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November 6, 2025
Sand Technologies
Every public sector leader feels pressure. Budgets are tight, and citizen expectations are high. Urban challenges, including traffic congestion, aging infrastructure and public safety concerns, require smarter solutions. In Europe and Asia, many cities have adopted a smart city approach to do more with less while meeting the needs and demands of growing populations. However, many US cities have been slow to adopt the smart city concept.
In an era of limited budgets, AI’s greatest strength lies in its ability to optimize resources and create significant efficiencies. City governments often lead with a reactive, crisis-management approach to governance. However, in smart cities, AI is a powerful force multiplier, providing practical tools that help governments break the reactive cycle and proactively improve residents’ quality of life.
At its core, AI optimizes resources in ways previously impossible. For example, a wastewater utility in the U.K. utilized data analytics and a digital model of its operations to reduce operational expenses by 15% and comply with environmental standards, avoiding £5-10 million in annual penalties.
In a landscape of competing priorities, AI provides the data-driven clarity government leaders need to make the toughest decisions. AI can improve strategy as well. For long-term planning, AI can synthesize demographic trends, infrastructure condition reports and public feedback to help prioritize capital improvement projects, ensuring investments in areas of greatest need to build a more equitable and resilient city for the future.
For AI projects to succeed, citizen buy-in is required. Are citizens ready for AI in their cities? The answer is a nuanced “yes,” but their readiness is conditional and must be earned through trust and mutual understanding. Sand Technologies surveyed 2000 people in the U.S. to assess their sentiment toward smart cities. The good news is that 67.8% of respondents believe that AI can have a positive impact on city living. However, 61.4% do not trust the government to use AI responsibly.
In a second smart city sentiment survey, Sand Technologies polled citizens in a mid-tier city, St. Louis, to see whether sentiment differed when excluding large, tech-friendly cities like New York and San Francisco. Directionally, the results were the same. However, in the St. Louis poll, distrust in the government’s use of AI was 10 points higher, with 71% saying they did not trust the government with AI. Municipal governments must incorporate citizen input into these projects, especially in smaller cities, to improve trust in AI.
According to the survey, residents are more than ready for the tangible benefits that well-implemented AI delivers. They want smoother commutes, faster pothole repairs and more responsive public services. They will readily embrace technology that solves their everyday problems and makes their city more efficient; however, concerns about privacy, fairness and surveillance may temper this enthusiasm.
AI is a tool. Think of it as a team of super-fast analysts who can spot patterns in city data, from traffic flow and energy usage to 311 service calls that are simply invisible to the human eye. This ability enables public sector leaders to respond faster with key services. Key AI capabilities include:
The most important concept to understand is to focus on the problem, not the technology. The goal isn’t to “use AI” for its own sake, but to solve a specific city problem with the technology. Follow this six-step AI strategy on the path to a smarter city.
Start with the “what.” Identify specific challenges and opportunities and choose use cases that deliver measurable impact. A good strategy for determining priorities is to poll the community. Is it traffic on Main Street? Slow permit processing? Let these real-world problems guide the approach. Successful smart cities align with their citizens’ priorities.
Tackling issues like chronic traffic congestion, slow service requests, or crumbling sidewalks delivers a tangible, immediate win that the public can see and feel. By demonstrating a clear return on investment for the community, leaders generate the essential buy-in, trust and momentum needed to champion larger, more complex and less visible smart city initiatives in the future.
A clear roadmap turns the AI vision into an actionable plan by balancing immediate wins and future growth, defining both short-term pilots and long-term scaling opportunities. Short-term pilots are small-scale, manageable projects designed to test AI’s potential, demonstrate value quickly and create momentum with minimal risk. Think of them as controlled experiments to see what works for the city.
At the same time, teams must identify how a successful pilot can be scaled across other departments or city-wide, ensuring that these initial efforts serve as stepping stones toward broader transformation, not just one-off novelties. To make this process effective, it’s critical to establish clear milestones to track progress, allocate the necessary resources (budget, talent and technology), assign specific roles to ensure accountability, and define key success metrics to measure impact objectively. This structured approach ensures that every AI initiative is purposeful, measurable and aligned with the city’s strategic goals from the outset.
Starting an AI journey with a small, well-defined pilot project is the most effective strategy for achieving long-term success. Begin with manageable pilots to test assumptions. Small projects offer public sector leaders and teams the opportunity to gain a deeper understanding of what is involved. The manageable scope creates a low-risk environment in which to learn, test assumptions and address unforeseen issues with data and processes.
The ability to iterate and refine the approach based on real-world feedback is invaluable. It allows teams to scale validated successes across the organization. A proven model becomes a blueprint for scaling faster and more confidently. A successful project delivers immediate value to stakeholders and builds crucial momentum and organizational buy-in, both of which are essential for future investment. The table below outlines good pilot projects for municipal governments.
Data powers AI. A building can’t stand on a weak foundation. The same applies to AI models. Ensure high-quality data and robust integration by implementing strong data governance and cleaning processes. Prepare data for AI by conducting an inventory of the city’s data. Is it accessible? Is it high-quality? Is it secure? Investing in good data governance is the essential, non-negotiable first step.
Preparing data before an AI project is the most critical step, as the quality of the AI’s output is entirely dependent on the quality of the input. Investing time in data preparation ensures that an AI project is built on a solid foundation, saving significant time and resources in the long run. Preparing data involves several steps to transform raw data into a clean, reliable dataset ready for an AI model. Learn more about preparing data for AI.
Upskill existing teams and hire AI experts. Cultivate a culture that embraces experimentation and change. Technology alone won’t deliver results; people and culture are the driving forces behind the AI strategy. Success requires a dual approach to talent: upskilling existing teams and strategically hiring specialized AI experts. Current employees possess invaluable institutional knowledge, and providing them with training in data literacy and basic AI tools empowers them to identify opportunities and champion new solutions within their departments. Complementing this internal expertise by hiring data scientists or machine learning engineers brings the deep technical horsepower needed to build and deploy complex systems.
However, even the most skilled team will falter without the right environment. That is why it’s crucial to cultivate a culture that embraces experimentation and change. AI implementation is an iterative process filled with trial and error. A culture that treats failures as learning opportunities, not mistakes, gives teams the psychological safety to innovate, test novel ideas and adapt quickly, ensuring that an organization’s AI capabilities evolve and improve over time.
An AI journey doesn’t end with a successful pilot; it begins there. The final step is to iterate and scale, transforming initial wins into lasting transformation. Leverage pilot learnings to refine strategy. Think of each pilot as a crucial learning opportunity, providing invaluable data on what worked, what didn’t and why. Teams must leverage these learnings to refine their broader AI strategy, making data-driven decisions on which projects to expand, adjust, or discontinue. Moreover, managers must address the people side of change, guiding their teams and the organization through the transition from the existing process to the new one enabled by AI.
Once a solution is scaled, the work shifts to continuous improvement. Establishing a robust system for gathering ongoing feedback from both employees and residents, while consistently measuring performance against Key Performance Indicators (KPIs), is essential. This continuous loop of implementation, feedback and optimization ensures the AI tools remain effective and aligned with community needs, allowing teams to move beyond one-off projects and achieve sustained, city-wide value.
Beyond these steps, public sector leaders may want to collaborate to build public confidence. Consider fostering partnerships with universities, tech companies, and community groups to leverage external expertise, share resources, and co-design innovative solutions that benefit all parties. Most importantly, lead with ethics and trust by embedding transparency, fairness and accountability into every initiative from its inception. Ensure the responsible use of AI. This requirement is fundamental to earning and maintaining public trust.
Partner with local universities, tech startups and community groups to bring in fresh expertise and new ideas. Public sector leaders should create opportunities with clear, mutually beneficial outcomes. The key is to move beyond simple contracts and build a true innovation ecosystem where private companies, academic institutions and startups can thrive by helping to solve civic challenges.
Another idea is to host a public innovation challenge or hackathon. Instead of prescribing a solution, the city defines a specific, pressing problem, such as “develop an AI tool to predict parking availability” or “design a system to optimize waste collection routes,” and invites companies, startups and developers to compete to build the best solution.
Using data and AI comes with profound responsibility. Be transparent. Develop clear, public policies on how data is collected, used and protected. Be proactive in ensuring that AI systems are fair, equitable and do not reinforce existing societal biases. Public trust is the most valuable asset in this journey. As the Smart City Sentiment results mentioned above show, people lack trust in the government’s use of AI. It is imperative to address ethics and trust issues.
To build public trust in AI projects, public sector leaders must prioritize radical transparency, meaningful public engagement and a robust ethical framework from the outset. Trust isn’t a byproduct of a successful project; it’s a prerequisite for getting started. For each AI initiative, create a public website or dashboard that explains in simple, clear language:
The Goal: What specific problem is this project trying to solve?
The Data: What data is needed? Where does it come from?
The Tool: How does the technology generally work, without overwhelming technical jargon?
Transparency demystifies the technology and respects the public’s right to know. It replaces suspicion with information, forming the foundation of a trusting relationship.
Cities must also create and publish clear, publicly available rules governing the use of AI. Include privacy and security as guardrails in this framework to ensure technology aligns with the community’s values. Guarantee that there is always a clear path for a human to review and override an AI’s decision. A public ethical framework proves that the city is thinking critically about the potential harms, not just the benefits.
Building trust is a two-way street. It requires moving beyond simply informing the public and actively involving them in the process. Consider hosting town halls, public workshops and feedback sessions to gather input before launching a project. Meaningful engagement gives residents a sense of ownership and shows that their concerns are being heard and valued.
Artificial Intelligence is no longer on the horizon; it’s here, and it’s offering practical solutions to some of the oldest and most difficult challenges in urban governance. For public sector leaders, AI is a powerful tool for delivering better services more efficiently, making communities safer and building a more responsive and resilient government.
The journey to becoming a smarter city may seem daunting, but it begins with a single, strategic step. Identify one key challenge, assemble a small team and explore how a pilot project could create a better future for residents.
| Public Sector Pilot Project Ideas | ||
| Category | Project | Benefit |
| Operations and efficiency | ||
| Intelligent document processing and workflow automation – AI can process documents such as forms, permits and license applications to extract data, flag errors and automate approvals. | Reduces manual hours on tedious tasks, improves turnaround times and finds errors. | |
| Budgeting and resource allocation – Analyzing historical expenditures, population growth and service demand optimizes financial planning. | Public sector leaders can identify trends and allocate resources more effectively, ensuring efficient use of public funds. | |
| Predictive infrastructure maintenance: AI systems analyze sensor and camera data to predict when infrastructure, such as roads, bridges and sewer pipes, will require maintenance. | Reduces the time and costs of expensive emergency repairs. | |
| Citizen services and engagement | ||
| 24/7 AI-powered chatbots handle routine inquiries, answer questions about services and guide citizens through applications. | Reduces call center traffic and allows citizens to get instant answers, even outside regular business hours. | |
| Sentiment analysis for feedback – AI can analyze public sentiment from social media, forums and surveys to help leaders understand community opinions and priorities. | Enables more responsive policymaking. | |
| Public safety and emergency response | ||
| Emergency response is enhanced by AI analyzing real-time data from traffic, weather and sensors. | Optimizes emergency services dispatch and provides faster response times during crises. | |
| Urban planning and sustainability | ||
| Optimized transportation – AI can analyze traffic patterns in real-time to adjust traffic signals and optimize public transit schedules | Reduces congestion and lowers emissions. | |
| Smart energy management – AI can optimize energy consumption in municipal buildings and public spaces. | Reduce energy use, prevent waste and achieve cost savings. | |
| Financial management and fraud detection | ||
| Automated auditing and fraud prevention – AI tools can quickly analyze financial reports and transaction patterns to flag irregularities and potential fraud. | Helps governments better protect public funds. | |
| Accounts payable automation – AI automates invoice coding and processing. | Reduces human error and frees up finance personnel for higher-level tasks. | |
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