Your Team Didn't Get Bigger. It Got Smarter.

Walk into almost any code enforcement department in America right now and you'll find the same thing: a whiteboard, a caseload, and at least one empty desk.

That's not a knock on city management. It's the reality of municipal government in 2025. Since early 2020, local government employment has declined by more than 300,000 workers. In state and local government, vacancies outnumber hires at a ratio of nearly 3.7 to 1, which is almost two and a half times higher than in the private sector. And as the National League of Cities notes, vacancies cause excessive workloads, which cause stress and turnover, and then those vacancies become even harder to fill. It's a cycle that never seems to break.

Code enforcement isn't immune to any of this. In fact, it may feel it more acutely than most departments.

The Vacancy Problem Is Uniquely Painful in Code Enforcement

Code enforcement officers aren't easy to replace. The job requires specific certifications, field experience, knowledge of local ordinances, and the kind of judgment that takes years to develop. It's not a pipeline anyone is actively filling, and a wave of retirements is making it worse. According to MissionSquare Research Institute, 46% of local governments expect the most significant wave of retirements is still ahead of them, and 61% have no succession planning process in place to handle it.

Meanwhile, state and local governments face compounding barriers to bringing new people in: burnout, long hiring timelines, and pay that rarely competes with the private sector. MissionSquare found that 93% of public HR managers have had to re-open recruitments due to an insufficient number of qualified applicants.

The result is that most code enforcement teams are perpetually operating at less than 100% while still being asked to cover the same geography, the same caseload, and the same community expectations with fewer people than the budget ever planned for.

Shorthanded Teams Fall Back on Complaints. Every Time.

When a code enforcement department is understaffed, the complaint queue becomes the lifeline. There aren't enough people to proactively patrol, so inspectors respond to what gets called in. This is completely understandable — and unsustainable

A complaint-driven model only sees what residents report. That means the abandoned lots on quiet streets go unnoticed. The slow-building blight in neighborhoods with lower civic engagement goes unchecked. The property sitting between two attentive block captains and an abandoned stretch where nobody's watching? That property deteriorates in the gap.

Shorthanded teams aren't failing at their jobs. They're doing exactly what a shorthanded team can do, which is respond. What they can't do is see everything. And that's exactly where City Detect comes in.

Good AI Doesn't Replace Your Team. It Gives Them Eyes They Don't Have.

City Detect's PASS AI™ works by mounting cameras on vehicles your city already operates, garbage trucks, utility crews, fleet vehicles already driving every street, every week. Those routine routes become systematic intelligence-gathering missions. Every pass through a neighborhood generates images. Every image gets analyzed by computer vision trained to identify indicators of urban blight, code violations, and property deterioration.

The AI doesn't make enforcement decisions. Your team does. But instead of walking into a Monday morning with an empty desk and a complaint queue, your team walks in with a prioritized, data-ranked list of verified conditions across the entire city, not just the blocks where someone happened to call.

Consider what that coverage looks like in practice. In Stockton, California, City Detect captured nearly 200,000 images across almost 40,000 parcels, surfacing 13,852 unique issues. In Prescott Valley, Arizona, the system flagged over 4,000 blight indicators across hundreds of undeveloped lots. In Greenville, South Carolina, 300 miles of storm-affected roadway were surveyed and roughly 1,200 high-severity damage indicators were identified.

None of those detections required a complaint. None of them required a staff addition. They required a system that could see what a shorthanded team physically couldn't.

The ROI Isn't Headcount. It's Coverage Per Inspector.

When city administrators evaluate AI tools, the ROI question is usually framed around efficiency: "How much time does this save?" That's valid. But for code enforcement departments dealing with chronic vacancies, the better question is: How much of my city is my current team actually able to see?

With a complaint-driven model and multiple vacancies, the answer might be: the blocks people call about. With the Good AI™ layered into existing operations, the answer becomes: all of it.

Your inspectors aren't being replaced; they're being pointed at the right places. They're spending their limited time on verified, prioritized issues instead of conducting speculative windshield surveys. They're covering more ground with better information than they ever could on foot.

The Good AI™ doesn't solve the staffing crisis. But it dramatically changes what a shorthanded team can accomplish within it.

Equity Is Built Into This Approach

There's a dimension to the volume-versus-complaints model that doesn't get discussed enough in city halls: fairness.

Complaint-driven enforcement concentrates resources where residents are most likely to call, which typically correlates with neighborhoods that are wealthier, more politically organized, and more comfortable engaging with city services. Communities that most need proactive code enforcement are often the least likely to appear in a complaint queue.

When AI-powered cameras go everywhere your fleet vehicles already go, every neighborhood receives the same coverage. That's not just operationally smart, it's a more defensible and equitable use of your team's limited capacity.

This Is What The Good AI™ Is Built For

City Detect was founded on the belief that AI should extend the capabilities of good people, not replace the human judgment at the center of municipal work. The system detects and flags. Your team decides and acts. The reports are actionable, integrate with existing case management workflows, and give your inspectors exactly what they need to prioritize their day.

Your budget didn't change. Your headcount didn't change. But your team's ability to see, document, prioritize, and respond? That changes completely.

Municipalities across the country are dealing with vacancies they can't fill on timelines they can't control. Good AI doesn't wait for the hiring pipeline to catch up. It gives your existing team the coverage they need to do the job right — right now.

Curious what Good AI could look like in your city? Contact City Detect to see how PASS AI is helping shorthanded teams do more.

Sources

1. National League of Cities — Attracting and Retaining Talent in City Government: Become a Great Place to Work https://www.nlc.org/article/2024/10/21/attracting-and-retaining-talent-in-city-government-become-a-great-place-to-work/

2. National League of Cities — Municipal Workforce Labor Shortage https://www.nlc.org/resource/improving-opportunities-and-boosting-economic-mobility/challenges/municipal-workforce-labor-shortage/

3. Urban Institute / Tax Policy Center — State and Local Government Jobs Still Haven't Recovered from the Pandemic https://taxpolicycenter.org/taxvox/state-and-local-government-jobs-still-havent-recovered-pandemic

4. MissionSquare Research Institute — Top Public Service Workforce Trends for 2024 https://www.missionsq.org/about-us/news-and-updates/media-inquiries/news-20240109-msriunveilstoppublicserviceworkforcetrendsfor2024.html

5. MissionSquare Research Institute — State and Local Workforce: 2024 Survey Findings https://research.missionsq.org/content/media/document/2024/4/WorkforceSurveyReport2024.pdf

6. MissionSquare Research Institute — 2025 State and Local Government Workforce Survey Resultshttps://www.missionsq.org/about-us/news-and-updates/media-inquiries/news-20250708-msqstateandlocalgovernmentemployersfacedfewerhiringchallengesinthepastyear.html