AI in Site Selection: Lessons from the Field
Earlier this year, I was asked to conduct a site search for a Chicago-based food processing company considering expansion into Indiana. As an experiment, I began the search using ChatGPT to identify available 150,000-square-foot buildings in Northwest Indiana.
ChatGPT quickly identified three suitable options and, impressively, pointed out specific requirements a building must meet to be considered food-grade, such as sanitation features, floor drains, and temperature controls. This was just the beginning.
Using ChatGPT’s Deep Research functionality, I discovered several AI tools now being used in site selection. Here’s a brief overview:
Artificial intelligence (AI) is transforming site selection by enabling faster, more data-driven decisions. Traditionally, site selection requires analyzing numerous factors—demographics, labor availability, infrastructure, logistics, customer behavior, and more—generating enormous datasets. AI technology can rapidly ingest and analyze these complex datasets, spotting patterns and relationships that would be difficult for humans to detect.
By leveraging real-time data, predictive modeling, and machine learning algorithms, AI helps companies forecast outcomes at potential sites—such as projected sales, operating costs, and workforce availability—and objectively compare alternatives. In practice, site selection consultants report that AI’s greatest strength lies in distilling and analyzing vast amounts of information. A recent industry survey found that 40% of professional site selectors already use AI platforms for day-to-day analysis, highlighting AI’s emergence as a powerful (but still human-guided) tool.
I also reviewed the 2024 report, The State of Site Selection, published by the Site Selectors Guild and DCI based on a 2023 survey, which confirmed that AI is rapidly becoming a core part of the site selection process.
Returning to ChatGPT, I followed its suggested process. It identified key organizations to contact regarding site location incentives, including the Northwest Indiana Forum and the Indiana Economic Development Corporation. Both organizations provided helpful clarification about available incentives.
Finally, I asked ChatGPT to compile the findings into a PowerPoint presentation, which I then turned into a webcast.
You can watch it on YouTube [here].
The Results:
Ultimately, the company decided to stay put, leasing additional space near their existing facility. But the real success story was the efficiency: the entire site selection process—from initial search to final recommendation—took less than three days, including time spent reviewing information and speaking with state and local economic development officials.
Takeaways:
While AI can’t replace human relationships—the heart of economic development—it can significantly accelerate the data collection and analysis phases. And while “boots on the ground” are still critical to verify conditions and build trust, AI is proving to be a valuable new ally for economic developers.