Skillfully revolutionizes hiring by using AI-powered, realistic workplace simulations that let candidates demonstrate real skills—not just resumes. This data-driven approach speeds up screening by 50%, cuts costs by 70%, and boosts candidate conversion rates 10x by focusing on proven abilities over paper credentials.
This episode of the SourceForge Podcast features a discussion on the future of hiring with Skillfully, an innovative platform that uses AI-powered simulations to assess candidates’ skills. We speak with Brett Waikart, CEO and Co-Founder of Skillfully. We discuss how Skillfully allows employers to evaluate candidates based on their actual performance in realistic job simulations, rather than relying on traditional resumes and credentials. This approach helps identify talent that might otherwise be overlooked due to biases in the traditional hiring process. The conversation also touches on the impact of generative AI on job applications, the challenges faced by recruiters, and the importance of focusing on skills and potential rather than pedigree. Brett emphasizes the need for a more accessible and meritocratic workforce, and how Skillfully’s platform aims to achieve that by providing a fairer evaluation of candidates’ abilities.
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Show Notes
Takeaways
- Skillfully uses AI-driven simulations to assess candidates’ real skills.
- Traditional hiring methods often overlook qualified candidates based on resumes.
- Generative AI has changed the landscape of job applications significantly.
- Employers are overwhelmed by AI-generated applications, making recruitment challenging.
- Skillfully provides a solution by allowing candidates to demonstrate their skills directly.
- The platform helps employers identify talent beyond traditional credentials.
- AI tools can automate job applications, increasing the volume of submissions.
- Skillfully’s simulations create a digital twin of job roles for better assessment.
- The shift towards skill-based hiring is essential for a more inclusive workforce.
- Skillfully aims to reduce hiring costs and improve the quality of hires. Large language models can be treated as building blocks for various applications.
- AI can create dynamic simulations for workplace challenges.
- Skill validation tools can streamline the hiring process and reduce costs.
- AI should address fundamental problems rather than reinforce the status quo.
- Bias in AI hiring tools can be mitigated by focusing on skill proficiency.
- Skillfully operates as a public benefit corporation with a social mission.
- The workforce is resilient and will adapt to changes brought by AI.
- AI can democratize access to education and job opportunities.
- Skillfully’s simulations assess candidates based on demonstrated skills.
- A skills-first approach can lead to a more equitable workforce.
Chapters
00:00 – Introduction to Skillfully and the Future of Hiring
01:19 – Brett Waikart’s Background and the Birth of Skillfully
07:54 – The Shift from Traditional Hiring to Skill-Based Assessment
12:00 – Generative AI’s Impact on Job Applications
18:22 – Skillfully’s Solution to Combat AI Overload in Hiring
23:00 – The Benefits of Skillfully for Employers and Job Seekers
26:52 – The Evolution of Skillfully and AI Integration
30:04 – The Agentic Revolution of Large Language Models
32:56 – Transforming Hiring with AI Simulations
36:45 – Addressing Bias in AI Hiring Tools
41:28 – Skill Proficiency Over Demographics
48:54 – Navigating Challenges in the AI Era
Transcript
Beau Hamilton (00:05)
Hello everyone and welcome to the SourceForge Podcast. Thank you for joining us today. I’m your host, Beau Hamilton, Senior Editor and Multimedia Producer here at SourceForge, the world’s most visited software comparison site where B2B software buyers compare and find business software solutions.
In today’s episode, we’ll be discussing the future of hiring with Skillfully, an innovative platform that’s redefining how businesses and companies identify, assess, and validate talent instead of relying on traditional credentials or keyword stuff applications, Skillfully allows employers to see how candidates actually perform before making a hiring decision through immersive role specific simulations powered by AI. Yeah, so instead of guessing how a candidate might handle a role, employers will actually get like a front row seat to watch them solve real problems, make decisions under pressure, and demonstrate the exact skills required all before an offer is ever made.
And I think at a time when companies are struggling to kind of cut through the noise of inflated resumes and generative AI job applications, I think Skillfully is stepping up at the right time with a much needed reality check. So to talk more about the platform, we’re joined by someone who knows the platform inside and out, CEO and Co-Founder of Skillfully, Brett Waikart. Brett, welcome to SourceForge Podcast.
Brett Waikart (01:23)
What’s going on, Beau? Thank you for having me.
Beau Hamilton (01:25)
Yeah, it’s great to have you here. I’m excited just to talk about this, this subject, the industry at large, what’s going on, to get started here. What can you just talk about like your background and history, behind Skillfully, but also just like was this platform sort of born out of a need to address the, you know, the flood of generative AI job applications, or was it more about like rethinking how we evaluate talents and in a world where resumes don’t tell the whole story.
Brett Waikart (01:52)
Man, what a good question. Beau, hey, first just want to say thank you again. Thank you for the chance to be introduced to your audience. Long time listener and a big fan of the show here.
Yeah, what an interesting time, to be building a company in the employment space. I personally have been in workforce development, in one form or another for about the last 15 years. Workforce development really just means ensuring that the talent pool has the skills that the workforce actually demands and requires, that there’s an alignment there, that we’re preparing people appropriately to be able to enter the world of work and then to succeed, to be able to create value, to be able to move through their career.
Skillfully started maybe about five years ago, I think is when we first kind of had the idea for what this was gonna turn into. And at the time I was running a different organization I had co-founded an up-skilling organization that was really focused on teaching critical skills in financial and investment literacy. It was a nonprofit. It was reaching into corners of the job market that were underrepresented in the workforce. And the idea was just literally, let’s teach skills that the American school system just wasn’t delivering consistently.
We were doing this at scale. We had between 15 and 20,000 students every year who were coming through our platform. And just pure luck of the draw, this is kind of luck of the universe how this works out. We decided early on that our teaching mechanism, the way that we would help those members develop their skills was through virtual simulations, software that we could write that would replicate how work is done today, how those skills are needed to be used. And the goal there was just to say, hey, if we can put folks into a realistic environment, then they’re going to be that much more proficient, that much more confident when they go into the real world to use those skills again.
What we kind of happened across is a couple years into doing that, we actually got a knock on the door from a couple of the largest employers in the financial services industry, think really big investment banks, Citi, Barclays, Bank of America, others. And they came in and they were one, looking to support the work that we were doing, but then all of them immediately were asking about, can we see the data? We’re so interested in the data that comes out of these simulations.
And what was really going on there is, our organization’s mandate was to reach into those underrepresented pockets of talent that were about to enter the workforce and to help them develop those skills. But what we were doing is we were building a really fascinating data set on job seekers who oftentimes wouldn’t show up well on a resume. These were students coming out of the community college systems, coming out of online universities, non-traditional pathways.
The way that recruitment was set up in the 2017, 2018, 2019 era, you’d submit a resume. If you were going to one of the target schools or one of the target pathways, you’d get an interview. If you weren’t, you’d get kind of ignored. We were developing this really interesting data, not on where they went to school, not on resume data, but really looking directly at were they proficient with the skills required in these different career paths in the financial and the investment services industry. And what we started doing is we started making introductions between these employers, basically introducing these employers to those students who were demonstrating the highest levels of proficiency in these simulations.
We sat back and we just started watching as first dozens and then hundreds of these students, they didn’t just get the interview, they got the first job. A year into the job, they were absolutely kicking down the doors and killing it and then moving on and getting promoted and advancing through their career. And if those job seekers had been evaluated purely on the basis of their resume, of their cover letter, of these kind of traditional credentials that make up how we’ve typically hired, they would never have been seen, they’ve never seen the light of day. They never would have been recognized for the talent, for the skill they have. But when we use these simulations to look directly at their ability, not these abstractions, not these proxies of ability, like someone’s resume, all of a sudden we started finding talent that these employers absolutely loved, were asking for more of, but we were really changing the process a little bit.
So, that was a formative experience, that insight that there was this kind of a new set of employability data available to us and that employers really had an appetite to be able to hire off of this information and that that information was very, very indicative of someone’s likelihood to succeed in a career. Okay, well that just broke everything wide open.
So Skillfully is a simulation-driven hiring platform. Exactly like you said, we focus on what somebody can do in the moment, not just how they might describe themselves. But that one fundamental change has completely reshaped employment. That happened before the commercial availability of large language models and generative AI agents, which has completely turned employment on its ear, but made that shift even more important. So that’s where we are today.
Beau Hamilton (07:49)
Yeah, that’s fascinating. And I think, you know, I’m glad that you’re tackling this issue, because it’s no secret that so much opportunity around landing a job is, especially at a college is pedigree, right? It’s who you know, it’s it’s less about proven ability, I feel like, even though it should be mostly on your skills, what you’re capable of doing, what you’ve learned and studied.
So I love to hear that you’re Yeah, you’re you’re kind leading with this belief that potential should be measured by performance and not privilege, I guess.
Brett Waikart (08:20)
Traditionally, that’s absolutely been the case. And the mind blowing research right now, and this has been validated over and over again, you’re absolutely right. Employment for the last 50 years, for the last 500 years probably, but for the last 50 certainly has been driven by, to your point, how good do you look on paper? How good is that resume? Did you go to the right school? Did you work at the right job? Did your keyword match how you describe yourself to the job description? Like that’s been the game of employment for decades at this point.
The near universal adoption of Gen AI tools like ChatGPT and Claude and Gemini and all of the rest, what that has meant is that that process of employment, of finding a job, of finding a job description you think you’d be qualified for and then writing your resume and submitting it and hoping for an interview. Right now, between 80 to 90% of resumes and applications that are submitted to any given job opportunity are enhanced or wholesale written by some type of an AI agent, some type of a model. And those models are not optimizing for a genuine representation of your ability. They’re not optimizing for truthfulness and they’re not calling out your flaws. They’re writing Shakespearean prose of why you are perfect for this job and why you have every skill that employer could possibly look for.
And that’s interesting that improves the quality of one person’s one applicant’s application material. But when you start thinking about 80 to 90 percent of the job market, the vast majority, millions and millions of people who are all using these tools at the same time, that overwhelms the system, that overwhelms recruiters. They’re suddenly just submerged in this tidal wave of perfectly keyword matched application materials and all of the filters, all of the tools they used to depend on, no longer work in this day and age. And it is a completely different set of tasks, it’s a completely different set of challenges today to be a recruiter, to be a hiring manager than it was even five years ago.
But that makes it, again, a fascinating time to be building a company in this space and to be building tools for those employers just because the need is so great and so universal.
Beau Hamilton (10:49)
Yeah, I came across this quote the other day, and I believe it was from Jensen Wong, CEO of Nvidia. And he basically just said, like, AI won’t take your job. AI will be used by somebody else who will take your job. And, know, I have a few unemployed friends who I frequent coffee shops with, and I’m always like torn on the advice to give out on how to build an effective resume, because like, on one hand, generative AI can do a lot of the heavy lifting and a lot of the polishing, you know, needed to stand out. But on the other hand, like you said, everybody’s using it. It’s overwhelming hiring managers. It’s also like overstating abilities. And it loses that like human quality, that human touch when it’s clearly written by AI.
And I’m just curious, like what, you know, with your industry perspective here, like what is your view on generative AI for job apps? Like, is it an effective tool for getting hired? Or again, like, has it just become overused? And like maybe a turnoff for job hiring managers?
Brett Waikart (11:50)
Listen, here’s the premise for everything else. The rules of employment, the rules of finding a job, the rules of finding your next generation of talent have fundamentally changed. They are different today than they were two years ago, three years ago. And the rest of the market, the job market, and the rest of the workforce is still catching up to that fact.
There’s early adoption of a technology and then there’s usually a more mature application once folks kind of understand the tool, the platform, whatever it is. There’s usually this shift. We have been headlong into that first phase of AI adoption in employment for the last two years. What that has meant is exactly what we’re describing right now. The vast majority of applications that are just being flung around the job market, and not applicants, not just job seekers, but the applications that they generate.
Some of these new generative AI tools, they don’t just rewrite a single resume for a particular job description. You’re not doing this in hand-to-hand combat. These tools essentially allow job seekers to automate their own job search process where if they pay $4.99 a month for whatever tool in the app store, they can basically upload some dusty old version of their resume and then click a single button and have the tools go out and scan every single job board, every single professional network for relevant jobs, and then automatically custom tailor the content of that resume, of that application material, auto submit it. And what’s happening now is that job seekers in the market are able to not just apply to a couple of jobs, maybe a dozen on a big day, folks can apply to hundreds, thousands of jobs in a given week or a month. And so the absolute quantity of applications that are out there in the job market right now, it is exponentially larger than it ever was before.
And now when you step back and look at that, you basically look at the effect of this, basically look at the job market as supply and demand dynamic. The amount of supply chasing the similar number of jobs has just become so much greater. It is so difficult. It is much more challenging today than it was two years ago to play by the standard rules of engagement, to sit there, craft your resume, submit it to those applications and hope you hear back. You have never had a smaller chance of success pursuing your job search in that fashion than you do today. And it’s only getting worse.
So this is part of the transition into that second, maybe more mature understanding of, how can these tools be used to improve employment? Instead of these tools just being used to mass generate application material and then, oh by the way, employers are trying to find the AI tools that can mass read all of that. We’re now in this world of AI bot talking to AI bot. We’re moving into this new chapter where instead of fighting against those odds, instead of saying, I’m just going to continue throwing a resume into this black hole and hoping that someday someone responds. The realization now is that AI actually allows a completely different way of hiring. And this is the really exciting part that folks are really starting to talk about and realize now. But the old way of hiring is saying, hey, I, as the employer, I can’t look over the shoulder of every single person applying to my job to see how they work. I can’t see their skills in action. So I’m going to rely on that applicant’s description, their resume, their LinkedIn profile, their hands, whatever it may be. And that’s how I’m going to start the recruitment process.
Well, that fundamental premise that an employer can’t see directly into how somebody works, that is no longer true with these new tools. Generative AI allows you as a user to basically rent that intelligence, to rent that ability, that compute. What companies like Skillfully, my company, are doing is we’re using AI not to focus on resumes, to focus on the old credentials of employment, but instead to create basically a digital twin of any given job that you might apply to. Skillfully is an assessment platform. It is a skill assessment platform. What we do are we build hyper-realistic simulations of how work is done, how work will be expected to be done, and any given role.
And when an employer uses a platform like Skillfully, they don’t need to ask an applicant for a resume. They don’t need to ask for a description. They can actually go straight to the source, straight to that applicant’s ability. They can create simulations, create a digital twin of that job opportunity that they’re bringing to the market. And they can say to applicants, hey, instead of writing your resume and spending 15 minutes there, spend 15 minutes just in the job, spend 15 minutes in this actual simulation of the work, and let me see exactly what skills you have. Let me see what you can do, not just how you would describe it.
And that fundamentally changes everything and opens us up into this beautiful kind of second wave of AI in hiring. But what we really focus on is giving folks the chance to lead in the recruitment process by demonstrating what they can do. There’s far less emphasis on where you may have learned those skills, the pathway you may have taken to get to that point. The only thing that matters to our employers is hiring the very best talent, the most skilled applicant in that moment. And that’s what this technology lets you do right now.
Beau Hamilton (18:04)
So in regard to like the AI job applications kind of flooding the market, I guess like in a nutshell, these tools are out there, they’re gonna be used. It is what it is. People are gonna use them. So companies need to find solutions to sort of combat it, right? And that’s kind of where you’re saying like Skillfully comes in and has an approach to kind of combat some of this AI slop that’s kind of flooding the market, right?
Brett Waikart (18:31)
That’s exactly it. And if you look at an employer’s traditional process, when they’re posting a job description, they’re probably going to take an old job description from the year before, from the last time they had to hire for this role. Maybe they’re going to add a little bit of content. They’re going to post it to their company’s career page. They’re going to post it to Indeed or LinkedIn or wherever else they’re going to go and use that. In the same amount of time that it takes for somebody to go in and to update that job description.
What they can do now is they can take that old job description and they can send it to a company like mine. They can send it to Skillfully. And what we can do is we will ingest that job description. We’ll also ingest a ton of publicly available data about that company, that employer, their product, their business. If that company has any other kind of knowledge sources like training manuals or videos on how to interact with colleagues or customers. We can take all of that as well, but it’s all automated. The actual recruiter doesn’t have to lift a finger for this other than sending us the files. What we will then do is we will, instead of rewriting that job description, we’ll take that as basically step one, but we’ll generate up a virtual experience, a virtual environment, our simulations to match the work that’s being described in that job description.
So before our first call with the recruiter, we can come to that meeting with a first draft of this digital twin of the job, this recreation, this virtual simulation of the role. That’s probably 90 to 95% accurate. That’s that close to how the work is actually expected to be done. And then the interaction with the employer is basically being an editor. Tell us what we got right, what we got wrong, where you want to turn up the volume, turn down the volume. But by the end of that first call, what the employer then has is that virtual replica of their job that they can start inviting applicants into. And then they can just go to sleep. They can walk away. They can come back a week later and just let all of these applicants come through and actually demonstrate that ability.
What the employer will then have is a dashboard that is gonna be ranking exactly who has the skills required for the job, who’s been able to demonstrate exactly the skills you as the employer is looking for. And we can stack rank those for you in terms of who has demonstrated the highest levels of proficiency with those skills so that when you come back to your recruitment process, you’re no longer spending time in this unending resume review. You’re no longer stuck in a tool. You have a very clear signal, a very, very clear lens you can look through to see exactly who you should be spending time with.
The end of the day, what this means is it is a tremendous amount of time savings for that recruiter. They no longer need to go through all of the motions with folks who just don’t have the skills for the role. Instead, they can focus their time on doing what they most likely entered the field of recruitment for, building relationships with highly qualified bottom of the funnel talent to figure out then, well, okay, you have the skills, are you a culture fit? What is your working style? How would you mesh with this environment? All of those kind of very human elements of the recruitment process that are getting lost today.
Bottom line for employers is that this will usually be turned into a 70 to 75% decrease in the total cost to hire purely because of those time savings. It’s a 50% shorter hiring cycle. And the folks that you do spend time with, those candidates that you do interview, you do meet, are between 10 and 20x. That’s 10 and 20 times more likely to convert to full-time hires than applicants that are suggested through regular conventional resume-driven processes. It’s such a striking difference.
Beau Hamilton (22:40)
Yeah. I appreciate you sharing those numbers because you kind of got ahead of me there. I was going to ask you not, I didn’t want to bore, bore anybody with, with statistics, but I think they’re really important here because you know, so often is, is AI trying the discussion is AI is making everybody efficient. And like there’s little niches here and there of like which areas of a company you can improve efficiency in. And obviously you’re concerned largely with improving the efficiency of recruiters and how they’re able to kind of, yeah, again, just comb through all these different, all these applicants.
Brett Waikart (23:12)
But also, it’s not just a single-sided benefit. And listen, that benefit is significant. We are a technology startup where a large part of our mandate is to build tools that are valuable and paid for by a large part of the market. This is a huge opportunity. Every employer needs this. Every employer hires. Everyone is experiencing this challenge. But it’s not just a one-sided benefit.
This old resume driven system that’s very focused on traditional credentials, very focused on pedigree and the pathway you took, who do you know, where did you go to school, where did you work? What that does is that severely limits the range of applicants who get to be considered for any given role and it completely discredits the huge proportion, the huge swath of the talent of the job market where these are students who might be coming out of the community college system. They may be coming out of public universities. They may be coming back after a few years away from the job market. And they have the skills. They have the abilities. But they just aren’t seen. Because that resume, for whatever reason, gets thrown out of consideration by some automated tool that is programmed to look for, no, no, we only really wanna hire from these 15 universities, or we only really want people coming from this background or from this network.
What we see is that when employers have a target school approach, typically they have maybe 10, 15 different schools that they’re really prioritizing that we wanna hire grads from there. If you take the top 15 schools in the United States and look at that total enrollment as a percentage of the total amount of students graduating higher education and entering the workforce every year, you as an employer are limiting yourself to between 2 and 3% of the eligible talent pool. You’re just not even looking at that other 97 to 98% of the potentially talented and highly skilled employees that could be your next hire.
When you flip that and you say, no, no, no, I’m actually gonna only concern, or at least first consider myself, consider the skill level of these applicants wherever they may have come from. I don’t care if they came from Harvard or Harlem, but if they have the skills for the job, I want to meet them. When you do that, you are creating a far more accessible pathway to career opportunity for those job seekers.
This is something that’s good for everyone in the job market to step away from this over-reliance on resumes and static credentials and to use AI, to use this new technology, to really be able to see someone’s potential, see someone’s ability in a way that you’ve never been able to before. That in itself is going to be the thing that will change employment for the rest of our lives. We are never going back. We will never have to go back to that world. And we’re at that kind of moment of adoption right now. The rocket ship has just left the launch pad. And it’s so exciting for both sides of the labor market, not just for employers.
Beau Hamilton (26:31)
Yeah, that is exciting. I’m curious to see like how this all plays out here and then in the coming. Everything’s happening so fast. I feel like it’s I mean, it’s hard to predict what will happen the next six months, let alone, you know, a year from now or five years from now. Now, for in regards to like your timeline for Skillfully like, were you always using AI in your product? Or was this something like a newer? Yeah, how did that work out? Because obviously, you’re dealing with simulations that just naturally speaks to AI. But like, what were you doing before this, this AI kind of boom we’re in?
Brett Waikart (27:03)
For sure. And this is such a funny thing too, but this is very common for tech companies. There’s always some form of a pivot. There’s always some form of an adjustment to kind of market conditions. When we started, we’d had this experience of basically as an education platform, teaching skills to job seekers from underrepresented backgrounds. The problem that we saw over and over again, was that people coming from those underrepresented communities, even if they were exceedingly qualified for a given role, were getting structurally overlooked. The system was stacked against them. The tools that employers were using were not oriented towards detecting skill, detecting talent.
It was basically detecting the appropriate credentials and the right pedigree. And that’s how they started their recruitment process. So when we began, this was before ChatGPT came out. This was before the, your point, the near monthly model updates and all of the new cycles and the hype around AI. We were a simulation driven hiring platform and the Problem Market Fit. PMF is always thrown around, but there’s a difference between problem market fit and product market fit. The problem market fit was there. We were correct in saying, hey, this is a huge limiting factor. This is expensive for employers. We’re wasting otherwise incredibly promising qualified talent. Okay, how do we solve that? And so we started creating these simulations about how work was done. We were focused on a couple smaller industries trying to figure it out. And the problem with simulations before the age of generative AI is that they’re difficult to make. They’re difficult to set up, to customize.
Simulations have been around for, I don’t know, for hundreds of years, forever, but they’ve always been so hard to do and certainly to do at scale. And that was kind of what we were running into. We could do this, but as soon as we wanted to reach beyond maybe a couple hundred applicants or a couple dozen different employment roles, the work was just too big for a small team like ours.
When we rethought the tech stack, so OpenAI comes out with ChatGPT, and that obviously takes the world by storm because it’s kind of illustrating the potential of these new large language models and what they can do. Large language models do more than just power a chat bot, than just power OpenAI or Claude. You can do so much more. And I guess what’s powering this agentic revolution right now, what we realized is that we could take these large language models and we could actually treat them, each individual large language model, we could treat it like an individual Lego brick. We could treat it like an individual building block. And if we started to stack these models, if we started to assign the models different tasks, different objectives, what we could do is instead of just creating a single chat conversation back and forth, you say something, I say something, instead of just simulating that, we could actually simulate a huge range of different virtual environments and interactions and workflows and tasks.
So what we did is we rebuilt our infrastructure, our backends. And we basically built it to treat large language models like little building blocks. And we started to rebuild some of these simulations. And suddenly, these simulations that otherwise were very pen and paper, they’re plain case studies, they’re static PDFs that you would respond to. Those turned into these dynamic, responsive, very realistic environments where we could recreate any kind of workplace challenge and not just creation of the simulation, those models could also power the evaluation of how well somebody was demonstrating skill in those simulations, how successful they were. So that change in the tech stack, we still had problem market fit, but we figured out how to make that product customizable to any of a million different workplace scenarios, and then the intelligence that comes from those simulations, the data on somebody’s demonstrated proficiency, what they can do in those simulations, all of a sudden that becomes economical. That becomes economical not to use just for a couple applicants, but you can use that as the very first step in your application process. You can do that as the first interaction between you, the employer, and an applicant to ask them to show in 10, 15 minutes of their time, what they can do.
Well, if we can do that for a couple dollars an applicant, a dollar an applicant, if we can basically continue to push that price down, well then all of a sudden we can propose a very viable alternative to putting up a job description on these platforms and then just trying to wade through the tens of thousands of applications that might come in. It’s very much a employment filter, a skill validation tool designed for the current age for the AI era.
But to your point, it wasn’t until we made that switch. We weren’t, we were never an AI company. That was never our aspiration when we started. And then all of a sudden we go out there now, we’ve been around for five years and people know us and we laugh about this. We were a simulation company solving that problem. And then AI just so happened to be the tool that really supercharged and enabled that product.
Beau Hamilton (33:00)
Well, yeah, just like you’re, you know, if you’re a job applicant or just, you know, somebody working to constantly like gain new skills and fit a new role, you kind of have to pivot and, you know, your company, obviously you pivoted, based off the market and what tools were available and where everything was going. And, you know, I think that’s obviously really important because the companies that aren’t pivoting, to these new technologies, usually utilizing AI where it makes sense, I think are going to be left behind and, you know, in a sense aren’t going to be higher. They’re not going to be around much longer, right?
Brett Waikart (33:32)
I think the point you’re making is a very, very important one. I think that for your audience, the challenge is trying to distinguish between products that start with a problem and then layer on the appropriate technology. They use the right tools to solve that problem. Finding those versus the companies that start because they’re enamored, or they’re excited about a technology and then they go look for a problem.
In hiring today, there was a rush to say, AI is the next wave, the next generation of technology. Let’s apply it to the old way of how we hired. People kind of assumed that the status quo would never change. We’ll always be in this world of descriptions, of job descriptions from the employer and skill descriptions in someone’s resume. And when you apply AI to that, what you find are there’s tons of tools out there that will say, hey, we’ll have an AI agent to review your resumes or to conduct virtual interviews. And what that’s really doing, those tools are just basically offering, hey, do you want another lane on your highway? Do you want to process a little bit more of these very corrupted, very AI enhanced resumes? Let me give you that. It’s garbage in, garbage out, you’re solving the wrong problem. Instead, if you can find those vendors that start from a problem and then they use AI as a way to solve that fundamental problem, you get fundamentally different outcomes. You get much different business results. This has the opportunity to be transformative.
A 70% reduction in your cost to hire is transformative for how a company thinks about attracting, acquiring talent, developing that talent, how they interact with the labor market, how they find skills. That changes things entirely. But if you’re just using AI tools to reinforce the status quo or to kind of have some marginal level of improvement, you’re just going to end up in exactly the same outcome.
Beau Hamilton (35:42)
Yeah. Yeah. Yeah. That was very true. Good answer. I, one thing I was thinking about too, was, you know, again, like your concern, obviously with, with eliminating some of the biases surrounding, the hiring and recruitment process, right? And, you know, related to AI is more companies turn to AI to streamline their hiring. There’s the really, you know, growing the real growing concern that these tools can like unintentionally reinforce existing biases, especially if they’re trained on historical hiring data. So I’m curious, how do you make sure your platform promotes fairness rather than just replicating the same old problems?
Brett Waikart (36:24)
That’s such a wonderful question. We’re a public benefit corporation, first and foremost. That’s a C-Corp, we’re a for-profit company, but we just have a social mission alongside our financial one. And to your point, it is the accessibility of opportunity. It is ensuring that this new world of work we’re moving into, opportunity is as accessible as possible.
It’s honestly, it’s grounded in the American dream. It’s grounded in this concept that no matter where you start from in life, that if you work hard, if you educate yourselves, if you prepare yourselves to have the skills that are needed by the workforce, that there is an opportunity for you to move up, to succeed professionally. And that is, that ability to be seen for your true potential, for your skills, that that is preserved. That’s the fundamental, that’s in the DNA of what we are doing Skillfully.
When you use AI in employment, the danger here, the incorrect way to apply it, is to just let it be a giant pattern recognition tool. And this is what happened maybe five, 10 years ago. Very few companies do it this broadly today. But basically, there were applications of AI that would say, hey, look at my top performers. I want you to just watch and just to absorb the profile of our star employees. Great. Help me find that in the talent market. Help me go find applicants that look and have the same ability and have the same profile as those star employees.
Okay, well, what ends up happening when you do that? The workforce is overwhelming. This is the example Amazon. They had an experiment around this, but they looked at their all-star developers who just coincidentally happened to all be white male of a certain background, of a certain pathway. And then there were rockstar engineers. had great skills and ability, but the AI engine was picking up on the background, was picking up on the demographics first before looking at the skills. And so the bad outcome here is that, well, talent it identified out in the job market was just purely homogenous matching that demographic. That’s problematic, but that doesn’t happen anymore. And to Amazon’s credit, they have an incredible, sophisticated, incredibly thoughtful hiring process today. They’ve learned a lot from that as well.
What we do is we focus our tool on specifically skill proficiency. So the functional proficiency with the specific skills that an employer is looking for. We do not look at your resume. We do not look at your demographic data. We don’t look at where you came from, where you went to school. The only thing that we’re doing in our simulations is comparing your demonstrated behavior, your demonstrated skill with our skill taxonomy, which is essentially one giant map of every single skill employers are looking for in the market right now and definitions of proficiency from a scale of zero to 10. Well, what does that actually look like? And what our system is doing, what our models are tasked with is essentially matching someone’s demonstrated behavior and simulations with the appropriate level of proficiency as dictated by that taxonomy.
Now, we have a very robust sampling process internally where that taxonomy is shared out externally. We make that available to auditors, to regulators, to companies that we work with. That’s transparent and it’s standard. Importantly, it’s a fixed rubric that all of our applicants will end up being compared against. That’s how they all get judged. It eliminates the subjectivity of evaluating someone’s proficiency, but it also admits it explicitly overlooks, does not consider any non-skill factor. So when you work with Skillfully, the data we’re gonna give you is purely a function of who has the highest level of proficiency with the skills you’re looking for, irrespective of where they came from, how they develop those skills.
Doing that, that’s the promise of a skills-first workforce, of a meritocratic, more accessible workforce of the future that’s gonna lead to a much more vibrant workforce, a more vibrant economy that everyone has the ability to participate in, not just those who have maybe traveled down specific pathways to get there.
Beau Hamilton (40:51)
Right. Yeah. That’s good that you’re kind of bouncing the, coming with it from the American dream standpoint. Obviously you got to recognize the biases at play. Are there? Just thinking this through some more and I appreciate you also painting a picture of a visual of kind of the simulations you offer. Is there a way to go in and tweak any of the criteria to kind of deviate from what you’re talking about of creating a focusing specifically on merit?
Or is that, that’s a pillar, that’s like built into the foundation of your product? You can’t go in and like tweak some of the demographic information.
Brett Waikart (41:28)
No, that is a pillar of our platform. That is not a selection criteria by any means. We collect that data when job seekers come through the platform, but that is never shared with the employer. That data, when we ask for it, is purely for us to ensure, to do our regular checks and audits that folks from different demographic backgrounds, coming from different pathways, that they are all being evaluated exactly the same. But for the employer, that is a pillar. That it does not matter the color of your skin, it doesn’t matter the name on your diploma, the only thing that matters, the thing that matters most to an employer is they want to hire the most skilled, the most qualified applicant possible. That’s what we help.
Beau Hamilton (42:15)
I love it. All right, well said. And yeah, I think that’s just a great distinction to be made and reassuring, I think, for listeners as well.
Now, before I pivot to a little bit of a role playing simulation and asking you some broader questions about the industry and, I want to just, can you just have one maybe key takeaway? Or maybe you can share one specific example of a simulation or a company you’ve worked with that has really maybe helped improve their hiring process?
Brett Waikart (42:46)
Yeah, yeah, no, for sure. We actually work with a really interesting group of employers that we didn’t expect would be a core customer for us. We work with a ton of global employment staffing companies, but folks that are looking to hire applicants for everything from paralegal roles, customer support and success, virtual assistants, chiefs of staff. Today though, on our platform, we have between 25 and 30,000 applicants for that body of roles coming through our platform every single month, demonstrating their skills in order to get a job with these kind of global staffing companies.
What’s really interesting is that there’s a bunch of requirements for that. First of all, the skills matter tremendously. These staffing companies are looking to find the most qualified folks to be placed into these roles. They are being held accountable. They need to be able to vet and validate somebody’s skill because it’s their credibility. It is the recruiter’s credibility on the line when they suggest and they place an applicant. So skill validation is paramount.
When you look at a global talent pool, there’s a beautiful opportunity in this, this idea that the internet, that AI has democratized access to education, to information. That’s absolutely true. There are incredible pockets of talent spread out all across the world. And when you are a global company, being able to search that full range, the full breadth of talent available to you is critical.
Another piece though is, well, business is done in English, in professional English. And so these placement firms don’t just need to think about making sure that the person has the skills for the job. An important aspect also is that person’s communication ability. And not necessarily, hey, do you speak with an accent or not? That’s not what matters, but articulation, precision, grammar, fluency, vocabulary with the actual vocabulary of that industry, those are all super important things. But traditionally, those have been very, very hard for any of these staffing firms to be able to identify outside of getting onto a Zoom call or getting into an interview with that applicant, and that doesn’t scale.
The market for this is millions and millions of open positions every single year that these placement firms are looking to fill. That means tens of millions of applicants that they need to vet and validate, but they just don’t have the time to interview and engage with every single one of them. Okay, well, enter our simulations. Skillfully can build up that very, very detailed replica of what it is to work in any of these given jobs. And then beyond that, our simulations are what’s known as multimodal. When you get into our simulation, one might be written chat, it might be spoken word, it might be a video call. We might be asking for assets coming in from all different kinds of file formats, whether it’s PowerPoint decks, spreadsheets, or Word files.
The range is unlimited, but when we have a voice simulation, when we are actually working in that verbal kind of modality, what we can also do is we can capture a voice print and we can analyze that voice print and use that as a basis to be able to generate not just very detailed skill data, but precise and accurate, what’s known as CEFR, English rating scores, the Common European Framework for English Fluency.
And so this is revolutionary. This completely changes the game for these RPOs and these staffing agencies, these global employment platforms, because again, the same time savings that are available to a company based in the US, maybe looking to hire locally, the same benefits are available to that global employer. They no longer need to spend their time validating skills or checking language fluency. We can take care of all of that automatically ourselves. So those companies then can hone in on the most qualified applicants.
Time to hire for those kind of firms is money. That means that you want to place and get onto the next one as quickly as you can. We enable that at a scale that’s never been seen before for those kind of companies.
Beau Hamilton (47:08)
Great answer. Yeah, yeah. I just, again, started kind of thinking all these different, you know, kind of rabbit holes of thoughts based off some of the things you were saying. I mean, again, I just, I think it comes down to, you know, a discussion I had actually yesterday where it was someone where I was just thinking, you know, so and so got hired. Was it because they, you know, sold themselves well? Was it because of their resume? You know, so much of it is just being seen, we’re in the first place and kind of getting through that initial first stage of assessment.
And then it comes time to actually prove yourself and show your skill. And yeah, I know, I just think what you’re doing is really, is really not only noble, but just, just it’s needed in this day and age with, with all this AI, again, AI slop that’s kind of flooding the market.
Now, now I also, you know, I want to kind of, wrap up this interview here with maybe a rapid fire sort of question and answer and do our own little like role playing simulation of my own where I’ll kind of pretend to be a recruiter asking you some questions based on your experience working in the HR and software development industry. And, you know, while I’m not an AI podcast host, it’s only a matter of time. Maybe this scenario will be built into your platform one day.
Well, the first question just is pretty generic, but like, how do you handle setbacks and failures and what advice would you give to others facing challenges?
Brett Waikart (48:38)
Remain focused on the point on the horizon that you’re moving towards. Remain focused on not the short-term setback, but the longer-term goal.
Beau Hamilton (48:51)
Now we live, next question, we live in obviously uncertain times with AI going on, having big societal impacts that might not be too positive, at least in the short term while we adapt, who’s to say, right? But how do you stay motivated and keep your team motivated during tough times?
Brett Waikart (49:08)
We’ve always been a very values driven organization. And one thing that we’ve benefited from is there’s quite a lot of human contact in the product that we build. If you’re building a tech company, sometimes it’s easy to get too lost in the code, lost in the software side, and you don’t necessarily get that exposure to what the product is actually doing, the role it’s playing, the impact it’s having in the life of the users that you’re focused on.
We’ve had a lot of existential near-death moments every startup does. And those are hard. Those are tough, for sure. What has consistently been a motivating factor to get through those hard times is we elevate the stories of those job seekers that we’re supporting that find opportunity, find success in their job search that otherwise would have likely been overlooked or unrecognized, had a different outcome.
I think that it’s easy to say, hey, a job is something you go to, you spend a couple hours to be able to get a paycheck and walk away. But I think the reality of it is that life is a journey for every single person. And that journey, the quality of it is dictated by the impact you feel you’re having on a cause that matters to you. And so when things are difficult financially, structurally, organizationally, being able to remain grounded in the facts, that we’re coming into work every morning to elevate, help support a job seeker who might otherwise have a very hard time, have a worse outcome, that helps. That makes a very real felt difference in the hard times.
Beau Hamilton (50:49)
We’ll put a five in that call up there. All right, now it says your CEO and Co-Founder of a company called Skillfully, I take it, which operates in the HR, tech and software development industry. What common misconception about your industry that you wish more people understood?
Brett Waikart (51:08)
That AI is going to come and take your job. The fear and uncertainty and doubt that there is some AI-powered robot that is going to steal your work, steal your job, make your life worse in the years to come, that Skynet is real. AI is going to disrupt at the task level, not at the title level.
This has happened with every major tech platform evolution, but the workforce is incredibly resilient. As AI starts to disrupt and starts to take over and automate certain tasks, human workers will always pivot to the next open spot on the value chain. They will adjust their time to work on the next most valuable problem. I think we’re entering an age of abundance in the workforce, an age of opportunity, an age of growth that will be powered by AI augmenting and supporting the work that we’re doing, I think that there’s way too much energy and calories being spent on worrying about this apocalyptic scenario of robots taking over the world. I don’t think we’re gonna be there any time in our lifetimes.
Beau Hamilton (52:18)
All right, last question I have for you, take off my monocle here. What’s something your competitors are getting wrong that you’re determined to do differently?
Brett Waikart (52:29)
AI is not just a tool for inferring information about somebody from some existing credential. It’s not just a tool for reading a resume and saying, this person probably has these different skills. AI is a tool for creating a new experiential layer to employment.
AI has such greater promise than adding an extra lane on the highway so you can process a couple more resumes or you can hold a couple more virtual interviews. It’s not a tool to do things the way we’ve always done them, just faster or just more. It is a tool that has the promise of completely changing, flipping on its ear how we hire. The world of work could be a very different but a much more fair, much more vibrant, much more accessible place for folks to be able to enter in their life and succeed and create and find value for themselves. That’s the world that we should be moving towards altogether, not reinforcing the world we came from.
Beau Hamilton (53:34)
Hmm, interesting. All right. Well, all right. That’s all I have for you today. We’ll follow up in three to five business days to see if you would like to proceed with hiring you for this role. Haha.
No, I appreciate you can entertain me with that role playing there. You know, as before we sign off here, any place you’d recommend people to send people to learn more about Skillfully.
Brett Waikart (53:57)
Yeah, our website is skillful.ly. Hit us up on LinkedIn. We publish a lot of content there, a lot of advice on how to kind of navigate this new world of work. Connect with me. I’m @Brett Waker on LinkedIn. I do love answering my DMs, so please reach out there and hopefully we’ll have the chance to speak again, though. This is a lot of fun.
Beau Hamilton (54:22)
Definitely. Awesome. Yeah. I really appreciate you taking the time to sit down and talk with us. I love this just casual conversation and, yeah, love to get in contact with you and hope the listeners do the same.
Brett Waikart (54:32)
Excellent. Thank you so much. Appreciate it.
Beau Hamilton (54:34)
All right. Thanks. Thank you all for listening to the SourceForge Podcast. I’m your host, Beau Hamilton. Make sure to subscribe to stay up to date with all of our upcoming B2B software related podcasts. I will talk to you in the next one.