City AI Connection is a series of posts on emerging ideas in artificial intelligence, with a focus on government application. These articles are initially shared exclusively with the City AI Connect community and subsequently with a broader audience via the Bloomberg Center for Government Excellence at Johns Hopkins University website, govex.jhu.edu. City AI Connect is a global learning community and digital platform for cities to trial and advance the usage of generative artificial intelligence to improve public services. The community is open exclusively to local government employees. To join, visit cityaiconnect.jhu.edu.
City AI Connection is a series of posts on emerging ideas in artificial intelligence, with a focus on government application. These articles are initially shared exclusively with the City AI Connect community and subsequently with a broader audience via the Bloomberg Center for Government Excellence at Johns Hopkins University website, govex.jhu.edu.
By Meg Burke with help from ChatGPT
Generative AI is reshaping our world, and while the technology holds immense potential for governments and public service, it brings challenges that no city should face alone. I’m lucky to be at the forefront of this as manager of City AI Connect, a global learning community for public sector professionals from over 400 cities to share, test, and discuss how they’re using generative AI to improve services and support AI implementation in local government.
Created by GovEx in collaboration with Bloomberg Philanthropies, City AI Connect is exclusively for government employees, providing a dedicated space for leaders to explore and develop AI solutions alongside peers worldwide.
Mayors and innovation leaders, who are hopeful that genAI could improve citizen engagement, enhance data-driven policy making, and optimize service delivery, are already thinking about how to implement the technology ethically, equitably, and with the utmost protection for resident data. By collaborating and sharing in the experience of adopting AI, cities can exchange valuable lessons, avoid needlessly recreating the same solutions, and chart a unified path forward.
Collaboration is not a one-size-fits-all solution for implementing AI, but it does offer a powerful way to drive meaningful progress. Here are some of the ways I’ve seen that cities can together and support each other in advancing and adopting generative AI:
Shared Challenges, Shared Solutions
When cities exchange ideas and experiences, they accelerate innovation while building public trust in the technology. Open dialogue allows cities to share strategies for addressing these challenges, enabling others to sidestep common pitfalls and adopt best practices quickly.
Cities like Austin, TX, which is testing AI for permitting, can provide valuable insights on ensuring responsible AI integration, addressing potential bias concerns, improving response times, and enabling residents to submit paperwork independently. This approach provides a model for others who see AI as a route for improving public services.
Other cities might be interested in looking at the work of Boise, ID which has created a Community of Practice for staff members who are interested in generative AI. This internal knowledge-sharing group fosters responsible experimentation, successfully incorporating new tools and approaches into everyday practice.
Do it Once, Do it Right
Cities often tackle similar problems individually, resulting in duplicated efforts and wasted resources. By connecting with peers, municipalities can pool resources, adapt existing frameworks, and save valuable time and money.
A city with experience operationalizing generative AI guidelines can mentor others who are just starting out and share lessons learned from the process. For example, Boston, MA was one of the first cities in the United States to put out genAI guidelines, and then share about their experience operationalizing the guidelines by posting the guidelines publicly and speaking about the process in public forums. Collaboration among cities engaged in similar work streamlines the learning process and enhances efficiency by facilitating idea exchange, document review, and discussions on developing and refining guidelines.
Setting Industry Standards Together
Generative AI is advancing quickly, but without collaboration, standards for ethical use, equity, and transparency may vary widely. Cities have the power to influence the trajectory of AI development by working together to establish norms that prioritize public good.
Tempe, AZ was one of the first U.S. cities to publish an ethical generative AI policy. The policy, which emphasizes human oversight, bias prevention, and data security, is designed to ensure the responsible use of generative AI in city services. Keeping ethics and transparency at the forefront of the conversation around generative AI is critical, especially as there are so many vendors and tools created using the technology. Cities working together and discussing ethical considerations could shape the implementation of generative AI in a way that will align with community values.
Lessons from Cities, Big and Small
Cities can learn valuable lessons from one another, regardless of size or location. Whether through published guidelines, training programs, or data governance frameworks—such as those recently developed in Buenos Aires, Argentina—urban areas can gain practical insights from both similar and vastly different environments.
Large cities often leverage AI to scale existing projects and reduce strain on staff. For example, chatbots like Sofia in Las Condes, Chile, handle resident inquiries, while AI is used in Memphis, TN to locate potholes and improve infrastructure planning. Smaller cities, on the other hand, tend to be more agile in piloting AI projects, often experimenting with creative solutions despite having smaller budgets. They also typically have stronger community engagement frameworks, allowing them to collect resident feedback on AI initiatives—an approach larger cities could benefit from.
The most effective AI integration will likely combine both strategies: the scalability of large cities with the agility and community-driven approach of smaller ones. By sharing ideas across diverse urban environments, cities can uncover innovative solutions that might not have emerged otherwise.
Building a Community
The road to integrating generative AI won’t always be easy, and cities need support including best practices and lessons learned from others as they try new technologies. A strong network provides opportunities for peer support, capacity building, and knowledge sharing.
The true potential of generative AI will only be realized through sharing lessons learned, use cases, and challenges faced by cities. Through learning from one another, setting collective standards, and building supportive networks, cities can harness this technology responsibly and equitably.
Meg Burke works for GovEx as the Community Manager for City AI Connect.
