Most cities are underusing AI. Here’s how to turn small pilots into a practical AI strategy that boosts service, cuts emissions and builds resident trust.
Why smart cities need bigger AI ambitions now
By late 2025, more than 80% of state CIOs say their employees use AI daily. That’s an adoption curve governments usually don’t see in a decade. Yet many city halls are still treating AI like a side project instead of a core strategy.
Here’s the thing about AI in cities: the risk isn’t “doing too much.” The real risk is doing a little bit everywhere and never tying it to outcomes that matter — like cleaner air, faster permits, safer streets or more reliable transit.
This matters because AI is quickly becoming the backbone of smart, green infrastructure. If your city wants to hit climate targets, manage EV charging, or run a resilient grid, you need more than a chatbot on the website. You need a clear AI strategy that’s tied to sustainability and resident trust.
In this post, I’ll walk through what experts are saying, what leading cities are actually doing, and a practical roadmap any city can use to go from experiments to meaningful, green outcomes.
1. The ‘cognitive Industrial Revolution’ for cities
AI in cities isn’t just another software upgrade. It’s more like a cognitive Industrial Revolution: software is starting to do work that once required people’s time, attention and judgment.
Tech leaders at the National League of Cities’ City Summit framed it bluntly: if you think you can sit this wave out, you’re “terribly wrong”. That’s not hyperbole; it’s a realistic reading of the adoption curve.
Why city leaders are underestimating AI
Most city leaders are:
- Over-focusing on narrow pilots (one chatbot here, a single analytics project there)
- Underestimating how fast AI tools improve year to year
- Ignoring the link between AI and core policy goals, especially climate and resilience
The reality? AI is already reshaping back-office government work: procurement, HR, communications, finance. The next wave hits energy, transportation, housing and the labor force. That’s where smart, green cities will either leap ahead or fall behind.
AI and green technology are now the same conversation
If your city is serious about green technology, AI has to be part of the toolkit. For example:
- Optimizing heating, cooling and lighting in public buildings can cut energy use by 20–30%
- Coordinated AI-based traffic management directly reduces congestion, emissions and fuel burn
- Smarter waste collection routing lowers truck miles driven and diesel consumption
When city leaders “don’t think big enough” about AI, they’re also not thinking big enough about climate impact, resilience and resource efficiency.
2. Where cities are actually winning with AI right now
Cities that are moving fast aren’t trying to build humanoid robots. They’re targeting specific, high-friction workflows and automating the boring parts.
Customer service: the low-hanging fruit
Customer service is the easiest and most visible win.
- Philadelphia, for example, uses an AI tool to help customer service reps give consistent, accurate answers to business owners.
- Many cities now run AI chatbots on their websites to handle common questions about permits, trash pickup, taxes and events.
This matters for three reasons:
- Speed – Residents get answers in seconds, 24/7.
- Consistency – The answer you get on Monday matches the answer you get on Thursday.
- Staff capacity – Human agents focus on complex cases instead of repeating the same answers all day.
Modern AI chatbots are no longer the frustrating scripts we all learned to hate. When they’re integrated with accurate city data, they’re good enough that residents actually use them.
Back-office AI that frees budget and time
Behind the scenes, AI is quietly attacking repetitive work:
- Drafting grant applications, council memos and RFPs
- Summarizing long reports for executives
- Categorizing and routing resident service requests
- Cleaning and structuring messy datasets
A recent survey of public employees found 80% believe AI will help with repetitive tasks and 75% say it will save time. That’s not a nice-to-have — that’s budget and staff capacity you can redirect to:
- Climate action planning
- Resilience projects
- Community engagement
Freeing up even 10–15% of staff time across a department can mean the difference between projects that sit on a shelf and projects that get built.
3. From pilots to impact: a simple AI roadmap for city leaders
Most cities don’t lack ideas. They lack a structured way to move from pilots to production without getting paralyzed by risk or complexity.
Here’s a practical roadmap that works at city scale.
Step 1: Start with one clear, measurable problem
Avoid the “AI for everything” trap. Pick one issue you can quantify:
- “Reduce average 311 response time by 30%”
- “Cut permit processing time from 6 weeks to 3 weeks”
- “Lower municipal building energy use by 20% over 2 years”
Tie every AI use case back to that target. If a vendor demo doesn’t move the needle on a real outcome, it’s a distraction.
Step 2: Use AI to write the business case
A smart way to get started is ironically using AI to justify AI:
- Draft a business case for an AI pilot
- Estimate potential time savings using existing workload numbers
- Model different scenarios (e.g., 10%, 20%, 30% efficiency gains)
I’ve seen city teams that took months to assemble a concept paper suddenly ship a solid draft in a day with AI assistance. You still review and edit — but the heavy lifting moves faster.
Step 3: Pilot fast, with clear guardrails
Successful pilots tend to share four traits:
- Tight scope – One department or one workflow
- Short timeline – 60–120 days, not “indefinite beta”
- Defined metrics – Time saved, error reduction, satisfaction scores
- Risk controls – Human review, audit logs, limited data access
You’re not trying to launch the perfect system. You’re trying to learn quickly, then iterate.
Step 4: Scale what works, kill what doesn’t
City leaders should be asking two blunt questions after each pilot:
- Did this change a meaningful metric (time, cost, satisfaction, emissions)?
- Can we safely scale it across departments or agencies?
If the answer to (1) is no, stop. If yes, invest in:
- Training and change management
- Stable funding and procurement paths
- Integration with existing systems (CRM, GIS, permitting, etc.)
This is where a lot of innovation “dies in isolation.” The antidote is a standard path from experiment → policy → citywide practice.
4. Using AI to drive green, resilient city outcomes
AI isn’t just about making bureaucracy faster. It’s one of the strongest tools cities have for hitting climate and resilience goals without exploding budgets.
Smarter energy use in public buildings
Municipal buildings — offices, libraries, schools, depots — are often some of the least-optimized assets a city owns. AI-based building management can:
- Predict occupancy and adjust heating/cooling dynamically
- Combine weather forecasts with historical demand to pre-condition spaces
- Detect anomalies that indicate wasted energy or failing equipment
In practice, that means:
- Lower carbon emissions
- Fewer emergency repairs
- More budget headroom for renewable projects
Traffic, transit and emissions
Transportation is usually a city’s largest emissions source. AI can help by:
- Optimizing traffic signal timing to cut idle time and congestion
- Predicting transit ridership and adjusting frequency and fleet deployment
- Coordinating EV charging to avoid peak grid stress
When these systems are tuned to sustainability metrics — not just speed — they support clean air, reduced noise, and safer streets.
Data as a core sustainability asset
One of the most important shifts experts called out: data is no longer a byproduct. It’s a core asset, right after people.
For green technology initiatives, that means:
- Tracking building energy use by hour, not just month
- Mapping flood risk, heat islands and vulnerable populations together
- Monitoring real-time performance of solar, storage and microgrids
AI thrives on this data. The cities that treat it as critical infrastructure — maintain it, govern it, share it wisely — will move faster than those that treat it as an afterthought.
5. Trust, transparency and “failing forward” with AI
AI in government only works if people trust it. That includes staff, residents, elected officials and community partners.
Building an AI culture in city hall
City leaders who are getting this right focus on five cultural moves:
- Trust – Be explicit about what AI is and is not doing. “This tool drafts emails; humans decide what to send.”
- Transparency – Document where AI is used, what data it touches and how decisions are reviewed.
- Shared values – Align AI projects with public commitments: equity, climate, accessibility, privacy.
- Failing forward – Treat early pilots as learning labs, not permanent infrastructure.
- Resident-centered design – Involve community groups early, especially where impacts are high (e.g., public safety, housing, benefits).
You can’t outsource this to a vendor. Culture is a leadership responsibility.
Practical governance guardrails
Strong AI governance doesn’t have to be bureaucratic. A lean but serious approach includes:
- An AI register listing all tools in use, owners and purposes
- Impact assessments for higher-risk use cases
- Mandatory human-in-the-loop review for decisions that affect rights or benefits
- Clear processes for residents to appeal or contest AI-supported decisions
- Regular staff training on bias, data security and responsible use
When this structure is in place, experimentation becomes much safer — and frankly, much faster — because people know where the lines are.
6. What you should do in the next 90 days
City leaders don’t need a 200-page AI strategy before they act. You need a short, focused plan and a willingness to experiment.
Here’s a realistic 90-day path:
-
Inventory informal AI use
Survey departments. You’ll discover staff already using tools like ChatGPT or office AI features for daily tasks. -
Pick two concrete pilots
- One resident-facing (e.g., chatbot or knowledge assistant)
- One internal (e.g., drafting grants, summarizing reports, energy analytics)
-
Set three explicit metrics
For each pilot, define success numerically: time saved, cases closed, energy reduced, satisfaction improved. -
Stand up light governance
Create a one-page AI principles document and a simple approval workflow for new tools. -
Communicate, early and often
- With staff: this is about augmentation, not replacement.
- With residents: what’s changing, what’s not, and how to give feedback.
If you do this well, you’ll quickly see where AI can support green technology projects, budget relief and better resident experience — not as separate initiatives, but as one coherent strategy.
AI is already embedded in how work gets done across government. The question isn’t whether your city will use AI; it’s whether you’ll use it intentionally to build a cleaner, fairer, more resilient city.
The cities that win over the next five years won’t be the ones that chase every shiny AI demo. They’ll be the ones that tie AI directly to climate goals, service improvements and community trust, then iterate relentlessly.
If your city is ready to move from talk to action, start with one problem, one pilot and one clear outcome — and build from there.