Ep 131: Scaling Human-Centric AI in Government and Higher Ed with James Regan

[00:00:09] Hey everyone, it's Bob. Welcome back to Elevate Your AIQ, your go-to source for insightful conversations on human-centric AI readiness, talent transformation, responsible innovation, and the future of work. Today I'm joined by James Regan. He's the CEO of Clutch, a social impact and human-centered AI consultancy based in Sacramento, California.

[00:00:29] James spent many years in public health, including pandemic response with California's Health and Human Services, before being tapped to become Deputy Secretary for Workforce Development in California, where he helped lead some of the earliest generative AI deployments in the public sector. He joined Clutch in 2025 as Chief Strategy Officer before taking on the CEO mantle earlier this year.

[00:00:51] James and I dig into what it really takes to build trust and reduce fear when rolling out new technology, how universities are rethinking AI readiness for the next generation of workers, and why real change has to start with people, not tools. I really enjoyed this conversation with James to hear about his impressive achievements and rewarding experiences, and I know you'll appreciate it as well. Thanks as always for listening to the show. Let's go talk to James. Hey everyone, it's Bob Pulver. Welcome to another episode of Elevate Your AIQ.

[00:01:20] Today I have Mr. James Regan. How are you today, James? I'm doing pretty good. I'm doing pretty good. Thanks for having me. Absolutely. Thanks for being here. Looking forward to digging into this conversation. You've been doing a lot of really incredible work, and I and my listeners want to hear all about it. James, why don't we just start with you giving folks a little bit of your backstory about your career, what you're studying in school, and all the ins and outs of your work in the state of California, and all the other great work that you've been doing.

[00:01:49] Totally. Totally. Like so many people, my career is not on a straight line. I started studying pre-med here in California and took a turn towards the end and went into public health instead of medicine. So related, but a little different discipline. I worked for the state of California out of school for a little over 10 years. Most of that was in public health, including a couple years of pandemic response here in California and helping with the rollout of vaccine and a bunch of other things.

[00:02:19] And then after about seven years with the California Health and Human Services family, I got asked to step into a workforce development role statewide, appointed by Governor Newsom to be the deputy secretary for workforce development in the state of California. So it was a really fun thing to see the overlaps between the public health work. And then also in my time in public health, I did a lot of product development and IT deployments for the sake of public health system organization, different things.

[00:02:48] So it was also a good fit at the time where the governor of California had issued an executive order on generative AI and government. And so I got to be on the executive team leading all the initial implementations of AI in California.

[00:03:01] And so that was really fun alongside a lot of higher education partnerships and figuring out how do we prepare the workforce for this huge wave of new emerging tech. And then a little over a year ago, stepped out of government. Now I'm with Clutch. We're social impact and human centered AI consulting firm in Sacramento, California. We do a ton of public sector education and nonprofit consulting.

[00:03:28] So similar mission, so similar mission, different side of the table, but it's all the same work and it's been super fun so far. And you've got a lot of clutch tying back to California. I mean, you've had some programs looking at, you know, AI literacy and readiness across the public sector and beyond, right? So definitely want to dig deep into some of those opportunities and initiatives.

[00:03:55] I mean, you were really early when it comes to deploying, I guess you and Governor Newsom, but just 2023. I mean, that was incredibly early to jump in. Really curious about how those initiatives were sort of scoped and how you sort of balanced knowing that, you know, it was so early to, you know, start digging in.

[00:04:17] It was very early and sort of the big AI wave. And I think that's part of what made it fun. It was one of the things that drove our philosophy was creating a safe space to learn by doing. So there was a handful of state agencies that sort of raised their hand and said, you know, hey, we have a pretty robust digital services programs for public services already.

[00:04:40] We would love to be the first in tackling what it means to deploy a generative AI tool in public sector. And, you know, we did land on a couple in transportation, a couple in health and human services, and one in the tax services space with the state.

[00:05:00] And they were all very different. I mean, ranging from some of the simplest uses at that time, not as like you said, it was very early at that time, not quite as common as they are now.

[00:05:12] But even just a simple search and retrieve for a call center agent to, you know, be able to serve more customers more efficiently over the phone, all the way to, hey, let's use all of the highway data to create a digital double of the highway system to try and figure out where are the areas that may require repair or attention on infrastructure,

[00:05:35] or how can we improve the roadway safety associated with these intersections pinpointed down very closely. So it was a huge range. And so we we took a swing. And what I loved about it was how often in government do you have the controlled and safe and secure sort of environment to say, we're going to try this and see if it works with a new emerging tech. I think it was a very interesting time. And some of them did.

[00:06:05] They worked really, really well. They're still in production now. And so it's it's exciting to see some of the outcomes there. I just I think it's amazing. I mean, obviously, to have, you know, that type of, you know, bold, you know, leadership and, you know, big state country is amazing. And I think it's such a great example for others to follow.

[00:06:26] And people will probably be surprised that all that was underway there for those who don't live in California or a neighboring state, just in terms of government to to move with with some level of speed, as well as the, I suppose, risk profile to to put it underway and invest in it in the first place.

[00:06:46] Absolutely. And we learned a ton, as you can imagine, you know, was challenging the procurement mechanisms because, you know, historically it was it was a pretty long process to buy an IT tool. And typically you bought something and it was it would stay exactly that way. You know, you were buying licenses or you were buying a custom product and then you would code freeze it and deploy it.

[00:07:10] And you didn't have to worry about that natural sort of evolution that happens that can happen with generative AI. And so we had to really tackle some of the procurement policy associated with generative AI tools as well, which was, you know, kind of acting like the front door. And then how what are the monitoring practices and governance practices associated with that in a public sector environment where there's a huge consequence of error if something were to go wrong with the tool.

[00:07:38] And so it was great. And we set up some secure sandboxes. And, you know, we had great tech partners right here in our backyard as well in California that sort of raised their hand and said, hey, well, for the public good, we want to team up on this with you. And so it was it was a cool thing to see people come together. Of course, it was also on the workforce side, like you mentioned earlier, it was challenged to make sure that people knew we weren't out to displace jobs. It was a lot of fear around that.

[00:08:07] And how do we support people through the change rather than, you know, feel like, OK, they're being left behind. And that also that experience while I was in government was also part of the inspiration behind the services that we now offer here at Clutch as well. I don't want to get people confused, but there's certainly a lot of sort of interplay and interdependency between, you know, some of your prior roles and the work that you're doing with Clutch,

[00:08:32] especially with some of the sort of, I guess, public-private sector, you know, partnerships that you've got going. I mean, one of those, just to fall on that thread around, you know, obviously Silicon Valley is in your state and as, you know, technology players, you know, elsewhere in Southern California as well. But you had a lot of opportunity to partner with some of the, you know, sort of frontier, you know, vendors in the space.

[00:08:58] I thought you could talk a little bit about, you know, Google's, working with Google's public sector team and some of the capabilities that they brought to bear. Yeah, it definitely is. It's directly related because for me, it was, it was about the underlying mission, right?

[00:09:14] And in government, it's the same underlying mission we carry here in Clutch, but in government, it was definitely interesting to see some of the mechanics and interrelationships between some of the larger frontier model owners, product owners, tech firms in a world now with generative AI, now a much more robust AI race that we all see a new headline for every week.

[00:09:36] So it's, it was definitely influential for me to be able to work alongside some of the truly the world leaders in AI at that time right here in our backyard to figure out what are the guardrails we want to put up. You know, everyone's always concerned about, you know, privacy and security, making sure that there's appropriate monitoring and AI observability into when the model is acting on any public sector data or any consumer data at all.

[00:10:04] And so it was a ton. I felt like I was, I was excited to be able to contribute at that level, right? It was just exciting for me in my career to be able to contribute at that level after working so long in, in public health and workforce to now feel like, oh my gosh, like this is, this is the front of AI policy. And we got to meet with people from, you know, around the world.

[00:10:30] Literally, we had represents, representatives from the EU and different European countries coming to learn what we were doing. And, you know, people coming from the federal government to learn what we were doing in other states were all kind of looking at us in a big way. And so it was a great thing to be a part of. And also professionally, I just always, I'm absorbing constantly. So I just felt lucky to be in the room with so many brilliant people across that process and really making an impact and using AI for good, really. Yeah. Is the whole mission.

[00:10:58] Just in terms of the, the adoption and the, the upskilling, I mean, I don't know how you phrase it from, from the workforce, you know, standpoint, but certainly, you know, on, on this show and elsewhere, I talk a lot about, you know, what, what it really means to be AI ready. And that's not just like skills and tools, but how do you get people into, you know, this mindset that, you know, it's here to augment all the things that we're, we're capable of.

[00:11:28] And I was just curious to kind of get your perspective on how you sort of instilled, you know, those, some of those concepts when you talked about overcoming people's, you know, fear of job displacement, which we still have today, you know. And so, yeah, just curious to get your take on all that. It's a good question. It's a big question. In my past roles, it was very much about consistency. It was consistency in messaging and making sure that we followed up with action.

[00:11:57] If we were talking with, you know, some of the largest unions who were concerned for their members, or we were talking with individual departments that had thousands of employees who were concerned for their job because we had this new generator AI tool we were deploying. It was about involving people. So as simple as it sounds, it just started with showing up and being transparent about what the goals were.

[00:12:24] And then if, if we didn't quite, if that didn't quite land right away, if there was still fear after that, then we would show up and repeat it again. And then follow it up with action that supported and then repeat it again. There was lots of reinforcement conversations, right? And then over time, in some cases it took months, like four to six months before it was like, okay, it's no longer adversarial. It's now collaborative. It's, we're really showing up in a certain way and we've proven it.

[00:12:54] That absolutely still translates in the projects that we help lead now in public sector. It absolutely translates. To me, we, we have experienced this in California and in other states where to be AI ready starts with that human centered and change management component.

[00:13:13] What are, and sometimes having been in government and now, you know, being a team member and a partner for government, sometimes government is really terrible at problem identification. And really great at saying, okay, this is the immediate thing. We need to handle this right away. And whether or not we're solving the root of the problem, we're going to put this solution in place so that the boat stops leaking. Right.

[00:13:38] And I think where it starts as a combination of that human centered design and design thinking component, which in my opinion is even more important now in a world of AI, where you could easily deploy an AI tool that does not solve the root of any problem. It's just a bandaid and then exacerbates the problem in some cases. And so what, what we've seen in success and a lot of deploying a lot of AI tools is exactly that.

[00:14:04] It starts with the humans, including the people who are frontline workers in a state department, a city department, a nonprofit to learn what are the things that actually hurt their day to day work stream and their ability to provide services to the public. And getting to that problem identification first and using the human centered design and design thinking principles that have been a practice for a long time that not always applied properly.

[00:14:32] In addition to change management practices that and everyone, we can get into this. I can talk about this all day. So you've unleashed me now on the change management pieces, which everyone has a different opinion of.

[00:14:45] We take the most, imagine the most robust and full service version of change management you can imagine, and then package that up into a method that is capable of keeping up with the rapid prototyping that exists now and rapid deployment of Vyde coded agents. And that's what we've developed here in the image of, I think what we always wish we had while we were in government and then offering that back as a service.

[00:15:13] And so it is like summing it all up. So it's a combination of that true problem identification through that human centered design, design thinking with the people and bringing them along so that they're included. It's not something happening to them, right? It's something that is happening with them and with their input and support. And then that last thing is the change management to reinforce that throughout the entire process of design, development, deployment of the AI tool.

[00:15:40] So first of all, now I know why everyone's coming to California to learn from you, because I would be thoroughly impressed if other states and other jurisdictions were doing everything that you just described.

[00:15:55] And yet at the same time, you know, having spent most of my career at IBM, learning all those things that you just mentioned around, you know, design thinking, empathy maps, understanding, you know, journeys, some of those fundamentals, not to mention all the things that IBM researchers and designers taught me around human centric design, accessibility design, you know, all of these things. None of that went away.

[00:16:48] I mean, when we talk about responsible AI, I mean responsible in both design and use of the technology. We are all, you know, builders as well as, you know, consumers and I guess potential beneficiaries of some of this technology. But you got to do it right from the first time and in government as well as in other places, you know, you don't have unlimited, you know, budget, right?

[00:17:14] So to do it right the first time so you don't have to pay to rework it and rebuild it again. And we see that so often, way more often than I would have hoped in government where there's huge investments and sometimes it takes so long that by the time you get the approval and the budget to implement, then the technology is outdated anyway. Right. So totally agree.

[00:17:40] Getting our entire services based around making sure that AI is applied in a safe and secure manner that actually solves a problem while also making sure the client gets the most out of their investment. It's not we have an interesting brand of consulting. We're not get the hooks in and then make sure we generate as much revenue out of that client as we can. Like we're we're for for profit and see we've got to stay solvent.

[00:18:07] But we can do that in a way that has integrity and showing up and actually being helpful and saying, hey, here are the things that you asked us to help with. We can do that at a rapid pace and making sure that we dispel some of the misconceptions around the cost of AI as well. That's changing dramatically and getting away from the idea that it's so costly and it has to be deployed in a waterfall format.

[00:18:34] And then it takes forever and ever to see the return on investment of that. And by the time you see the return on investment, it's considered a legacy system and you got to start all over anyway. You know, like it's all of that is what our service exists to avoid. We are a service that was designed around the idea that it can be done very rapidly and iteratively.

[00:18:59] It doesn't have to be this big bang eight or nine figure investment, even on a system wide or statewide software deployment. It can be done in a fraction of the time, including the change management, which we have in a method that allows us to do that stakeholder engagement and design thinking and then the resistance and empathy mapping in a fraction of the time. Right alongside the rapid deployment of even a vibe coded AI agent off the shelf.

[00:19:28] Yeah. So you're basically taking the foundational sort of principles that we should all be working from going back to design and software before there was software as a service, just regular software. Some of these core principles, you know, some of these core principles, you know, still applied and then and then pairing that with all the benefits of, you know, what what modern solution development, you know, affords us. Right.

[00:19:52] Exactly. And to your point about making sure that the our clients and partners get the most out of their investment, particularly public sector clients that have very limited taxpayer dollars, we want to see the highest ROI possible out of those. And one of the things that we have started to I don't want to say fall into, but it was as we were providing this AI readiness service and AI enablement service, that's really our primary one.

[00:20:16] And we also started to be asked by partners to do post deployment, like if there was off the shelf licenses that were purchased, but then the client had purchased, say, a packet of 10,000 licenses of productivity tool.

[00:20:34] And but they weren't using it and the adoption wasn't high, then we were asked to almost function in a in a CSM or enablement function post deployment as well for off the shelf tools to make sure that the client gets the most out of their investment. It's part of the possible workshops. Hey, here's what it's capable of. All right, let's run through some use cases and personas that you have in your organization that would make it so that all right, even in your day to day, super simple, super easy to digest.

[00:21:01] How can this help you and be a force multiplier rather than something that is scary? Yeah, no, that's excellent. When you're in that space of, you know, public sector, you've got a lot of I mean, it's a big ecosystem. You've got a lot of stakeholders that you try to balance. So some people may get tripped up by maybe focusing on a particular, you know, cohort or archetype when you do some of those some of that design work.

[00:21:27] So how do you guys how do you guys handle that in terms of like, well, I guess what's when you think about being human centric and all of this work? I mean, is there a particular persona that you sort of start with and then layer the other ones, you know, onto that? So the reason my framing is like I'm thinking about like, so since you did a lot of work on the on the workforce strategy and the State Department of Labor and I imagine and things like that.

[00:21:58] But I can tell an acquisition in general. A lot of people talk about being human centric and the process, but then they then they immediately start focusing on like giving the recruiter back time. And it's like, that's nice. They're, you know, they're probably a human being as well. And I want them to be efficient and effective, of course. But but the primary human that I think about in recruiting is the candidate. They're the one being judged.

[00:22:25] They're the ones whose personal information needs to be handled, handled with sensitivity and privacy or whatever. So how do you how do you think about the different sort of human stakeholders across your constituencies? How long do you have? Yeah, it is a tough one because we have certain domains of specialty. One of them, like you mentioned, is workforce development. It's it's in my professional background. It's something I love working on.

[00:22:53] If it's done right, it could be the best tool ever for economic mobility and and financial stability for families. In this case, there are so many ways that we're seeing, you know, emerging tech and AI applied towards this. Also in a way that is a causing damage through that, like you said, the privacy risk and also the risk of algorithmic bias and the hiring process, for example, and things like that.

[00:23:19] One of the things that I will say is the biggest focus that we've seen in the biggest need out there isn't even in the hiring process. It's just in skills matching for the job seeker to have a tool that helps them match the skills they input with jobs that are available in the market. Because, you know, in all of my work in workforce development, that was one of the biggest barriers.

[00:23:46] In some cases, it wasn't even that a job seeker didn't have enough jobs to choose from. It's that they weren't aware that they were qualified for certain jobs. Their materials didn't showcase the qualifications they had for certain jobs. It was a translation problem. You know, it was one skill to another. In particular, we saw this quite a bit, probably not surprising, with service members coming out of the military and transitioning to civilian life as a veteran.

[00:24:14] It was one of the biggest pools that we put so much effort into. And applying AI towards skills matching and career mapping, to me, would be the number one most amazing support tool for the job seeker.

[00:24:32] As opposed to what we see most often, like you mentioned, is AI is applied towards digital recruitment for the sake of the recruiter, which is great if it makes the recruitment of the right person more efficient. But if there is a gap in the candidate pool's ability to find that that job is available in the first place, then we have a big problem.

[00:24:55] And it becomes an equity problem in addition to an actual economic problem of people who are in the job market dying to earn a new job or even in their off time have re-skilled with AI skills and then don't know how to translate those new skills to what is in demand by an employer. And this is another area I could probably talk about all day, but it is truly like being able to create that taxonomy of skills and roles.

[00:25:23] You know, and there's obviously all kinds of resources at the federal level and there's all the industry code system and, you know, things like that. But that doesn't always keep up either with some of the new and emerging roles that we see out of emerging tech, in particular in cybersecurity, artificial intelligence, data science, and how that applies to jobs that, you know, even in healthcare.

[00:25:48] All of a sudden being recruited into the healthcare system with knowledge of digital tools, AI tools that are being used in direct patient care or coordination of care. And so I know I gave you a way longer answer probably than you were asking for, but it truly is to me like when you're talking about a helpful rather than harmful way of applying AI for the sake of supporting a candidate. That to me is the number one thing that comes into mind.

[00:26:16] If there was an AI tool for skills matching and job seeking, you know, going the opposite way of a recruiter, it's the push rather than the pull. Then I think that would be absolutely amazing. Yeah. I mean, thank you. Yeah. Absolutely. Very thorough answer. And now I have like six different, you know, as we can go down, but, but this has come up a lot.

[00:26:37] I mean, literally even in the last, you know, 24 hours, I've talked to lots of people on about this and it came up on just a community call a couple hours ago where the CEO of a labor market intelligence, you know, vendor was, was on the call talking about some of these and the, the, the sort of dynamic. That are needed to understand how to match up the right skills with the right profiles and the right, you know, geographies. Where can we hire from?

[00:27:03] What's their total cost of sort of cost of, of ownership or, or total cost to get this particular work done? Where can I find the humans with those skills? Do they need to be co-located with the rest of the team? Does this even need to be done by a human or should say, should this be done, continue to be done by a human? Or can, is this something that we can build, you know, AI agents and agentic workflows to, to handle and things like that.

[00:27:30] So there's a lot that goes into the, the, the dynamics of how, you know, the labor market, the availability of talent, this, the half-life of skills. How does this all work?

[00:27:42] And you also have the, the gap between where you have AI exposure to the reality on the ground, which is, you know, individual companies' ability and wherewithal to actually absorb investments on the technology side instead of, you know, the human labor side. But there's a, there's tremendous sort of inefficiencies.

[00:28:07] I think you were getting at this tremendous inefficiencies in a way that people find work or work finds people or doesn't find people. But there's a lot of people on the sidelines on the bench that could at least be doing, you know, fractional gig, you know, part-time work.

[00:28:26] So that there's, there's way too many qualified and willing and able talented people that are on the sidelines, not by any choice at all.

[00:28:38] Totally agree. And part of the work that I did do in the past was around skills-based hiring and really trying to look at how do we move in, in particular for us at the time it was in public sector, move away from the default to a four-year degree as a proxy for skills necessary for a huge variety of jobs.

[00:29:01] Even if that degree wasn't in the field of study associated with the job, you know, making it so it's a little bit more targeted and opening up the field, you know, in, in California in particular, the last data I saw, it ended up being right around two thirds of people never pursued a four-year degree, right? It was two-year degree, it was certifications, it was trades, which of course we're seeing, you know, skyrocket in popularity now again, and the resurgence of certain manufacturing as well.

[00:29:31] And so one of the things that we, I am looking forward to is that, you know, a lot of, of skilled workers and individuals who have earned their career through mechanisms and learning outside of a, of a traditional degree program have, are on the sideline now.

[00:29:54] Even though I think it's a perfect time in particular for, with this wave of AI and emerging tech, with skills that are suited for those smaller sort of, the, it's, it's like a smaller amounts of time. There's quick hits of education that you can get through certification or a badge.

[00:30:16] You can get in self-driven courses, you can get in very low cost, you know, education, affordable education through training provider or a community college. It's a perfect time because the misconceptions on needing a CS degree to be a cybersecurity professional are starting to drop out, right? And so even someone who has a lot of skills that are relevant to a lot of different career paths may not know it or believe it.

[00:30:45] They may go through a variety of, of iterations of a role. And I really would love to see this sort of push through the, this wave of AI and employers to be a mechanism to tap into that broader labor market. People who are highly skilled would just take a little bit of investment, you know?

[00:31:05] So, I mean, as an employer myself, I understand that sometimes we get stuck in that realm of like, okay, I need the turnkey candidate, someone who is going to be able to, I can airdrop them into this job and they barely need any training and then we're off to the races. It's just not realistic anymore. The skill sets that are going to become in demand now are ones that haven't been around long enough for that to be true.

[00:31:26] And so how do we get past that hump in, in what the employers are generally looking for when it comes to AI, cyber and emerging tech skills, where we make the investment, make the investment in growing the talent for those people that you were mentioning there on the sidelines right now, but highly capable.

[00:31:45] Yeah. I just, I don't know, as a, as a process efficiency guy at heart and someone who believes in, in human potential, I just, this is, this is like unacceptable to me. Right. And that's why it's so encouraging when I hear, you know, some of the work that you're doing, I, I'm actually cautiously optimistic that some of the things going on at the, at the federal level, some of the things that Keith Sonderling is doing with the Department of Labor. I mean, some of those programs sound amazing.

[00:32:15] I mean, the proof will be, you know, how effective they become, but in theory and principle and everything that I've read, there's some sound, you know, programs in there that could make a meaningful difference. But I still contend that based on what I see across the, the talent ecosystem and the talent lifecycle, there's still a lot of opportunity that ties to some of the things that you were just talking about.

[00:32:38] Like if anybody can just sign up to be an Uber driver and I could like, as soon as we're done here, I could go hop in my car and go drive people around for two hours if I wanted to. So why are we, why do none of the, you know, gig and freelance platforms seem to have that level of efficiency? Like why hasn't anyone built a real, you know, Uber for, for, for AI, you know, whatever work doesn't have to be for AI, but you know what I mean?

[00:33:05] Like there's just, there are other disciplines and domains where someone could just fractionally, if you don't need to be a licensed, you know, whatever, then, you know, why, why don't we have the mechanisms? I think there's some structural things, whether it's insurance liability or we're just a litigious society and that could cause all kinds of downstream impacts.

[00:33:28] But it just seems like if we think about this, you know, collectively, there's, there's a lot of solutions that probably are long overdue. A hundred percent that, that skills and opportunity matching component. I haven't, I haven't necessarily seen, like you said, platform at scale, that effectiveness that I would hope for. There are some that are getting there, but you're right to have a sort of a matching platform for gig work.

[00:33:58] You know, Hey, I've got this opportunity. It's, it's fractional for six months. It's X pay per hour. Right. And then you match up and they're verified skills. One of the things that I will say that you find interesting, and this exists in some, I think some other states as well, but California is pushing right now the, they're calling it the career passport. It's essentially a universal learning and employment record system, right? Just an LER.

[00:34:23] And it's exactly that idea where a job seeker has a profile on there and it's a, it's a content aggregator into a profile of all of my historic learning and employment records, degree programs at any level. And then employers sign up to be a part of the ecosystem, essentially, where then if they have a job posting, whether it's fractional or full time, they can post that.

[00:34:51] And then on top of the ability for job seekers to be matched to jobs that are posted in there, the, the job seeker has all of their information in there. They don't have to chase it down. They don't have to go, Hey, I mean, a college two year to two year degree. I got 10 years ago. And then I got a certification for my last employer two years ago. And then I got another certification last year. Now I'm unemployed and I'm job seeking. And I'm only looking for part time.

[00:35:20] And it's a benefit to them because of that content aggregation piece on the LER side. And then on the employer side, it's a benefit because those skills and credentials are already pre verified. So as far as verifying the skills, the platform guarantees the verification with the source. You know, it could be community college, university, a training provider or credentialing authority of some kind.

[00:35:47] So the employers, a lot of the work has been done for them. And so if that project ends up being as successful as I hope it is, could be a decent example of what you're describing. And, and I really hope it is. Yeah, no, that's exactly where I'm going. In fact, I think my last comment on the community call, someone said, well, what's the future of, forget how they phrase the question. What's the future of the resume or what's, what do we do without resumes or whatever? And I said, well, you create verified digital credentials that the candidate owns.

[00:36:17] And then you, Mr. Recruiter, could do outbound sourcing against the validated, you don't have to check referrals. You don't have to streamline, you know, background checking and, and, you know, all the other, you know, checks that people have to do, your credentials. It's all stored there. I don't have to take the same assessment twice as a candidate, right? I don't have to do any of that twice. And it's there. And it's, I don't know if blockchain is the, is a foundational, you know, component of it, but it's got to be some way that it's validated so that you can't manipulate it.

[00:36:46] And then you just have to make sure that when you hire that person, that the same person that you thought you hired is the one that showed up to do the work. Yes. But I don't want to go, I don't want to veer off into that conversation coming up on the midterm elections and getting all political. But, but yeah, that's exactly, that's exactly where I'm going. I just think you add so much efficiency and effectiveness to what it is you're doing, whether you need somebody for a week or a month or a year.

[00:37:12] There, there's plenty of gig platforms, but I just find them all to be inefficient. I mean, I literally have to go in as a, you know, freelancer, I have to go in and see what new projects are there. Like a lot of them don't even tell you, you know, what's there. They certainly don't do a lot of outreach. Like you should, it's a manual effort. If somebody says, I saw your profile, you know, you might want to throw in a proposal to this, you know, opportunity or whatever.

[00:37:40] Well, if you weren't there, how was I ever going to find that? It's like you were saying before, James, like around, like you're just waiting and hoping that the right people happen to see your, your job post in the limited time that it's up there, that the right people that perfectly match are going to create this, you know, magical, you know, win-win scenario. I mean, we've proven over the last couple of decades, it just doesn't really work out that way most of the time.

[00:38:10] Nope. The post and pray method does not work. That's right. It does not work. Yeah. Yeah. I mean, I already feel like there's so many takeaways for, for my audience. So I'm just thinking about like, you know, what else, what are some other like sort of observations that you've, you've seen or, or, or talk to me a little bit about like where, what are some of the initiatives that Clutch is, is working on now?

[00:38:33] Like either within California or beyond and beyond maybe public sector, like what else are you thinking about? Yeah. Well, one of the things that continues to grow in demand, maybe not surprising that I, that Clutch and I are really, really focused on is a change management method.

[00:38:56] That sort of human centered change management method that can keep up with the rapid change in the workplace, both from AI and a variety of other factors. So much change in culture and expectation in the workplace with, you know, the remote based work or hybrid based work that, of course, became popular through the pandemic for safety reasons. And now people are grappling, all employers are grappling with that.

[00:39:22] So one of the things that we have developed and are getting ready to launch is a new to the world change management method that takes into account the cognitive science of the human trauma curve. Understanding that nowadays, when someone hears a change is happening in their workplace, to their job, to the expectations of by their employer, there's a similar physiological response to trauma.

[00:39:52] And coupling that with a digital tool that operates on a composite basis to actually quantify and give a resistance score to an individual based on a variety of composite factors that we've developed across the last year and in concert with university partners and some cognitive science experts.

[00:40:16] And allowing us to not only quantify with a number, but then allows us to essentially tailor and customize the approach and communication strategy, the empathy mapping, the persona based approach to change management planning, even in organizations of 10, 20,000.

[00:40:42] And then, because then it can be aggregated before AI rollout ever begins. We can collect this information at the individual level and roll it up and know, oh, this section of the organization is slightly more resistant to this type of change. This one isn't. This one's in the middle. How do we tailor our communications and the way it rolls out to each of those role based personas and reduce resistance at an exponentially faster rate and increase adoption than in how we approach the communications and bring them in to be a part of the process?

[00:41:11] And so that product is set to launch here pretty soon. And I am, I'm saying this and people laugh at me, but I think we're going to break the internet because it's the first time in a long time that a new rapid based but quantifiable yet human centered change management method has been done like this. Maybe ever, but certainly not in decades.

[00:41:37] And so it seems like the perfect moment for us as an AI enablement and readiness firm because we don't go and sell AI products. Our entire thing is we want AI products to work for the client base to the best of its ability. How do we drive that adoption and reduce fear? And so that is one of the biggest things we're working on right now. I would say I am so excited about it. I could easily talk with you for another hour, but I don't know that the listeners are, you know, they might be tired of me. I don't know.

[00:42:07] But that is one of the biggest things because I thought some of the change management methods out there weren't rising to the moment with the rapid deployments and prototyping. And unfortunately, a lot of AI tools are being deployed in a way that reinforces the fear and doubt of its effectiveness. We're here to shatter that problem. So that's one of the big things that we're working on right now. I'm super proud of it, actually. Wow. Sounds really exciting. I definitely agree that people are using.

[00:42:37] So we talked earlier about using some of these, you know, 10, 20-year-old or more, you know, methodologies as we think about being responsible by design and human-centric or whatever. But I do agree on the change management side. I mean, the phrase itself almost seems a little stale at this point. But you're right.

[00:42:58] You know, some of those, I think we'd have to cherry pick to say, you know, these things are still very relevant and need to apply, but certainly overdue for an overhaul, I would say. So that's very encouraging with what you're doing. It also seems very complementary to everything that I talk about when we talk about AI readiness.

[00:43:18] And, of course, I use the acronym AIQ, but really not just, you know, people thinking about their skills and, you know, prompting and, you know, learning different models and the tools or whatever. But to what end, right? Like, what does this mean? What does this mean for me, for my team, for other people that I have to interact with and collaborate with and people I'm taking care of in the healthcare space?

[00:43:43] And so it's a broad sort of Venn diagram across, you know, responsibility, human centricity, and then, you know, tools, skills, mindset, adaptability. It sounds like you guys are way ahead of the curve, even in the private sector for companies trying to figure this all out. You, James, you mentioned some of the university, you know, work.

[00:44:11] I was curious because we've talked a lot about current workforce, whether people are gainfully employed or not. But I care very much about, you know, the future workforce as well. And so, you know, I'm on the East Coast in New York and my daughter's about to go off to school also on the East Coast. But I have cousins that teach at UC Davis. My niece just graduated from UC Santa Barbara. So I'm a little bit familiar with the UC system.

[00:44:41] And so you've got a lot of collective intelligence across the California ecosystem. And so I'm just curious if you could unpack the work with the university system a little bit. Totally. We work with the universities in a couple different ways. I mean, one, just I call it the macro and the micro workforce development.

[00:45:03] There are some cases where we are hired by college or university to help with AI enablement through the like internal education and workshops on the part of the faculty or staff of a university. So that then in particular, when it comes to the student services and efficiency of the university as an organization is AI enabled.

[00:45:26] Because I think one of the biggest fears that we're carrying in sentiment across the state among students is not knowing which degree program or which education track to pick. While also making sure it's something that isn't going to be obsolete by the time they're done because of AI or emerging technology.

[00:45:44] And so one of the things that we help do is try and provide that baseline AI literacy and education and a little bit of AI enablement in the delivery of that educational experience for matriculated students going through a degree program. Right. This is future workforce, future leaders. And that in turn would hopefully give all the students the opportunity for exposure and using the tools as they're going through their degree program. Right.

[00:46:10] Because otherwise they're going to hit the workforce and they're going to already be operating at a deficit and not having the skills associated with the AI tools that employ a lot of employers expect you to have. It's table stakes now. That's the micro pieces, you know, assisting with trying to change that culture and AI, use AI enablement for the sake of the student experience. The second one, I would call it like the macro, right?

[00:46:35] The workforce development at large in rethinking how do we prepare the future workforce while to reinforce the degree programs that are absolutely necessary for certain career paths, while also with the expansion of work-based learning programs. A lot of expansion happening here in pre-apprenticeships, non-traditional apprenticeships, as well as traditional apprenticeships, all of which are being evolving.

[00:47:02] They're evolving according to how AI affects that career path. And so we do help with that curation of employer groups, advisory groups, alongside the universities and colleges in a particular community to build a really clear, beneficial talent pipeline through work-based learning programs as well. So it's both the macro and the micro. We love helping individuals in a classroom and seeing that aha moment.

[00:47:31] And then also seeing the system level changes through the work we do on talent pipeline management and work-based learning programs. Wow. So, James, I mean, you got it all figured out. Yeah, right. Yeah, for sure. We definitely are trying to make progress in a big societal challenge, right? Like, that's what we're in it for. It's what Clutch was founded on. We have an amazing ecosystem of partners. I definitely do not want to, you know, make it seem like we're doing all this on our own. You know, there's large and small, right?

[00:48:00] We love our small business partners that can do things in really nimble fashion. It's one of the things that sets Clutch apart as well. We're a medium-sized organization, right? It's nimble enough while also being able to handle big things. And then, of course, large partners. You know, Google Public Sector is a partner of ours really close. It's good to have product partners for things you really believe could make a difference.

[00:48:24] And then also have brilliant developers you work with who can handle things like our partners at Mosaic Data Solutions and others. So it's truly a team effort. It's, we have curated an amazing group of partners that I can't wait to see what we do in the world. Amazing. Yeah, no, I'm really impressed, honestly. I mean, I just think there's so many pieces to what you've had to do.

[00:48:51] I think your background and, you know, working in the public sector there has certainly been to your advantage. But it seems like, you know, you've really created some strong, impactful partnerships and you've impressed upon them that, you know, these are our sort of expectations when it comes to how we think about AI and data.

[00:49:13] And, you know, the dignity that you're trying to give people back in terms of the workforce, you know, readiness, making them realize what they might be capable of just because no one's exposed you to these career paths or these skills.

[00:49:29] I mean, they may have, you know, both natural talent and the ability to learn new skills quickly, but without the exposure, without opening their eyes to what else is possible, you know, they are going to stick with their sort of limiting beliefs. And so if you can shed some of that, I mean, that's part of the battle, right? It's just that mindset shift. And we're fighting every inch of the way, let me tell you. It is a battle. Yeah.

[00:49:57] But I really appreciate the conversation. I mean, any chance to talk with other brilliant like-minded people, thank you. Thank you so much for having me on. And we're just going to keep finding a good fight. Awesome. Well, I wish you the best of luck. And I really, really appreciate you coming on and sharing these great stories and all that you've accomplished, James. So best of luck to you and the team at Clutch. Again, thank you for being here. Of course. Thanks for having me. Thank you, everyone, for listening. We will see you next time.