Find the right solution for your business.
Explore SolutionsWe recently sat down with Fernando Rodriguez-Villa, the CEO and co-founder of AdeptID, for a conversation about the future of hiring and recruitment. AdeptID is a company that builds talent-matching software, leveraging AI and machine learning to revolutionize the way employers find and connect with the right candidates.
In this interview, Fernando shares his thoughts on how AdeptID’s technology is bridging the gap between underemployed talent and employers struggling to find the right skills. He also discusses the key trends in the hiring and recruitment sector, his predictions for the future, and offers valuable advice for those navigating today’s labor market.
Join us as we delve into the world of AI-driven recruitment and discover how AdeptID is transforming the hiring landscape.
How is AdeptID’s technology revolutionizing the hiring process?
At AdeptID, we build talent matching software. My co-founder, Brian, and I worked around data science and machine learning for most of our careers—both professionally and academically—myself on the product and commercialization side and then Brian as a researcher and data scientist himself.
We came together to start AdeptID a little over three years ago. We felt like there was an opportunity to apply a lot of the technology that we’d spent our careers with to what we think is a matching problem between talent demand and training. We felt like there was this critical mismatch where, on the one hand, there is a labor force of folks who are underemployed for one reason or another.
And then on the mirror side were employers that were struggling to find talent with the skills that would make them successful, and this felt like the kind of matching problem that data science could address.
So we’ve all come across recommendation systems, whether book recommendations on Amazon, what we might enjoy watching next on Netflix, or even what clothing items should go in a Stitch Fix box. These recommendation systems have been phenomenally successful and transformative for all sorts of industries, but under-applied in the world of hiring and talent, particularly around talent that didn’t have a traditional, linear career trajectory.
I think a lot of the builders in this space and a lot of the organizations have this kind of pre-built notion that most people in the workforce look like the people who are on LinkedIn—they usually have a four-year degree and a sequence of jobs (without gaps) that show linear progress of increasing title and pay. The reality is that isn’t necessarily true for many of the folks who are on LinkedIn. It’s certainly not true of the majority of the workforce.
So if most of the workforce doesn’t have the same “data trail” that you would use to train a recommender system, what’s the technology that’s being built for them?
At AdeptID, we felt like there was an opportunity to build something that was not only impactful but economically viable for those many employers trying to find talent in new places.
On top of that, there’s a ton of generative and predictive AI that goes beyond the typical book recommendations. We’re training that technology on a ton of data about how people move from job to job. Then we use something called an API, or an Application Programming Interface, to plug into the back end of other tools, so the majority of the people who are interacting with AdeptID’s technology aren’t actually logging into our platform. They are logging into an ATS or a job board and those recommendations are coming from AdeptID.
What are some examples of AdeptID successfully bridging the labor gap?
One example is our work with a vocational training provider called Year Up, which has about 40 sites across the country. They focus on identifying opportunity talent and training them for success in professional environments like large technology companies or financial services firms. Historically, they’ve had an incredibly manual process of recommending their students to employers who want to hire from Year Up.
With us, they’ve been able to start incorporating a predictive model that, as Year Up learns about its individuals, suggests the employers that are the best fit for them. It takes into account a lot of the really interesting data and observations of these expert professionals.
The technology is being implemented entirely in support of people who know what they’re doing. We like this as a case study because there’s often apprehension that machine learning and data science are coming to replace the role of individuals. But every time we’ve seen it successfully used, it’s been in a situation where the tech is empowering individuals to do their jobs even better and at more scale.
In our very early days, we were engaged by a hospital in the Boston area to test out our technology. Through data in their applicant tracking system, we were able to show that a significant portion of their successful pharmacy techs had previously been cashiers at Dunkin’ Donuts.
So what is it about that Dunkin’ Donuts cashier experience that is predictive of success in a seemingly different role, going from fast service to healthcare? There’s a lot about that environment, perhaps during rush hour in Boston, that is predictive of being successful in other high-stakes roles.
There are some cases where we’ve been able to use the technology to make observations that sound pretty intuitive when you say them out loud, but get lost in tons and tons of data if they’re not being applied the right way.
What key trends are you observing in the hiring and recruitment sector with new technologies?
I think there’s a lot of interest in AI, so many of the tools or tech providers already serving learning and recruitment are debuting their own chatbots and announcing a raft of AI features. It’s already in the consciousness and there’s going to be a lot of experimentation.
Because so much of it in the last 12 months has been very hype-driven, there’s reason to assume that a fair number of those tools are going to fall flat. There’s potential for issues, and it’s worth acknowledging that the way these cycles normally play out is that there’s a fervor. Some things will work and some things won’t.
The example we tend to fixate on is mobile. Not too long ago, mobile was this big technology theme everyone was really excited about. Every Fortune 50 boardroom needed to have a mobile strategy, and it became a whole focus area for venture firms and tech teams. Then, people kind of stopped talking about mobile, but the reality is we’re all on our phones all day. That technology wave stopped being talked about explicitly but became incredibly pervasive in our lives.
Similarly, I can imagine that before we wake up and start our workday, we’ll have interacted with 40 to 50 different generative AI-informed tools. It just won’t be as obviously stated. So I think there will be AI all over the recruitment space, but maybe it won’t be marketed as such.
What are your predictions for the future of hiring with these evolving technologies?
We try to be really hopeful. I think there’s a lot of potential.
We’ve made it a lot easier and removed a lot of the friction between job opportunities and candidates. That top of the funnel has been largely solved. Anyone can go on Indeed, ZipRecruiter, or JobCase and there is no shortage of jobs they can apply for. And on the reciprocal end, when even a relatively anonymous employer like ourselves puts a job ad up, we know that 48 hours later, we’re going to have a couple hundred folks that have expressed interest. So that part of the friction has been removed.
What we’re hopeful for, and what we think is the next phase, is going from quantity to quality. Those same job seekers who have an easy time clicking on a hundred “apply” buttons on Indeed will tell you that their experience is not particularly positive. They feel like they’re not being given a fair shot, that the employer is not getting a real impression of who they are and what they’re capable of.
So I think being able to use technology to take those impressions of people, understand much more deeply what’s possible and understand the right 10 people for you to interview for a job—or on the search side, the right 10 jobs that you should be applying for based on everything we can learn about you, will create a much more positive recruitment and job search experience for firms and folks.
What advice would you offer to those navigating the labor market today?
I think fit is this kind of sometimes cheesy notion, but I think it really is a salient one. A lot of cheesy notions can have a lot of truth in them.
I would say for both job seekers and employers hiring individuals, really make sure that the place is a fit. Make sure that the place is consistent with what you want to be doing, with work you find interesting, and that affords your family kind of what it takes for money not to be a distraction.
I think there’s that notion of fit and finding something that is worth pursuing and worth being pretty dedicated to. We all have friends, loved ones, or ourselves who have to take opportunities ahead of us when we’re able to find that perfect fit, and there are a lot of constraints on folks, but I think pushing for fit is really important on both sides.
I think it matters frankly even more for employers to make sure that when you’re filling a role, you’re filling it with someone that is really consistent with what you’re trying to build. Partly because, as a lot of firms in the tech industry in particular have learned over the last 18 months, money doesn’t grow on trees. Companies have had to make cuts and that’s been a tough thing to walk back.
So make sure that what you’re getting into on both sides is something that you’re willing to really invest in and see through. Fit is important.
