agency that is tasked with thinking about skilling the nation. And it became very clear to us, and this was really the first time that we began looking at the mid-career and older worker population, and there was obviously a need in Singapore to focus on mid-careers. But then we started looking at the statistics across the OECD, and what we found is when you look at the long-term unemployed across the U.S., Europe, 40% to 50% plus are age 45 plus. And so then we thought, okay, well why is this? And then we began tunneling into the data, and we realized: If you are age 50 and you become unemployed, the odds of you finding employment after that, it just falls off a cliff.
Denver: Wow.
Mona: And so we began to realize, okay, we are here to be in service to all adults who need support as they transition to a new career, or as they’re unemployed. And so, we actually changed our mission… it was around 2019 or thereabouts… to include learners of all ages.
And then we began doing research and programming for mid-career and older workers, and one of the pieces of research we did was with the OECD, which we released last year. And so, the challenges in terms of the bias that mid-career and older workers face is actually quite stunning.
So, when you ask employers: When you look at CVs of job candidates, which are most fit for purpose? When it comes to the age 45-plus population, like only 15% of CVs of mid-career and older workers are considered fit for purpose. Then when you ask those same employers: Well, of the mid-career and older workers that you happen to have hired, how are they actually performing on the job? 86% plus are performing as well if not better than their younger people.
And so there’s just a lot of perception bias, and we see that in our programming. So, even though our mid-career and older workers have the same competency level in the program as their younger peers, it can often take six months to get someone into a job versus three months for someone who is younger. Now, we are happy, though, to see that once they are in the job, they have strong performance; they have strong job retention, but it takes longer and it takes longer partly because of this perception issue.
And we actually just released research this week about the use of AI by mid-career and older workers, and the reason why that was interesting to us and an important question to ask is because when you ask employers why are they hesitant about mid-career and older workers, it tends to be about technology. It’s about a perception that they won’t use technology as well, that they don’t learn new processes and new things as well as their younger peers and so on, and there is so much change obviously happening with AI. So, this felt like a really important question to understand.
So, just briefly, I’ll just share two things from that research. So, we surveyed thousands of employers and mid-career and older workers across U.S., U.K., France, Spain, Ireland. So, first of all, for roles that involve use of AI… and that actually is now many roles involve some use of AI tools… employers are three times less likely to consider a mid-career and older worker for that role than someone who is younger. So that is one, and that is within the context of shrinking job vacancies. So, this is amplifying age biases.
The second thing we found is that it is true that the share of mid-career and older workers, like only about 15% plus, are using AI tools today. However, for those who are using it, they are largely self-taught. They are finding great benefits from using these tools, and they are power users, so they’re using it multiple times a week, if not daily.
The real stumbling block is the use case. So, there’s other research which we’ve seen as well, which shows that AI tools are actually producing the greatest boost for those who are early in their work experience… so they are nascent workers. So, how you support someone who is an experienced worker and marry their experience with AI tools? That use case, employers have not really fully fleshed out, and it’s only when that happens that we’re going to begin to see this uptake.
So, there are a number of calls to action, if you will, both for mid-career and older workers, but also for employers.
Denver: Yeah and I think a key thing you note in the report too is that just because a lot of mid-career and older workers are not using AI, doesn’t mean they can’t learn to use AI…and as you say, the great benefit of having that coupled with experience. I sometimes feel that experience is just not as valued as it used to be because we live in such a TikTok…whatever happened this morning is the most important thing… and what happened five or 10 years ago doesn’t make any difference to today. But it really does. You know, some of these things are timeless, and they’re universal.
Mona: Fully agree. So, I’ll put a fact point behind what you just expressed. We asked employers in our survey last year: So, here are two CVs. One of them, the individual had five years of work experience, the other 25 years. So, you can intuit age without seeing it. Employers equally valued the person with five years of work experience as they did the person with 25 years. So, work experience is important, but they’re equally valuing them.
Now, you can ask why… so there could be wage level considerations that go into that. But there is also a question about institutional memory, the ability to do pattern recognition …and coupled with technology, how do you get the best out of the tools that we have today? And that is, I think, something that the world of work needs to grapple with a lot, and we’re still learning.
Denver: Yeah, yeah. Let’s dig a little bit deeper into AI. What do you think the impact of AI is going to have on the future of work? We know it’s going to have a big impact. And what are you doing to help prepare your cohort, so 6- to 16-weekers, to get prepared for that change, or that evolution?
Mona: Well, I’ll share the effects that we’re already seeing and that I could attempt to forecast. So, we are already seeing that job vacancies are declining. And why? Because employers right now, as they bring in AI tools ..and the majority of employers are rolling out AI tools in the workplace… they’re asking themselves, “Okay, so to achieve the productivity benefit, so maybe I don’t need 10 people in this role. Can I get by with six people? Maybe with four people?” So, employers are very much reflecting on that, and as they do that reflection, they have reduced their hiring as a result.
So, we see a lot of freezes; we see a lot of reduction, and I expect that that trend is not just for this year, it’s going to continue into next year. I don’t doubt that there will be some new roles that are created as a result of AI tools, but it hasn’t kicked into force yet. So, that is the first trend, and that obviously makes it harder for someone who is coming from a non-traditional background and who does not have at least three or four years of work experience in that domain.
Second thing that we are seeing is that when it comes to certain populations, and we just talked about it for mid-career and older workers, that because of perspectives that you need to look a certain way in order to make the best use of AI– and that is, typically, be a university graduate and be younger, and so we are seeing that perception bias in hiring.
The third thing we’re seeing is in the hiring process itself. So, one of the things that AI tools bring is that it will write your cover letter for you with some prompts; it will make sure that your CV has all the right buzzwords, et cetera. And so employers are seeing a much higher volume of applicants than they typically saw in the past. And as a result, they are also using AI screening tools themselves looking for all of those buzzwords and so on. And so, it is becoming much harder to break through as a result. And again, why is this relevant in our work is because we have non-traditional candidates who are different and so on. So, it’s making it more fraught to be able to get the job.
And you even… I mean, there are an abundance of articles about individuals who have applied to hundreds of jobs, and they have not been able to break through and so on. So, that’s what we see right now.
So, what are we doing about this? So one, employer relationships have never been more important. So we are blessed to have 18,000 employers who have hired from us, and about 70% of our employed graduates are hired by repeat employers, and so those relationships are very important.
Second, we have introduced AI modules to all of our professions because in our view, everyone needs to know the basics of how to use AI, and so we’re rolling this out across all of our professions in all of our geographies.
And then third, we’ve selected about 10 professions where we are doing a much deeper dive on how do you use AI tools. So, for example, for our software developers, how do you use things like CoPilot because it is just becoming such an important part of the role.
And then, we’re also using AI to try to make our own work more efficient as well.
So, those are just some of the things that we’re doing, and I’m sure that it’s not enough and that we’ll need to do more.
Denver: Yes, it’ll go. It is interesting though that analytics are ruling the roost, and the analytics are writing the cover letter; the analytics are reading the cover letter. But if you want to be better than everybody else, since everybody’s using the analytics, you’ve got to do something different, something individualized… as in customized, something that’s a little bit out of the analytic mainstream. And that’s how you differentiate yourself as being the best, or certainly better.
Let me go back to what you said at the very beginning, the five countries you started in, the 17 countries you’re in now– What have you seen as the cultural differences in employment practices across these countries? What aspects of your work are universal, and what aspects of your work are really pretty much customized to certain parts of the world or individual countries?
Mona: So, let me start with that last part. So, what we found is that the seven steps are indeed universal. So, the “what” is universal; the “how” can be context- specific. And here’s what I mean by that.
So, when we mobilize employers, in some countries, we can find that there are anchor employers who will hire hundreds of our people. We do have employers that have now hired like 300, 400 of our graduates. In other countries, it’s actually much more atomized. It is dominated by small and mid-sized companies, and so we need to have a lot more coverage across employers, versus saying, “Okay, these 10 employers are our anchor, and then we just need a set around them.” And that yields a different business development strategy.
Recruiting. So, we recruit our learners, we look for the same thing in our learners. But in some countries, it’s dominated by social media– that is how we recruit. In other countries, it’s because we have relationships with ministries of labor or employment agencies. And so the “how” is what varies, but the “what” is fully consistent.
And so that’s the first piece, and that lends to: Okay, what is the same across countries, versus different? So, in some countries, we’ve been able to partner with governments, which is really important to being able to take existing workforce dollars and deploy them in a way that yields higher return. And when I say higher return, I mean higher employment rates, greater income for those who are employed. Some countries don’t have workforce funds that can be leveraged in that way, and so that results also in a change of how we engage.
So, it’s things like that, but I think what’s been most heartening for us is that the seven steps are truly the seven steps. And it requires that holistic approach to get someone, not just employed, but to have sustained employment and wellbeing. And the nearly 50 million data points, which we now have and we can look over time across all of our geographies, that’s what it shows.
And then what I will say: where there is greater variation is by profession. So, tech jobs have been very durable. I mean, up until recently they have been very durable. Customer service jobs, depending on the nature of it. There have been professions that we’ve just said, “Look, we can’t get to a living wage within two to three years, and so we should not offer this.”
So that’s the benefit of hindsight.
Denver: You have a core curriculum…I love it…that travels well.
Dollars. Sustaining a global nonprofit like Generation, it requires some significant resources. How do you approach fundraising, earned income, which I’m sure is a big piece of it. What are those primary sources of revenue? How challenging has it been? What else are you thinking of doing?
Mona: Absolutely. There are three sources of funding for our network. So, there is philanthropy, there is government funding, and there is employer funds.
I should say our programs are free to our learners. We do not charge them because we did not want that to be a barrier to access.
So, globally where we are, essentially, it is about a third is government money, and then we have currently…employer money is only about 5% and I’ll explain why in a moment, and then the rest is philanthropy.
What we found post-pandemic is that it became very challenging to have employer payment. You will recall the mass layoffs during the pandemic, and at that point, employers were essentially saying, “Look, just be grateful we’re even giving your people a job, let alone paying you,” and we took that trade off. For us, it was more important that we were able to get our people into a career, earning income, and on a trajectory. Now, we are trying to get that back.
And so, we imagine that our government funding will grow as our government partnerships grow. The employer portion needs to be greater, and we’re trialing a number of things on that front. Philanthropy will always be an important part of what we do, but we very much want to be able to emphasize outcomes that last.
This is one of the most important things for us, and I’ll just say, Denver, literally hundreds of billions of dollars get spent in the workforce space every year by government, by philanthropy, by others, and the ROI on that spend, when it is even measured… and I should say when it’s rarely measured,and when it is, it’s only about 12 to 18 months out. Programming has a negative ROI and those that do have a positive, it’s only like, two to three percentage points versus the counterfactual. We think that should change.
So, this is why we measure durability; this is why we measure outcomes at two to five years out, and we really want to be a part of catalyzing a shift in how philanthropy, governments, peers, how we all measure our outcomes because that’s going to make all of our work better… if we know that the change lasts. And if the change isn’t lasting, then what interventions do we need to do to make it last?
Denver: Yeah, that is a steep hill. I mean, we talk about measuring impact all the time, and it is just so, so difficult.
I want to ask you a little bit about your workplace culture because the thing that has really struck me with this whole conversation is how data-driven you are in everything that you do. Talk a little bit about the workplace culture at generation.org,–what you do to shape and influence it, and the impact that being so data-driven has on the workspace.
Mona: Yeah, so without data, you can’t make decisions.
Denver: A lot of people do, Mona,
Mona: So, I will reframe. Without data, you can’t make robust decisions.
Denver: Yes, I agree.
Mona: So, essentially, we knew from the very beginning that if we were able to implement our program successfully, that our data could be as important as the programming itself.
That’s why we have put so much effort into measuring the whole life cycle of, okay, so what does our learners’ life look like when they are applying to Generation? So socioeconomics, education attainment, a number of things. What is their performance in the program? And then, what is happening in terms of employment and income and wellbeing at three months out, at six months out, at one year out, and then annually thereafter, up to five years out?
So that’s what gives us the nearly 50 million data points, and then we analyze that. And then we have dashboards for our programming, so that on any given day, you can see how many cohorts, what’s happening inside the cohort. And so every day, we are taking decisions on the basis of the data that we have. That’s how we can turn on a dime and know, okay, this something didn’t work in this cohort. We’re not just going to launch the next cohort. We need to change X, Y, and Z accordingly.
And so, what I will say is that measuring the durability of our outcomes, so two to five years out, it now costs us 1% of the total cost per learner to do that. That’s scalable. That’s replicable.
This is why I say, one of our great aspirations is to be able to also support peers in this space, or funders, and governments to measure the same because we’ve now truly built a machine behind it and a set of tools, and we have high data completion. I mean, we always seek to make the data completion even higher, but we’re at a point now where we feel the entire space could be doing this, and we’d like to be a part of partnering and collaborating with other stakeholders in the space to do that.
Denver: That is fabulous. I can see that you use data primarily to learn and to get better, but you also do it to raise money and show funders the impact of your work. And I’d be curious, Mona, have you seen the benefits of that from funders? Funders always say they want data. You’re giving them data, and then some. Have you seen them step up and say, “Wow, we don’t see this, here’s more than we’ve given in the past.” Or, is it not having quite the influence that one would hope?
Mona: So within the philanthropy community, there are certainly funders who care deeply about outcomes, and this data absolutely makes a difference with that community.
There are also members in the philanthropy community who are focused more on what we call breadth, which is the number of people trained, at least in our world, or the number of people served.
And so, we gravitate towards those that care about breadth, depth, and durability together, as opposed to breadth alone. And for those who care about depth and durability, this data absolutely makes a difference… and the fact that we can do it globally.
Denver: Tell me about your leadership philosophy. What are those guiding principles that you bring to the organization and everything that you do and maybe how they’ve evolved since starting the Generation?
Mona: Oh, big question. So, I think, first and foremost, and quite a lot of this is underpinned in our organization values as well: Solve problems that matter.
There are so many things that need to be tackled in the social space: big, complex problems because I mean, frankly, in the social space, everything is big and complex. If it was simple, then, it would’ve been solved long ago.
Denver: In another space.
Mona: So, it’s very much about big, complex problems. How we do that globally and interdisciplinary? You know, there is never a case where there’s only one part of Generation that’s trying to crack something. It is always how our six parts of the Generation network working together to figure out how we tackle this. It’s very much interdisciplinary and global. It matters a lot in everything that we do.:What is the universal versus the context-specific? And there’s got to be data, right? So those are the things.
I think at a more personal level to me, it is always about: How can we unleash the entrepreneurship energy that someone has, the hustle that someone has, to be able to be in service of the mission? And how do we make sure that we’re getting enough of the organization excited… and our stakeholder group more broadly? How are we making sure that what we have learned in Spain we’re taking to Kenya? What we learned in Kenya we’re taking to Mexico, and so on? And that requires that we are not just talking, but listening to each other.
So, it’s things like that are very important in how we think about our organization culture.
Denver: I’m going to try another big question. So, let’s see how this one goes.
Mona: We’re on a roll.
“So you don’t know what you don’t know until much later in life, but your path would’ve actually been very different had you seen that earlier.”
Denver: And I’m going to look at your younger cohort, and knowing the young people who come and you work with, and knowing the employers that you work with, if you could redesign the global education system from scratch without any constraints, what would it look like?
Mona: Okay, and so you’re talking now at school level?
Denver: Yeah, at school level and right throughout university, but right from the beginning. I mean, I talk to folks as well. I don’t know this obviously the way you do, but there’s a gap. There’s a knowledge gap between what students are getting out of school and what employers are looking for. And I’m just wondering what you would do, if you could do anything, to close that gap.
Mona: So, a couple of things. One, when I look at school education, I do not believe it needs to be grouped by age. It should be grouped by skill and capability and so, You can find second graders operating at the level of fifth graders. You can find sixth graders who could actually benefit from support in certain areas. So, I think I would love to see much more multi-age cohorts. By the way, I think post-secondary can be much more intergenerational as well, and the workplace is certainly intergenerational. So, I think that we’ve gotten into a world of age defines where you slot in. It doesn’t need to be that way.
Second, embedding these mindset and behavioral skills much more intentionally from an earlier age would benefit all of us… and naming it as such. So, forward orientation– How do you make a plan and track your progress against that plan and develop workarounds when your plan isn’t working, or whatever it may be?
We can all benefit from that much earlier. So, it’s not the teacher or your parent or whomever telling you what to do, but that you’re actually saying, You know, hey this! And there are some school systems or certain programs that are seeking to do that. It needs to become much more widespread than what it is today.
I also think that being able to identify : What are your interests? requires someone putting a buffet in front of you so that you can try this and try that. And unfortunately, for so many school systems, they’re so resource-constrained that they’re not able to put much of the buffet in front of you. And so you don’t know what you don’t know until much later in life, but your path would’ve actually been very different had you seen that earlier.
I think at the secondary school level, having much more deliberate engagement with employers in terms of, “This is a day in the life of my job; this is what’s great about my job, and this is what’s not so great about my job.” It is often optional. “So, hey, go to this career fair!” Bake this into the education experience. I mean, professions are changing so rapidly. You know, my daughter right now is in seventh grade. By the time she gets to 12th grade, it’s just going to be an entirely new world of work.
Denver: Absolutely.
Mona: Let children see this much earlier on. And then certainly at the university level, bake in work experiences, and not just in the final semester of your senior year. No. These things should be coming in much earlier.
And by the way, there are different options. Not everyone has to go to college after 12th grade. Maybe you want to try work and then go to college, if that is what you want later on. But we’ve got to get out of this sequential, rigid motion that so much of the world is on.
So, that is what I would do if I could re-engineer.
Denver: Well, I love your five ideas, Mona, and I hereby nominate you as the global minister of education. You can decline if you’d like, but the nomination has been entered.
Finally, Mona, what’s your vision for generation.org’s impact over the next decade, and how do you hope your work will be remembered?
Mona: You’re just.. Can’t resist the big questions.
So, as I mentioned, we are officially celebrating our 10th-year anniversary, so this is timely to think about what is going to happen.
Denver: It really is. it’s almost like mid-career for a lot of organizations at 10 years. That energy that got you started, you almost say, “Okay, where are we at?” and you begin to start to think what the future is going to look like.
Mona: Yeah, so we have actually begun thinking about what it looks like, at least for the next five years. So, clearly, we want to be able to support more and more learners across the world to engage in our programming and to achieve economic mobility.
So, we imagine that over the next… so between now and 2030, that we’ll be able to reach anywhere between 400,000 to 500,000 graduates. Another aspiration links to what I was describing earlier about durability. I mean, we want to be a part of building, leading a durability movement so that much more of the sector is not just tracking durability, but that we are able to make this a systemic part of how philanthropy and governments and peers are taking this, as “This is our standard practice.”
So, we have a lot of aspirations around this, and by the way, it doesn’t have to stop at workforce. You know, frankly, this is applicable to public health; it’s applicable to other arenas in the social sector. But we’d love to hold hands with our peers and stakeholders and really make a push on that.
So, those are at least two of our aspirations.
Denver: Well, those are two great ones, and it’s movement generosity, in some ways. Not only do you want to do it for yourself, but you want the sector across the board to do it, because it’s going to be better outcomes for everyone.
If listeners should want to learn more about generation.org or financially support this work, tell us about your website, Mona, and the kind of information they can find there.
Mona: Absolutely. So, generation.org is indeed the website. And what you find there is an overview of our approach, our impact metrics, third-party impact evaluations, stories of our graduates so that you can see the people and the journey that they’ve been on. And that’s what lies behind the data and is in front of the data in terms of what we’re seeking to do.
And if you’re an employer, we welcome you on our website as well. We would love to have more and more employers and funders as well, but that’s essentially our site.
Denver: There you go. Well, thanks, Mona, for being here today. It was a pleasure to have you on the show, and I want to thank you for such an interesting conversation.
Mona: Denver, thank you so much.
Denver Frederick, Host of The Business of Giving serves as a Trusted Advisor and Executive Coach to Nonprofit Leaders. His Book, The Business of Giving: New Best Practices for Nonprofit and Philanthropic Leaders in an Uncertain World, is available now on Amazon and Barnes & Noble.