Within the credentialing ecosystem, we talk about digital badges and LERs (Learning and Employment Records) with such frequency, that we often forget to ask ourselves core questions. What do employers actually want? Do they care about LERs and badges? In this episode, we are joined by Ian Davidsion, Chief Growth Officer at SmartResume. He shares what employers actually want, how to talk to employers about credentials, and why AI has accelerated the need for better communicating verifiable skills.
Matt Sterenberg (00:01.418)
Ian, welcome to the podcast.
Ian Davidson (00:04.005)
Hey, thanks for having me. I’m glad to be here.
Matt Sterenberg (00:06.902)
I wanted to have you on because you are entrenched in the credentialing conversation, specifically in terms of how credentials communicate information about a candidate. How do credentials potentially impact employability? And you posted a series of blog posts that I thought were really interesting about what employers actually want and how does credentialing and our credentialing conversations fit into that?
So real easy question to start off Ian, what do employers actually want?
Ian Davidson (00:42.173)
I think ultimately on top of making good hires, employers want to be efficient. There are so many inefficiencies in the process and they’re growing. The process as I would describe it is there’s two ways to find a potential person to hire. The one is to promote a job posting and say, who’s going to come to me? And depending on the employer, there’s a wide range of outcomes who comes. Some employers who don’t have big grants and don’t know how to navigate the space, they don’t get a lot of candidates. Others get thousands.
was talking to a professional from the Bank of America who said they get over a thousand candidates within posting a job within a day or two. They don’t even look at it the whole thing. They can’t, right? They look at the first handful and they invite those into interview and they do most of their vetting there because they can’t rely on the resume to be a really good judgment of who a person is. You also have a lot of challenges that we’ll get into here, but in general, what they want is to be efficient.
They want to be able to identify someone who has what they’re looking for, but who also wants what they’re offering. So a lot of times people are now using agents, AI agents to apply to any job. And so they’ll find some candidates, they’ll spend some time, they’ll try and contact that candidate and that candidate’s not interested in what they have to offer. So there’s a lot of inefficiency built into the process. Who has what I’m looking for? Who will be responsive and engaged? And who can I hire quickly because I can verify what I need to verify.
I can understand the candidate and it can be very obvious that they’re good use of my time.
Matt Sterenberg (02:15.31)
Yeah. And you have a very provocative title in one of your blogs, which is employers don’t want badges, which is, you know, we’ve had a number of badging conversations on this podcast. I know you’re generally like, you know, you’re pro badges, but what are we missing? Right? Like I’m pro badge, you’re pro badge. But to say that they don’t want them, I think is obviously like an eye catching title, but like explain that a little bit more for me. Like, what are we missing by saying, you know,
Ian Davidson (02:30.001)
Shut.
Matt Sterenberg (02:45.058)
badges is going to solve this. What do employers really want to get? What are we missing?
Ian Davidson (02:50.865)
Yeah, I want to be, like you said, I am a huge fan of badging. I’m a huge fan of the learning and employment record ecosystem. At some point I can tell you how I discovered it and why I decided to bet my career on the idea that learning and employment records can solve a lot of problems in hiring. But I think we have a tendency to lead with technology and lead with theory.
and say, what’s really cool because what it is, is this portable asset that comes in a digital format that can be moved between platforms. And it’s going to verify that an institution is standing behind an individual on this claim. And the claim can be anything. could be, and it’s the future because who needs resumes when you can have all of this data coming in, et cetera. Like you can rattle it off. You know, I remember a few years ago, Sean Murphy from the Walmart Foundation said, we have to teach people to spell L-E-R.
And I think at the time I agreed with him, but now I think we have to teach people to sell the benefits of digital badging without using any nomenclature that employers don’t understand. And what I think is really important, Matt, is one of the reactions that I got from one of the blogs I posted from Taylor Stockton at Learning Economy Foundation was like, hey, we’re not all in this for the employer.
And I’m not here to tell everyone to change everything they do because employers don’t care about what badges or LERs are. I am saying that if we view one of the primary benefits, not the only, but one of the primary benefits of digital badging is that it creates economic mobility for people, then we need to make sure that we’re solving problems for employers and making that data valuable to them as
And so yes, I don’t think that they are fundamentally looking for the way we describe things. We can get into what they might be looking
Matt Sterenberg (04:44.396)
Yeah, think, you know, credentials are currency, right? And if it’s going to have any value as currency, it needs to be exchanged for something. There needs to be a trust component. People need to understand what it is, trust that it means something and communicate something, right? Communicate some level of value. So I do think that’s, that’s really critical. I want to double click on the LER piece, the learning and employment record, just cause I want to, I’ve been in
part of many sessions I’ve sat in on it. But sometimes I sit back and I go, what is it really? And I want to synthesize that for people and make sure that we really define it. Tell me a little bit more about how you started on this path. And what’s in an LER? Really? Let’s make sure we fully define it.
Ian Davidson (05:32.679)
How I started on this path is I transitioned from traditional media, but online media, into a jobs marketplace. So I used to work in marketplaces that were selling ads to consumer brands in real time. And the amount of data points that were used to drive that currency, know, cookie data collects billions of data points every day, was enormous. And then I transitioned as a zip recruiter
And I said, wait, the only data points we have are a job post and a resume. And it was a shocking transition. As I studied the resume problem, it became very apparent to me that people don’t know what could be most marketable about themselves. You ask your average job seeker in an interview, what are your top five skills? I have done this over 50 times. They will tell you what they did in their last job. They will not talk about the skills that will transfer to the new job.
And so we create this kind of stasis in the hiring environment where it’s very hard to see how someone will advance their career and how the next job could be better than the last because of their skills. I’m not going to preach about skills-based hiring. think people understand its benefits, but learning and employment records are really a collection of data standards. That’s the way I define it. It’s open badges, it’s comprehensive learner records, and it’s the learning and employment record resume standard.
and also verifiable credentials, which kind of blend across all three. But it’s data standards that allow records of employment and learning to be verifiable and transferable. That’s one way to define it. Another way I tend to think about it is learning employment records become more valuable and aggregate, right? When they can start to help you understand the shape of a person, help an individual understand their skills.
and help employers understand things about that person that they wouldn’t necessarily understand if they just ask the person about themselves. That’s where the value comes from. But at its most fundamental, digital badges are a type of learning and…
Matt Sterenberg (07:40.886)
It’s funny you bring up like the people that are best at describing themselves often don’t necessarily have those skills and the people that have a hard time describing like what they’re good at and their skills often exhibit a lot of those skills, right? I think it’s an interesting thing. Like how do we define what truth is? And this record helps us get to some source of truth or validity, right? I just think about the data problem though. Like you outline the infrastructure of what it could be, but it’s like,
man, it seems like a lot of people working together. How do we make this ecosystem function? And I know that’s something that you’re thinking about every day. And that’s part of your work is how do we get all these people to cooperate and collaborate to adopt the standard? Man, that’s a big problem. That’s a challenge to get like to adopt any standard, right? Is difficult.
Ian Davidson (08:35.613)
Yeah, I think the open badge standard has a lot of traction, right? I have encountered platforms that are issuing digital badges and you always have to kind of ask the question or go look, is it an open badge? And I think the percentage of times that it’s an open badge has grown from less than 10 % to over 50 % or more.
Matt Sterenberg (08:55.479)
What is an open badge for those that don’t know?
Ian Davidson (08:58.695)
An open badge is a data standard from one ed tech. Open badge 3.0 combines open badge and the verifiable credential standard. That’s the technical answer. The practical answer is it is a format with which to recognize, for the most part, singular achievements. You have developed a skill and we have validated it. You have completed this micro-credential program. You have earned the certification or even you have earned a degree or even
you worked at this job from this time to this time, and this was your job description. All of those are relatively singular and can be recognized in a digital format that allows for it to move between systems and be verifiable. That’s the power of a data state.
Matt Sterenberg (09:42.862)
So help me out with something because I think from the higher ed perspective, okay, we can adopt a standard and you have state systems, you have common course numbering, you have courses that are similar, like how do we better describe what you learn in this environment or we’re gonna credential you on your way to a degree.
There’s a lot of people doing really cool work. So I think from like the better describing what someone’s educational experiences, I think we’re actually like, we can get there. The thing that I have a hard time wrapping my head around is, okay, I’ve worked somewhere for eight years. How do we get the information from my employer in there? How are they going to put in that work? You know, because I think if we just had a system that’s like, let’s help you get your first job out of college that I can conceive of because we have
a relatively closed ecosystem of higher ed, although there’s tons of different flavors, but the employer piece, how do we get them to buy in or like, what is that actually like, how are they going to validate my skills? Cause we all know there’s job description, job descriptions out there that are like, it’s a fancy job description, but the skills themselves are probably much different or what you’re going to be doing on a daily basis is much different. How do we actually get the employers?
to better describe what you did and what’s the incentive for them to do that?
Ian Davidson (11:14.983)
Yeah, the employers definitely suffer from a prisoners dilemma.
Ian Davidson (11:22.631)
the first year or two at SmartResume, we were not gonna go try and pitch employers on verifying their workforce because they would just say, wait, you want me to make it easier for people who work for me to go get a job? And so, I think that would be, I always tell people, I think that’s the final piece that will snap together. And I think it will happen when they see the value that they get when other employers do it. I think there are some employers leading the way. But in general,
Matt Sterenberg (11:35.126)
Yeah, right.
Ian Davidson (11:51.685)
I don’t think we need them to do it. If anyone from ADP or a massive payroll processing platform is listening to this call, or if anyone from state or federal government is listening to this call, they actually know who is employed at what employer from day to day based off of tax records, payroll records, et cetera. And so there are ways to get to someone’s applying to my job. Did they really work somewhere for the dates that they have that…
could help us jumpstart the whole ecosystem. And then there are ways to get employers comfortable with like, okay, maybe I don’t say what they do. Maybe I don’t have to think about the validate with the skills, but we know how many calls we get from background screening companies. Was Ian really in this role from the state to the state? Maybe I will issue just that. And that becomes a piece of the picture. And then employers are up-skilling employees generally through external systems like a DeGreed or a Coursera, et cetera. And when those platforms validate the skills and learning, you can start to see how the pieces come together.
Matt, the thing that I have as a challenge for higher ed, who I think is a good bulk of the listeners to this podcast, is yes, you can credential the learning. Think about credentialing behavior as well. So employers have told organizations like SHRM, they’ve told people like me, they confided in their job board providers. Our biggest challenge
for the average job in the country is how do I identify people who will show up for the interview, show up for day one, day seven, and if I invest in them and help them develop their career, they will be coachable and they will stay, right? Where would you get that type of signal? Well, who shows up to class? Who goes for teacher, know, extra time office hours with
teachers. Who completes these programs and then completes the next one? Who uses the tools provided by the university to navigate career paths? Who shows up for career coaching? Who comes to the job fairs and the resume sessions? That’s the secret layer of data that is, I think, much more interesting to a much wider universe of employers than what classes did they take and what micro-credentials did
Matt Sterenberg (14:12.062)
And we even don’t even have to have the answer right now, right? If we had data flowing back and forth, they could make their own determination about what has made an impact for employee retention, right? You know, like it’s, think showing up to class is obviously like a good indicator of whether someone’s going to show up to work. That might be it, but it also could be, you know, actually what we found is the best hires we make are
someone who was in a college sport, it could be any little thing like that. And obviously there’s a good fit for each individual company. But if it was better data, they could then look at the data over time and start to actually do an analysis of like, what did we learn from the data and what this person ended up being at our company, right? That’s kind of the cool possibility too.
Ian Davidson (15:05.538)
Can I illustrate that through my favorite story about career services? Peter Thorsett is at Alamo Colleges. He’s part of their microcredential program. You probably know him if they’re Canvas credential customer. But he’s worked in career services previously. He told me about this one student. I forget the student’s name. Let’s call him Barry. And he just loved Barry. Barry was personable and likeable. And Igor and Ernest showed up and asked for help. Really wanted to have a good career.
but he just didn’t do well in interviews. And Peter became very invested. And so he would call employers and say, I’m telling you, this kid will make a great hire. He may not interview well, give him a fair shake. And so he came back from an interview and he asked Barry, you know, how’d it go?
Well, what was the hardest question? And he said, well, they told me that the most important thing they’re looking for is teamwork. And they asked me questions about how I’ve demonstrated teamwork. And I just could tell that I didn’t give them the answer they were looking at. Peter said, Barry, you’re the captain of the basketball team. Did you tell him that? Barry was like, no, why would I tell him that? That has nothing to do with business. You know?
Matt Sterenberg (16:15.438)
You
Ian Davidson (16:20.432)
And so to me, to your point, just give data, give signal, because Barry doesn’t think that’s relevant when he goes into an interview. And yet, almost guaranteed would have got the job if he should.
Matt Sterenberg (16:20.653)
Yeah.
Matt Sterenberg (16:32.238)
That is a great example because the question isn’t the question is about teamwork. And if you’re on a basketball team, that’s kind of just part of the operation. You kind of don’t think about it because you’re so close to it. Right. And they could have said, tell me when you’ve been a leader inside and outside of work.
That question he probably wouldn’t have been able to answer, but he’s not sure what they’re looking for. So he’s trying to give them the answer. And that’s also part of the problem with these job interviews is you have one shot at it. You’re trying to give them the answer that you think they’re looking for in a truthful way. And you’re missing things. Like, I wish you would have answered this way. Be like, I didn’t quite know what you were looking for. And you don’t have the questions in advance, right? So it’s a difficult proposition.
In your piece, you also highlight AI. The employers are using AI to filter through resumes, candidates are using AI to create their resumes or write cover letters, whatever it may be. And it seems like if you’re a candidate, you’re using AI. How do I stand out? How do I write the perfect cover letter or resume based on the job description? And it ends up being a zero-sum game, because it just floods the applicant pool, potentially.
It seems like no one wins. And so I think AI is a good accelerator for some of the things that you’re talking about. You shared a little bit at the beginning, but I would love for you to highlight some of the challenges with AI, both from a candidate’s perspective and from the employer’s perspective.
Ian Davidson (18:13.395)
Yeah, AI is creating so much urgency for what we can bring to the table as a digital credential community. Dave Wangel, the founder of Identify and Smart Resume says, our story was lacking a villain and now we have one. Right? And that’s not to beat up on AI. It’s just storytelling is part of how you get things to grow. Right? So yes, I have experimented.
putting my stock resume into ChatGPT and saying, me look like a fit for this job. And I don’t think ChatGPT or me in doing so is doing anything particularly unethical, right? It is doing.
Matt Sterenberg (18:54.624)
It still seemed like you when it sped out the resume. It didn’t seem like a different person.
Ian Davidson (18:57.727)
And I always say, actually, Matt, what are the two biggest rules that you were told when you were writing your resume to follow?
Matt Sterenberg (19:09.452)
I wish I could tell you. I wish I could tell you. don’t. I mean.
Ian Davidson (19:14.835)
How long can you resonate with?
Matt Sterenberg (19:15.562)
I, well, what I always thought one page, two page maximum.
Ian Davidson (19:21.759)
Okay, and what should you do to make yourself look like a fit to that employer?
Matt Sterenberg (19:27.68)
I think you would align it with the job description or try to cut. Yeah.
Ian Davidson (19:30.463)
Those are the two things that everyone says. And that’s what AI is doing. You can give AI 100 pages and it’ll make it two. Yeah. And so it’s just doing what we were all told and we would reward the job seekers who put the most effort into doing these things. mean, you’ll probably remember hearing stories of people being like, just put the job description itself in your Word document and then make it white text.
Matt Sterenberg (19:36.263)
plug in the job description, plug in your resume. It’s like,
Ian Davidson (19:56.37)
and the machine will read it and you’ll look like a fit. Like there are all these hacks and I’d say that is unethical. But in general, AI is just doing what we’ve told job seekers to do. The problem is when it spits it back and I’ve worked for for-profits my whole life, if I apply to a nonprofit, it’s going to use nonprofit language and say that everything I’ve done is mission driven and I know how to navigate philanthropies rather than business development. It makes enough of a change that it’s not an honest representation of myself. It’s a facsimile of me. But
Matt Sterenberg (20:00.4)
yeah.
Ian Davidson (20:24.447)
You know, it goes really far. And if you give it a thin resume, the average resume, then the embellishment increases. Because for someone like me, it’s more shaping a lot of content back. So there’s problems with it. I think we can all acknowledge that. And the biggest problem is not only does it allow people to look better and more relevant than they probably are, but smart job seekers, which is a growing percentage of us, are using AI agents to apply to jobs where we literally never even click any
We just say apply to jobs in this area that have this, and especially for remote jobs, the applicant volume has gone up by 300%. And the range of applicants has condensed. The good candidates look more like everyone else. The bad candidates look more like the good candidates. And so employers are screaming from the mountaintops, this is raising costs to hire, they’re having to validate skills, they’re having to interview more people. It’s problematic. And so they’re trying to fight it with AI.
and from everything I’m hearing, it’s not working. Yeah, AI versus AI. Yeah. So yeah, it’s a massive problem. And as you can imagine, the opportunity is to give AI, here’s a refutable fact about this person. And that’s what digital managers do. Perhaps not an irrefutable fact, but you can say…
Matt Sterenberg (21:27.424)
It’s AI versus AI, you know, on both ends. Yeah. Yeah.
Ian Davidson (21:49.119)
This institution is saying this about this individual and that is a trust anchor that can be used to improve the process.
Matt Sterenberg (21:57.165)
Yeah, and it feels like, yeah, we don’t want to create a system where the people that get the opportunities and interviews, for instance, are just the best at applying. They’re best at the game of getting a job rather than doing the job, right? And that’s kind of the system we’ve created.
Ian Davidson (22:14.429)
or even worse, the system we created long before this is the best at interviewing, right? Because there’s so much bias. I’ll admit, when I was an early hiring manager, I liked hiring other guys in their 20s who would play ping pong on the break and can talk sports at lunch. And like, I was an idiot. I was young. Like, I didn’t know how to identify the best talent and I didn’t get very good training on doing it. Yeah, the whole process is rife with problems and bringing better data to the table solves.
Matt Sterenberg (22:19.416)
Yeah.
Matt Sterenberg (22:42.338)
Yeah. Well, Ian, I’ll give you the last word. Is there anything else we didn’t highlight related to this conversation about what employers want, LERs, talking about skills? Is there anything that you want to highlight that we haven’t yet talked about?
Ian Davidson (22:57.619)
Yeah, I would say that everyone who is working on these types of data and these signals, their heart, I always have found is in the right place and the work that they’re doing is important and it should march on and continue. And the more people who do this, the better the volume of data grows, the better the signal is. Everything’s becoming better and it’s inevitably marching in a fabulous direction that’s going to create a ton of economic value for a lot of people and a lot of good society.
we want to accelerate it, supplement what you’re doing with the lowest common denominator things. How can I verify my students identity just like I do on the student ID card as a digital credential? So if they want to work remote, employers can know that that is a work authorized resident of the United States or a citizen, you know, et cetera. And they go to the top of the pile because employers are terrified of hiring
people representing themselves as Americans who aren’t. That should be something you should be able to do for 100 % of your student body as a service. Think about the attendance records things that we’ve talked about. Think about signals that are simple and that you can measure across anyone. Think about credentialing the time cards of your own workforce. Who shows up on time? Right? And just contribute. We tend to focus in on highly specialized things where we’re only credentialing 15 % of our student body as a result.
Focus on common denominator, go ask employers what they would find valuable, and that’s a rocket fuel to this whole thing because you can increase the volume of people that you can support. And surprisingly, by going down to basic things that you can measure across entire populations, you increase the value.
Matt Sterenberg (24:38.606)
Well, Ian, I always appreciate what you have to share because you have a really interesting perspective on it and you’re always kind of pushing the envelope but doing it in a way that’s really pragmatic. Ian Davidson, thank you so much for joining me today.
Ian Davidson (24:52.575)
Thank you, Matt. This was a lot of fun.
