In this episode, Corey Jones explains how AI is creating a critical window of opportunity for workforce professionals to proactively address job displacement risks while leveraging innovation, data insights, and practical tools to expand career pathways and better align with evolving employer needs.
Podcast Transcript
00:00:05 – 00:0028 Introduction
What could happen if we take our workforce to new heights?
Workforce on the Mic, presented by NAWDP, brings you inspirational stories, innovative solutions, and expert insights that are shaping the future of the workforce.
Tune in for dynamic conversations that motivate and transform the workforce development community.
And now, on to the episode.
00:00:28 Alexis Franks
Good morning, good afternoon, and good evening, all you workforce warriors across the country.
My name is Alexis Franks, and I am your director of membership with the National Association of Workforce Development Professionals.
And in today’s episode, we’ll be discussing a topic that is on everyone’s mind right now, and that is artificial intelligence, or AI, in the workforce system.
So as we know, AI is really reshaping the work that we get done all around. And for our field, this moment brings big opportunity and responsibility.
So today’s guest will help us to understand how AI shows up in real and practical ways in the workforce system, what it means for our workforce program, and for practitioners everywhere.
We have with us Corey Jones, Chief of Policy and Planning for the Governor’s Workforce Board of Rhode Island. Welcome, Corey.
00:01:34 Corey Jones
Thank you, Alexis. Thanks for having me.
00:01:36 Alexis Franks
Yes, absolutely. So here, what we typically do is pass the mic. So I’m going to pass the mic over to you. And Corey, if you want to just kick us off and tell us a little bit more about the work that you do and your journey in workforce.
00:01:52 Corey Jones
Yeah, so in my role, I do policy forecast, policy analysis. For folks who are familiar with our federal workforce program, WIOA, I oversee our local policy. I play a role in our state policy as well.
I formerly served as the policy advisor for Governor McKee. And when I came to the Department of Labor and Training, I actually started as a legislative liaison. So I was doing a lot of the leg work, but I had a desire to dig deeper into policy and, you know, after a year doing ledge work.
I started my tenure with our state workforce board. And when ChatGPT came out, I was excited to start analyzing how generative AI will impact our local job market.
I had a background in computer science. So as a young man, I thought I would be a computer scientist and decided that while I like tech, that I really want to work with people and make people’s lives better. And so AI was a really cool opportunity to intersect two of my passions.
And ever since it kind of came to fruition in the modern kind of generative AI movement or technology revolution that we’re seeing now, I’ve been just really deep into it and seeing how not only will it impact our state, but also the job programs that we fund.
And you know, how can we make better decisions with the information that is available to us? And you know, with the programs that we operate.
00:03:23 Alexis Franks
Well, Corey, I’m so glad that we get to have you because of your excitement. So for so many people in workforce right now, we’re still trying to navigate and understand how to use AI in our workforce programs, how they can help support the customers that we serve every single day.
And it can be a little bit nerve wracking to try to implement those changes into our programs and what that looks like, and how we can understand the benefit for us to be able to integrate those systems and that technology.
So we’re so glad to have you today. And I’m going to jump right in because of where we are.
If you are nervous or if any of our listeners today aren’t really sure where to start, what would you describe as the current moment that we’re in right now with AI? And how is it influencing our workforce system?
00:04:17 Corey Jones
Yeah, so I think we’re at a really important window of opportunity. I think that the window of opportunity I describe is a moment where we are not all the way in the thick of it, but we are far enough to have some type of intuition or forecast of what’s to come. And I think the greatest debate is when will these forecasts come to fruition, right?
So, you know, there’s been reports that 35%, 50% of the job market will be augmented or displaced from this technology. And really the debate, is it going to be this year? Is it going to be in three years? Is it going to be in five years?
And then there’s a sliver of people that kind of have this perspective that the jobs will be created needed before, you know, that moment hits.
I think the folks that are doing the forecasting, you know, for myself at least, I’ll tell you that there’s no way for me to accurately, myself personally, forecast when the jobs of tomorrow will arrive and how many jobs will exist, right?
So I think this window of opportunity allows us to get a grip on, you know, what jobs are under, are at risk and, what jobs are potentially going to grow.
Using this window of opportunity, I think that will be the difference between having another tool for productivity or this being the kind of technology that brings about, dare I say, another recession.
So I think if we use this as a tool and we use it in a favorable way, we can do a lot of really cool things with it, we can lower the barriers of entry.
Imagine a student that maybe wasn’t able to get a four-year degree or didn’t think a four-year degree was right for them being able to take a two-year course and be a vibe coder instead of a software engineer.
I think it also opens up new industries when you look at, you know, technology that wasn’t able to be created until these new base models were available. And also when you look at the jobs that are growing from data centers and that are growing from the individuals who are training these models, those are also windows of opportunity.
So I think if we don’t act now, we’ll pay the price tomorrow. And, you know, this is an opportunity. But if we allow this opportunity to pass, then, you know, we are going to be in a reactive situation rather than a preventative situation.
00:07:09 Alexis Franks
I like that you said that, Corey, because we do as workforce professionals tend to focus on the future. Where are the jobs going? Where are they taking us? What’s in demand now? What will be in demand in the future?
But knowing the now piece, it seems more to be a little bit more important. What are we doing now to address the barriers of our customers? How can we use AI to support those needs? How can we use AI to support our needs as we’re working in these programs as well? And there seems to be a lot of push around that at work.
So I’m glad that you brought that up, that we may not always know what will happen in the future, but we can, there are things that we can do to address the right now and how we’re working in our programs.
And I’m glad that you brought that up. It kind of moves us into my next question for you. In what ways is AI really changing how we analyze and interpret labor market information?
00:08:09 Corey Jones
So the data is still the same. The same labor market information that we’re reading now is, hasn’t necessarily changed very much, but I think the assumptions are changing.
So the real questions we have to ask is how vulnerable is each occupation to AI? How much of our job market is made-up of these occupations that are vulnerable? And then how can we make some targeted assumptions, right?
So for instance, you know, when you look at some of the data we did, one of the top quantitatively exposed occupations to AI is teachers, right?
And, you know, a targeted assumption would be that I think teachers kind from a qualitative perspective, people will be more reluctant to augment those jobs, right? So while these might be at the top of the list, and kind of thinking about it from a student and a parent and a teacher themselves perspective and a systems perspective, I think that that is something that was at the top of our list, but we also are like, we are less concerned about teachers than we would be about accountants who, you know, in the finance world, they’re one of the fastest industries to adopt a new technology because they’re going to save some money, right?
So how can we take these qualitative and historical trends and, you know, experiences and try to use it to make some informed assumptions about the data that, you know, we’re looking at.
Another thing that we were able to do is there’s a occupational study that takes the human abilities by Felton, and it says, you know, there’s 52 human abilities. This is how much they can be augmented by AI.
Those 52 human abilities correlated to occupations in the SOC codes, you know, now this is the exposure score based off the amount of those abilities that can be augmented by AI. Here’s how exposed these occupations are.
So what we did was we took that information and we wedded it with our local job market data. So that’s something that I encourage every workforce board to be doing right now. And that’s going to show you the potential quantitatively of each of these jobs to be augmented by AI.
The next step we did was kind of analyze it from that qualitative perspective, and then add on these newer studies from Microsoft OpenAI themselves, where they call them applicability scores, where they take 200,000 automized conversations from these AI-based models, and then they use that to kind of make assumptions or to show what occupations are already being augmented by this technology and to what degree.
So I think all that information is very helpful, using it and comparing it to the data that we’ve already had. And then, asking these new assumptions and how AI may impact and then take it a step further.
Go and talk to the businesses that are on that list of highly exposed and have high employment in your state. For us, that’s the hospitals, right? And you know, we’ve talked to the hospitals, they’re already implementing. So we want to make sure that we’re working with them to know how are they implementing it.
You know, our hospitals said they’re focused on attrition, right? So that was a positive thing for us.
But when we’re looking at the job data and we’re saying, typically, we need 500 nurses, right? We’re thinking about it a little differently because we know that as they’re losing employees, they are using AI to augment certain occupations. And nurses is not one, particularly, I think,
Pharmacist tech technicians and billing specialists are definitely going to be roles that folks will be looking at attrition and augmenting there. And then also what was interesting about the hospitals is they’re looking at it from an innovation perspective.
So, you know, there’s a real opportunity for job growth when you’re finding ways to address, you know, healthcare concerns. Potentially, you know, this could create some innovation that could grow, you know, our our hospitals if that’s done successfully.
00:12:36 Alexis Franks
Corey, that’s a really good point that you brought up, too, because for those individuals that may not understand the data piece of all of this information, right, there’s still specific tasks that you can do.
There’s actions that you can take to make sure you’re addressing the needs of the business as well as the customers that you’re serving. So even if you’re listening to us today, if you’re one of those workforce warriors, that says, maybe I don’t understand all of the data. It’s important that we’re still having the conversation with the businesses too, to really understand how they’re already using AI and how it’s affecting the occupations that they’re looking to feel.
And then we can be reactionary or create a plan to address those needs of the businesses. So I’m really glad that you mentioned that too. And it’s still important for us to do our research. If you don’t know who to talk to, definitely not we’ll be glad to help point you in the right direction, but we want to make sure that you have access to that labor market information that you need as you’re moving forward and planning for your workforce program.
So thank you, Corey. I know we talked a little bit about the hospitals, but are there anything, is there any other occupations or roles or skill sets that we see appearing as a result of AI and maybe even ones that are specifically workforce related?
00:14:01 Corey Jones
Yes. So interestingly enough, there’s a company called Mercer, and Mercer was the fastest growing, is currently the fastest growing company in the country, in the world. And what Mercer does is they were originally an Indeed competitor. They were basically a jobs board website, but they pivoted during the kind of generative AI moment. And I believe they pivoted beforehand, and that’s why they were really ahead of the curve. And now they hire individuals for AI training.
So. When we, you know, think of AI and generative AI, we all, all, you know, always think of OpenAI, ChatGPT, Claude, right? And these are what we call base models. But these base models have what we call an API.
And the way I describe an API is, imagine if you could buy a car and, you know, you can just buy the engine and put any engine into any frame, right? That’s kind of how an API is, right? It’s like the engine of that website, of that, you know, software.
And so the API of these base models are then plugged into these AI wrappers or these kind of secondary models that are more specific. So a lot of the AI companies really made a bet that AI, you know, the chatbots as we know it, will be able to do everything.
And these other companies said, wait, if we get, you know, what we call a rag, which is a specific database of information, if you made a chatbot, it’s like the files you upload to that chatbot that allows it to understand your work.
They realize if we get a rag and we customize it and integrate it into our platform, we’re able to be much more accurate, much more efficient, and do what we needed to do better. And so some of these companies are making proprietary solutions for their own company. And then some of them are making software solutions that they can sell.
And so a popular rag I use or AI wrapper is Granola, right? And so, you know, the jobs at Granola, those are new jobs, right?
That, you know, new entrepreneurs and startup founders were able to kind of rise and, you know, create those new occupations. They’re not new occupations, you know, as we know them, but they’re new jobs, right?
Granola is a note-taker, and it records your conversations and transcribes, and there’s a ton of different ones like that.
So what Mercer is doing, it’s hiring experts.
So say you’re a lawyer who’s the top of your firm and you have a process and procedure that you use to kind of go through your cases. What Mercer would do is hire you to train his AI model to recreate that kind of procedures and rules and understanding that you execute.
And so, you know, the CEO of Mercer would say that he believes in the future, everyone will be training models. That’s a little dystopian for me.
But, you know, it shows you where, you know, the CEO of, you know, the fastest growing company in the world right now, where his headspace is on where he thinks the future of AI jobs will be. I think there’s a challenge with that.
And the challenge is if, you know, I’m an entry level and I, you know, you know, employee and I just graduated college, how am I going to get this new job, right, that is really, you know, for senior level people who’ve been doing the work for a really long time, while at the same time, AI is augmenting entry-level positions, right?
And so we’re watching the kind of AI revolution play out just like, you know, everyone says, right, every kind of, you know, economic revolution creates new jobs, and, you know, so we lose the old jobs and we get new jobs, but this time, the career ladder is becoming fractured.
And that is going to be a severe challenge for us as workforce professionals, because we’re going to be more dependent on apprenticeships. We’re going to be more dependent on, you know, a program we have is called Work Immersion, where we subsidize a percentage of employees who are new to the industry or new to the occupations, first 400 hours of work, right?
So we are going to have to create on-ramps for companies to kind of incentivize them to hire entry level positions. Again, I think that it’s also short sighted because, you know, we know that these entry level positions bring experimentation.
When you’re new to a job, you know, you’re more likely to not just ask the person next to you, you know, what do they do? You’re going to say, well, maybe it should be done this way. Why haven’t we did it this way?
And that brings experimentation and new ideas to to companies that sometimes you know, things are being done the same way it has been for 20 years.
And so, I think, you know, while it might save them some revenue, the companies that are really going to win are the ones who are going to continue to find ways to hire those entry-level positions while also, you know, finding ways to make their work more efficient, increase their revenues and utilize AI in that fashion.
But those jobs, you know, are jobs that already exist. They’re jobs at data centers from the data center explosion that AI has brought about. And then they’re also these senior level positions training AI models.
00:19:51 Alexis Franks
That’s very interesting. And I don’t think I’ve heard that idea before, but it does make a lot of sense for how we’re exploring AI career pathways and how those shape into progression. I think that’s definitely something.
And recently, we’re talking more and more about having competency-based occupations and pathways and skills-based pathways. That’s also another thing that might have to be addressed if we’re looking at a more fragmented as you said, career path in AI.
So those are all really good things to think about from a workforce perspective. How are we going to be addressing those needs?
And you’re right, we’re watching it play out in real time. So there’s a lot of work for us to do and to continue to do so.
Corey, I thank you so much and I appreciate the time. And I have one more question for you. I don’t want to hold you up too long.
But for our workforce warriors that are listening today, if they’re starting out new with understanding AI, how they can use it, how they can help their customers to use it to their benefit, is there any advice that you would give them in even a small way that they can improve their service delivery or their case management and the work that they do?
00:21:09 Corey Jones
Yeah, so first I want to say, shout out to JFF, Jobs for the Future. They have some incredible material on career counselors and how they can specifically use different tools in their work with AI. And so that reference is incredible.
But a few things that I’ve used is Lightcast, they have some awesome tools that you can utilize for AI with LMI work, as well as some other things that I haven’t completely explored yet.
But Google has a really cool interview prep tool that I really liked using. I think, you know, interview preps are one of the most kind of underestimated elements in, you know, the work we do in career counseling.
It can be sometimes a little awkward to, you know, do some mock interviews, right? And to do it with AI, I think it adds like a level of kind of comfortability that someone may, you know, not have with, you know, a career counselor who may be a stranger to them.
And then also just, I think, for me, the greatest application is career exploration. 40% of freshmen in college are undeclared for their major.
Really, we don’t talk about this enough, but the idea that you go to college and you choose whatever pathway you want to be is really new in the Western world.
Most times in society, you did what your parents did. And I think it presents a real challenge. It’s probably one of the reasons we have the worker shortage. I think it’s a company to the reason why we have such quick kind of, such high turnover and turn in certain occupations.
But I think using it to kind of explore what the, you know, what is the day like of a nurse? What is the day like at, you know, working at a data center, right? How many people will like talk to, how much. You know, I think those questions are really helpful. And then also, you know, being able to use it for policy work, for market demand, right?
How many, if I want to live in this community for the rest of my life, how many jobs in computer science are available? Am I going to be able to get a cybersecurity job in my hometown? You know, using, and I think the best way to use it is to take official data, whether it’s from BLS, whether it’s from, you know, a state workforce website, and then upload that data into your AI, you know, chat bot, and then ask you specific questions about that data.
That’s the best way for me. It reduces hallucination. It allows you to use the most accurate information, and then it also allows you to kind of analyze it in a way that, you know, you may not analyze it on your own.
00:24:01 Alexis Franks
That is great. And Corey, here on Workforce on the Mic, we do have what we call mic drop moments. So I think you’ve given us a couple of our mic drop moments for this episode. And if I could sum it up in one word, I think it’s explore. We want to encourage our customers. We want to use it in our workforce programs, but the way we start is just to explore. You brought up some really good resources that we can and look into.
And hopefully our workforce warriors listening in will take advantage of some of those things. But really just to take that time to explore is where we can start and really use the information and learn how we can use it in the best way possible. So Corey, again, I want to thank you for taking the time to join us today on this episode of Workforce on the Mic. We hope to continue to work with you and see all of the great work that you’re doing in Rhode Island. And thank you again for joining us.
00:25:01 Corey Jones
Thank you for having me. And if you know anyone has questions or like some of those studies that we cited, you know, reach out happy to help. And you know, again, Alexis, thank you so much for having me.
00:25:11 Alexis Franks
Great. Thank you.
00:25:13 Alexis Franks
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Conclusion
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