Data-Smart City Pod: "City Leadership in the AI Era"

Date: November 19, 2025
Host: Stephen Goldsmith (Professor of Urban Policy, Bloomberg Center for Cities, Harvard University)
Guests:

• Carrie Bishop (Lead, Data & AI Initiatives, Bloomberg Philanthropies, Government Innovation Team)
• Rochelle Haynes (Managing Director, What Works Cities)

Episode Overview

This episode delves into the evolving role of city leadership at the intersection of data, artificial intelligence (AI), and public engagement. Host Stephen Goldsmith is joined by Carrie Bishop and Rochelle Haynes, two leading voices in city innovation, to discuss practical strategies for leveraging data and AI to drive better community engagement, improve service delivery, and foster a new culture of data-driven leadership within city governments.

Key Discussion Points and Insights

1. Guest Backgrounds and Perspectives

• Carrie Bishop:

  • Former Chief Digital Services Officer, City of San Francisco; now at Bloomberg Philanthropies leading initiatives geared toward enhancing cities’ data and AI capabilities globally.
  • Focuses on helping cities apply these tools to their most pressing challenges.
  • “It’s really great to get to see not just the US but… across the globe, how cities are using data and AI.” (01:25)

• Rochelle Haynes:

  • Native New Yorker with experience in NYC’s government (affordable housing, social services, homeless services).
  • Heads What Works Cities, providing free capacity-building and certification for city governments on data governance and management.
  • “We are the international standard of excellence on what it means to be good, well managed local government.” (02:33)

2. Rethinking Community Engagement with Data & AI

Host’s Take:

• Community engagement historically has been “decide-first, defend-later,” with limited incorporation of real community feedback. (03:26)

Rochelle’s Perspective:

• Emphasizes “intentional engagement” by meeting communities where they are and using data to inform decision-making.
• Savannah, GA Example:
  • Surveyed residents and performed a physical asset inventory before making park investments.
  • Outcome: 37% increase in youth park engagement and better use of underutilized facilities.
  • “They actually surveyed residents and asked them, what would you like to see in this park… They also did an inventory of physical assets… and then came up with a plan.” (04:30)
  • AI/data used to map assets, saving money through smarter procurement.

Carrie’s Perspective:

• Cities have “a gold mine of data” in existing records (i.e., survey responses, 311 calls) that has often gone underutilized.
• AI allows cities to process and interpret vast amounts of qualitative feedback previously unread or shelved.
• Warns against redundant engagement; leverage what’s already known to deepen engagement without fatigue.
  • “Communities really don’t like it when you ask them the same question 50 times over and refuse to listen to the answer.” (09:40)
• Envisions a future where community organizations train AI models to express neighborhood perspectives to city leaders—co-designing solutions using AI.
  • “How do we encourage our community-based organizations to up their literacy around AI and grab a hold of these tools? …Giving you, through AI, like, the tool you need to understand how we, the community, view this.” (09:50–10:28)

3. Community AI Literacy & Access

Host’s Challenge:

• How do we teach communities to use generative AI for problem-solving (e.g., understanding local disparities in public services)? (11:14)

Rochelle’s Response:

• Generative AI can democratize city processes if education and access barriers are addressed.
• Importance of meeting the public “where they are”—libraries, community centers, civic orgs.
• Suggests “train the trainer” programs: equip civil servants with AI literacy so they can, in turn, train the public.
• References international examples, e.g., Belo Horizonte identifying Wi-Fi gaps, surveying residents, and providing multilingual access and public hotspots.
  • “How do you start to create spaces… awareness around tools, really just hold workshops, libraries, spaces where there’s youth.” (13:01)
  • “WI FI access is not enough. You also need to make sure there’s centers that people can go and access.” (14:25)

4. Data/AI Literacy Inside City Halls

Stephen’s Question: “If you were mayor, what are the top steps to broaden AI literacy in city government?” (15:00)

Carrie’s Response:

• There’s an abundance of public-sector-focused AI training content, including free resources from What Works Cities.
• Stresses the importance of focusing on use cases that matter:
  • Connect training to real, relevant problems to get employee buy-in.
  • Use “sandbox” approaches so civil servants can experiment in a controlled setting (Cities: Santiago, Boston, Seattle).
  • Some cities (e.g., San Francisco) give employees access to tools like Copilot, then monitor and evaluate implementation.
  • “If you stay rooted in problem as opposed to just AI for everyone, that is a better way to stay grounded to the use case…” (16:55)
• Governance and ethical considerations are essential; leaders must calibrate between caution and innovation.

Stephen shares the “rat story”:

• Example of starting with real city problems—Chicago’s city analytics center used data to predict rat infestations.
  • “Pick the problem and use the data to show how you can solve it.” (18:41)

5. Advice for City Leaders: AI, Data, and Culture Change

Rochelle:

• Effective use of AI/data begins by identifying pain points with broad support (examples: illegal dumping in Mendoza, blight detection in Newport News) and using responsible technology to address them.
• “The more that we can uplift these positive examples of use cases… more and more this will stop feeling scary and feel like the very practical tool we need.” (21:44)

Carrie:

• Real progress happens when mayors treat AI and data as core leadership and culture-change issues—not just “for the data nerds.”
• Successful mayors are those asking for data in every meeting and making it a management priority.
• “This is a culture change and it starts at the top… this is a leadership challenge.” (22:43)

Memorable Quotes & Moments

• Carrie Bishop:

  • “AI allows us to take that data and understand it, process it in a way that like we’ve never really had the tools to do before.” (08:22)
  • “Mayors that are asking for data in every single meeting, those are the mayors that are actually making this change happen.” (23:19)

• Rochelle Haynes:

  • “Community engagement is about actually centering the community in a thoughtful and intentional way… not having your plan already.” (04:14)
  • “Generative AI… can democratize these processes… How do we start to create spaces and put in spaces education and awareness around tools?” (11:56)
  • “It’s really getting keen on what are the spaces. And most places have this, right? …Public libraries still matter.” (13:40)

• Stephen Goldsmith:

  • “Problem-solving as a contagion.” (20:03)

Notable Examples

• Savannah, GA: Data-driven parks investment, asset mapping, and procurement efficiency. (04:30)
• Belo Horizonte, Brazil: Wi-Fi gap analysis, multilingual public Wi-Fi access. (13:50)
• Chicago, IL (“Rat Story”): Data-driven pest management as an early city data use case. (18:36)
• Mendoza, Argentina: AI for detecting illegal dumping. (20:43)
• Newport News, VA: AI for proactive blight/code enforcement. (21:10)
• San Francisco, Santiago, Boston, Seattle: Varied approaches to city employee AI literacy and experimentation. (17:39)

Important Timestamps

| Timestamp | Topic/Quote | |-----------|-------------| | 01:02 – 02:33 | Guest introductions (Carrie Bishop & Rochelle Haynes backgrounds) | | 04:11 – 06:24 | Rethinking community engagement and Savannah case study | | 07:27 – 10:33 | Leveraging generative AI for community feedback/Data as a resource | | 11:56 – 15:00 | Bridging AI literacy for communities & civil servants; digital equity | | 15:39 – 18:36 | Data/AI upskilling for city employees; experiment “sandboxes” | | 18:36 – 20:08 | Importance of real use-cases: The “rat” example | | 20:08 – 23:34 | Forward-looking leadership advice for mayors | | 23:34 – End | Closing reflection and thanks |

Conclusion

This episode underscores that successful AI and data utilization in cities is not just about technology, but about intentional leadership, culture change, and authentic community engagement. Both city staff and residents need upskilling, access, and a real stake in the use of these transformative tools. Ultimately, prioritizing real problems, respecting community knowledge, and leveraging existing data resources are at the heart of smarter, more equitable cities.