Why Place, People, and Planners Still Matter in the AI Era

Artificial intelligence is showing up everywhere. From predicting traffic to identifying where future housing might go, it’s being touted as a revolution in the way we plan and manage our communities. In geospatial planning — the work of mapping land, infrastructure, and environmental features — AI seems especially promising. But the truth is more nuanced. These tools are only as reliable as the data and the people behind them, and without planners and geospatial experts, the results can be misleading.

Much of the problem begins with the data itself. Planning relies on property boundaries, zoning codes, environmental overlays, traffic counts, and dozens of other datasets, often compiled by different agencies on different timelines. AI can’t magically resolve inconsistencies or fill in the gaps. If one county updates property maps every year while a neighboring county waits five years, the AI will crunch both as if they’re equally accurate, even though they aren’t. Experts are the ones who notice these discrepancies and make sense of them.

Scale adds another layer of difficulty. A model might identify a neighborhood as ideal for new development based on regional averages, but those averages won’t reveal the industrial lot with buried fuel tanks or the aging stormwater pipes underground. AI is not equipped to recognize the small, local details that can make or break a project. That’s where planners, with their on-the-ground knowledge, remain indispensable.

The way cities describe their land uses and zoning codes also complicates things. What one city calls “mixed-use” might mean high-rise towers, while another uses the same label for small corner shops with apartments above. AI trained in one place doesn’t automatically understand the categories somewhere else. Human professionals, however, can interpret these systems and explain them in ways that both policymakers and the public understand.

Then there’s the question of explanation. Planning decisions don’t just need to be correct; they need to be defensible. Residents want to know why a new school is being built on one block instead of another. City councils need evidence that stands up to public debate and sometimes legal scrutiny. Black-box algorithms don’t provide that. They may output a recommendation, but without someone to interpret and connect it to familiar evidence, the result carries little weight. Planners and GIS specialists bridge that gap, turning data into clear stories.

It’s also worth remembering that planning is not just a technical exercise. Communities are shaped by history, culture, and politics as much as by data. An AI might highlight a stretch of farmland as prime for suburban housing, but perhaps that land is under long-standing conservation protection, or perhaps the community values it for agriculture and open space. Negotiating these competing values is a human responsibility, not something an algorithm can handle.

Even if the technology were flawless, most local governments simply don’t have the resources to run complex AI systems. Planning departments are often small and underfunded. They may not have the money for expensive software or the staff to retrain models as new data becomes available. By contrast, human experts are adaptable and can make practical sense of the tools they already have.

None of this is to say AI has no place in geospatial planning. It can process huge datasets quickly, spot patterns humans might miss, and save time on routine tasks. But the real value comes when it is used alongside human judgment, not in place of it. AI can be a powerful tool in the planner’s toolbox, but it still needs a steady hand to guide it.

The rise of AI doesn’t signal the end of traditional planning — it marks a new partnership. Algorithms can crunch the numbers, but only people can connect those numbers to the stories, challenges, and aspirations of real communities. The future of planning won’t be built by machines alone. It will be shaped by people who understand both the power of data and the power of place.