B (4:25)

When I was deputy mayor of New York for Mike Bloomberg, the mayor was into measuring everything, really. And I look at the mayor's performance scorecard and I was responsible for that part of the operation and presenting it to him. And we had hundreds of things we measured, But I was never sure that those measurements produced outcomes anybody cared about. How does listening to the sentiment, grading the anonymized sentiment in the social media world compare to the way a city would measure its own performance?

C (4:57)

So I think measuring the online sentiment is just one way to bring this to life. But the more interesting question to me is how does measuring sentiment or perception generally be an outcome metric or an outcome perspective? I would go as far as saying that measuring people's satisfaction or happiness is the North Star metric, is the target equation that local government is working for. Think about it. If at the end of the day, a city saves money or collects more money, it doesn't give out dividends to council members. Hopefully it uses those resources to serve their community better, to invest in the areas that people care about the most with the goal of making people happy with life in Their community. In my experience with most public servants I've met, there is a real passion to making sure that people's lived experience in the city is a good one. And that is what organizations are optimizing for. So the question I've been obsessed with is can we measure that instead of trying to measure leading indicators that might lead lead us to it. And I'm very confident to say that, that it's something we can do.

B (6:04)

You've watched our group of stat performance cities. Look at what stat would be, what performance management would be in a generative AI world. I'm thinking about generative AI as the way a city or a community would discover the causes of things that annoy them. Right. Or discover a solution to a problem. Like why is that pothole here every six months? Right. Or why are so many people slipping on this part of the sidewalk? So when you watch these cities experiment with building their own agents, what do you predict the future is for generative AI in performance management in behalf of a city?

C (6:52)

Thinking back to the goal of stat programs of performance programs, they don't exist to give somebody a report card. We're not doing this to clap and say, hey, we're doing great. We're doing this to actually improve services, to recognize what are things that could work better and how they can work better and then make those solutions and measure and see if we are actually doing better. And the current way or the historic way we did statistically has a very, very long lead time and it's a very resource intensive process. Right. There's a lot of people that need to do a lot of work and spend a lot of time in order to get that done. So I've been extremely inspired by the group in seeing how we can crunch those capabilities to make that cycle of stat a lot faster. Basically, I think there's four levels of improving stat that I personally am excited about. Number one is let's do that with less resources. Right? We don't need so many people in the room and so much staff time and so much expensive technology to get all this data done. That's the basic level. Like do the same thing with less resources. I think the next level, which already starts to make this better, is do it in less time. One of the lowest hanging fruit that I already saw, you know, the agents that our group members have done, is that we shorten the cycle between I have a question, I get the root cause answer to it and I can take action on that root cause. Before that, you'd see a number you'd ask the staff, hey, could you tell me what the reason is? They'll say, great, yeah, in two weeks, we'll circle back to you after we've investigated, and then, you know, we'll schedule that meeting two weeks after, etc. Etc. Now we can, using these agents. I've seen that, for example, with the 311 analyzer that Steve from Seattle has built, we're able to get to the root cause immediately while we're in the meeting without knowing the question ahead of time. And that already quenches time, and that's value. And then the next levels of value is we can recognize patterns and root causes that we didn't even know to ask for. That's, I think, the next thing we're aiming for. And then eventually we can open up those capabilities to everybody, even beyond the city, to community members and to other folks to ask those questions. But right now, the thing that we're already starting to see from that group is we can crunch the time and the resources that require us to answer those questions.

B (9:05)

Let's say that hypothetically, the deputy Transportation Director wants to explore the data to determine how to be better at their jobs, how they could be more responsive. How do you imagine, six months from now or a year from now, they could use generative AI to ask questions which would help them do their work better.

C (9:31)

The simple version of this is when we have a good definition of what success looks like. So, for example, for a transportation director, that could be time and traffic, or that could be amount of people that are using public transportation, or amount of people that are using alternative mobility solutions, or of course, as I would say, the satisfaction of people with ability to move around the city. Once we have those target equations well defined, we first can use GENAI to wrangle all the data easily to see how we're doing. With tools like MCP servers, we can now easily build agents that pull data from a lot of different siloed sources and present them easily and ask questions over them easily. I think the next level would be that we can ask why, like, hey, average time in traffic or average trip time. Let's dive into is there specific roads or intersections where travel time is longer, or are there neighborhoods where we're seeing a lot of people convert trips to micromobility? Show me that specific example and I can ask those questions in a human language in the same way that I would ask a knowledgeable staff member. So in a case like this, we would have an MCP server and the data would flow and Then we'd have agents that give us an easy, presentable overview of the data and we can ask follow up questions on them in a human language.

B (10:44)

So now let's bring the two parts together. And many mayors across the country are frustrated by the fact that crime is down, but their public feels like they're less safe. Right? They feel like crime is up. How would a mayor or city official investigate the difference between the data on safety, it could be any subject called safety and the sense of safety?

C (11:11)

First, I'll say it's one of the biggest challenges that local government is facing. And specifically in public safety, it's. It's emerged as a real challenge. In a lot of places. The perception of a problem is something different than the state of the problem itself. But the perception is really important a, because as we said, that's the North Star. But also if we take safety specifically, you know, a parent will decide if their kid can walk to school, not based on recent homicide numbers. They would decide that question based on do they feel safe in their neighborhood and do they feel like it's safe to walk to school? So that perception is reality and is an essence that we need to solve. You know, I was just meeting yesterday with one of my favorite law enforcement partners. We run recurring monthly surveys for them on sense of safety across the city and trust in law enforcement. One of the interesting things we saw there was that over the last year, across almost all demographics, sense of safety and trust in law enforcement has improved. There was one specific community where sense of safety went down dramatically, which is the Hispanic Latino community. But at the same time, their trust in law enforcement, in the city's law enforcement went way up. We were able to use our generative AI agent there to ask a few questions and follow ups that the reason sense of safety is down for that the Hispanic Latino community is because of things that are happening on the federal level. And that actually shines a really positive light on the city's police department, who the residents now trust more because they have less trust in the federal government. So I think step one to answer your question directly here is to be able to understand that this is something that you need to measure separately from measuring crime data and then track in a stat process. Track both of them together in a lot of our law enforcement customers. And lapd, for example, is one of our leading examples. This data is embedded into the CompStat process, which is, you know, the law enforcement version of a city stat program. And they're looking at perceptions and sense of safety alongside the Crime numbers. And a lot of times they're moving together because people feel more safe if there's less crime and if the police is doing a great job. But in some cases we see a divergence between them and there we can create interventions that are different, that are messaging and communication interventions to allow change of perception and not just policing interventions.

B (13:36)

Let's imagine a year from now you're talking to the deputy mayor of New York or mayor of Indianapolis or any other kind of random choice and that person wants to increase trust in government by being more responsive, by fulfilling the expectations of residents better. How would you set that up? What changes would you make? How would you arrange the flow of information so that you could actually produce more satisfaction and trust?

C (14:11)

It's a great question. And I know to some extent every mayor and city manager and police chief are asking themselves this question. The first part of my response would be that you can't manage what you can't measure. So the first step would be to measure it. Our approach to measure this, and today I'm proud to say we serve about 200 million people in the 400 cities we work with, is that you need to put a few capabilities in place of listening and measuring people's perception and satisfaction. Both representative community wide surveys analyzing of what people are sharing on social media, post interaction surveys that ask people that got services from the city directly how is their experience on that specific service. Putting those data collection infrastructure in place and bringing all that data together I think is step one. Gen AI is already playing a really big role in that. But a year from now I'm thinking about, hey, we can capture voice data a lot more effectively. Hey, we can survey people in much more interesting ways by mimicking a video interviewer to get more information from people. So there's a lot more exciting stuff coming down the pipe there, there. But after we've collected that information, the next step is to analyze it and review it on a regular basis. And to your question before, to cross reference it with other metrics that we're looking at that are operational metrics. And the thing that we can do there, hopefully in a year from now is very easily ask our agent. Are we seeing a parallel trend between perception data and satisfaction data and the operational data? And if not, where do we see the gap and what could be driving it?

B (15:49)

Well, that sounds compelling. Maybe I should sign up for another stint as a deputy mayor in some city.

C (15:56)

I'm sure every city will be lucky to have you.

B (15:58)

I don't know. Thank you. Well, this is Steve Goldsmith with IL Federer from Zen City. An interesting conversation about the future of responsiveness and the definitions of it. Thanks so much for your insights, for your kind of volunteer work with our cities, and for telling us today how cities can develop more trust. Thanks so much.

C (16:18)

Thank you. Talk soon.

A (16:21)

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.