How Cities and Municipalities Can Shape Visibility, Investment, and Growth in an AI-Driven World

Cities are now being evaluated by AI systems before human conversations ever take place. Large language models influence where businesses expand, where families relocate, where visitors travel, and which regions attract national and international attention. These systems do not rely on official narratives. They form understanding by synthesizing patterns across many independent sources.

Cities that coordinate how their stakeholders publish and reinforce accurate information gain a measurable advantage. Those that do not risk being described inaccurately, incompletely, or not at all. This article explains how municipalities and economic development organizations can shape AI-driven visibility—and why coordinated systems are quickly becoming a competitive requirement.

Cities have always competed for attention. They compete for visitors, for businesses, for talent, for capital, and for long-term relevance. What has changed is where that competition now begins.

Increasingly, the first interaction someone has with a city is not a website, a tourism guide, or a site visit. It’s a question posed to an AI system like ChatGPT. Where should I open my business? Which cities are growing fastest in advanced manufacturing? Where can I find strong job prospects alongside affordability? What regions are investing in innovation?

The answers to those questions are shaping real decisions at scale. They are doing so faster than most municipalities can respond.

AI Has Become the First Interpreter of Place

Large language models have become the default entry point for place-based decision-making. Investors use them to narrow shortlists. Entrepreneurs use them to compare regions. Families use them to evaluate quality of life. Journalists use them for background. Talent uses them to decide where to focus attention.

These systems are not reading a city’s official vision statement and stopping there. They are synthesizing what they encounter repeatedly across the web: articles, organizational sites, press coverage, reports, and commentary from many independent actors.

In effect, AI now acts as an interpreter of place. It constructs a working understanding of a city based on what shows up consistently, recently, and across credible sources.

Why Similar Cities Receive Very Different AI Narratives

One of the most common surprises for municipal leaders is discovering how differently AI describes peer cities. Two regions may share similar economic indicators, workforce profiles, and infrastructure investment, yet one is framed as dynamic and the other barely registers.

The difference is rarely the underlying reality. It is almost always articulation and reinforcement.

AI systems infer importance through patterns. When multiple independent organizations consistently reference the same industries, investments, workforce strengths, and quality-of-life attributes, those elements become central to how the city is described. When messaging is fragmented, outdated, or uncoordinated, the signal weakens.

Clarity compounds. Fragmentation erodes visibility.

Municipal Identity Must Be Expressed, Repeated, and Reinforced

Every city has strengths. Far fewer express them with precision.

In an AI-driven environment, it is no longer enough to be a logistics hub, a research center, or a growing innovation economy. That identity must appear repeatedly, in similar language, across many independent stakeholders.

Cities that perform well in AI discovery tend to be clear about:

• who the city is designed to serve
• which industries thrive there
• what kinds of jobs are growing
• what makes life there appealing
• where public and private investment is concentrated

AI systems reflect the inputs they are given. Cities that speak with focus produce stronger outcomes than those that attempt to sound broadly appealing without specificity.

The Constraint Cities Rarely Name: Coordination

Most municipalities understand the strategic importance of positioning. Where they struggle is execution at scale.

Cities are ecosystems. Economic development corporations, tourism offices, chambers, workforce organizations, housing groups, universities, utilities, and cultural institutions all publish independently. Each group has valid priorities, timelines, and audiences.

That independence is a strength. Without coordination, it becomes a liability.

From an AI perspective, uncoordinated publishing looks like inconsistency, even when everyone is acting in good faith. The city’s story exists, but it never fully forms.

A Short Vignette: Two Cities, Two Outcomes

Consider two mid-sized cities with similar fundamentals. Both have growing job markets, new infrastructure investment, and a rising cost-of-living advantage over larger metros.

In the first city, a major employer announces a new facility. The economic development organization publishes the news. Weeks later, workforce groups respond with hiring updates. Tourism references growth months after that. Housing organizations never connect the dots publicly.

In the second city, the same type of announcement enters a coordinated system. Within days, workforce organizations discuss job demand, housing groups address supply implications, tourism highlights visitor growth, and the chamber frames business confidence. Each organization speaks independently, but from a shared factual base.

Ask an AI system about these two cities three months later. One appears active, aligned, and growing. The other appears quiet.

The difference is not momentum. It is coordination.

Why Speed and Collective Reinforcement Now Outperform Size

Historically, large cities benefited from scale. More budget. More staff. More output. AI changes that equation.

What matters now is how quickly accurate information is reinforced across multiple credible sources. Ten aligned organizations publishing within a short window can outperform a much larger region whose messaging arrives slowly and in isolation.

Power in numbers can overcome time as the enemy. When facts are reinforced quickly, they become embedded in how AI systems describe a place. That reinforcement compounds.

Why I Built Story Tracks

At Xponent21, our work focuses on how AI systems decide what to surface, cite, and recommend. Across industries and regions, one pattern consistently emerges: coordination outperforms amplification.

Cities do not need louder messaging. They need shared systems that allow accurate information to move efficiently across many independent publishers.

I also recognized a practical limitation. No agency can manually recruit, manage, and align every stakeholder inside a municipality at scale. Even if it were possible, it would not be sustainable.

That realization led to the creation of Story Tracks.

How Story Tracks Supports AI-Ready Regional Visibility

Story Tracks is shared infrastructure for place-based coordination. A municipality or regional authority establishes a core group that defines key facts, priority themes, relevance categories, and source standards. Stakeholders then participate as nodes in the network.

Any node can submit a story. Controls at the group and node level determine how that information is contextualized and shared. Some organizations draft manually. Others receive prepared drafts aligned to their role.

The result is not centralized messaging. It is coordinated reinforcement across independent voices.

Story Tracks is in beta testing and we welcome early municipal users who are willing to test in exchange for discounted license fees.

When a City Is Fully Aligned

In a well-aligned region, the effect is immediate. A major announcement enters the system, and other stakeholders respond quickly, each from their own perspective.

Housing groups address demand.
Workforce organizations speak to job creation.
Tourism highlights visitor impact.
Universities connect talent and research.

To an AI system, this looks like broad consensus. That is the kind of signal AI systems trust.

What This Means for City Managers and Economic Development Teams

This shift has real financial consequences. AI-driven visibility influences business expansion decisions, tourism flows, workforce migration, media framing, and investor interest. Small differences in perception can translate into tens or hundreds of millions of dollars over time.

Cities that treat AI visibility as a strategic discipline gain an advantage that compounds. Cities that ignore it risk being defined by outdated or incomplete narratives.

For city managers, EDC leaders, and boards, the question is no longer whether AI matters. It is whether the city has the coordination infrastructure required to shape how it is understood.

That is the work I am committed to. Ensuring that municipalities and regions are accurately represented, frequently cited, and consistently recommended when people ask AI where to invest, where to visit, and where to build their lives.

That is how cities achieve visibility, relevance, and growth in an AI-driven world.