AI smart cities need partnerships that actually scale

Cities don’t fail at “smart city” projects because they lack ideas. They fail because good ideas don’t survive procurement, pilot fatigue, and messy data ownership. By late 2025, most city leaders I speak with have the same frustration: there are too many point solutions, too many dashboards, and not enough outcomes residents can feel.

The partnership model discussed by Venturous Group and Arup—highlighting networks of citytech partners and joint platforms like Neuron—gets one thing right: AI in the public sector is a team sport. No single vendor, city department, or systems integrator can pull off AI-driven e-governance, infrastructure optimization, and citizen personalization alone. It takes shared standards, clear responsibility, and solutions that can be “right-sized” for different operators.

This post is part of our series “Mākslīgais intelekts publiskajā sektorā un viedajās pilsētās”, and the focus here is practical: how partnerships make AI adoption in cities faster, safer, and more scalable—without trapping municipalities in endless pilots.

Partnership is the missing operating model for city AI

Answer first: City partnerships work when they function like an operating model—shared goals, shared data rules, and shared accountability—rather than a one-off contract.

A smart city project that includes AI (traffic prediction, energy optimization, automated case triage in e-pārvalde) needs more than software. It needs:

• Domain owners (transport, housing, utilities, permitting)
• Data stewards (privacy, retention, data quality)
• Technical delivery (integration, security, MLOps)
• Frontline feedback loops (operators and citizen experience)

When these pieces live in separate organizations, partnership stops being a “nice-to-have” and becomes the only realistic way to deliver.

The Venturous Group + Arup conversation is a good reminder that collaboration isn’t just about funding or branding. It’s about building repeatable mechanisms so one city’s solution doesn’t have to be reinvented from scratch for the next.

What partnership changes (in real terms)

Partnership shifts cities from “project procurement” to capability building. That means:

1. Reusable components: identity, consent, data pipelines, sensor standards
2. Shared validation: what counts as “works” (accuracy, latency, uptime, fairness)
3. Common playbooks: incident response, model monitoring, vendor offboarding

A strong partnership structure reduces two killers of public-sector AI:

• Integration surprises (the “we didn’t know the legacy system can’t export that field” moment)
• Orphaned pilots (a demo that never becomes a service)

A simple truth: if the partnership can’t survive personnel changes, it isn’t a partnership—it’s a temporary alignment.

Scaling AI requires “right-sized” solutions, not one-size-fits-all

Answer first: The fastest path to scaling AI in cities is designing solutions that can run in multiple governance and ownership contexts—large capitals, small municipalities, and private operators.

The podcast’s emphasis on scalability and right-sizing is exactly where many city AI programs go wrong. Vendors often build for one “ideal” customer: a large city with strong IT capacity and clean data. Reality looks different.

A medium-size municipality might have:

• A small IT/security team
• Fragmented data across departments
• Limited ability to run custom models
• Procurement rules that favor low operational overhead

Meanwhile, a privately owned campus district or transport operator may have excellent telemetry but different legal constraints.

A practical “right-sizing” checklist for AI city services

If you’re evaluating an AI-enabled platform (buildings, traffic, waste, citizen service), use this list:

• Deployment flexibility: cloud, hybrid, on-prem—without feature loss
• Data minimums: what’s the lowest quality/volume of data it can work with?
• Model options: pre-trained vs locally trained vs rules-based fallback
• Operations burden: who monitors drift, incidents, and retraining?
• Procurement fit: modular pricing and phased adoption, not all-or-nothing

The key is to avoid platforms that only succeed when conditions are perfect. Cities need tech that degrades gracefully.

Where this connects to AI in e-pārvalde

E-governance is a great example of right-sizing done well. A city can start with low-risk AI assistance (routing requests, summarizing case notes, suggesting responses) before moving to higher automation.

That staged approach works only if the partnership supports it:

• One partner provides the service design and citizen journey
• Another handles secure integration with case management systems
• Another ensures compliance and auditability

If the solution can’t scale from “assist” to “automate” without a rebuild, it’s not designed for public-sector reality.

Smart buildings are the most underrated AI entry point

Answer first: Smart buildings are where cities can prove AI value quickly—because energy, comfort, maintenance, and safety generate measurable outcomes.

The Neuron smart building platform (a joint venture between Arup and Venturous Group) points at a strong strategy: start where measurement is clearer. Buildings give you direct feedback loops:

• Energy consumption trends
• Occupancy patterns
• Equipment failure signals
• Indoor air quality metrics

And unlike some citywide initiatives, building stakeholders often share a clearer incentive structure: reduce costs, improve comfort, meet ESG targets, and avoid downtime.

A concrete AI use case: predictive maintenance that finance teams understand

Predictive maintenance is often described vaguely. Here’s how it looks when it’s real:

1. Sensors/logs collect runtime, vibration, temperature, fault codes
2. A model predicts probability of failure within a defined window
3. Work orders are prioritized based on risk and operational impact
4. The city/operator tracks avoided emergency callouts and downtime

This is measurable. And measurable outcomes are what help partnerships earn trust across departments.

Why buildings matter to the “smart city” narrative

Buildings aren’t isolated. They connect to:

• District energy systems
• Emergency response planning
• Mobility patterns (events, commuting peaks)
• Citizen services (libraries, schools, community centers)

A city that gets AI-enabled building operations right builds the foundation for broader infrastructure intelligence.

Citizen personalization is powerful—and politically sensitive

Answer first: Personalization in smart cities should be treated like a public service design challenge, not a marketing feature, because it changes trust dynamics.

The podcast touches on the idea that citizens will be able to personalize their city experiences based on preference. That’s plausible—especially as digital identity, consent frameworks, and AI assistants mature.

But personalization in the public sector has a hard constraint: it must be fair, transparent, and optional.

What “personalization” can mean without crossing the line

Good personalization improves usability without creating discrimination. Examples:

• A city app that defaults to your preferred language and accessibility settings
• Notifications tuned to your location and service needs (waste collection, roadworks)
• AI assistance that helps you complete forms faster using previously provided data (with explicit consent)

Where cities get burned is when personalization becomes opaque profiling.

Partnership is how you keep personalization ethical

No single vendor should own the full personalization stack. A healthy model splits responsibilities:

• The city defines policy, consent, and red lines
• Technology partners implement privacy-by-design and auditing
• Civil society/academia can help validate fairness and impact

If you want AI in public sector services to stick, you must make it governable.

Personalization isn’t “AI doing more.” It’s the city doing less guessing—and more listening.

A partnership playbook for AI-driven smart city delivery

Answer first: The most effective city partnerships combine a shared data layer, clear governance, and procurement that rewards outcomes—not pilots.

Here’s a practical playbook I’ve seen work across city technology programs.

1) Start with a shared problem statement (not a product)

Write a one-page brief that includes:

• The service outcome (e.g., reduce permit processing time by 20%)
• The users (citizens, operators, inspectors)
• The constraints (data limits, legal, staffing)
• The measurement plan (what you’ll track weekly/monthly)

If partners can’t agree on this page, they won’t agree later when tradeoffs appear.

2) Build a “minimum viable data pipeline” before fancy models

Cities often try to buy AI before they can reliably move data. Flip that.

A minimum viable pipeline includes:

• Data inventory and ownership
• Access controls and logging
• Basic quality checks (missing fields, duplicates)
• A simple API or export mechanism

Once the pipe is stable, models become far easier to deploy and maintain.

3) Demand auditability: decisions, data, and model behavior

For AI used in e-pārvalde or infrastructure operations, require:

• Model versioning and change logs
• Input/output traceability for a sample of cases
• Bias/fairness tests relevant to the service
• Human override paths and escalation rules

This is how you avoid “black box” controversies.

4) Make scalability a contract requirement

Scalability isn’t a promise. It’s a set of deliverables:

• Multi-tenant capability (for multiple agencies)
• Standard integrations (common CMMS, GIS, ticketing)
• Documentation and training assets
• An exit plan (data portability and vendor transition)

5) Use pilots, but treat them like production

A pilot should include:

• Real users, real workflows
• Real security controls
• A go/no-go decision date
• A budget path to scale if it works

Otherwise, it’s a demo.

Where to focus in 2026: fewer platforms, more interoperability

Cities are heading into a budget-sensitive year, and there’s less patience for “one more dashboard.” The winning approach is interoperable building blocks that multiple partners can assemble around shared governance.

For the Mākslīgais intelekts publiskajā sektorā un viedajās pilsētās agenda, that means prioritizing:

• AI that improves e-pārvalde throughput and service quality
• Data-driven infrastructure management (maintenance, energy, resilience)
• Traffic and mobility analytics tied to operational decisions
• Citizen experiences that respect consent and transparency

Partnership isn’t the headline—it’s the engine. If your city wants AI that actually scales, start by designing partnerships that can carry the weight: data responsibility, operational ownership, and long-term maintainability.

What would change in your city if every AI project had a named data owner, a clear scaling path, and a built-in exit plan from day one?