Data Driven Leadership

Data-Driven Future: How Generative AI Can Shape the Public Sector

Guest: Chris Hein, Director of Customer Engineering for Public Sector/SLED, Google

In this episode of Data Driven Leadership, host Jess Carter engages with Chris Hein, director of customer engineering for the SLED team at Google. Together, they illuminate the path of Generative AI as it shapes aspects of public life like education, privacy, and efficiency.

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What if the futuristic world of generative AI is closer to the public sector than you think?

In this episode of Data Driven Leadership, host Jess Carter engages with Chris Hein, director of customer engineering for the SLED team at Google. Together, they illuminate the path of Generative AI as it shapes aspects of public life like education, privacy, and efficiency.

Chris shares his firsthand experience, unveiling the innovations and real-world applications that are transforming how governments leverage data. From tech enthusiasts to public sector professionals, this episode is a must-listen for anyone curious about the cutting-edge intersection of AI and government.

Don't miss this opportunity to peek into the future with one of the industry's leading voices.

In this episode, you will learn:

  • How generative AI and data can be used in the public sector
  • How using generative AI and data can improve early childhood education
  • Privacy and security concerns and solutions for using generative AI in the public sector

In this podcast:

  • [00:28-03:03] Who Chris is and the role of the customer engineering team at Google
  • [03:03-5:13] What the SLED team at Google does
  • [5:13-12:22] Problems the public sector faces when trying to adapt to technology and data usage
  • [12:22-18:47] How generative AI and data can be used in the public sector
  • [18:47-33:37] Where AI and data are headed in the education sector
  • [33:37-36:36] Resultant and Google’s partnership

Our Guest

Chris Hein

Chris Hein

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Chris Hein runs a team of 50+ customer engineers for Google Public Sector. His team is responsible for architecting technical solutions to solve Public Sector problems. He’s been with Google for 9 years as a Solution Architect, Technical Evangelist, and now Director of Customer Engineering. During that time Chris has helped Fortune 10 companies and state governments alike work more like Google. He is also a member of the Executive AI Principles Fellowship at Google focused on ensuring the ethical use of AI both internally and externally. His goal is to help public sector and educational organizations become more efficient and effective in furthering their mission using best-in-breed technologies and methodologies.


Jess Carter: The power of data is undeniable and unharnessed. It's nothing but chaos.

Speaker 2: The amount of data, it was crazy.

Speaker 3: Can I trust it?

Speaker 4: You will waste money.

Speaker 5: Altogether with duct tape.

Speaker 6: Doomed to failure.

Jess Carter: This season, we're solving problems in real time to reveal the art of the possible. Making data your ally. Using it to lead with confidence and clarity, helping communities and people thrive. This is Data Driven Leadership, a show by Resultant. Welcome back to Data Driven Leadership. I'm your host, Jess Carter. On today's episode, we're talking to Chris Hein, director of customer engineering for the SLED Team at Google. Let's get into this. Hey, Chris, what's up?

Chris Hein: Not much. Great to talk to you, Jess.

Jess Carter: Awesome. Thanks for being here. What does it look like? Let's start with like, your role. What does it look like to be the director of customer engineering for the SLED Team at Google? Like, unpack that for me. Can we do that?

Chris Hein: Yeah. So let's take a step back and talk about what customer engineering is at large. Yeah, that's a weird term in and of itself. And so the way that I look at it is that the customer engineering team is really responsible for being that technical interface that really we need to know enough about both the Google technologies that, that the Google cloud stuff that we work with day in, day out, but also really having kind of customer empathy in terms of what do they look like, how are they, what's their day-to-day experience, right? How are they currently on their mainframes and their old J2EE experiences? And what does that look like and how do you bridge that technical gap from where they might be today to where we want them to be from a Google Cloud perspective?

And then the other kind of part of that that's really important as we think about it in the public sector and SLED is, it also has to be it can't just be about the bits and bytes. My team, we are almost as much solution architects as we are engineers. And so because when we talk to public sector entities, they don't talk in terms of, oh, what you know, like what is Kubernetes or, you know, how does that how is that potentially going to be useful for application stack? They talk to us about, I have a problem with fraud or I have a problem where I can't take online applications after 6 p.m. because my mainframe doesn't accept them, right? And so the other thing that's important for my engineering team is that we have to be way more conversant in the day-to-day experiences of our customers. And that's I think that the reason that you put customer into that title is customer engineers. You really do need to be both the engineering side, but also highly, I am highly empathetic to what the customers are going through.

In terms of me, like, what do I do as a director here? So I've been at Google for almost ten years. I've been here for nine and a half years. I started as an individual contributor and have been a manager and director for the past five or six. And for me, personally, you know, my responsibility is really to try to help set that technical vision for our overall go-to-market teams, right? How are we positioning ourselves? How are we making sure that we are meeting the customers where they are with what they really need from us? And how does the overall engineering team support that? And so, you know, across the SLED market, I've got about 60 folks that are responsible for helping out. And so I oversee those teams and make sure they're all hopefully marching in the right direction.

Jess Carter: That is awesome. And for those who don't know what is SLED?

Chris Hein: SLED is state, local, and education. And so those are the three major components in terms of who we specifically deal with in terms of state entities, local entities, as well as education. That could be anything from kindergarten through 12th grade to higher education as well as inside of our SLED business at Google is also like education technology companies that interface directly with those types of organizations.

Jess Carter: Awesome. So I'm excited to hang out with you. I did not realize that you've been there for nine and a half years, so I started at Resultant nine and a half years ago. So this is like we got to check on our start dates for a second and so you have to figure that out. So I'm excited to see some of this and talk to you about it because I've been on the public sector side serving like most of my clients have been public sector state agencies. And one of the reasons I'm in love with that sector and I just find it fascinating and I don't know about you, but it sounds like with some of the allusions you made to the customer side, and the empathy, is the problems are really unique. Like each state has totally different citizen layout and you are serving, you know, underserved populations that look completely different, and how you navigate to an application solution for unemployment insurance depends on if you have citizen groups that don't use the Internet by, you know, practice or faith or culture.

And so some of the unique solutions we've had to provide to citizens to understand what is the real population need? And for me, it was Indiana v. Nevada, and they were totally different. And so I just love that the problems are always really kind of custom if you get to know and empathize appropriately with who's the state, who are the citizens in it, what are the groups we're trying to serve, that every solution is unique and it's not because we're just trying to sell something twice for more money. It's because the solution actually needs to adjust. So it sounds like your team deeply appreciates that. It's kind of why they exist. Is that fair?

Chris Hein: Yeah, I agree. It's really interesting. There's two halves to it, right? Because I think in some ways one of the great things about working in this space is if you solve a problem once, I guarantee you that same problem in some format that exists 49 other times, right? Maybe a few extra if you include D.C., Puerto Rico, those folks. So that problem set is the same. But to your point, it's never a full-on just like, oh great. And then you just take that you duplicate the code base and you're off to the races because how you solve that will be this whole set of both constituent experiences of how many languages do we need that to be, and do we need that to be something that they can do over the phone?

Also, in government, it's almost always going to have some paper component too, because we're not really done with that. And so there's all these kinds of constituent-facing experiences that you have to account for. And then you also have to just assume that they are going to be coming from multiple years of they've been solving this problem somehow for however long, right? Hundreds of years sometimes, right? And so you also have to meet them where they are to make sure that your solution isn't heavy-handed because they'll know and they'll push back. When you say, oh, yeah, we're just going to have all your people do this differently from here on. And it's like, no, you don't understand how difficult that ask is.

So it is this really interesting process of it's useful to be able to to know, oh, we did this for the state of Indiana and thus we have a good idea of how we might do it for another then it is really important then to dive into those details and be able to understand the nuances of difference from from state to state in terms of legislation, population, etc..

Jess Carter: Yeah. Oh absolutely. Love legislation, it’s always fun, too. So every July one we're sort of checking in to see what needs to be implemented now versus in January one, because sometimes there's like, some time to get everybody adjusted and prepare for the legislation. But yeah, I think that that is, I don't know. I think that that is really astute and it also, I think you mentioned, even empathizing with the worker. So with the person who's trying to run the process.

I'm a little bit of a history geek, too. So one of the things I love is when we're trying to use today's technologies to manage a program that was created through policy during the Great Depression and you're like, this is so neat and we might need some adjustments. And some of those are software and some of those might be policy. But I get to play in one side of that house, not the other. And so I just think it's neat to look at how like censuses change and how the needs of the citizens change. And we weren't solving a substance use disorder in the 1920s the way that we are in 2023. But there were other problems that we were solving back in those days. And the way that we can solve them is largely some of that. To your point, you can't, you don't have to throw the baby out with the bathwater. There are solutions to complex human problems that we can borrow.

Okay, So here's one of my questions. I don't know how many solutions you play with or if you have, you know, agencies like corrections versus unemployment versus motor vehicles. But do you have a few of like, the solutions you guys have been working on that you just think or one that you're like, I just really like, it was my favorite? I just really enjoyed the solution. Like, I don't know, can we have that conversation?

Chris Hein: Yeah, we could. We could definitely talk about that. And I think there's a variety and each one is near and dear to my heart in different ways. Sure. Which of your children do you love the most? But the way that I look at that is so for some of them, right? Like one of the ones that we've had a great partnership with for five years now is the city of New York's Cyber Command, right? And so they are the Cyber Defense Agency for the city of New York. And they've been using our stuff to be able to help protect the New York network from attack over the past five years. And so that's always been one that like, it is always exciting when you think about security and helping secure because that is, you know, one of the other interesting things about working in public sector is public trust matters so much and there's no there's no gaining it back when it's gone like that. That is usually years of having to reinforce like, hey, you can trust us, you can trust us because when you screw up like, it's a big deal. And so that's obviously been something that both in New York City as well as in a bunch of other places that we've done work in their kind of cyber security aspects that I do find really exciting.

But then there's other, kind of more practical ones, right? I think that we've been helping in unemployment agency is like, Wisconsin is a great one to look at where Wisconsin's they call it DWD. You know, they've been really looking at hey, they were on a mainframe, was like I think it was legitimately a 40-year-old mainframe. Yeah. And they've been working with us to say like, alright, great. Like, what does that next version of this look like? And how do we get from A to B and what are some of the ways that we can do that? And they've been really, you know, innovative and looking at, okay, there are you know, I like to think about the way that they're doing it because they're they're approaching it from a user-centric perspective, which is, you know, a very Googly thing to do, which is to say, hey, the the experience of calling in for information about my unemployment claim, what if we just improve that because I don't have to go rewrite the whole mainframe, but I can improve there. And then what if I wanted to do more documents coming in? Again, it's a paper document process. Can I take that funnel and accelerate that funnel and then while I'm still chipping away at that, that legacy mainframe in the background? So that's certainly something that I think is, it's really reproducible because there's so many of these instances that are these large legacy application bases and the rip and replace model is really, really hard in government. And so this idea of taking it and chipping it away from it from a user perspective is something that I think is a really, you know, something Google likes to bring to it. And I'm always appreciative when our public sector entities meet us in that journey.

Jess Carter: That makes sense. And I also think, I’m so curious if you hang out with your friends and they believe you when you're like, I'm not kidding. My client is on a mainframe from 40 years ago, like it was 1977 when this thing was born. It's real, right? Like those are, this is actually happening, but you're also implementing generative AI. So it's the fact that they can accommodate new technology alongside technology that may deserve to die a certain kind of death. And so it's just entertaining to me that we can hold those both truths together and still make a difference in citizens’ lives.

And to your point, I mean, I've been on multiple rescues where they tried to do it all at once, and it took seven years and multiple failures and millions of dollars spent of citizen dollars, and they didn't get anywhere. And so you kind of had to trim the fat and say one way or another, most of these projects end in, how do we take the right pieces and move them over? Over time, we will get to, we no longer need the mainframe, but I just think it's interesting. So, and you've done some work with generative AI, I'm so curious to hear if you could… well, you talk about a mainframe. You can also introduce how that can be used in public sector. I'm just fascinated.

Chris Hein: I mean, yes, it has certainly been the last about nine months of my life, I've pretty much only talked about generative AI. It's been really, really exciting.

Jess Carter: Me too, but I think, not true.

Chris Hein: Yeah. Yeah. I'm super fun at parties at this point. This the really fascinating thing when I look at generative AI in, in public sectors, I am actually seeing you and I were chatting just before the show started about like, you know, are they actually going to do anything with this? Like they're always so far behind. Again, they're on the legacy mainframe, right? What's been fascinating is this has been the only time in, you know, in the six years that I've been directly doing this, this has been the only time where I've seen them leaning in really strongly into a new technology and saying, how do we do this? How do we do it responsibly? How do we do it ethically? But we want to.

Jess Carter: You're saying like state agencies are like, really excited about it. Okay.

Chris Hein: Yeah. I mean, I've had multiple conversations at from governor level to secretary level to director level, and all of them from top to bottom are, they're coming to us saying we want to figure this out. This is important to us. And so, you know, what's been exciting about that, even to the point of a mainframe, one of the things that as we start to talk to them about what these large language models are actually quite good at, you know, one of the things is code conversion. They are very, very good at understanding these legacy code bases and help suggest ways to then convert that into something modern and reusable. And so that's been one of those problem sets of, you know, you do have some of these states that are running on these core systems where literally there's no staff under the age of 50 that understand the framework. Yeah. And so you have this very dangerous kind of experience of what happens is, those folks start to retire and how are we going to maintain these systems?

Jess Carter: Right.

Chris Hein: Using something like generative AI to say, hey, let's document the code base, tell us what it does, right? Just literally tell me what this thing does, because oftentimes that knowledge has lapsed and we've gone from the organization to, hey, let's rewrite that thing, right? Let's do that. So that's one area. But I'd also point out the other two things that are getting agencies excited today is looking at the 10x-ing of the customer experience, the consumer experience, right? And so this is using these conversational agents, these conversational AIs to be able to like, really like you can point it at their website and say, now you know everything about this website and suddenly it's both the best site search that you've ever seen on a government website and it answers questions accurately based on the ground truth data that's there on the website. You know, so those are things that they are really excited about because suddenly they can lower their amount of incoming call load and raise the user experience that comes to them.

The other the last one that I'll mention, then I'll pause and take a breath is an idea of giving their workforce superpowers, right? Because they, you know, one of the things that I love about working in the public sector is that you come across these people who, they are so mission-driven, right? They care so deeply about, you know, doing the thing that they are there to do, whether that is making sure people get benefits, making sure that the state runs effectively, keeping roadways safe, like all of those different areas of their jobs. And so they have to be able to be good at a lot of things to do those things effectively. And so if you start to say, hey, what if I gave you access to a like, a side-by-side super agent who understands how to write code, who understands how to write a SQL who understands your data and can do that analysis with you and in combination with you, and can help you to to suggest next steps of what you might do in your job day-to-day?

Those are things that, you know, when you say that to someone like, hey, what if we could give that to your workforce? It's like their eyes light up, like, wait, if you could make my employees… I was actually just reading a study somebody just did this morning, that somebody did something where it reduced the time to do their job by 40% and their results were 18% more accurate because they had access to an LLM to help them with that particular specific task that they were responsible for doing. That's the kind of stuff that gets me jazzed, but it also is really getting the public sector jazzed.

Jess Carter: That's amazing. I have so many questions. You're going to tell quickly that I'm not you and I have not spent nine months studying generative AI, but I want to. I want to push the envelope on a couple of things. So, efficiency and state government leveraging it for sure. I already want to talk, I want to geek out about DWD agencies, workforce agencies, reemployment. They usually have some solution that tries to do labor market support or spider in job postings from all these places. I'm thinking generative AI would be so neat to pull the right, like, identify unique job postings and actually post those where people have one single source of truth. There's obviously value propositions. Let me go gangbusters on you and then you can then you can dive into security because we know that that's where this should head.

I have always been really excited about the idea of, like, when I was working…start this way, when I was working on state agency projects, it was usually system transition. So it was like, an unemployment insurance system or something. And so those happened for a half-decade of my career. Then we started to understand data in public sector, and then a lot of my projects became data projects because we realized that there were human problems at a substance abuse or substance use disorder or COVID. These human problems that surveyed multiple agencies and not just one source system, social determinants of health were required. We needed to know somebody’s wages, somebody's job, where they work geographically, what their health records are. To start to answer some really unique problems, I get geeked out about this crazy idea and you can tell me why it's stupid and then we can talk about security. If a state ever took on an entity resolution where they could actually understand a unique citizen and a bunch of their datasets across state agencies, don't put on your aluminum hat yet. And do you think that there's a world where we could ever train generative AI on that data and have unique citizen data where we could really understand more effective policies that are evidence-based, based off of that data? Like, is that crazy? And I'm in like, I don't know, like 1984 world.

Chris Hein: I mean, we are living in the future. So that's fine. You know, we've reached this point where sci-fi is no longer all that interesting to us. I don't think you're that far from what's actually our near-term experience, right? I think about this in a lot of ways, like because again, one of the other halves of my sector is education. And in education there is this concept that is really starting to resonate across is looking at it and saying, could you create a customized tutor to every student, right, that understands every grade they've ever gotten? Wow. Understands every test they've ever taken? Where do they typically struggle? Where do they excel? What are the types of questions that they need to be provoked? They could actually like, stand with them over the course of years, right? And really take them through all the different elements of what they're going to need to learn. That's not science fiction anymore. Those are things that we can do.

And so I look at that. Then I extend that same concept to what you were just talking about, which is to say, again, some of this is the dirty business of data wrangling, which is a very, very difficult thing to do in the government space. But as you think about this, if I can raise some of those datasets up a level, right? One of the, one of my big mantras is typically you don't have to break down the data silo. You just have to extract, right? Get it to a place where you have access to it. I don't want to go disrupt every system everywhere because it's too hard on too many people. If I bring that data up to a place where I can actually get access to it, then as a constituent, if I come to a government website, what if or if I'm a caseworker helping a constituent if I can take that that top layer of data that has fed from all these silos? And I then ask a generative AI agent, hey, like, what do I need to know about Chris, right? What are some of the interaction points that we've had with him over the past 25 years? He's lived in Illinois, right? Like, are there things where we're going to do, what are things that have been tried in the past, right? To your point about like, substance abuse, like if you start to look at substance abuse as a problem, that tends to be a corrections problem, an education problem, a health problem, all these different silos, right? So if I if I can abstract that up a level and then I give this generative AI agent access to that abstracted level, now suddenly it knows enough about Chris to be able to answer things at a rational level in terms of what are some good policy things versus some interesting things, which again, it just it takes this, it takes this cruft work of like an amazing case agent would have to go dive into six different files, open up each one of them, whereas instead, it could just be right there in front of them. When they start to have that conversation with somebody that they need to help.

Jess Carter: This is so cool. I'm hearing like, Yeah, we're going to do it, Jess.

Chris Hein: I think so. That is yes, it's the shorter version of what I said is.

Jess Carter: Yes, I love it so much and it makes tons of sense. And I'm excited, especially the idea of a tutor like I just to enable improved learning. That is super exciting. Okay, give me one second. Chris, can you hear my dog? No. Okay, that. Never mind. I'm not going to let her in. She's whining at the door and I'm afraid she's going to start scratching. So if I have to stop, I apologize. Okay. Okay. Then one of us has to take on the, like, the security element, right? Because it does sound crazy. And there are going to be people who are like, I don't want you to have that access to my data. And do I have any right to, can I tell you, no, you can't do that with my data? So do you want to jump on your soapbox here about security?

Chris Hein: Yeah. I mean, I think this is where both its security and its privacy, right? Both of those things matter a tremendous amount. And I think there's multiple aspects to this, right? So let's start with security writ large. Then we'll move to security specific to generative AI and what governments should be thinking about there.

Security writ large, this is where, you know, government has, often they will use compliance to mean security. And what I mean by that—and this is archaic terms to most humans and forgive me for delving into it—but in government, they'll look at you and say, are you FedRAMP certified? Or if you're in public safety, are you CJIS? Yeah. Which stands for Criminal Justice Information Systems. Now, what you need to know about that is that it's a checklist, right? And it's important. And I'm not trying to tell people not to worry about it, but what it does is it tends to obfuscate where the problems really lie. And so real security does not come from a checklist, right? Real security comes from going after the ways that companies like Google have invented new paradigms of security, because, again, we're one of the most attacked networks in the world every single day because people would love to get access to all the things that Google knows. And so taking the lessons learned from that, that it is one of those things where, you know, I wish, you know, it's kind of like buying insurance, like nobody actually likes their insurance policy. It's not like they're excited about paying that on a monthly basis. Cybersecurity ends up being a very similar topic because it's like, hey, that sounds fine. But like, so you're asking me to spend money and I get no actual constituent value except that the potential bad stuff, right? So that's the cybersecurity writ large. My mini rant there, my, my mini-rant on the generative AI side is now it's a privacy conversation, right? And that's where I do think it's important to do a couple of things.

One is you do need to make sure you're understanding government workforces need to understand the differences between consumer tools and enterprise tools. So we've seen lots of headlines about people that put stuff into Chat GPT—it's the more well-known generative AI tool out there—that was not theirs to give, right? They’re putting corporate information into Chat GPT and guess what? That's now somewhat owned by someone else, right? You know, so we come back and we say the same thing of, hey, Bard is this amazing tool that Google has produced. It's a consumer. It's a consumer tool. And so if you need to be doing that cool stuff that I talked about, about understanding your constituents and writing code and all of those things, you need to be doing that in an enterprise context so that your data is secured under that umbrella, right? It's your data, it's your terms. You don't have to have any fear of leakage or privacy concerns that are going to come from it. And you need to be able to express that back to the constituents because you don't want, the last thing you want is for someone to say, oh, wait, you know, my city of Chicago is using Google to understand whether I should get a benefit or not.

Jess Carter: Right?

Chris Hein: No, like Google knows none of that, right? Like, these are underlying core systems that are important to do correctly. So there are kind of these multiple layers of both privacy and making sure that you're using enterprise versus consumer tools when you're looking at things like generative AI.

Jess Carter: Okay, so we play this back for you and make sure I understand because I think this is really neat is, you know, like I said, where I kind of started with, you know, C agencies were looking at getting off their mainframe and that was sort of the key objective in the early 2000s. Then it was how to use their data as an asset. It seems like now an emphasis on data as an asset is how do we better leverage generative AI for efficiencies, for support, but not necessarily to replace core functions or of course is an amplifier of a sort. Is that the right way to look at that for now?

Chris Hein: Yeah, absolutely. I don't think that most of the governments that I talk to, very few are in any way like, hey, how do I reduce their workforce by using these tools? Most of them are saying I can't hire enough workers as it is, right? Like, it's a really tough market out there for public sector entities to hire in. So given that, how do I extend what they can, what they can do? And then on the data side, I do think that there was this, you know, government came along on, they're like, hey, we should we should treat data as an asset. But there are still fairly large blockers in what was being created of, they still didn't have the expertise in those systems to really make it an asset. It just was like, okay, so you're doing some amount of co-mingling and that's good. But they still didn't have the ability to go really get information out of it. And so this is removing that barrier writ large and start to say, how do you use natural language questions and queries to gain access to those data sets to really say, what should I know about people in Illinois who are on Medicaid? What and how does that compare and contrast people in Oklahoma who are on Medicaid and then being able to look into that be like, oh, well, how are the policies different and having something that can actually supply those answers? It's a really it's a real big step change for our public sector friends. Yeah.

Jess Carter: For sure. Do you think when you were meeting with an agency talking about some of the opportunities here or education space when they asked this question: So we've got data, but it's not very good and we haven't really invested in our data governance. Is that a limiting factor on the efficacy of generative AI, or not really at all?

Chris Hein: It becomes a, I don't like this phrase, but it's what's in my head. I'm going to use it anyway. It comes to crawl, walk, run conversation. Okay. Right. And so, you know, what we say then is, hey, I can still enhance experiences with out-of-the-box tooling that do not require you to supply me with a whole bunch of clean, well-regulated data. And that's useful, right? And you should use that, right? It's the, that kind of like, cool vision of the future that you painted of like understanding someone, understanding where they're coming from, and being able to really work with them. Yeah. At this point now, you're going to have to if you haven't done it already, you are going to have to do some more hard work around your data ecosystem and making sure that it's well put together and thought through. And so it's kind of one of those things where are things you could do without having done that hard work. Yep. And you should, right? It's just that like, the amazing stuff is probably going to be, you know, something that does require some forethought and an expertise in getting it done. Okay.

Jess Carter: Well, then can I put you on the spot on? Can you grab a crystal ball and tell me if we've talked about where public sector has been? What do you think is next? What's the next big hurdle?

Chris Hein: If I were home, I'd grab my son's magic eight-ball and shake it around a little bit. You know where we're going. I do think that as you look across the states that I've talked with and the localities that I've talked with, one thing is clear, which is it kind of comes back to something that you you were pointing out, which is you hear these we're on a seven-year modernization journey, which I always look at and squint at. Because I’m like, well, seven years. What you're building is now legacy. You're right.

Jess Carter: You're right.

Chris Hein: You're not getting where you think you're going. And so what I think we're starting to see a lot more energy put into and a lot of the toolset is catching up to what they would need, which is to say that modernization process should be in the months, not years kind of category. And so that's what I'm excited by. And it's, you know, as we've really been working in this space, that's what we've seen as like, part of this was made far more clear during the pandemic because what happened in the pandemic was governments didn't have, they didn't have the time to be able to say, hey, build me a plan that in seven years you can improve my vaccine information system. Right? Right. No! People needed to be able to register for vaccines. They needed to be able to get information about them. And that had to happen in weeks, not years.

Jess Carter: Right.

Chris Hein: And what I think was exciting about that is that the government saw that it worked, that it was possible, right? And it wasn't always their same old vendors that they had been using, right? Those were not necessarily the ones that actually fixed the problem for them so to speak it. So I think that if I were to look into the future, you're going to just see there's a lot more acceptance of the fact that these next generations of tools, they might be able to do what we say they can do, and they might even be able to do it in government, which was always like, oh, you can't do it in government. That won't work. The same here. And so I'm excited about that. I think that I think we're hitting this tipping point where people are going to be more willing and eager to jump into the pool with us.

Jess Carter: I mean, a world where technology isn't to blame for why you can't implement your policy is going to put some real pressure on the legislative process.

Chris Hein: Yes.

Jess Carter: So challenge, accepted. Okay? Yeah.

Chris Hein: Yeah. Suddenly, suddenly the wheels go a little bit. If you can't gum up the wheels with with the fact that. Oh, well we can’t do it because of X, Y, or Z.

Jess Carter: Right.

Chris Hein: It's an interesting next conversation.

Jess Carter: It is an interesting next conversation. That is awesome. Okay. Chris, last question here. I believe that Resultant may have won an award for partner of the year for Google. I'm curious if you could tell me more of like, to you, what does that mean? I just don't know much about it. Curious if you could unpack that for me.

Chris Hein: Yeah. First of all, congratulations. Second of all.

Jess Carter: I had nothing to do with that.

Chris Hein: But to me, when I think about in the public sector space, what makes our great partners, you know, so much of this comes back to, in some ways my very first kind of points around being customer engineers and being customer empathetic and solving, you know, solutions versus just, you know, tactical single things that are being asked of us. And so when I look at Resultant and I look at the work that's being done, what's really exciting is when there are these moments in time where you all are coming to us and saying, hey, here's how we're going to solve this. And we're like, it's great. What do you need from us to make that happen? Right? And that's where it suddenly becomes this real, true partnership.

And, you know, it really is not to be hokey about it, but it becomes this really great three-way partnership of the government entity, the partner, and then us. And suddenly, like you get the, you know, the old one plus one equals three kind of scenario where it really does become a much broader win for across all three of those where the constituents are getting better experiences. The government's happy about the operational efficiency that they're seeing. And then both Resultant and Google are both coming out of it ahead from a commercial standpoint as well. So I would say, you know, thank you all to your teams for being such great partners to us in that respect.

Jess Carter: Thank you guys for listening. I'm your host, Jess Carter. Please don't forget to follow the Data Driven Leadership wherever you get your podcasts and review. Let us letting us know how these data topics are transforming your business. We can't wait for you to join us on our next episode.

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