Use Case Podcast – Storytelling about retrain.ai with Shay David

Announcer: 00:02 Welcome to RecruitingDaily’s Use Case Podcast, a show dedicated to the storytelling that happens. Or should happen.

Announcer: 00:10 When practitioners purchase technology. Each episode is designed to inspire new ways and ideas to make your business better as we speak with the brightest minds in recruitment and HR tech. That’s what we do. Here’s your host, William Tincup.

William Tincup: 00:26 Ladies and gentlemen, this is William Tincup, and you are listening to the Use Case Podcast. Today, we have Shay on from retrain.ai, and we’ll be learning about the use case, the business case, cost benefit analysis, however you’d like to put it, of why his customers and prospects choose retrain.ai. Let’s jump into introductions. Shay, would you do us a favor and introduce both yourself and retrain.

Shay David: 00:53 William, thank you and great to be here. My name is Dr. Shay David in my full honorary designation and I’m the co-founder and CEO of retrain.ai. Retrain.ai is a startup company who is solving the big skills gap emergency. We are developing what we believe is the world’s largest, most actionable, skills framework, helping organizations hire faster, retain longer and develop their talent smarter by putting together a system that understands data that [inaudible 00:01:28] and we unified into a system that understands data about people, about jobs and about training pathways.

William Tincup: 01:38 I love this. Okay, Shay, where did you do your doctorate? Where did you get your PhD?

Shay David: 01:41 I did my PhD work at Cornell University and my postdoc at Yale University. Both on issues of access to knowledge systems, large scale open information systems and access to knowledge.

William Tincup: 01:55 That’s fantastic. One of the things I wanted to ask you about was the competency frameworks that people have used. Historically, we’ve fought incompetencies. I’ve never seen a company actually… I’ve seen a lot of companies create competency frameworks, but not proliferate the organization all the way into recruiting and job descriptions and comp and succession planning. Really, really operate the business with competency models. First of all, I wanted to get your take on that. Have you seen some of the same things with competency models?

Shay David: 02:30 Yeah. I think that in order to see competencies, we need to understand that there’s a system and I use a chemistry metaphor, but the system is broken down into atoms and molecules and in our view, competencies are somewhere in between that hierarchy. The underlying atoms of that is skills, skills capitalist. So we talk about skills, including both domain skills and core skills and personal attributes. But those skills build up into larger structures that include capabilities and competencies and tasks and roles and occupations and understanding the relationship between all of those objects we think is key. So there’s been a lot of attempts, both government attempts, private sector attempts, to build these frameworks. In many cases, part of the challenge is that it’s a very dynamic area. The market keeps changing. People keep changing. And if you have a framework that is not adapted, but is not dynamic, then soon enough, it’s not actionable anymore.

William Tincup: 03:29 Yeah. It’s almost like a book being published is, well at least as they say, and the moment it’s published is the very moment that it’s out of date.

Shay David: 03:38 Exactly. There’s an old joke. The skills framework version of it is an old joke, but two old ladies meeting in the park and one wants to introduce her grandkids and she says, “Oh, this three year old is the lawyer and the four year old is doctor.” Unfortunately, no, no. All the grandma ever wanted her kid to be an Android developer, but probably Android development or Python development, for the matter or data science, are probably the jobs of the present, jobs of the future, some of the most well paying and in need jobs. Those did not exist.

William Tincup: 04:15 When that conversation.

Shay David: 04:17 [inaudible 00:04:17]. Right? Nobody thought those were going to rise.

William Tincup: 04:21 And that gets to the point of being agile or having agility with your framework. I love how you broke down skills, because most of the folks in HR and in recruiting breakdown skills into hard skills and soft skills, but you had actually talked about it in a couple different ways. Take us back through that if you don’t mind.

Shay David: 04:40 Yeah. So again, I’m using the word skills as kind of a reserved keyword when we see… Yeah, we actually talk about very different sync. Skills being kind of an overarching name. For some of the core capabilities, we have hard skills and soft skills. We have domain skills. We have personal attributes. And to that extent, this is true. Not only about people, but also about organizations, certain organizations to have a different understanding of what skills are required on per job basis. A project manager at Google is very different than a project manager at Bank of America, is very different than a project manager at Tyson Foods.

Even though the title might be the same but if we look about the skills, we will see that in the definition of those roles, on a peer organization basis, we would be having a different interpretation about what those skills even mean. So I think that any framework, to be successful and to be actionable, the first order of businesses is to be able to analyze the data and to answer the basic questions is what do we talk? What do we mean when we talk about skills? And to be able to break it down to such fine granularity. So when we want to put it together in order to create world definitions and then obviously in order to be able to create matches between people and roles and potentially offer even more useful things like career pathways, training pathways, then we have a unifying language, a unifying language of skills.

William Tincup: 06:12 You know the way you talk is A so eloquent, but it reminds me of how people talk about the human genome. And kind of understanding the underlying DNA and underlying that and going piece by piece, brick by brick. I love it. So let’s talk a little bit more about retrain and how you help organizations. So the problem, I… It’s evident what the problem is, but let’s talk about the solution.

Shay David: 06:44 So, let’s spend just 20 seconds talking about the problem. The problem is not just the problem. The problem is an emergency. We call that the skill gap emergency. And what is that skill gap emergency? We believe that the number one problem organizations have today is getting access to skilled labor. Putting aside what is now probably some recessionary cycle. We think that is relatively short term. The long term secular trend in the market is that there’s just not enough skilled labor. Based on Bureau of Labor statistics data from then of Q1, more than 500,000 open nursing jobs in the US alone, 300,000 driver jobs, 350,000 programmer jobs. So there are hundreds of thousands of skilled laborers that are missing from the job market. And for many CEOs, maybe 77% of fortune 500 CEOs, definitely think that the number one problem that they have arresting business growth is access to skilled labor.

If you’re a large pharmacy network and you want to open a pharmacy in North Chicago, and based on your specs, you need two pharmacists and five pharmacy technicians, but you only got three. You cannot open the pharmacy, right? That is a very significant hindrance to business growth, on one hand. So that’s half of the emergency. The other half of the emergency, and that if you look at the Bureau of Labor statistics data, you would see that there are probably only one job seeker for every two open jobs. And again, that might change in the next few quarters because there’s some recessionary pressure going on, but long term, that’s not going to change. And the second half of the problem is that statistics is misleading because it does not include people that are not looking for a job. When we talk about job seekers, and when we say that there are two jobs for every job seekers, that does not mean that there are two jobs for every person that doesn’t have a job.

It just means that there are millions of people that are no longer looking for a job because they gave up. And why did they give up? They gave up because they don’t have the skills that are requisite to participate in this modern economy. That’s the second part of the emergency. So how do we take those two very severe tragedies that are kind of developing in slow motion in front of our eyes, businesses that can grow and individuals that can make a living, and how do we start fixing that? So at retrain, we believe that there’s no pixie dust and no panacea, but what we definitely need as a formidable starting point is a map. And that’s what we’re building, the Google Maps of that labor market. We start with building an understanding of the labor market at the level of granularity and actionability like never before. We do that by developing an AI model that we’ve trained on billions of data points from the labor market. It’s a system that understands natural language.

It reads documents about people, about jobs, about training pathways. So it could read a LinkedIn profile. It could read a CV. It could read a job description. It could read the core syllabi. It reads all of these documents. And it’s already learned from billions of data points and it’s developing an understanding that we model as a very significant, very large knowledge graph to understand what are the occupational opportunities that exist today, and to be able to predict, a little bit into the future, what those opportunities are. So when there are new occupations, such as Android developer to go back to the example we were using earlier, the system automatically learns it. It does not need a hierarchical taxonomy. It does not need an expert group to meet every two years and talk about jobs of the future. It just reads job boards. It reads LinkedIn profiles.

It reads data from real time government and private sources and it learns on the fly. So that’s iteration one, creating the map. Iteration two is being able to place organizations and individuals on that map so that we know here’s what the occupational opportunity at Bank of America or Wells Fargo or Citibank is right now. Here’s what it means for Blue Cross Blue Shield right now. These are the jobs that are open. These are the job requirements that are open. That’s iteration two. Iteration three is adding individuals on that. Here’s a job seeker. Here’s a student that’s looking for an apprenticeship. Here’s an older person who’s looking for a career. They’re not ready to retire. They’ve been in the workforce for 25 years. They’ve never really written their CV. How could they expose their skills? How could they map themselves into this knowledge draft so that they can get closer to that vocational opportunity?

And the last step is to be able to work together with our training provider partners, to be able to show a training pathway. For individuals that don’t have a direct match, what could they learn? Whether on their time or their dime, and most preferably, on an employer’s time and dime so that they can re-participate in the economy, hopefully before they get expend from the economy. And good examples is that would be, for example, within our banking clients, many banks, for example, are closing down branches on Main Street, moving to online banking. What happens to all those people? Do they go home or could they be redeployed in the more digital arms of those banks? We think the answer is the latter, but how do you take them on this journey? That would be a good example. So, that’s the type of holistic approach that we’re trying to bring to this market.

William Tincup: 12:11 And for the audience sake, what iteration are we at right now?

Shay David: 12:16 So we have built the first version of all three of these. We’re focusing on the first two but we’re probably working with several partners on iteration three. And we think that within the next few months, we’re going to start to be able to talk about very large scale deployments. The mission we give the companies is to help 10 million people in the first five years. But we now realize we actually need to up that because we’re moving fast and the emergency is larger than we thought. There are 10s of millions of people around the world that are affected by this. There are some estimated say that between a third and half of the workforce would need to be upskilled and risk skilled within the next decade [inaudible 00:12:59] millions of people.

William Tincup: 12:59 Sorry to interrupt, Shay. This isn’t a new problem. This has been around for a long time, right? I mean, in terms of when manufacturing jobs left America, it left some of those cities that were heavily indexed on, in manufacturing. It left those workers without jobs to do because they had built up skills in a certain area. You’re bringing a refinement to it in terms of, like you said, the Google Maps for this. Being able to understand where skills are, what I would assume, from breadth and depth to what those skills are, transferable skills, et cetera. And then be able to put people on a pathway quickly to kind of re-skilling deployment.

Shay David: 13:42 Absolutely. Absolutely. And I think it goes even earlier than that type of transition.

William Tincup: 13:47 Yeah, good point.

Shay David: 13:50 We can actually talk about four industrial revolutions. And for those interested in the highly recommended book about this is Carl Frey’s, The Technology Trap, which literally makes the data driven examples of showing how in every given previous industrial revolution, the first thing, the steam engine, the second being electrification, the third being the information revolution, the force, which we’re living through now, is the AI and kind of syncing machines revolution, or the automation revolution, if you will.

In every one of the three iterations prior, the end result was that there would be more jobs created than jobs destroyed. The challenge is that sometimes took decades. The impact for organizations that didn’t adapt fast enough, and definitely for the individuals who got caught behind, were devastating. Businesses went out of business and individuals couldn’t put food on the table. What’s different, I think, about this time is that one, because we’re talking about automation of not only manual labor, but also mid-skill jobs as well, more jobs are at risk and more jobs are susceptible to automation, one. Two, it’s happening at record speeds. People don’t completely understand how deep we are in that forced industrial transformation, if you will.

William Tincup: 15:18 And if they don’t, they’re being blindsided by it one way or another. It’s almost like there’s a Moore’s Law for skills.

Shay David: 15:27 Exactly. Exactly.

William Tincup: 15:29 We all know Moore’s Law, but not necessarily think of it in terms of our own skill development.

Shay David: 15:34 Absolutely right. And I think that the implication of that, and if it’s following Moore’s law or faster, is that because exponential, those estimates, about half the people needing to be re-skilled, if they’re not true today, they’ll be true in a year or two from now because the repeated the change is so big. But I think there’s a nuance, which is important to state, which is the headlines that we might read in the popular press is, “Oh, robots are coming for your job. Everybody’s going to be gone.” That is probably not going to happen anytime soon. I think the risk is emergency is not about the fact that robots are going to take everybody’s jobs, it’s that we’re not going to have enough people to operate the machines and that the skills of the future are all about humans and machines working alongside one another. So to go back to the banking example I was giving earlier, what did it mean to work at a digital arm of a neobank?

It doesn’t mean that you’re working on your own. It means that you’re working within semi-automated processes and that you’re working within call center environment. And that there are algorithms working alongside you when the bank is making, say a credit or loan decisions and whatnot. That’s a type of skill that most individuals within the banking industry have never learned and do not possess. So how do you teach for that? Or to give an example from healthcare, we’re talking about personalized medicine, genetic testing, things like that. How would the large healthcare organization introduce genetics into portfolio products and services? What does it mean for doctors? What does it mean for nurses? What does it mean for new occupations, like genetic counselors? Et cetera, et cetera, et cetera. Consider a transformation like that and you realize that it touches pretty much every aspect of their organization. Everybody needs to learn new skills.

So how do you manage that? The answer for most people is oh, sticky notes on the fridge in the cafeteria and low and behold, that’s just not good enough, not a reality 2022. And definitely not due to the acceleration of all these transformations that we’ve seen through COVID and definitely now through some sort of economic meltdown. These types of outside pressures and, again I’m referring back to Carl Frey’s book, when there’s economic pressure, what happens is the technological transformation gets accelerated because employers perceive that as a way for cutting cost. And when there’s economic pressure, like COVID or like inflation, the result of that is that automation is going to get accelerated. So everything we’re talking about, it’s already an emergency. It’s going to get much worse before it gets better.

William Tincup: 18:12 It’s interesting because the way you’re talking is this relentless pursuit of skill acquisition and skill development, both from a company perspective and also from an individual’s perspective, from an employee or candidate’s perspective, that it isn’t every three years you retool or every five years or 20 or whatever, you’re constantly rethinking and looking at your own skills as an individual. But as a company, you’re also looking at the skills that you have and how the business is changing and what you need to do to acquire new skills, train up other skills within the force that you already have and deploy skills.

Shay David: 18:55 Absolutely. Because I think skills is what makes the difference between failing businesses and good businesses, between good businesses and great businesses. If you are a… Not to talk about a funny skill, but if you can run the 100 meters dash in under 11 seconds, you’ll be high school neighborhood champion. If you can run under 10 seconds, you’ll be Olympic gold. So that 10% difference, might make the difference between gold or not. And by implication, that is probably true for many other occupations and many other skills. So it’s about constant developing and it’s about the difference that makes a difference. How could we help identify the critical skills that employees across the board need to learn in financial services, in healthcare, in banking, in manufacturing, in retail, and how could we convince individuals that there’s a future for them within an organization and outside an organization?

And when we’re thinking about industries of mid skill or low skill labor, particularly around retail, food and beverage, consumer packaged goods, we know that the attrition rate is horrendous. People come in, they stay on the job very, very short wise. You go to McDonald’s, somebody flips your burgers, you’ll be there a year later, two thirds of the people are not going to be there because there’s almost two thirds, 66%, annual rotation. So we need to think about this as a more holistic approach, both on the talent acquisition side, the talent developments and talent management. Giving people the capability to develop the skills as they move from job to job and being able to take ownership of their careers, interest of skill development. So could I imagine employees, for example, owning something like a skills passport or skills wallet knowing, hey, I’m only going to do this job for McDonald’s for a few months, but maybe I learned a few interesting skills while I was flipping burgers, because I’ve also learned teamwork and deescalation.

Maybe my next job is going to be an assistant barista at the Starbucks, et cetera, et cetera. And I can imagine my career, even though I might not stay at this given organization, differently, for the organization, the big benefit is being able to keep employees. And I think people understand that. I talk with a lot of chief learning officers and chief HR officers and chief information officer, chief technology officer across the board. And I think that they’re all feeling the same pressure. How could they put their employees on pathways that allows the employees to stay in control and allows the employees to understand that they’re getting something back from the system? And allow the employees to identify with the brand because the brand is giving them what they need, which is a long, long, long range career opportunities. Whether within or outside the conference of a given organization.

William Tincup: 21:47 It’s interesting because the way that you talk about skills, at least historically, we thought of them as more of happenstance. You look backwards at the skills that you’re built and I think one of the things that I love about what you’re doing is you’re looking at skills more like Lego blocks and building blocks. You just give that example of McDonald’s flipping burgers. I’ll build or I’ll create these skills and I’ll then be able to take those skills with me to wherever I go next.

Shay David: 22:16 Correct. And I think that’s exactly right. And, again, because it’s data and it’s hard to visualize. So we’ve been going back and forth on many of these metaphors, like Lego blocks or absences or DNA, but it’s exactly that. The commonality within all these metaphors is that there are smaller building blocks that are building a larger hole. And that larger hole is me, as an employee, wanting to feel meaningful, wanting to feel that I’m learning something, wanting to feel that I’m in control, understanding that my career pathway extends beyond a single employer and beyond a single job. And the biggest difference is that, maybe in our parent generation, you learn something, maybe you went to university, maybe not. Maybe you just finished high school, or maybe you spent a few years in college, but then you went to work for the local utility or for a defense contractor for the auto manufacturer. And you stayed there for the next 30 years. Today’s workforce is not like that at all.

William Tincup: 23:09 No. And neither is the employer. They’re both sides.

Shay David: 23:12 There’s a good chance that you’re going to develop three, four or five, maybe 10 or 12 careers. Whether some of those might be with the same employee or not. But you need to reinvent yourself every few years because the technology is changing so rapidly and it’s a great accelerator. And again, I think that COVID was just a dramatic accelerator. If we talk about the future of work as a 15 year horizon, then COVID just accelerated the timeline to four or five years, of which two years have already passed.

And in that sense, it’s a combination of rapid adoption of technology, of remote and flexible work, of the rise of the gig economy, of the coming into maturation of the cycles that started 20 years ago with eCommerce and services moving online, of mobility. A lot of these things… None of these things are new, but it’s kind of the perfect storm in the sense that they’re all coming together. And this is what creates the pressures on the system as we know it right now. And I think that employers, the type of CHROs, CEOs that I talk to, they understand that it’s no longer business as usual. We cannot go on using the same outdated HR systems of yesteryear. There’s been a lot of systems that have been implemented at the time that computing meant green screens with blinking cursors. This no longer works.

William Tincup: 24:36 I wanted to ask you. Dumb question alert, but the stat that you gave in terms of people that apply for jobs, one and two, I think it was, then why people don’t apply for jobs? My perception of that is that they’re defeated, but they feel defeated because they don’t… Either they don’t have the salad skills or they don’t feel like they have the transferable skills or they can’t tell the story of their own skills. Or maybe they just don’t even have the skills. But it’s like, I used to tell people this bit around hunger. We produce enough food in the world to feed all the people in the world. We have a logistics problem.

Shay David: 25:20 Yep.

William Tincup: 25:21 Right?

Shay David: 25:23 Absolutely. It’s a distribution problem. There are more calories. There are more calories being generated any day of the year than being consumed and much of it is a double distribution problem. Because on one hand we have many people going hungry. On the other hand, we have many people going obese.

William Tincup: 25:39 That’s right.

Shay David: 25:40 So it’s a double distribution problem. And I think you’re right. I think it’s a good analogy. Because I think that the skills economy is also kind of winner takes all. On one hand, you have very sought after, relatively small class of people, car freight calls them symbolic analysts. These are people that move symbols for a living. They could be creative, they could be Photoshop designers, they could be programmers. They could be writers. People whose job it is symbolic analysis. And then there’s the rest of us, you and I probably fall in the first category, lucky for us, but many other people do not.

And how do we imbued digital skills and the people that otherwise would not have immense digital skills recharge? I think, to your earlier point, I think a lot of people that fell out of the labor market gave up. The labor market in its current form could not give them the sense of hope and meaning that they had and more people, and especially kind of the middle aged generation, I think they gave up on the possibility. I think about skills as a ladder. And if you were a mid-management shift manager at an order manufacturer and your job got automated away, you’re falling from the skill ladder. You’re moving away from a unionized $25 per hour type of job that allowed you to go to Mexico for an annual vacation, put two kids in college and drive a nice car. What are you going to do?

You could be guard at the shopping mall working for $9 an hour. You fell off the skills ladder and you need help climbing up the skills ladder. And the first thing you need is the belief that everybody could be upskilled and re-skilled, that it is a ladder and that there are steps that you can take, but somebody needs to help you see this. And those old systems, both on the public sector and government side, and on the private employer side, they just don’t do that. This is why we think that we really do need new types of system. This is why we call it talent intelligence. It’s both intelligence and its meaning as an intelligence system, but it’s also intelligence. It’s kind of its military meaning of collecting intel and understanding where vocational opportunity lies.

William Tincup: 27:56 Last thing, because I could talk to you all day and I know that you’ve got other things to do, is iteration four where you connect everything that we’ve learned. The where, the what, the who, and the how. Where we connect those things to training programs and retraining programs. So we can redeploy those skills and again, get people up on the ladder, and there’s hope that there is a ladder for them, and then that they can actually reach the next step of the ladder. Talk us into that a little bit more, because I know it’s, first of all, everything you’re doing in iteration one through three, before that, gets you to the place of actually then being able to impact training and re-skilling people so that they can have hope. And again, I love the phrase that you came into the show with. It’s an emergency. An emergency’s not going to go away until we get our heads out of the sand, collectively, and face the emergency as it is

Shay David: 28:55 Correct. But I think that there is a ladder for everybody and I’ll give you one example. We have collaborations with organizations, let’s take Coursera as an example. Which is a lot of the content is there. The beauty about something like Coursera is that [inaudible 00:29:11] has been developed at some of the top universities in the world and by some of the top employers in the world. It’s all available. Anybody could go up and sign up for much of the content on Coursera for free.

The challenge with that is that just having access to the content is just not enough because if you don’t have the map of where vocational opportunity lies, and if you don’t have a strong sense of what career opportunities lie ahead, and if you don’t understand how training pathways connect to career pathways and the motivation for actually going and learning on your own if you’re out of the labor market or even if you are in low level jobs in the labor market and you want to move up, you just don’t have the motivation and you don’t have the compass ahead of you to tell you where to go. And that’s part of the reason why the completion rates in massive open online course is so low.

There’s debate about the statistics, but it’s in the low single digit, maybe under. In some courses, under 1% of the people. Some people come in, they see some courses, they do some tests, but very few people actually learn and go through the completion of the courses. That’s what we’re hoping to change with these partnerships. Which is, what if there was a training program that actually had a job at the end? Where you knew that if you were on that training pass, you are in the starting phases of a new career pass. That dramatically increases both the motivation and availability of resources from the employer perspective. Which, to remind you, going full circle back, is looking for that type of talent.

William Tincup: 30:42 Yeah. What I love about this is you’re taking something that’s been historically invisible, and maybe even a bit amorphous or ambiguity and you’re making it visual.

Shay David: 30:56 Yep. Our hope is to partner up and down the ecosystem. So in conversations with people like Guild Education and Breed and people like some of the more traditional vendors and the up comers, like Metaverse.io, and many of the companies that are really committed to changing this and creating opportunity. And what we’re hoping is to be able to be the partner in that ecosystem, building the platform, building the intelligence and helping everybody with the type of data we’re masking so that they can build their business better.

William Tincup: 31:30 What you’re building is absolutely beautiful. I know it’s mission driven. It’s beautiful. It’s elegant and it’s beautiful and I absolutely appreciate your time today.

Shay David: 31:41 Thank you. And I invite the listeners to check out our website, retrain.ai, and sign up for a trial and come work with us. If you are a developer or salesperson, we’re hiring a lot. And thank you, Will, for the opportunity. I’m very passionate about this and very happy to discuss it and take questions. If those come, please do send us emails, inquiries, come and help us build this.

William Tincup: 32:05 I love it. Thanks for everyone listening to the Use Case Podcast. Until next time.

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