Latest / Real State / Leading Through AI: What's Next for State Government
Transcript
- 1 00:00:00,000 --> 00:00:03,360 What is coming next that state leaders should be watching for? 2 00:00:03,360 --> 00:00:07,040 I don't think there's much doubt that what's coming next is a genetic AI. 3 00:00:07,040 --> 00:00:13,120 This is Real State, a podcast from CSG South. 4 00:00:13,120 --> 00:00:19,680 Join us as we talk with policymakers and public leaders shaping state-level decisions across the 5 00:00:19,680 --> 00:00:25,040 South and explore how ideas move from policy to real impact. 6 00:00:25,040 --> 00:00:30,320 [MUSIC] 7 00:00:30,320 --> 00:00:35,280 Hello and welcome to Real State. I'm your host, Lindsey Gray. Back in July, 8 00:00:35,280 --> 00:00:40,160 we recorded some real-state episodes while at the Southern Legislative Conference, 9 00:00:40,160 --> 00:00:45,600 where state leaders from across our 15 state regions and beyond are discussing the issues 10 00:00:45,600 --> 00:00:51,680 that will shape the future of state government. And few issues are evolving faster than artificial 11 00:00:51,680 --> 00:00:58,800 intelligence. For states, the conversation around AI has quickly moved from beyond the basics. 12 00:00:58,800 --> 00:01:06,240 Leaders are now asking what tools are most effective? What problems can AI help us solve? 13 00:01:06,240 --> 00:01:13,280 How do we manage the risk and how do we prepare for what's coming next? AI, including 14 00:01:13,280 --> 00:01:21,840 generative AI, a genetic AI, and machine learning is now ranking as one of the top priorities for state CIOs. 15 00:01:21,840 --> 00:01:28,320 But successful adoption isn't just about deploying new technology. It requires thoughtful decisions 16 00:01:28,320 --> 00:01:34,320 around governance, security, data, workforce readiness, and public trust. 17 00:01:34,320 --> 00:01:40,080 Joining me today is Doug Robinson, Executive Director of NASCIO, 18 00:01:40,080 --> 00:01:46,560 the National Association of State Chief Information Officers. Doug works with CIOs 19 00:01:46,560 --> 00:01:53,680 across the country and brings a unique perspective on where states are today and where this technology 20 00:01:53,680 --> 00:01:59,280 is heading. He was also gracious enough to lead an awesome session at the Southern Legislative 21 00:01:59,280 --> 00:02:04,720 Conference this year. Doug, welcome to Real State. Pleasure to be here, Lindsey. Thank you for the 22 00:02:04,720 --> 00:02:11,280 invitation. Doug, you have a unique vantage point where you're talking with technology leaders 23 00:02:11,280 --> 00:02:17,680 across the country. When you look at where states are today with AI adoption, how would you describe 24 00:02:17,680 --> 00:02:25,360 this moment? Are we still in the experimentation phase or are states beginning to think about AI as 25 00:02:25,360 --> 00:02:33,600 a long-term transformation strategy? Certainly beyond experimentation. I've characterized 26 00:02:33,600 --> 00:02:41,840 2026 as the year of adoption. So we've moved for the most part, and again, I'm generalizing 27 00:02:41,840 --> 00:02:47,040 from the states, our motto at NASCIO, if you've seen one state, you've seen one state. So I have to 28 00:02:47,040 --> 00:02:54,000 look at kind of unevenness, but for the most part, about 80% of the CIOs report that they already have 29 00:02:54,640 --> 00:03:01,920 AI solutions, generative AI solutions in production. So I would say beyond experimentation and cautiously 30 00:03:01,920 --> 00:03:09,360 moving. So it's kind of a combination of adoption and kind of risk management. What are you hearing 31 00:03:09,360 --> 00:03:17,840 from state CIOs? And are they excited, cautious, or a bit of both? A little bit of both. I think 32 00:03:17,840 --> 00:03:25,600 there's certainly a lot of excitement about the possibilities. We have not seen a large percentage of 33 00:03:25,600 --> 00:03:34,000 these pilot projects move to scale and by that, I mean beyond an agency or beyond a certain line of 34 00:03:34,000 --> 00:03:39,680 business or a project. So when we look at our data, they're fairly specific in what I'll call kind 35 00:03:39,680 --> 00:03:46,080 of the functional domains of government, health and human services, transportation, tax and revenue, 36 00:03:46,080 --> 00:03:52,160 those are the large ones. But in terms of scaling across the entire executive branch, those have 37 00:03:52,160 --> 00:03:58,480 been limited to a lot of the productivity tools, but not something that we're saying. So I think that's 38 00:03:58,480 --> 00:04:05,760 kind of the cautious nature of what their role is, part of their role is risk management, regardless. So 39 00:04:05,760 --> 00:04:13,360 I think they're looking at the exciting opportunities, transforming how states serve their citizens, 40 00:04:13,360 --> 00:04:19,840 but also doing that cautiously because you just can't let the beast loose without those guard rails. 41 00:04:19,840 --> 00:04:26,560 A lot of attention goes to the future potential of AI. But I want to talk about what is available 42 00:04:26,560 --> 00:04:33,680 today? What AI tools or capabilities are already mature enough that states can realistically 43 00:04:33,680 --> 00:04:40,640 begin leveraging them? Yeah, so that's a good question because it's again mixed, but I'll generalize for 44 00:04:40,640 --> 00:04:47,120 the most part what's widely available are the productivity tools that are already built into 45 00:04:47,120 --> 00:04:54,480 collaboration platforms. So things like Microsoft, co-pilot and Google Gemini, those are widespread. Other 46 00:04:54,480 --> 00:05:00,960 states have direct through CIO directives are an executive order by the governor. They've deployed 47 00:05:00,960 --> 00:05:08,000 other tools like chat DPT and Clawed and others throughout the executive branch for employees to 48 00:05:08,000 --> 00:05:14,080 utilize. So those are widespread. Again, what is more limited are the more specific 49 00:05:14,080 --> 00:05:19,920 applications within a line of business. So you have to recognize that the states aren't, they're not 50 00:05:19,920 --> 00:05:26,480 building AI tools. These are commercial products. That's one of the challenges in terms of acquisition 51 00:05:26,480 --> 00:05:34,560 and procurement is all of these products today are you can say putting quotes powered by AI. So every 52 00:05:34,560 --> 00:05:40,320 solution they bring in most likely already has AI embedded in it and that's kind of one of the 53 00:05:40,320 --> 00:05:45,600 challenges is making sure that you have an inventory of what's available when you look at the states that 54 00:05:45,600 --> 00:05:51,760 do have inventories. You'll see it's predominantly commercial products that are focused on document 55 00:05:51,760 --> 00:05:57,840 preparation, generation, chat bots. Those are the kind of leading use cases across all the states. 56 00:05:57,840 --> 00:06:03,040 Where do you see the biggest opportunities for AI to improve how government operates? 57 00:06:03,040 --> 00:06:09,520 Whether that's internal efficiency, citizen services, cybersecurity or decision making. 58 00:06:09,520 --> 00:06:14,720 My speculation and certainly based on the evidence out there is the greatest opportunities are to 59 00:06:14,720 --> 00:06:21,200 enhance citizen services. But that comes with the challenge of trust. And so I think that's the one 60 00:06:21,200 --> 00:06:29,680 thing that CIO's and agency leaders are looking at is they see opportunities to apply beyond things like 61 00:06:30,800 --> 00:06:36,640 responding to questions out of chat bot or doing immediate translation services much more 62 00:06:36,640 --> 00:06:44,640 transformation from end to end cybersecurity. Almost 90% of the states today are using AI in 63 00:06:44,640 --> 00:06:52,160 cybersecurity for threat detection to examine anomalies to scrub logs. Things that take a lot of 64 00:06:52,160 --> 00:07:00,000 human capacity can be done with AI. So we see that as another from cybersecurity ops. Recognizing 65 00:07:00,000 --> 00:07:08,160 that the blade swings both ways. So the bad actors are also using AI extensively to craft 66 00:07:08,160 --> 00:07:14,400 very convincing, fishing emails and to do things in social engineering that weren't available for. 67 00:07:14,400 --> 00:07:21,120 So that's certainly part of it. I think the other area that I'm really looking at, because I think 68 00:07:21,120 --> 00:07:26,880 there's tremendous opportunity there is fraud detection, fraud prevention. So in a states of 69 00:07:26,880 --> 00:07:32,960 classically done kind of what's called the pay and chase model. You've seen a lot of headline news 70 00:07:32,960 --> 00:07:38,000 over the last several years certainly since COVID about the level of fraud and public benefit programs 71 00:07:38,000 --> 00:07:44,960 and just public distribution programs. It is is significant and in the major challenge for that is 72 00:07:44,960 --> 00:07:51,280 identity management and identity verification. So I think the states are testing AI several 73 00:07:51,280 --> 00:07:59,520 state revenue and tax departments are using AI already to look at how to advance that through AI 74 00:07:59,520 --> 00:08:05,280 as opposed to some of the traditional fraud tools. So that's another I think really strong use cases 75 00:08:05,280 --> 00:08:11,120 fraud detection. Are there use cases you were seeing emerge across the states that you think could 76 00:08:11,120 --> 00:08:17,040 become standard practices over the next several years for our states? We're capturing those with our 77 00:08:17,040 --> 00:08:23,600 regular surveys and we're just now analyzing our 2026 data to look across the word kind of to see 78 00:08:23,600 --> 00:08:30,960 where are those were the patterns of of use cases. Again, top of the list is in things like case 79 00:08:30,960 --> 00:08:38,400 management and benefits management with with cautions that ultimately its humans making a decision. 80 00:08:38,400 --> 00:08:45,120 We have a number of state legislative bodies that have already enacted legislation and the past bills 81 00:08:45,120 --> 00:08:51,600 that prohibit AI from making final things like Medicaid eligibility termination or insurance. 82 00:08:51,600 --> 00:08:58,560 However, those tools can be used to obviously streamline the massive amount of rules and 83 00:08:58,560 --> 00:09:04,720 regulations that case workers have to absorb. So we've seen good examples of that good examples 84 00:09:04,720 --> 00:09:12,240 and helping citizens navigate complex bureaucratic structures because they don't understand the rules 85 00:09:12,240 --> 00:09:19,120 of the road and so a lot of those business rules can be embedded in in AI some very fascinating things 86 00:09:19,120 --> 00:09:26,800 and natural resources some very discrete kind of use cases of things like predicting the 87 00:09:26,800 --> 00:09:33,360 ovulation cycles of stripe bass. We got that one. I thought that was very interesting. So a lot of 88 00:09:33,360 --> 00:09:42,000 things California has embarked on a fairly significant effort to look at things like accidents 89 00:09:42,000 --> 00:09:47,440 and to use data historical data that again and previously this wasn't possible with the with the 90 00:09:47,440 --> 00:09:54,320 capabilities and the compute powers to look at 10 years of historical accident data and make 91 00:09:54,320 --> 00:09:59,200 a some type of revelations around the causal factors of that. So that's what they are doing. They 92 00:09:59,200 --> 00:10:05,200 have two major initiatives around vehicular and pedestrian accidents and they are even embarked 93 00:10:05,200 --> 00:10:11,040 on that where they are looking at terabytes and terabytes of data that has been going to put 94 00:10:11,040 --> 00:10:17,040 together for AI analysis. So a lot of good things in those. Which you I think if you look at 95 00:10:17,040 --> 00:10:24,480 and just think about what are the major the functions of state government and the major large 96 00:10:24,480 --> 00:10:28,240 agencies that's where you're going to find a lot of activity because they're trying to reduce 97 00:10:28,240 --> 00:10:36,000 the overall burden but I think certainly the promise in the future is enhanced citizen services 98 00:10:36,640 --> 00:10:44,240 with with AI tools. Many states have launched AI pilots or created task forces to study potential 99 00:10:44,240 --> 00:10:50,400 applications but moving from pilot project to something that truly changes how government works 100 00:10:50,400 --> 00:10:57,360 can be challenging. So what separates states that are successfully implementing AI from those that 101 00:10:57,360 --> 00:11:03,440 are still trying to determine where to start? That's the question on the table today. Certainly as we've 102 00:11:03,440 --> 00:11:11,040 seen the states that have moved that if you look at the data from a year ago 90% of our states reported 103 00:11:11,040 --> 00:11:18,880 that they had pilot projects and only about 30% said they had moved those into production. So fast 104 00:11:18,880 --> 00:11:26,960 forward to 2026 our latest data indicates about 80% of them report that they've moved these pilots 105 00:11:26,960 --> 00:11:33,200 into production. Now some of them will not succeed. They will find issues with those but for the most 106 00:11:33,200 --> 00:11:41,040 part they're reporting that I think strong governance strong oversight, strong measurable impact and so 107 00:11:41,040 --> 00:11:48,080 be basically measuring along the way to see how these things are operating because they advance and 108 00:11:48,080 --> 00:11:53,920 evolve so quickly you have new models that come out all the time and those new models obviously can 109 00:11:53,920 --> 00:12:00,160 impact the direction of the project. So they're looking to stabilize those governance is a big part 110 00:12:00,160 --> 00:12:06,320 of that. The other certainly most defining challenge of all states is data quality and data integrity. 111 00:12:06,320 --> 00:12:14,560 I always remark years ago CIO on our board said to me, "Doug, for a bad data I wouldn't have any at all." 112 00:12:14,560 --> 00:12:21,040 And so that's one of the challenges is how do you make sure that you are ingesting high quality 113 00:12:21,040 --> 00:12:28,240 reliable data into your models that you're using to make those decisions? And I think the third 114 00:12:28,240 --> 00:12:34,560 thing which is a touchstone for all of this is workforce readiness. So we're seeing a lot of states ramp 115 00:12:34,560 --> 00:12:39,920 up their capabilities and in the workforce they're combining it with their cyber security training 116 00:12:39,920 --> 00:12:45,840 perhaps. Some cases they're making it mandatory if you're using an AI tool then you're required to 117 00:12:45,840 --> 00:12:50,800 receive some type of training and that training is going to focus on responsible and ethical use. 118 00:12:50,800 --> 00:12:57,360 And so that's a that can be a major issue. There will be missteps. I am confident that something 119 00:12:57,360 --> 00:13:03,120 will happen and states will learn from that. But and right now it's they're moving ahead but they're 120 00:13:03,120 --> 00:13:09,120 being cautious. Are there foundational investments whether that's cloud modernization, workforce 121 00:13:09,120 --> 00:13:14,240 training or governance that states need before they can fully take advantage of AI? 122 00:13:14,240 --> 00:13:18,880 Yeah well I've mentioned that governance is certainly at the top of the list which is those 123 00:13:18,880 --> 00:13:26,640 is the shared kind of decision model with in my area certainly of focus is the only the executive 124 00:13:26,640 --> 00:13:34,960 branch. So you have to have stakeholder engagement. I think you you bringing in outside experts as 125 00:13:34,960 --> 00:13:38,720 you have many states that are they're not only bringing in industry but they're bringing in the 126 00:13:38,720 --> 00:13:44,480 university. They have coupled their projects with university efforts. Mississippi is a good example 127 00:13:44,480 --> 00:13:50,320 of that. They have a project called Maine which is the Mississippi AI network and it's a combination 128 00:13:50,320 --> 00:13:57,280 of state government and the private sector and industry and higher education that are that are 129 00:13:57,280 --> 00:14:02,240 involved in that. I think that's kind of a good collaborative to the matter of model to look at 130 00:14:02,240 --> 00:14:08,400 other states have done very similar. So that's that's that's the challenge is how to how to kind of 131 00:14:08,400 --> 00:14:16,080 navigate all of that. Procurement is is another piece of the acquisition cycle is another kind of 132 00:14:16,080 --> 00:14:22,000 foundational aspect you have to get right. And those that comes with some challenges because 133 00:14:22,000 --> 00:14:29,920 states today don't have that maturity in how to require things like disclosure from you know all 134 00:14:29,920 --> 00:14:35,680 their vendors during association. So they're going to have to kind of work through you know all of that 135 00:14:35,680 --> 00:14:41,280 because there's tremendous liability if they if they don't workforce readiness as I've already 136 00:14:41,280 --> 00:14:46,320 mentioned in a couple of times is another one of those foundational pieces when you look at some of 137 00:14:46,320 --> 00:14:52,880 the challenges not not is not not not I think a couple of policy areas that have not been given 138 00:14:52,880 --> 00:15:01,280 enough exposure one is things like public records laws right so how do you make sure that you're 139 00:15:01,280 --> 00:15:07,200 aligned when you're using these tools to the existing open records laws that are in the state and 140 00:15:07,200 --> 00:15:12,560 for almost every state it's very similar record as a record regardless of physical form or 141 00:15:12,560 --> 00:15:20,240 characteristics. So for the states AI results AI prompts and AI results used by state agencies are 142 00:15:20,240 --> 00:15:25,440 part of the official public record but the majority of the states today are not handling it in that 143 00:15:25,440 --> 00:15:30,080 way. So they're going to have to advance the maturity of that not surprisingly technology is 144 00:15:30,080 --> 00:15:35,760 always way out in front of the policy framework and in the policy foundations in most states. So 145 00:15:35,760 --> 00:15:41,600 they always have to speed those up and move quickly. So let's stick with the governance theme. 146 00:15:41,600 --> 00:15:48,800 Nassio has highlighted AI governance as a major priority so this technology touches every agency 147 00:15:48,800 --> 00:15:54,960 every part of government so given that from your perspective what does a strong governance model for 148 00:15:54,960 --> 00:15:59,440 state governments look like? Well when you think about you think about governance particularly in a 149 00:15:59,440 --> 00:16:06,880 technology space there are certainly lots of different opinions about architecture essentially the 150 00:16:06,880 --> 00:16:14,800 the the the the blueprint of government as we describe it and so you have to be able to have a 151 00:16:14,800 --> 00:16:23,120 venue, a policy process, a collaborative model to have those shared decision rights and if you 152 00:16:23,120 --> 00:16:30,960 look at the state today, overall majority of them there is a directive of some type of coming from 153 00:16:30,960 --> 00:16:36,800 the executive branch and from the state CIO which outlines what what is their AI framework? What 154 00:16:36,800 --> 00:16:42,160 is the responsible use? What are the ethical guidelines? What are the guardrails that you have to adhere 155 00:16:42,160 --> 00:16:49,520 to? A simple example would be employees are not permitted to use personally identifiable citizen 156 00:16:49,520 --> 00:16:56,160 information right when they're using these tools they can't they can't train the models using state 157 00:16:56,160 --> 00:17:00,720 data that's kind of a guardrail it's across all states so they're all very common they're they're 158 00:17:00,720 --> 00:17:06,400 borrowing from each other and looking out to success and so you know strong governance looks at that 159 00:17:06,400 --> 00:17:11,520 that collaborative approach and shared decision it can't be the big hammer type of authority that 160 00:17:11,520 --> 00:17:17,520 we've seen and it can't be the rigid you know you cannot use these tools it has to recognize that 161 00:17:17,520 --> 00:17:22,800 these tools are in place they're now ubiquitously available and there's got to be a flexibility 162 00:17:22,800 --> 00:17:31,520 around those around those use cases so it has to be taken seriously this is a a transformational 163 00:17:31,520 --> 00:17:38,160 technology in terms of its use but it can also inherit risks and so I think that's the role of 164 00:17:38,160 --> 00:17:45,440 of strong governance is having those conversations and adopting a rubric that works in in that in 165 00:17:45,440 --> 00:17:51,520 that state depending on their operating model so when you talk to state leaders what are the biggest 166 00:17:51,520 --> 00:17:58,560 concerns you hear around AI adoption they don't know what they don't know and I think that's the 167 00:17:58,560 --> 00:18:06,400 the major concern is you know they they they expect surprises and they're trying to mitigate those 168 00:18:06,400 --> 00:18:12,960 and so without understanding the complexities and again you even some of the data scientists and 169 00:18:12,960 --> 00:18:20,320 developers don't understand how these models are evolving and the constant flux in the marketplace 170 00:18:20,320 --> 00:18:25,600 and also the constant changing there's new new models and new capabilities that seems like coming 171 00:18:25,600 --> 00:18:32,080 out every week and so it can be very confusing because the states are trying to kind of put a 172 00:18:32,080 --> 00:18:38,800 stake in the ground at a point in time and it's constantly it's constantly moving and it creates 173 00:18:39,360 --> 00:18:44,480 you know some some friction perhaps and in whether it's because there might be some 174 00:18:44,480 --> 00:18:51,120 agency that really wants to move quickly but there's a level of uncertainty there about 175 00:18:51,120 --> 00:18:58,400 you know what the consequences might be so they've been very careful to test things go to the sandbox 176 00:18:58,400 --> 00:19:04,000 reevaluate and I think that's a big part is constant and continuous evaluation and some states 177 00:19:04,000 --> 00:19:09,440 the others report to me that they're not doing that very well yet because of the magnitude of that 178 00:19:09,440 --> 00:19:15,840 there's certainly as you know winzie a lot of hype around AI right it's at the higher that's kind of 179 00:19:15,840 --> 00:19:20,320 high hype cycle but you've got a temper that with the reality of the role of government 180 00:19:20,320 --> 00:19:28,400 and also the fact that look at the latest results from pew around trust in AI 70% of those 181 00:19:28,400 --> 00:19:35,840 respondents certainly US citizens said they don't trust AI 70% so they don't trust AI and yet we 182 00:19:35,840 --> 00:19:41,760 have state government agencies and leaderships trying to deploy AI across you know all matter 183 00:19:41,760 --> 00:19:47,920 of state services and that creates some some challenges and direct conflicts with what citizens want 184 00:19:47,920 --> 00:19:56,240 to see taking the matter of citizen data protection a step deeper states have a responsibility to 185 00:19:56,240 --> 00:20:03,440 protect citizens information and maintain trust how should leaders think about privacy transparency 186 00:20:03,440 --> 00:20:11,200 and ethical use as AI becomes more integrated in state governments again a broad constituency of 187 00:20:11,200 --> 00:20:17,360 collaborators internally so a specific example of we host the community of practice of the state chief 188 00:20:17,360 --> 00:20:21,760 privacy officers we also heist the state chief information security officers those are 189 00:20:22,640 --> 00:20:29,280 typically part of the CIO organization but not always and we are we we always recommend that the 190 00:20:29,280 --> 00:20:35,520 chief privacy officer be fully engaged in those decisions in fact in a couple of states the CPO 191 00:20:35,520 --> 00:20:43,120 is actually leading their AI effort so we now have over half the states that have a chief AI 192 00:20:43,120 --> 00:20:48,400 officer already appointed and many more that are operating in that role not without equivalent 193 00:20:48,400 --> 00:20:54,480 title but they they operate in support of that so we're seeing the growth of another chief physician 194 00:20:54,480 --> 00:20:59,920 but with tell them that they they have to have communications together and that's a big part of 195 00:20:59,920 --> 00:21:06,960 that to make sure that they are following the appropriate privacy there's no federal privacy rules 196 00:21:06,960 --> 00:21:11,680 there's no federal privacy legislation so each state have crafted their own and some of them 197 00:21:11,680 --> 00:21:17,760 you know are are very restrictive in terms of what the states can do so they have to make sure 198 00:21:17,760 --> 00:21:23,760 that they're aligned with that and that again requires that they they understand the the magnitude 199 00:21:23,760 --> 00:21:31,520 of a state data states have the most amount of personal information about all of us 200 00:21:31,520 --> 00:21:36,880 than any other entity from essentially cradle to grave right they have they issue the births 201 00:21:36,880 --> 00:21:43,520 certificate so they have all that information they host the gold standard for identity which is 202 00:21:43,520 --> 00:21:49,280 the driver's license and then of course they have a whole set of transactions which they're constantly 203 00:21:49,280 --> 00:21:54,960 collecting personally that have all information from citizens and that has to be protected and 204 00:21:54,960 --> 00:22:02,400 certainly fire walled off from a lot of these AI tools AI obviously is moving very very fast 205 00:22:02,400 --> 00:22:09,520 so many leaders are just getting their arms around generative AI now here comes the agents with 206 00:22:09,520 --> 00:22:15,600 more advanced capabilities what is coming next that state leaders should be watching for I don't 207 00:22:15,600 --> 00:22:21,600 think there's much doubt that what's coming next is a genetic AI as you mentioned that is from you 208 00:22:21,600 --> 00:22:28,640 know again our recent data about the next wave what's the the most what will be the most impactful 209 00:22:28,640 --> 00:22:34,560 emerging technology in the next three years it's two thirds of our our state CO said a 210 00:22:34,560 --> 00:22:40,800 genetic AI as they see that actually as surpassing generative AI because of its capabilities 211 00:22:40,800 --> 00:22:48,320 its capabilities to have actually navigate and and and make decisions of complex business rules 212 00:22:48,320 --> 00:22:54,400 that many citizens are not surprisingly are confounded by call it navigating the pirate map 213 00:22:54,400 --> 00:23:00,000 it's very difficult to understand that so a gentigate AI and those agents can actually give them the 214 00:23:00,000 --> 00:23:05,120 tools again comes with a lot of inherent risk a lot of security risks and privacy risks but 215 00:23:05,120 --> 00:23:11,040 they see that as the next capability to use agent technology they're using it a number of states 216 00:23:11,040 --> 00:23:18,720 already using a gentigate AI internally they're using it to make a kind of simple decisions along 217 00:23:18,720 --> 00:23:25,600 along those complex business rules internally I see them opening that up two citizens in the near 218 00:23:25,600 --> 00:23:33,040 future so it is is not widespread is I think the maturity is uneven the adoptions uneven but they 219 00:23:33,040 --> 00:23:40,080 see the promise of what of what that can do and there's certainly plenty more that generative AI 220 00:23:40,080 --> 00:23:47,840 can do when you get down into get down into the large enterprise scale by that I mean cross boundary 221 00:23:47,840 --> 00:23:53,520 interoperability across multiple agencies right now that is a fairly low level and so we're going 222 00:23:53,520 --> 00:23:59,520 to see that we see that build up but I think it's exciting to look at the I guess the innovation 223 00:23:59,520 --> 00:24:08,640 that you're seeing across the states and the fact that old old the legacy modernization topic is 224 00:24:08,640 --> 00:24:14,080 always kind of the top of the list for states see I just modernization and general every state CIO 225 00:24:14,080 --> 00:24:20,160 aspires to have a modern environment these tools can help leak accelerate that but again with 226 00:24:20,160 --> 00:24:26,480 they're doing it very cautiously understanding that things that will certainly could go wrong and 227 00:24:26,480 --> 00:24:30,960 will go wrong at some point in the future is they're trying to mitigate the risk as much as they 228 00:24:30,960 --> 00:24:36,720 can and provide guidance through their policy memorandum and their direction so that'll something 229 00:24:36,720 --> 00:24:42,960 will solidify over the next several years beyond a genetic AI what other emerging tools or 230 00:24:42,960 --> 00:24:49,520 capabilities do you see that could be especially valuable for states over the next five years I 231 00:24:49,520 --> 00:24:57,440 think we're looking at the tools that were originally deployed at a fairly low level during 232 00:24:57,440 --> 00:25:04,880 during COVID one is low code no code development and there's also vibe coding which involves AI 233 00:25:04,880 --> 00:25:11,200 but I think low code no code is really bringing the capabilities of application development out of 234 00:25:11,200 --> 00:25:17,440 the software solution shop and giving it to the lines of business meaning that that state 235 00:25:17,440 --> 00:25:23,840 employees who know the business process can now use modules and kind of plug and play 236 00:25:23,840 --> 00:25:29,760 to develop solutions we saw that happen during COVID that has become certainly more widespread 237 00:25:29,760 --> 00:25:37,360 quantum certainly something that states are just now looking at we have a couple of states that 238 00:25:37,360 --> 00:25:43,680 have quantum readiness task forces and looking at that that the the danger with quantum from the 239 00:25:43,680 --> 00:25:50,720 states perspective is the security concerns and the capabilities that that actors using quantum 240 00:25:50,720 --> 00:25:57,920 capabilities can actually bust the encryption levels of many of the encryption tools and the 241 00:25:57,920 --> 00:26:03,040 algorithms that states use today to protect their data so many states are encrypting their data at 242 00:26:03,040 --> 00:26:08,000 rest they're using encryption for transport their websites all have capabilities where they're 243 00:26:08,000 --> 00:26:15,040 encrypted with tokens and quantum has the certainly the capability in the future to break those 244 00:26:15,040 --> 00:26:21,360 encryption and not and do it in a relatively rapid format not hundreds of years that we talked 245 00:26:21,360 --> 00:26:27,600 about before so that's one I think an area that is receiving more attention it kind of was at the 246 00:26:27,600 --> 00:26:33,760 high of the hype cycle and then went down onto the trough for a while's blockchain and so we have 247 00:26:34,480 --> 00:26:39,760 renewed interest in blockchain and a number of states Texas has been very aggressive new blockchain 248 00:26:39,760 --> 00:26:45,680 commission lots of activities with but blockchain but I think the poof is in the pudding to see 249 00:26:45,680 --> 00:26:51,520 exactly what the use cases are they never really materialized except that a fairly nominal level 250 00:26:51,520 --> 00:27:00,160 four or five years ago so that's a another one I think the the one area which I think is really 251 00:27:00,160 --> 00:27:07,120 fascinating from a kind of mixed technology both with data and with AI and with mechanical systems 252 00:27:07,120 --> 00:27:12,800 or drones on manned aerial systems so that is something that's here wrote about almost a decade ago 253 00:27:12,800 --> 00:27:18,480 and we've seen that come to fruition now there's even more advanced capabilities with that 254 00:27:18,480 --> 00:27:23,600 particularly powered by AI solution so we've up the game a little bit but every state today 255 00:27:24,400 --> 00:27:32,080 they are using a small unmanned drones to do a lot of work and they're highly highly productive so 256 00:27:32,080 --> 00:27:37,520 cost effective so I think that's another area we're going to continue to look at and at the Southern 257 00:27:37,520 --> 00:27:43,920 Legislative Conference we have policymakers legislative staff and leaders from across our 15 state 258 00:27:43,920 --> 00:27:50,320 Southern region from your perspective what are Southern states doing well and where do you 259 00:27:50,320 --> 00:27:57,040 see opportunities that exist yeah I think that's a that's an excellent question Lindsey because you're 260 00:27:57,040 --> 00:28:04,400 looking at kind of where are those two intersect and in my certainly career in technology in the 261 00:28:04,400 --> 00:28:11,440 in the public sector space I don't think I've seen previously as much activity and interest 262 00:28:11,440 --> 00:28:17,440 around a kind of what I would call a technology stack from a state legislative bodies and and 263 00:28:17,440 --> 00:28:27,920 deliberators 1500 plus bills introduced in in in 2026 almost over a thousand bills introduced in 2025 264 00:28:27,920 --> 00:28:36,160 and so they certainly kind of latched on with a high degree of interest of what's going on I think 265 00:28:36,160 --> 00:28:42,800 particularly concerned about the private sector use of AI and that's where most of these 266 00:28:42,800 --> 00:28:46,960 bill the majority of these bills are now focused on that but they're still a significant amount several 267 00:28:46,960 --> 00:28:54,080 I think over 300 bills this 2026 session focused on state government use and so when I look at kind of 268 00:28:54,080 --> 00:28:59,840 what they are concerned about a lot of it has to do with governance so they're they're looking at 269 00:28:59,840 --> 00:29:04,160 you know creating or already strengthening task forces and commissions that have been 270 00:29:04,160 --> 00:29:09,520 legislated be mandated to look at that they're concerned about the what I would call the 271 00:29:09,520 --> 00:29:14,880 inappropriate use of AI by state government and a lot of those have to do with topics I already 272 00:29:14,880 --> 00:29:20,640 mentioned things like ethical and responsible use decision making many of them have prohibited very 273 00:29:20,640 --> 00:29:27,600 clearly they prohibited state agencies from using AI to do things like behavioral health therapy 274 00:29:27,600 --> 00:29:33,760 they don't want chatbot therapists providing citizen services they don't want AI making 275 00:29:33,760 --> 00:29:38,880 determinations on eligibility things like Medicaid things like unemployment insurance things like 276 00:29:39,440 --> 00:29:45,760 benefit programs and so I think that's what the legislators have seen a lot of states in the south 277 00:29:45,760 --> 00:29:51,440 have taken the lead for that Georgia has a very excellent governance program and they have some 278 00:29:51,440 --> 00:29:58,240 widespread applications I mentioned what Mr. Cippy is doing north carolina also has that I don't 279 00:29:58,240 --> 00:30:03,760 want to single out all these states because the we don't rate and rank the states so they think 280 00:30:03,760 --> 00:30:08,960 that's a that's a challenge for me to look at all the great things are doing Texas has robust 281 00:30:09,360 --> 00:30:16,000 set of policies they were an early leader in enterprise policies around around AI but I think 282 00:30:16,000 --> 00:30:20,800 they're all focused on predominantly the same thing is making sure they have a strong foundation 283 00:30:20,800 --> 00:30:29,680 on the governance side to make those those policies and you know the legislative body can help by 284 00:30:29,680 --> 00:30:35,440 you know supporting them through the enactments that we've seen because almost all those states have 285 00:30:35,440 --> 00:30:43,040 a number of bills around around AI as we close I'll end with a question we ask all of our guests 286 00:30:43,040 --> 00:30:48,800 what is the best piece of professional advice you have ever received and how does it shape the way 287 00:30:48,800 --> 00:30:53,840 that you approach leadership today or in my career I've received a lot of advice 288 00:30:53,840 --> 00:31:02,720 I would say probably the one a couple things one is one is a quote 289 00:31:04,160 --> 00:31:10,160 that someone gave me 30 plus years ago I still have it on a little frame on my desk 290 00:31:10,160 --> 00:31:19,280 the life you lead is the lesson you teach and so I always think about that from a leadership 291 00:31:19,280 --> 00:31:25,920 perspective is that people are watching people are paying attention whether it's your team in my 292 00:31:25,920 --> 00:31:31,680 case whether it's my my 56 state territorial and district colonial members and they're they're 293 00:31:31,680 --> 00:31:38,400 watching so I always I look at that a lot talk about the fact that that that's a kind of a leadership 294 00:31:38,400 --> 00:31:46,320 mantra about whether and works in life it works in your personal life that works with your children 295 00:31:46,320 --> 00:31:51,920 right you're always you're always watching and so I think you know being you know ethical it if I 296 00:31:51,920 --> 00:32:00,400 high degree of integrity I think the other piece couple with that is something that I think one of 297 00:32:00,400 --> 00:32:08,000 my leaders in I work for some some excellent leaders over the years and and I think one of them always 298 00:32:08,000 --> 00:32:14,720 said you know stay humble be humble and I think that's that's important and then I try to certainly 299 00:32:14,720 --> 00:32:21,840 exercise that as much as I can I'm not very humble about about Nassio and the great work that 300 00:32:21,840 --> 00:32:28,560 that we are doing it's a great association and provide leadership for the states but you know 301 00:32:28,560 --> 00:32:34,960 have be humble about what what I'm doing so that's probably the my best advice for people Doug thank 302 00:32:34,960 --> 00:32:40,160 you for joining us at the Southern Legislative Conference this year and on our real state episode 303 00:32:40,160 --> 00:32:45,200 we are grateful to have an organization like the National Association of Chief Information Officers 304 00:32:45,200 --> 00:32:50,480 that is committed to helping state governments keep up with the ever changing landscape of technology 305 00:32:50,480 --> 00:32:57,440 our missions of supporting state governments strongly align and we deeply appreciate your partnership 306 00:32:57,440 --> 00:33:03,200 thank you Lindsey for being here and to our listeners thank you for tuning in to Real State for more 307 00:33:03,200 --> 00:33:11,360 conversations like this and more about the work of CSG South visit csgself.org I'm Lindsey Gray let's 308 00:33:11,360 --> 00:33:25,760 keep it real thank you for joining us today on real states where we keep it real about what really matters 309 00:33:25,760 --> 00:33:45,000