The Intuitive City, Part Two: Testing What Cities Don’t Tender For
By Dr. Amy Hochadel | 17 April 2025
— A follow-up to “Data as Empathy,” exploring what it takes to move from insight to integration.
It’s one thing to understand people. It’s another to design with them.
In recent years, cities have made significant progress in collecting and analyzing data to sense how they are being used. But knowing what’s happening and knowing what to do about it are two different things. Turning empathy into action—that’s the harder part.
The traditional model for testing innovation in cities is deeply flawed. Pilot zones and “sandbox” areas might create controlled conditions for experimentation, but they rarely capture the complexity of real urban life. Cities aren’t tidy, and neither are the challenges they face. That’s why real innovation requires a different kind of testing environment—one that reflects the full unpredictability of a living, breathing city.
When a whole city operates as a testbed, the bar is higher. Solutions can’t be speculative. They need to be safe, operationally sound, and socially acceptable. In other words: near-market. These are innovations that are technically ready but haven’t broken through the procurement barriers that keep new ideas locked out of old systems. Cities don’t tender for innovation. That doesn’t mean they don’t need it.
The Role of Near-Market Testing
There’s a quiet crisis in urban innovation: solutions exist, but systems don’t know how to absorb them.
Across sectors—from mobility to energy to public realm design—there are technologies and models that could be deployed tomorrow. But they stall. Not because they don’t work, but because cities haven’t seen them work in context.
That’s where near-market testing comes in. This isn’t about prototyping in labs—it’s about putting ready solutions into real-world conditions to gather the data and evidence city systems need to move forward. It’s innovation under pressure: with people moving through it, infrastructure pushing against it, and policy constraints wrapped around it.
For example, consider adaptive lighting systems that respond to footfall and ambient noise. These technologies are deployable. But they often remain in the R&D phase because few cities are set up to evaluate their impact outside of static benchmarks. Near-market testing fills that gap—not by isolating innovation, but by embedding it directly into the grain of daily life.
This approach doesn’t just validate solutions. It surfaces the friction points that often get missed in sanitized pilot projects—user discomfort, policy misalignment, edge-case failures. And that’s the kind of feedback loop urban systems actually need if they’re serious about scaling what works.
The City That Listens—Even When No One’s Speaking
Urban systems have traditionally relied on explicit signals—service requests, citizen complaints, policy interventions. But cities are full of quieter signals. Not everything that matters shows up in a dashboard. Not everyone reports an issue. Often, they adapt silently.
A truly intuitive city doesn’t wait for feedback forms. It senses patterns in behavior, in movement, in ambient conditions. It understands when routines shift, when a space goes underused, when a service fails to connect—not because someone says so, but because the data shows it. This isn’t surveillance. It’s attentiveness at scale.
With AI and ambient sensing, recognition happens across multiple modalities. A visitor shouldn’t need to download an app to access a building. A resident shouldn’t need to log into five platforms to interact with city services. The more intuitive the system, the less visible it becomes. The interface is the city itself.
This isn’t a vision of passive consumption. It’s a different kind of engagement—one where citizens don’t need to shout to be heard, because the system is already listening. And the feedback loop isn’t occasional; it’s continuous. A service doesn’t just fail once and get fixed. It learns, in real time, to adjust.
What Cities Miss When They Don’t Evolve This Way
Many cities still define innovation as something to procure, rather than something to participate in. The result is a backlog of good ideas with nowhere to go. Technologies sit on the shelf. Startups burn out waiting for pilot approvals. Public servants are asked to “be innovative” without the tools, space, or mandate to act.
But when cities begin to operate more like adaptive systems, something shifts. You stop talking about digital transformation as a goal, and start treating it as a method—one that embeds itself into governance, planning, procurement, and even maintenance.
The payoff isn’t just smarter infrastructure. It’s public services that become more responsive without needing to grow in complexity. It’s public spaces that adjust without needing to be redesigned from scratch. It’s a shift from bureaucratic control to urban intuition—where systems respond not because someone programmed a specific outcome, but because they’re trained to recognize and respond to what matters.
This is not about creating the perfect city. It’s about creating cities capable of learning.
Learning Cities or Lagging Cities
Whether you’re managing a megacity or a midsize municipality, the questions are converging. How do we deliver public services that adapt faster than bureaucracies? How do we build trust without friction? How do we design systems that get smarter, not just more digital?
These challenges aren’t unique to any region or income level. Rich or resource-constrained, North or South—even cities designed for the future—are wrestling with the same core tension: how to evolve practically, affordably, and humanely, in a world that won’t wait.
No city can solve this in isolation. The real breakthroughs will come from collaboration—not at the level of technologies, but at the level of ideas, methods, and shared risk.
The cities that lead will be the ones that learn. Together.