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Data-Smart City Pod
How GenAI Can Actually Boost Public Sector Creativity
00:24:18
This episode features Mike Sarasti, former Chief Innovation Officer and Director of Innovation and Technology in Miami and a leading advocate for government transformation, in conversation with host Stephen Goldsmith. They unpack how GenAI and rapid process mapping are revolutionizing public sector efficiency, not by shaving seconds off legacy workflows, but by making space for human creativity and curiosity. Mike shares real-world examples and explains how city leaders can democratize AI tools and clear bureaucratic tedium while guarding against hype and automation overreach.
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- [00:03]
A
From DataSmart City Solutions the Bloomberg center for Cities, this is the DataSmart CityPod. This is Stephen Goldsmith, professor of Urban Policy at the Bloomberg center for Cities at Harvard University with another one of our podcasts. Welcome back. Thanks for joining us again today. We have an old friend in the world of data and analytics in cities, Mike Sarasti, who is currently an entrepreneur. He calls himself a musician. I call him entrepreneur working with cities. But I know him from his work in Miami Dade and city of Miami. Hi, Mike. Nice to have you back.
- [00:43]
B
How are you doing? Good, sir. Good to see you. This is fun.
- [00:46]
A
You have so many different personalities.
- [00:51]
B
Try to keep everybody on their toes.
- [00:54]
A
This is a podcast so people can't really see you, but I do see your guitar over your right shoulder.
- [01:00]
B
Get that guitar. I'm over here in my studio space. Yeah. Which has been spending a lot of. A lot of time. And after my city life, which is great.
- [01:08]
A
Well, I want to go back to your city life before we start, but while we're talking about guitars. Okay.
- [01:13]
B
Yeah.
- [01:14]
A
What do you think the role of creativity is to AI business? Process transformation.
- [01:22]
B
We're going right into it. Yeah. I'm constantly having this conversation with my. In my AI chat group as they're sending me AI generated songs and telling me how they made things. So while I appreciate that more accessible, this idea that, like, creativity and tools that were historically really expensive, you know, like having to go into a studio before was quite inaccessible for a lot of musicians. And I do enjoy the idea that people can go in and do more now just from the tools that are available on their desktop. I think it's really important for us to continue to celebrate craftsmanship in these things that we are doing. And I think that applies to if we suck out the creativity of government work. It's something I used to talk about a lot. I think fundamentally we're all creative beings to. And problem solving is an act of creation. So even as the AI overlords give us a lot of these tools that say, oh, we're just going to do this for you. Preserving that weight on craft and the spark that comes from creation when you're solving a problem from a different way is really important to preserve.
- [02:32]
A
So before we go back to the beginning and start the podcast the way we're supposed to, I have one more question based on the guitar. And I know we gave you questions in advance. None of these questions did we give you in advance. So just. So back when I was mayor of Indianapolis, I read this as I recollect anyway, Wall Street Journal article about Southwest Airlines. And the story was essentially that in order to turn around planes at the gate more quickly, they didn't go look at United or American. They went to look at pit stops at NASCAR or Indy 500 to see how it is. They change the tires and oil in 29 seconds. Right. So you're an expert. We're going to talk about the connection between AI and business process reengineering, but in terms of creativity, right, the guitar in the background. I think too often in government, the definition of efficiency is making an old process slightly less worse as contrasted to breakthrough, imaginative, creative thinking that lets you understand and imagine a whole new process. How do we generate the creativity to think about the new processes as contrasted to just making the old processes slightly more efficient?
- [03:54]
B
Yeah, I mean, I think so much comes from, from play, right? I mean, it's called playing music. Playing a guitar like that is not accidental. And I think way too often in like the process efficiency space, it's all like, let's just make it faster, shorter, faster, anything you can do and kind of move on. The part does give me some excitement now if we can clear out some of the tedium and create more space for play, more space for actual human collaboration. Because you're offloading all of these tedious things, a lot of great things are going to come out of that. Right. If you, if it's actually clearing out space for human interaction, for the jamming part of things and you're, you're sort of set up with these great tools like that is the exciting part. Shaving off a few seconds, you know, is a little bit of a dopamine hit, you know, off a process. But the breakthrough stuff does come out when you have sort of some freedom to do it, you know, and historically, maybe you had to take a whole retreat and people are mad because they've got a bunch of work on their desk. Well, what if they're not mad that they have a bunch of work, work on your desk because you've been able to clear some of that stuff out. Now they can really immerse themselves in kind of the play part and the discovery that comes from the stuff humans are good at, tell the stories about what we're doing and what does that turn into and the sharing. So hopefully that's the direction we're moving, where we have more time to do that. I worry that we're just, if we're not careful, we're just like, more optimization, more optimization and say, all right, well, that's let's shift from that and let's make it a little bit more fun.
- [05:26]
A
So now that you've answered the podcast questions, let's go back to the beginning and start. So who are you? Give us a couple minutes about your Miami Miami Dade experience and then we'll talk a little bit about your current work.
- [05:40]
B
Great. So I've been in government in or around government for about 20 years now. I started at Miami Dade county, did about 10 years there. I came in initially in a survey research capacity because I had done that towards the end of my grad school, but it was in the department that was running 311 in MiamiDade.gov, so a lot of UX customer focus and towards the end of that time period started getting really involved. The open data movement was happening and sort of immersed myself in that world. I was sort of my entry point to data. We were also building a 311 CRM internally. So I was around a lot of technology teams building, you know, customer centric focus. Not in an IT department, but definitely a technology enabled team thinking about user experience and customer interactions. And then towards the end of that, I got asked to join City of Miami as the city's first Chief Innovation Officer, which is really cool. I love saying that, like wasn't important that it was me, but it was important that the city had designated a role for innovation. About two years into that they said, well, do you want to do the IT department director role as well? So I got a Chief Innovation and Information Officer added on and through that. Got to work on everything under the sun around technology from data to, you know, obviously the building of tech and the website and did a lot of work around that. But got to keep the innovation portion of it, which was largely grounded in a lot of analog things. We lifted Denver's Peak Academy program and set up an instance of the program at the City of Miami which was a crash course and a lot of the process work and tools and techniques to do process mapping and process re engineering and always tried to make a connection how we were building better technology and better systems through the lens of process. And all of those things together sort of brought me to what I've been doing the last three years, which is kind of using some of the newer tools at our disposal, including AI to do rapid process mapping and discovery. So all the things that we were doing that might have taken us weeks and years earlier trying to leverage the new tech to do it in hours and days, hopefully in some cases.
- [07:44]
A
So Mike, let's pretend For a second. I'm the chief administrative officer of a major jurisdiction. Give me your river work pitch. Right. What should I be doing that would allow me to transform the way I engage my residents? What is it about AI and business process that's particularly appealing?
- [08:05]
B
Well, I'll connect the origin story a little bit to what I mentioned, which is a decade or so ago, along with some of our mutual friends, we were thinking a lot about open data and the concept of like machine readable process came up. You know, we were trying to get people to release data. They're like, oh, you already had the data, it's in the PDFs. And the shift was like, all right, no, that's fine that you're publishing a big giant PDF with a bunch of tables and numbers in it, but how do we start to make this data machine readable so that we can do a lot more stuff with it and make it open and available? That was great. Somewhere along that journey I started to get frustrated because, yeah, we had sort of the outputs of the data, but we didn't really have all the information about how things worked. So the shift for me started going from like machine readable data to machine readable process. You know, we were doing these kind of post IT note based process maps and I'm starting to see these post it notes up on a wall and those started to look like cells on a spreadsheet for me. Right. Each step has all this metadata around it, et cetera, et cetera. So how things work I think is fundamental to, you know, being able to fix a process. And a lot of times we skip that. Not because we don't want to do it, because it's been very, very. So what we did with Riverwork was we sort of became obsessed about how we make that discovery and being able to map out all those processes. Not tedious. And it turns out that a lot of the things that Gen AI is good at, pattern recognition, among them, is that we can actually source processes very, very quickly. So whereas it used to take you months, sometimes years to map out a series of processes, in many cases we're doing that in days. Where does that help you? Obviously the simple is like just have SOPs documented. That has all kinds of benefit when you've got turnover, someone's leaving, someone's being onboarded. But it can also be very, very powerful if you're mixing it up from a performance management perspective with other data points. Right. Because if you are also taking those processes and you are creating a structured version of that, it Turns out that it makes the data a lot richer.
- [10:06]
A
Take a breath here. You said a lot of stuff without naming a client. Give us an example of where you've done this permitting or just name the subject where you've process mapped with this much velocity.
- [10:21]
B
Yeah, finance, we have a big place that has mapped out. We were able just recently to map out 100 processes in a little over 30 days. If you would have asked me to map out 100 processes as a CIO, I would have told you that we've got a three year project on our hands and we did it in a little over 30 days. What did that do? That particular department was in the middle of the software implementation. So that is going to help with their transition. But in addition to that, we're doing conversational. The way we do it is we do conversational interviews with individual employees. So this is not like a select group of people that we're bringing into a room and the loudest person in the room gets to determine the outcome. We interview everybody in the department because everyone gets a conversational AI interview. We built a conversational agent that asks you about your process. So you, on your own time, Steve, can make a phone call, you describe a particular process. Everybody could do that at the same time if they want. So you could have 100 contributors, 300 contributors. You also get multiple perspectives. So if you've got three contributors on a process, it's not driven sort of by one individual. You can create a composite of that process where it's sort of crowdsourced. So that's a great example. Another example is a municipality that wanted to actually implement more gen AI, but they didn't really know where to start because they didn't have their processes documented. So it's a little kind of meta. We're using AI to kind of launch an AI project, but we were able to rapidly source. All right, here are the actual steps in the process. Now can we apply a different prompt that analyzes those steps and looks for opportunities to implement AI?
- [11:59]
A
So let me see if I can make the question more complicated. Your answer was really too good. Let's see if we can make it more obtuse. When I was deputy mayor of New York, licensing and permitting reported to me. It was in my portfolio.
- [12:13]
B
This is my favorite, one of my favorite topics, Steve. So let's go. I'm ready.
- [12:17]
A
We worked on efficiency, which made each of the. I'll make up the number 15 agencies that touched. A restaurant permit would try to be a little better or a building permit, but of Course that wasn't really all that helpful to the poor New Yorker actually applying for the permit.
- [12:34]
B
Yeah.
- [12:35]
A
So as you talk about the processes and improving them, let's talk about where there's interconnectivity across the agents, agencies, all of which are using different systems. And how do you process map 10 agencies or six agencies into a combined customer facing solution?
- [12:57]
B
Yeah, that's great. It turns out that those connections tend to be a little bit more difficult to get at. Obviously if you get in with somebody in a room, those things start to emerge. The agent is designed to just be curious and to put you in a position to just tell stories about your work. So it turns out is that individuals are describing a process to you and that the questions are not as structured as they usually are. They tell you, oh yeah, this is how the process usually works. But then I've got to send this document over to Stacy in this department and then Bob's over in this department as they're in this kind of conversational telling you the software that's problematic and their pain points, you can let them speak freely. And then after the fact in processing that transcript using Genai, you can establish those relationships. Right. So you might ask a question across 10 transcripts, how many times does Steve show up as a node across of these transcripts? Well, it turns out Steve is very important to the process across all these areas. So that's when the prompt engineering sort of after the fact on the transcript. Now that you've got these transcripts, turns out you can ask a lot of questions that I hadn't even thought about asking at the beginning. You don't have to think about all the possibilities, just do the interview. And now it's like the ability to go back to go, I wish I would have asked that question. Well, it turns out that because we've done it this way, you can go back, ask that question, you can establish the nodes and we're storing and processing the data to put it in a standardized format. So the interview we actually converted into like a JSON file that you can establish those relationships and make some of those connections.
- [14:30]
A
So since you're in the consulting business, how about some free advice?
- [14:34]
B
Yeah, let's do it.
- [14:35]
A
So I've got a group of major cities, like seven or eight of the largest cities in the U.S. and we're working on modernizing performance management, what you would know as stat management. And one of the goals is to help mid managers be able to use AI tools to discover process problems or causation problems. Something Other than just how to more quickly respond to a problem. So how do we partially democratize the use of generative AI to access data? So what should I be telling these cities in terms of data literacy training? Let's say you've done the process mapping, but we want to have more. I love that word, curiosity. We want to have more curiosity by mid managers about why they're filling the same pothole 10 times. How do we take the next step?
- [15:31]
B
So if we're asking about specific skill sets that I think people should be developing in this like Gen AI world and leveraging some of this tech and things that are possible today that weren't possible before, we should be getting very good at prompt engineering. And every time I think about is this a good use of Gen AI or not, I always kind of parse it into two different categories in the way I think about it. One is AI in general is very, very good at pattern recognition. Right. So if it's something that is being able to detect a pattern, generally that makes me feel better. If we're in a spot where Genai is making a bunch of choices for you, right? It's doing all the writing, it's writing the entirety of the song. It will produce those kinds of results with very, very short prompts. Right? Like I see these things that are like one sentence. Oh, it spit it out. This looks great. That universe of AI worries me quite a bit. Could probably do a whole podcast on my critiques of that side of it. But if you understand that it's really good at detecting patterns and you can get really in depth with the prompt, you know, you don't have a one sentence prompt. If you can have a three page prompt that effectively acts as filtering on large volumes of data. That is something that wasn't available to a manager before. You know, your ability to go in and have a dialogue with data is something that is quite possible these days. You're not waiting for the database engineer to get freed up. And I think that as being something new is a skill set that people should lean into because it's very, very accessible. You can practice on your own with your own stuff. You can take a document that's a PDF and you can have a conversation with it and beef up your ability to prompt it. I think those skills are really, really important and very valuable for a performance management. I did a little experiment kind of in the lead up to this where I took 10 different transcripts on a process and I hadn't done this before from a performance management lens, but Started designing a bunch of metrics based on what employees had said about their business process to sort of start forming something that could be a little bit programmatic. Where are all the instances of rework in this process? You know, where are all the handoffs, which is probably a good indication where you might have some rework. At what point does it hit the customer? How many times is it coming back? Were there any indications of things like that that can help you design a good number of metrics? And it's not something you need a high degree of technical skill to do.
- [17:54]
A
So a couple more questions. One, with respect to these issues you've mentioned that are entangled among agencies and people, what do you want from a leader to set the right stage, right? Whether it's the CIO or the mayor or the cao, what structures or what messages would be most empowering?
- [18:18]
B
I'll start with what I don't want, what I think is dangerous. There are a lot, and I'm not in it anymore. But I can only imagine, and obviously what I've heard, how many AI companies are coming into the mix and saying something like, oh, just give us all your data and we're going to have AI is going to be doing automagically do this, this and that. I think that's quite dangerous one because as most of us kind of in this know, a lot of these data systems weren't. It's a sliver of what's happening, right? Like the systems themselves weren't designed to be able to give you all the nuance. The data isn't digitized by Google. I think that's very important to know, which means you're still coming back to humans. The knowledge is still trapped in people's brains. So I would say definitely continue to focus on the human experience. You have new tools at your disposal to make that side of the shop richer. And again, the data you have is like a sl. It's only we've been working, Steve, as you know, in our realm, we've been working with like, what we got. It's usually like the data that's easier to get to. Those things kind of tend to float to the top of a performance management program, right? Because it's what we've got. And this moment, if anything, is like the potential to enrich those data sets with natural language nuance. And like, think about that as structure, I think is very, very, very powerful. That's the kind of part of it that doesn't replace the human experience. It's actually helping amplify the human experience. So in summary, be cautious of the people that are promising you all this stuff that AI can sort of do and lean into the things that are really, again, going back to some of the things we talked about earlier, more opportunity for human creative stuff. You're going to reduce the tedious stuff that is generally clogging up the system from human creativity and elevating the potential for all these other things to emerge. That I think is an exciting stance for leadership to take.
- [20:12]
A
Have you worked with any cities or counties that are concurrently involving their communities in the redesign process?
- [20:23]
B
No, I think we've had some inquiries and honestly maybe this is partially on us because I think there's a push or a pull to do the chatbot right. Get people involved in the public. I'm not sure that technology is quite there yet for the same reason. Right. Like the information you need for those things to work well is not often digitized. We haven't gotten to that point yet. So I'm often pushing people back like let's make sure you understand what the organization looks like. But I do think the potential for conversational AI again, the same way that we might be asking employees to describe their experience can transform what's happening in terms of customer feedback. We've often had these kind of crude tools that we've had for a while, the survey just the nature that it was very difficult to comb through open ended responses, meaning we often weren't doing a lot of open ended responses. That limitation is not really a problem anymore. You can put people in a stands to tell those stories fully and then use some of this tech to look for patterns in that and kind of shape and develop programs. So I hope we're on our way there. I do think the tools are there as long as make sure it's the right ones.
- [21:28]
A
So let me ask you this in closing. I've been around long enough. I'm so old, I was at the beginning of the E Government movement because it's the beginning of the digital analytics center movement. In terms of changing the way cities operate. I feel like this is more powerful than either of those. So our listeners are city and county and state leaders. If we want to accelerate change, take advantage of these tools. We'll put a caveat, understanding the risks and the privacy questions and the algorithmic bias. But if we really wanted to accelerate transformation, what should we do?
- [22:06]
B
I think you need to use the tools. That's always the first thing that I, I say there are some cities that we see, they're just like we're going to cut off, you know, access to this. I think that's a mistake. I think it's important that we develop a certain amount of intuition using these tools so that you understand the choices that are being made in some of these responses. You know, there's a whole lot of discuss around, like, sycophancy of the prompt, you know, and always telling you you're doing great. You know, you can start to pick up on those things only if you're using the tools, right? You're always. When you get the first response, I'm like, oh, my God, that first response was amazing. If you talk to these tools long enough, that starts to deteriorate a little bit, and you're like, okay, I know what's happening here. So I think to accelerate change and also to accelerate the kind of change we want to see, we need to start putting these tools in people's hands and encouraging people to not let go of their agency on these things. You're not looking for that first response from this thing, right? You. You want people to think about it again with curiosity and a process of discovery.
- [23:06]
A
So I thought your answers today were great until the last one, because I've been looking to my AI for confidence building because it more often compliments me than my own colleague. I thought that was personal.
- [23:22]
B
I thought it was personal, which depends on which part of the day. Sometimes you just need that. As long as you. That that's what you're going in for, it's all good.
- [23:30]
A
All right, well, Michael Sarasti, this has been a pleasure. Steve Goldsmith here from Harvard Bloomberg center for Cities. You've got a lot of experience, and it shows, and I hope you can help your clients provide better quality services for their citizens. So thank you so much for your time.
- [23:47]
B
Thanks so much for having me, Steve. Great to see you.
- [23:52]
A
If you like this podcast, please Visit us at datasmartcities.org and find us on itunes, Spotify, or wherever you get your podcasts. This podcast was hosted by Stephen Goldsmith and produced by me, Betsy Gardner. Thanks for listening.