Data-Smart City Pod: How Cities Can Measure What Actually Matters

Date: March 4, 2026
Host: Steve Goldsmith (Bloomberg Center for Cities)
Guest: Eyal Feder-Levy, CEO & Co-Founder, Zen City

Episode Overview

This episode explores how cities can shift from measuring traditional performance indicators to tracking what truly matters to residents—namely, community sentiment and satisfaction. Host Steve Goldsmith is joined by Eyal Feder-Levy of Zen City to discuss the evolution of “stat” programs, the rise of generative AI in civic performance management, and strategies for increasing resident trust and responsiveness. They investigate how advanced analytics, AI tools, and new methods of community listening can produce a more accurate, data-driven understanding of constituent priorities and lived experiences.

Key Discussion Points & Insights

1. Rethinking What We Measure in Cities

• Sentiment vs. Traditional Metrics:
  Feder-Levy describes his shift from classic performance indicators to focusing on community sentiment as the key to evaluating city success.
  • Town hall meetings and surveys only catch the opinions of a vocal minority (“the same 10 people”), which skews city leaders’ perception of resident needs and satisfaction.
  • Using technology, Zen City seeks to make resident input quantifiable and actionable, offering leaders the opportunity to budget and prioritize based on broad-based sentiment (03:00–04:25).

“We became very passionate about: can we use technology to give a data-driven answer to that question?... Hearing from many, many people and putting numbers behind them... really drove a change in the usage of this to drive decisions around policy and budgeting and messaging.”
— Eyal Feder-Levy [03:45]

2. The North Star Metric: Resident Satisfaction

• Outcome-Focused Governance:
  Feder-Levy argues that maximizing resident happiness should be the ultimate goal of local government, rather than measuring proxy indicators (‘leading indicators’) that may not resonate with community priorities.
  • He describes satisfaction as the “North Star metric”—the main result city leaders ought to pursue (04:57–06:04).

“Measuring people's satisfaction or happiness is the North Star metric, is the target equation that local government is working for.”
— Eyal Feder-Levy [05:15]

3. The Impact of Generative AI on Performance Management

• Transforming “Stat” Programs:
  Goldsmith poses the question: can generative AI (GenAI) fundamentally improve government responsiveness and learning?
  • Feder-Levy outlines a four-stage progression where AI first reduces the resources and time needed to analyze data, then identifies new patterns, and finally empowers community-wide usage of analytical tools (06:52–09:05).
  • With AI agents, departments like transportation can more quickly diagnose root causes and take timely action (citing the Seattle 311 analyzer as an example).

“Now we can, using these agents... get to the root cause immediately, while we're in the meeting, without knowing the question ahead of time. And that already crunches time, and that’s value.”
— Eyal Feder-Levy [08:18]

4. Real-World Applications for City Officials

• AI-Powered Decision-Making:
  In practical terms, a transportation director or other official could interact conversationally with AI tools, pulling together data from multiple sources and exploring “why” questions as easily as talking to a human analyst (09:31–10:44).
  • Feder-Levy stresses that defining success metrics (e.g., traffic times, transit usage, satisfaction with mobility) is a crucial prerequisite for effective AI deployment.

“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... in the same way I would ask a knowledgeable staff member.”
— Eyal Feder-Levy [10:30]

5. Addressing the Perception Gap: Safety and Reality

• When Crime Stats Improve but Public Feels Unsafe:
  Many mayors encounter a persistent gap between crime statistics (often improving) and residents' perceptions of safety. Feder-Levy discusses the need to measure subjective sense of safety separately from hard crime data (11:11–13:36).
  • Example: In one city, sense of safety among Hispanic/Latino residents decreased (due to federal issues), even as trust in local police improved.
  • Monitoring both crime stats and public perception enables targeted interventions, such as better communication, not just policing changes.

“Perception is reality and is an essence that we need to solve... They would decide [if a child can walk to school] based on: do they feel safe in their neighborhood?”
— Eyal Feder-Levy [11:29]

6. Building Trust Through Responsive Measurement

• Blueprint for Increasing Satisfaction and Trust:
  Feder-Levy’s playbook for city leaders:
  • Install listening and measurement infrastructure (community-wide surveys, social media analysis, post-service feedback).
  • Harness GenAI and related technologies to gather and organize this data efficiently.
  • Regularly cross-reference perception/satisfaction data with operational metrics to spot gaps and drive responsive changes (13:36–15:49).

“You can’t manage what you can’t measure. So the first step would be to measure it.”
— Eyal Feder-Levy [14:15]
“Gen AI is already playing a really big role... A year from now, I’m thinking about: hey, we can capture voice data a lot more effectively... survey people in much more interesting ways by mimicking a video interviewer.”
— Eyal Feder-Levy [14:45]

Memorable Quotes & Moments

• On outdated public engagement:
  "The same 10 people that always show up... provides a very skewed view of their ‘community.’" — Eyal Feder-Levy [03:19]
• On performance management’s purpose:
  "Stat programs... don’t exist to give somebody a report card. We’re doing this to actually improve services, to recognize what are things that could work better..." — Eyal Feder-Levy [06:57]
• On the future of AI-powered feedback:
  "We can survey people in much more interesting ways by mimicking a video interviewer to get more information." — Eyal Feder-Levy [14:48]

Noteworthy Timestamps

• 03:00–04:25 — Why classic town halls and surveys fail to represent true community sentiment, and how tech can help.
• 06:52–09:05 — The four-stage evolution of performance management with generative AI.
• 10:44–11:11 — The challenge of reconciling perception (sense of safety) with empirical data (crime statistics).
• 14:11–15:49 — Step-by-step playbook for building trust and responsiveness using data and AI.

Conclusion

Through practical examples and future-looking insights, this episode makes a compelling case that cities must start measuring what actually matters—people’s satisfaction and perceptions—using advanced tools like generative AI. Feder-Levy’s advice foregrounds the necessity for representative engagement, cross-functional data analysis, and continual adaptation of new technology to enhance trust and responsiveness in government. For city leaders, the message is clear: meaningful measurement drives meaningful change.