WEBVTT NOTE { "characteristics": "public.accessibility.transcribes-spoken-dialog", "language": "en-US", "language-name": "English (US)" } 00:00:00.560 --> 00:00:05.719 From Deloitte, this is Government’s Future Frontiers, the podcast that asks questions today 00:00:05.719 --> 00:00:10.240 to help create tomorrow. I’m Bill Eggers, the executive director of the Deloitte Center for 00:00:10.240 --> 00:00:15.160 Government Insights. And in this episode, you’re going to hear a conversation recorded live in 00:00:15.160 --> 00:00:21.840 Barcelona, Spain, at the Smart City Expo World Congress. It’s an influential event on urban 00:00:21.840 --> 00:00:27.680 innovation where key players from all over the world come together. That makes it an ideal place 00:00:27.680 --> 00:00:34.279 to explore this episode’s subject: AI-powered cities and leading practices in enhancing performance at 00:00:34.280 --> 00:00:40.199 City Hall. You’re going to hear my conversation with two innovators, Rochelle Haynes from What Works 00:00:40.200 --> 00:00:46.359 Cities and Suma Nallapati from the city and county of Denver. You’ll learn about some of the 00:00:46.360 --> 00:00:52.759 latest examples of how AI is transforming life for city residents, and my guests explain how they 00:00:52.759 --> 00:00:59.200 see artificial and human intelligence working together to create a better future for all. 00:01:00.200 --> 00:01:07.160 Well, welcome. We’re at Smart City World Expo right here with thousands and 00:01:07.160 --> 00:01:14.000 thousands of other Smart City enthusiasts. And I’m really excited about this panel today 00:01:14.000 --> 00:01:20.360 because it’s with two individuals, innovators I’ve admired for a very long time. So for the panel, we’re 00:01:20.360 --> 00:01:25.280 going to have you two talk a little bit about yourselves. But first we have Rochelle Haynes from 00:01:25.280 --> 00:01:31.960 Bloomberg Philanthropies, What Works Cities, Results for America. And then we have my 00:01:31.960 --> 00:01:38.880 friend Suma Nallapati, who is now the chief AI and information officer for the 00:01:38.880 --> 00:01:43.920 City of Denver. So welcome. And why don’t we start off by you two saying a little bit about 00:01:43.920 --> 00:01:48.880 yourselves and what your roles are and so on. Wonderful. So, Bill, first and foremost, thank you so 00:01:48.880 --> 00:01:54.240 much for having me. And I’m the managing director of What Works Cities. What Works Cities is the 00:01:54.240 --> 00:01:58.680 international standard of excellence on what it means to be good, well-managed local government. 00:01:58.680 --> 00:02:03.960 I’m excited to be leading this work. I’m a former public servant of New York City, where I focused on 00:02:03.960 --> 00:02:08.760 affordable housing development, social services, and homeless services work. And so, this role allows 00:02:08.760 --> 00:02:13.479 me to combine my experience in both the public sector work as well as nonprofit and 00:02:13.480 --> 00:02:18.480 philanthropic sectors. And what’s exciting about What Works Cities and what we’re doing right now 00:02:18.480 --> 00:02:23.920 is that we’re connecting city leaders with the information, the data, and the technical assistance 00:02:23.920 --> 00:02:29.159 skills they need to be more data-driven, evidence-based in their approach, but also be ready for the 00:02:29.160 --> 00:02:36.080 future, in which we all know AI is a big part of that future. Thank you. Suma. Yep, speaking of AI, 00:02:37.520 --> 00:02:42.239 it’s everywhere, and I’m very proud to lead the city and county of Denver, and I made Mike 00:02:42.240 --> 00:02:47.440 Johnston the AI strategy, along with our technology strategy for the City and County of 00:02:47.440 --> 00:02:51.960 Denver. I’ve been in the public sector and private sector. I’ve worked with Deloitte in my public 00:02:51.960 --> 00:02:58.589 sector roles, and also in the private sector, and also with Bloomberg Philanthropies. We just concluded 00:02:58.589 --> 00:03:04.589 our Denver AI summit focused on public sector challenges and where AI can play a part. So very 00:03:04.589 --> 00:03:08.869 excited to be part of this amazing conference with so much thought leadership. And thank you, 00:03:08.869 --> 00:03:14.990 Bill, so much for having us. How did it come about, your new title of chief AI officer? It’s pretty 00:03:14.990 --> 00:03:20.669 symbolic of the work that’s being done. Our resources are constrained, right? The budgets are 00:03:20.670 --> 00:03:27.470 constrained. So, AI is a tool in our toolbox to help us leverage capabilities which may 00:03:27.630 --> 00:03:34.390 not be offered to us otherwise. So very excited. And also, responsible AI is important. Data 00:03:34.390 --> 00:03:40.030 security, privacy, all those are very critical in this framework. So, the title is a culmination of a 00:03:40.030 --> 00:03:46.910 long, hard-fought journey. Being in IT for almost 30 years. And I started out with machine learning, 00:03:46.910 --> 00:03:52.229 deep learning, large language models. And this is a natural progression. It’s again, how do you take 00:03:52.229 --> 00:03:59.109 data and use the outcomes to help with residents and their resident experience. So, 00:03:59.109 --> 00:04:04.429 it’s all about the experience for our residents. Wonderful. Rochelle, What Works Cities has been 00:04:04.430 --> 00:04:10.750 around government performance at the city level, getting certified, looking at how to improve that 00:04:10.750 --> 00:04:16.670 performance. And now you’re looking at expansion. You’ve expanded into South America a little bit and 00:04:16.670 --> 00:04:22.269 into Canada. Talk a little bit more about What Works Cities, what it does, how do you get 00:04:22.269 --> 00:04:27.910 certification, and what you’re looking at doing from an expansion perspective? Absolutely. And, 00:04:27.910 --> 00:04:33.749 first and foremost, I would acknowledge Bill. Bill is one of our What Works Cities standard committee members, and 00:04:33.749 --> 00:04:38.709 he’s been a champion of our work from the very beginning. And as I mentioned, What Works Cities is 00:04:38.709 --> 00:04:43.349 an international standard of excellence. It really helps cities benchmark their assets as well as 00:04:43.350 --> 00:04:47.789 their risks. We’re taking a look at things like, how do you manage your data? What does your data 00:04:47.790 --> 00:04:52.950 governance policy look like? Are you building evaluations into your processes and your policies 00:04:52.950 --> 00:04:58.149 that you’re designing. And for cities, we’re not just asking you to map these assets and risks. 00:04:58.190 --> 00:05:03.670 We’re combining that with free technical assistance that we provide from experts and partners 00:05:03.670 --> 00:05:10.149 throughout the US, as well as LATAM and Canada, to really help you build that internal capacity. We 00:05:10.190 --> 00:05:15.229 currently have over 100 cities that have certified with our program in North, Central, and 00:05:15.230 --> 00:05:20.869 South America. We’re thrilled about that, and what’s really exciting, I have over 220 cities in 00:05:20.869 --> 00:05:27.070 the network, over 1,800 city leaders. So, this is a global network of leaders around the world that 00:05:27.070 --> 00:05:32.589 are connecting with one another on how to drive data-driven decision-making, how to make decisions 00:05:32.590 --> 00:05:37.229 that connect to the residents, and also how to get at the root of the challenges that you all are 00:05:37.230 --> 00:05:42.108 facing. And what’s powerful about the network is it’s global, and what you find is the 00:05:42.109 --> 00:05:47.630 conversations you’re having in the US aren’t that different from LATAM. We’ve created a space 00:05:47.630 --> 00:05:52.750 for leaders to connect with one another. So, it’s a network of doers, as I like to say, where 00:05:52.790 --> 00:05:58.069 networkers are doers and collaborators that are sharing best practices with one another. Fantastic. 00:05:58.070 --> 00:06:03.829 And I’d love to ask this question to both of you. What’s on everyone’s mind right now, of course, is 00:06:03.870 --> 00:06:10.828 AI. So Smart City’s AI basically, you know, very, very connected. Now, Suma, you have 00:06:10.829 --> 00:06:17.149 an early background in that. And I remember I first started writing about AI in 00:06:17.150 --> 00:06:22.749 government in a book over 20 years ago, and those were like rules-based engines and everything. Not 00:06:22.750 --> 00:06:28.070 nearly as sophisticated as what we see today. Can you talk a little bit about the role that AI is 00:06:28.070 --> 00:06:34.670 going to play in government performance and innovation and helping citizens? Absolutely. 00:06:34.709 --> 00:06:40.389 Housing, affordability, government inefficiencies, right? Why does licensing and 00:06:40.390 --> 00:06:47.390 permitting take as long as it does? Can AI be applied, right? There’s all these complex rules 00:06:47.390 --> 00:06:52.549 around all of that. The human intelligence needs to be augmented by AI in this 00:06:52.549 --> 00:06:58.268 scenario. So, I’m very excited. We’ve started actually with information governance. 00:06:58.269 --> 00:07:02.789 That’s important. Policy is important. Responsible AI is important. But once you establish those 00:07:02.790 --> 00:07:08.909 guardrails, the potential for AI is limitless in my mind. How do you take the transformational 00:07:08.910 --> 00:07:14.789 work and give it to humans? They want meaningful work. Our employees want meaningful work. They just 00:07:14.789 --> 00:07:19.469 don’t want to do the repetitive, mundane tasks. They want to use their intelligence in ways that 00:07:19.469 --> 00:07:25.868 will help our residents, right? So do that with human intelligence, but leave the transactional 00:07:25.869 --> 00:07:31.469 work to the bots. So that’s where the repetitive, mundane tasks get eliminated in our workflows, and 00:07:31.469 --> 00:07:36.589 we focus on the truly meaningful work. So, we’ve been able to do quite a bit already. We’ve 00:07:36.589 --> 00:07:41.189 introduced this platform which sits on our Denver gov website. It’s called Sunny. It’s integrated 00:07:41.189 --> 00:07:46.100 into all our back-end systems using data. Our teams are curating it for hallucinations. It doesn’t 00:07:46.100 --> 00:07:52.139 read off of the internet, right, as an example, and we just look at ways to help our residents. It 00:07:52.140 --> 00:07:57.739 answers, Sunny answers, questions in 72 different languages. And Bill, if I have to hire 72 different 00:07:57.739 --> 00:08:03.779 translators, that’s simply impossible, right? Like people that may be hungry, right? They can give up 00:08:03.779 --> 00:08:08.539 a lot of things, but if they have their phone, what we are saying is I’m hungry, is there like a place 00:08:08.540 --> 00:08:13.619 where I can get food tonight? And they’re asking that of Sunny. For me, that’s powerful, because they 00:08:13.620 --> 00:08:19.498 may feel a lot of shame in calling a call center, but they’re able to do that much more easily on 00:08:19.540 --> 00:08:25.939 the platform, and it creates a case. We follow up immediately, and it’s a much more compelling 00:08:25.940 --> 00:08:32.699 platform than just someone calling on the phone. So, we are finding ways again, worked with MIT, a 00:08:32.700 --> 00:08:39.099 startup, to have a licensing and permitting software integrated into our whole process, and 00:08:39.099 --> 00:08:44.619 it’s getting us more than 30% efficiencies already. That’s wonderful. Yeah. I think a lot of 00:08:44.619 --> 00:08:51.619 the regulatory areas and permitting and even housing and using digital twins, and I would call 00:08:51.619 --> 00:08:56.459 regulation as code where you’re bringing that in. And even around procurement, there’s so many ways 00:08:56.460 --> 00:09:02.419 to both speed things up for citizens and businesses, but also to do what we call scaling 00:09:02.419 --> 00:09:09.379 the human edge, really focusing on what humans can do better and so on, and taking away some of the 00:09:09.379 --> 00:09:16.179 manual tasks. Rochelle, you spoke on the main stage yesterday on AI 00:09:16.340 --> 00:09:20.979 and AI-powered cities. Let’s hear a little bit of your thoughts on this, and especially as it 00:09:20.979 --> 00:09:27.820 connects to what you’re doing at What Works Cities. Yeah. So yes, I had the pleasure yesterday of 00:09:28.060 --> 00:09:32.780 participating in the AI and urban transformation conversation. What I love about that conversation 00:09:32.780 --> 00:09:38.139 and all the conversations we’re having about AI is that we’re centering cities. Cities are at the 00:09:38.140 --> 00:09:43.538 forefront of the global revolution, and they’re going to be at the forefront of the AI revolution. 00:09:43.700 --> 00:09:48.979 Cities have the opportunity to do a really big thing right now and revolutionize how they show 00:09:48.979 --> 00:09:55.579 up by leveraging AI technology. And so, for me, what I’m excited about is that cities are 00:09:55.580 --> 00:10:01.780 actually trying AI. I think in the past, sometimes the public sector is a little bit reluctant to adopt 00:10:01.820 --> 00:10:07.699 technology, but there is a willingness among cities and leadership within cities to try. What I 00:10:07.700 --> 00:10:12.938 find is that cities are most comfortable doing pilots that feel really practical right now. So, 00:10:12.939 --> 00:10:19.539 how do you practically help me process permits and applications? How do you practically help me 00:10:19.580 --> 00:10:24.659 do preventative work like detect wildfires? We’re seeing that in Austin, Texas. They’re using AI 00:10:24.700 --> 00:10:31.419 technology to detect wildfires. In Recife, Brazil, they’re using AI technology for flood prevention. 00:10:31.420 --> 00:10:35.498 But what’s great about all of these is they’re connecting with their residents. They’re not 00:10:35.499 --> 00:10:40.379 building it in a vacuum and making citizens think that there’s some sort of big brother thing 00:10:40.380 --> 00:10:46.058 that’s behind the scenes. They’re being upfront and transparent and cocreating with residents. 00:10:46.059 --> 00:10:51.259 And so, for me, when I think about this moment in cities, I think this is a moment to revolutionize 00:10:51.260 --> 00:10:58.019 how cities operate. I think it is going to free up time, as you mentioned, for city staff, and it will 00:10:58.020 --> 00:11:02.859 free their time so they can be creative and innovative and not just in crisis mode. I’m a 00:11:02.859 --> 00:11:06.419 former public servant. We spent more time sometimes in crisis mode than we did in 00:11:06.419 --> 00:11:12.218 innovation. I think AI is going to create the space for cities to have that, staff to kind of have 00:11:12.219 --> 00:11:16.579 that space to be more innovative and creative with how they think. And then I think on the other 00:11:16.579 --> 00:11:21.859 side, it’s an opportunity to democratize information for residents so residents can access 00:11:21.859 --> 00:11:25.780 information within their city halls. They can get the answers to the questions if you need the meal 00:11:25.780 --> 00:11:30.419 for the night, but it also allows government to be more transparent. And when you have a transparent 00:11:30.419 --> 00:11:35.659 government, you can have a citizenry that is actually engaged. And so I’m, as you can tell, very 00:11:35.659 --> 00:11:42.499 excited about what AI can do, and I think there’s work to be done, right? To clean up data, organize 00:11:42.499 --> 00:11:48.299 data, make sure we’re addressing biases. But at the same time, we don’t have to let the perfect be the 00:11:48.299 --> 00:11:53.299 enemy of the good. We can try and test in a way that feels safe and comfortable and engages our 00:11:53.299 --> 00:11:58.459 residents. Well, let’s get into the data piece a little bit, because that’s a big piece of the AI 00:11:58.780 --> 00:12:05.500 puzzle right now. And we’ve heard from a number of folks here over the last two days, just about 00:12:05.539 --> 00:12:11.339 the big problem with data silos, both within different government departments, levels of 00:12:11.340 --> 00:12:17.098 government, but also between the public and private sector, where at a city level, a lot of 00:12:17.140 --> 00:12:22.979 transportation data, a lot of other data sometimes is held by the private sector. What do we do about 00:12:22.979 --> 00:12:28.658 the data piece of this and being able to start to break some of those silos so we can get the most 00:12:28.659 --> 00:12:34.259 out of AI. And Suma, you’ve been in this area a long time, so we’d love to hear from you. I really 00:12:34.260 --> 00:12:38.379 like what you said, right? Let’s not wait for data to be perfect before you start the AI journey. I 00:12:38.379 --> 00:12:44.259 think they both can coexist. We can learn from each other on those two tracks. Right? 00:12:44.299 --> 00:12:50.619 Like, how do you start the journey? How do you make your AI and iterate? That’s important. Bill, to 00:12:50.620 --> 00:12:54.979 answer your question directly, let’s start from the resident and work our way backward. Right? 00:12:55.020 --> 00:13:01.299 Rather than having to try to merge all these data silos and stuff, let’s see what the 00:13:01.299 --> 00:13:08.099 resident wants, ask the questions, and then build your data pipelines to match that. Right? And you 00:13:08.099 --> 00:13:12.179 want to meet the residents where they are. And again, when they’re interacting in the private 00:13:12.179 --> 00:13:17.699 sector, they’re interacting with very sophisticated AI. It’s curating the content based 00:13:17.699 --> 00:13:23.098 on their preferences. And when it comes to public sector, why is it so difficult? Right? That’s the 00:13:23.099 --> 00:13:29.339 question to ask. What are those interactions that are most common? Start with that. Start with an MVP, 00:13:29.379 --> 00:13:34.729 start with a pilot of those data attributes and then go from there. So that’s the 00:13:34.729 --> 00:13:40.289 only way to get started. And Bill, as you know, I worked in the state and we built myColorado. 00:13:40.330 --> 00:13:46.849 Right? And again with myColorado, the app, the app was the easy part. How we got the data pipelines 00:13:46.890 --> 00:13:52.649 behind it was the most time consuming. But learning from those kinds of experiences, it’s all about 00:13:52.650 --> 00:13:58.209 the resident. And then that makes the equation easier. That’s a great explanation. And Rochelle, 00:13:58.209 --> 00:14:02.928 what are your thoughts on the data piece? So one of the things you touched on was residents. 00:14:02.929 --> 00:14:08.089 Right? And so we know that sometimes data is flawed or can be biased old data sets. This is a 00:14:08.090 --> 00:14:13.849 moment to clean that up. Use the data that you have. But at the same time find accessible new 00:14:13.849 --> 00:14:18.968 collection methods that allow residents to engage with you and collect new data. And one example 00:14:19.010 --> 00:14:24.169 I’ll highlight of this, like in action in a city that’s part of our network, is Fort Lauderdale, 00:14:24.169 --> 00:14:29.889 Florida. They were going to do investments in stormwater. There was excessive flooding. Before 00:14:29.890 --> 00:14:34.209 they did the investments, they did the GIS mapping to identify where there are floods. But then 00:14:34.209 --> 00:14:38.569 someone said, hey, let’s also have focus groups and conversations with residents so residents can 00:14:38.569 --> 00:14:43.729 tell us where there’s flooding. And to no one’s surprise, beyond just the GIS data, they 00:14:43.730 --> 00:14:48.770 identified, I think it was like 25 additional sites where there was flooding that 00:14:48.770 --> 00:14:54.489 data alone didn’t pick up. So, it’s this combination of the resident engagement data, as 00:14:54.490 --> 00:14:58.889 well as the hard-core data that you collect through methods that allows you to be more 00:14:58.890 --> 00:15:04.329 informed. And I think that’s the approach we need to have with AI. It’s like, let’s use what we 00:15:04.330 --> 00:15:09.890 have, get started, but use this as a moment to clean that data up. But I think at the end of 00:15:09.890 --> 00:15:14.129 the day, we don’t have to wait for it to be perfect. And I think that’s the biggest takeaway. I 00:15:14.130 --> 00:15:18.569 think some of the messaging before has been everything has to be perfect before you start. 00:15:18.569 --> 00:15:24.169 It’ll never be perfect, but what you can do is start to leverage small pilots as the 00:15:24.169 --> 00:15:29.529 conversations that you can have internally to do this. And that’s really also how our certification 00:15:29.569 --> 00:15:33.770 is designed. You can’t get certified without talking to one another. It’s purposely designed 00:15:33.770 --> 00:15:39.889 that way so it creates that interconnectedness. That’s wonderful. Suma, you just launched a 00:15:40.009 --> 00:15:46.089 Denver’s Sunny chatbot, and I assume it’s called Sunny because Denver has more sunny days than 00:15:46.450 --> 00:15:53.369 any other cities. Yeah. 360 days of sunshine. How is that going, and what does it do? 00:15:53.409 --> 00:16:00.009 It’s a great platform. Again, the beauty of Sunny is how well it’s integrated to our back-end 00:16:00.010 --> 00:16:05.168 systems. Right? So, it’s not just a chatbot front-facing. It’s integrated to the back end. Technology 00:16:05.169 --> 00:16:12.168 is working behind the scenes. We have had more than 40% of our call volumes with 311 go 00:16:12.169 --> 00:16:19.128 through Sunny now. So that’s a lot. In how long? Within one year. Within one year, that’s amazing. Right? 00:16:19.129 --> 00:16:25.769 And again, when I say resources are constrained, that 311 team hasn’t grown, but our interactions 00:16:25.769 --> 00:16:31.849 have gone up. And Sunny is able to augment the staff. Right? And it’s going extremely well. CIO 00:16:31.849 --> 00:16:37.729 100 award-winning platform and a lot of other cities are coming to us on how we got started 00:16:37.729 --> 00:16:44.369 with that, and it’s been truly one of the biggest things for us in the recent past 00:16:44.370 --> 00:16:49.889 with how we were able to integrate the data behind the scenes, clean up the data, but also make 00:16:49.889 --> 00:16:54.968 it very easy for the residents to interact with city government. Well, congratulations on that, 00:16:54.969 --> 00:17:01.168 because a lot of the early uses of AI have been sort of back office and everything, and people 00:17:01.169 --> 00:17:07.810 were afraid to do things citizen-facing too early. Well, we’re almost done. But I want to ask one last 00:17:07.810 --> 00:17:13.849 question to both of you. Looking out ten years from now, how do you think cities will be 00:17:13.849 --> 00:17:20.329 transformed by AI, by digital twins, IoT, even 00:17:20.329 --> 00:17:27.169 quantum computing, and other emerging technologies. Oh, the cities of ten years from now, 00:17:28.050 --> 00:17:34.968 I will say they will be labs of innovation. I think AI is going to automate tasks that should 00:17:34.969 --> 00:17:41.609 be automated. AI is going to free up staff time to have the space to think big 00:17:41.729 --> 00:17:48.569 about policies, about approaches and frameworks and innovative design. I think AI is going to have 00:17:48.569 --> 00:17:55.169 city halls where citizens are able to get the resources they need in a more efficient way, and I 00:17:55.169 --> 00:18:00.329 think it’s going to be an exciting time for all of us. I think that, you know, in some ways I’m at 00:18:00.329 --> 00:18:04.169 the five-year mark, not even a ten-year mark, because I think there’s probably not even the 00:18:04.170 --> 00:18:09.529 evolution of the AI technology on how far it can take us with city halls, but I think it’s going to 00:18:09.530 --> 00:18:14.929 be a city hall that is deeply responsive. Think about a city hall that has a dynamic dashboard, 00:18:15.209 --> 00:18:21.809 that can predict flooding, that can get ahead of the wildfire, right, that can use a digital 00:18:21.850 --> 00:18:27.999 twin to build affordable housing in a different way and be able to use that digital twin to 00:18:27.999 --> 00:18:32.760 explain it to their residents. Right? And so, there’s more trade-offs. Exactly. And explain the 00:18:32.760 --> 00:18:38.040 trade-offs. And so, citizens feel more engaged. And I think that’s where we’re headed. Right? And so 00:18:38.040 --> 00:18:44.439 it’s really exciting time. Great. Suma. I would say, Bill, the recursions are happening so fast with AI. I 00:18:44.480 --> 00:18:49.359 can’t predict how it’s going to be in the next six months. Right? It’s happening so very fast, which is 00:18:49.359 --> 00:18:54.599 all great. Right? How do we keep up with that pace? We all have to increase our literacy or awareness, 00:18:54.599 --> 00:19:01.478 our education and all of that. I think with all of it, my hope is that we 00:19:01.479 --> 00:19:05.879 serve more residents. Maybe our health outcomes are better. Maybe we get better water, better 00:19:05.879 --> 00:19:12.358 living, better resources for our residents, where they truly feel that they belong in the city and 00:19:12.399 --> 00:19:17.078 that they are being serviced with the right resources, whether it’s AI or something else. We 00:19:17.360 --> 00:19:24.079 democratize the data to more effectively serve our residents that demand more. 00:19:24.160 --> 00:19:29.039 They want more. They deserve more. And I think AI is going to get us there. Well, I’m also looking 00:19:29.040 --> 00:19:35.479 forward to flying taxis because I hate things like congestion. And that’s been on the radar 00:19:35.480 --> 00:19:40.639 for 10 years. I guess you can go to Dubai. You know, I support that. As a native of New York City 00:19:40.680 --> 00:19:46.480 and who sat in traffic many meeting days, I support that. Well, thank you two very much. It’s so 00:19:46.480 --> 00:19:52.639 good to see you and have a great rest of the conference. Great. Thank you. Thank you. Well, that’s 00:19:52.640 --> 00:19:58.199 it for this episode. We’re out of time. Thanks to Suma and Rochelle for such an inspiring 00:19:58.199 --> 00:20:03.560 discussion here in Barcelona. If you’d like more from Government’s Future Frontiers, you’ll find all 00:20:03.560 --> 00:20:07.919 our previous episodes wherever you get your podcasts. And to make sure you don’t miss new 00:20:07.920 --> 00:20:12.559 episodes, be sure to follow the show on your favorite podcast platform.