AI-Powered Cities: Why ‘Thinking Bigger’ Means Thinking Greener

Most city halls are missing the real story about artificial intelligence.

They’re asking, “Can AI help my call center?” when they should be asking, “How do we run an entire low‑carbon city with AI?” That gap in ambition is exactly what tech expert Jonathan Reichental was calling out when he said city leaders “aren’t thinking big enough” about AI.

This matters because cities are responsible for more than 70% of global CO₂ emissions, and they’re under pressure right now—heading into 2026 budgets—to cut costs and cut carbon at the same time. AI is one of the few tools that can realistically do both.

In this post, I’ll connect the dots between that National League of Cities conversation on AI and the larger green technology story: how AI can help cities shrink emissions, stretch limited staff, and build trust with residents instead of burning it.

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AI is the new operating system for sustainable cities

AI isn’t just a productivity tool for local government. Used well, it becomes the operating system for a sustainable, low‑carbon city.

Reichental describes this moment as a “cognitive Industrial Revolution.” He’s right—and if you care about climate and resilience, that should be a wake‑up call. The same algorithms writing emails for city staff can also:

• Optimize building energy use
• Synchronize traffic lights to cut idling
• Predict water leaks and grid failures
• Target the right residents for efficiency programs

The reality? AI for green technology is already working in three core city systems:

1. Energy and buildings – AI can manage heating, cooling, and lighting based on occupancy and weather, cutting building emissions by 10–40% in many pilots.
2. Transportation and mobility – Traffic optimization alone can reduce congestion and associated CO₂ by double digits in dense corridors.
3. Infrastructure and utilities – Predictive maintenance reduces waste, extends asset life, and avoids carbon‑intensive emergency fixes.

City leaders who frame AI as a narrow “tech project” miss this systems view—and leave money and carbon reductions on the table.

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Start small, but design with a big green goal

Panelists at the City Summit made one thing clear: you don’t have to start big to think big.

Philadelphia CIO Melissa Scott talked about rolling out an AI tool to help customer service reps give accurate information to business owners. That’s not flashy. But it’s smart.

Here’s why this approach works—and how to connect it to sustainability:

1. Pick a use case that already bleeds time

The fastest AI wins sit where staff are overwhelmed with repetitive inquiries: permits, trash collection, transit schedules, utility billing. AI can:

• Draft accurate responses
• Surface current policies
• Guide residents to the right programs

Now connect that to green goals:

• Route callers to solar permitting guidance instead of making them hunt
• Prioritize outreach for energy‑assistance and weatherization programs
• Promote shared mobility and transit options instead of defaulting to car parking answers

The workflow is the same; the content shifts the city’s climate trajectory.

2. Use AI to build, not replace, your team

Scott’s line is the right one:

“This particular tool will enhance the customer service reps; it won’t replace them.”

For green technology, this mindset is crucial. You want planners, sustainability officers, and engineers using AI as a force multiplier, not fearing layoffs. That looks like:

• Sustainability teams using AI to pre‑draft climate action plans based on existing data
• Planners asking AI to generate scenario analyses—e.g., “What happens to emissions if we shift 15% of car trips to transit?”
• Grant writers using AI to assemble first drafts of complex funding proposals for EV charging, resilient microgrids, or building retrofits

Micah Gaudet’s advice on “just use ChatGPT to write grants” sounds almost too simple. But if that grants program funds a new community solar project or heat pump conversion, the climate payoff is very real.

3. Make chatbots climate‑aware from day one

Reichental said modern civic chatbots “should be like a basic thing.” I’d push that further: a climate‑literate chatbot should be basic infrastructure.

If you’re deploying an AI assistant on your city website, train and configure it to:

• Proactively suggest green alternatives (EV charging maps, transit routes, bike infrastructure)
• Explain rebates and incentives for heat pumps, insulation, rooftop solar
• Help residents calculate their home’s approximate carbon footprint and connect them with local programs

You’re not just saving staff time. You’re putting climate action in every resident conversation, 24/7.

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Data is the fuel: how AI turns raw numbers into lower emissions

Reichental said, “Data is the most important aspect of every organization, aside from people.” For green technology, that isn’t a slogan—it’s literal.

Cities already sit on mountains of climate‑relevant data:

• Smart meters and utility usage
• Traffic counts and GPS traces
• Building permits and zoning data
• Transit ridership and service patterns
• Sensor feeds on air quality, heat, and flooding

On its own, this data just eats storage. With AI, it becomes real‑time climate intelligence.

Practical ways cities can use AI on sustainability data

1. Optimize building energy performance

   • Use AI models to learn occupancy patterns and suggest schedule changes for HVAC systems in municipal buildings.
   • Run simulations on which buildings should be retrofitted first to get the largest emissions reduction per dollar.

2. Smarter transportation planning

   • Analyze congestion and idling hotspots, then test signal timing changes in silico before reprogramming intersections.
   • Identify neighborhoods where short car trips dominate and prioritize safe cycling infrastructure or on‑demand shuttles there.

3. Targeted climate resilience

   • Combine floodplain maps, stormwater models, and social vulnerability data to find the highest‑risk blocks.
   • Use AI to forecast where extreme heat will hit hardest and time cooling center openings or tree planting accordingly.

1. Waste and circular economy

   • Use AI vision systems at facilities to track contamination rates and then tailor outreach by neighborhood.
   • Predict when equipment upgrades will yield the greatest efficiency gains.

If you’re a sustainability director or city CIO, here’s the key move: tie every new data project to a specific emissions or resilience outcome. No more “data for data’s sake.”

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Balancing AI risks with climate urgency

The NLC panel was realistic about AI’s risks: bias, opacity, and resident mistrust are all real. Scott framed the solution as building a culture of “trust, transparency and shared values.” That’s exactly the right frame, especially for green technology.

Here’s what that looks like in practice.

Build a “fail forward” lab, but set guardrails

Innovation without guardrails is how you end up with biased policing tools and secretive algorithms deciding public benefits. Innovation with guardrails is how you pilot sustainable solutions safely.

Set up:

• Clear AI use guidelines: What’s allowed, what’s prohibited (e.g., no black‑box systems in life‑or‑death decisions, no secret deployments in public space).
• Pilot sandboxes: Time‑boxed trials for things like energy optimization, traffic management, or virtual inspectors for low‑risk inspections.
• Transparent evaluation: Publish simple metrics—“This pilot cut downtown building emissions by 12% and saved $400k/year.”

Residents are much more likely to support AI in city operations when they see tangible climate and cost benefits—and when they know how the tech is governed.

Keep people at the center

Reichental warned that AI will hit “energy, transportation, economics and the labor force in very big, profound ways.” He’s right, and that can cut both ways.

For sustainable cities, the goal should be: climate wins that also create fair work and healthier neighborhoods.

That means:

• Training frontline workers—facilities staff, operators, inspectors—to use AI tools instead of outsourcing everything to vendors.
• Co‑designing AI projects with community groups, especially environmental justice communities that often bear the brunt of pollution.
• Measuring success in more than just cost savings: think emissions reduced, indoor air quality improved, asthma rates lowered, commute times shortened.

If you get this balance wrong, AI becomes another “tech imposed on people.” Get it right, and it becomes a visible part of how your city keeps residents safe, comfortable, and included as the climate changes.

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A simple roadmap: from chatbot experiments to climate‑smart operations

Most cities don’t need another 200‑page AI strategy. They need a short, opinionated roadmap they can start on this fiscal year.

Here’s one that works and stays grounded in green technology.

Step 1: Map AI opportunities to climate goals

Start with your existing climate or sustainability plan and ask:

• Where are we stuck because of staff time? (e.g., building outreach, grant writing, data analysis)
• Where are we flying blind because we lack real‑time insight? (e.g., grid congestion, heat islands, water loss)
• Which climate actions would benefit most from automation or prediction?

From that list, pick 3–5 high‑impact, low‑risk AI use cases.

Step 2: Launch one “front‑office” and one “back‑office” pilot

This mirrors what the NLC panelists described.

• Front‑office: An AI chatbot or assistant that helps residents with energy rebates, solar permits, transit options, and waste questions.
• Back‑office: An AI‑assisted analytics project—such as optimizing municipal building energy or simulating traffic changes to cut emissions.

Make sure both pilots have:

• A named owner (not “the IT department” in general)
• A simple success metric tied to climate and cost (e.g., “20% more solar applications processed,” “10% less electricity use in target buildings”)

Step 3: Upskill staff—not just the IT team

If you want AI to stick, it can’t be a side hobby. Bring in basic AI literacy training for:

• Sustainability and climate teams
• Public works and utilities
• Transportation and planning
• Finance and budget staff

Show them concrete prompts and workflows—not abstract theory. I’ve found that once a planner sees AI instantly draft three land‑use scenarios or a sustainability officer sees a first‑draft grant appear in seconds, adoption takes care of itself.

Step 4: Scale what works, shut down what doesn’t

“Experiment, pilot more than you’ve ever done before,” Reichental urged. I’d add: kill pilots quickly if they don’t move climate or equity metrics.

After 6–12 months:

• Roll successful tools into standard operations and budgets.
• Publish a short public report on climate and cost impacts.
• Retire pilots that don’t deliver and redirect that energy.

This build–measure–learn loop is how AI becomes part of the city’s green technology backbone, not another abandoned innovation project.

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Why thinking bigger about AI means thinking greener

City leaders have a choice in 2026 and beyond: treat AI as a convenience feature, or use it as core infrastructure for a low‑carbon, resilient city.

When you connect AI to green technology, three things happen:

• Every productivity gain becomes a potential emissions reduction.
• Every data project becomes a way to harden your city against heat, floods, and outages.
• Every resident interaction becomes a moment to move people toward cleaner, healthier choices.

Most cities aren’t thinking big enough about AI yet. The fix isn’t a moonshot project; it’s a clear decision: from now on, AI pilots must serve climate, equity, or resilience—not just convenience.

If you’re responsible for sustainability, technology, or infrastructure, this is the moment to link your AI agenda to your climate agenda and treat them as one roadmap. The cities that do that over the next two years won’t just be “smart cities.” They’ll be the ones that stay livable.