Top 5 Tips for Economic Developers in the Age of AI Site Selection

If you market sites and communities for a living, here’s something worth knowing: The first review of your site increasingly isn’t done by a person. Our team at Site Selection Group has been building and using AI tools that scrape site listings, digest RFIs (requests for information that we send to communities during a project), and score sites against project requirements. The first screen of your community may happen entirely in software, before any human looks at your site.

As a result, this early AI usage should change how you market what you have. Here are five tips from our side of the table.

1. Post your sites online with complete information where site selectors and companies actually look

An AI scraper can only evaluate what it can read. The minimum bar is acreage, zoning, and distances to utilities, but the bar is rising fast. It’s now common to see communities attach due diligence reports right to their listings. We recommend you share as much as you can while honoring nondisclosure agreements (NDAs) and protecting sensitive information.

A few practical points: 

• First, make your listings machine-readable. A site flyer saved as a scanned image can be invisible to a scraper, and so is anything sitting behind a “contact us for details” form. Put the data in text on the page or in text-based documents. Second, don’t assume an AI scraper will find your community’s website. Post your sites where selectors and companies already pull from—your state’s Zoom Prospector platform is a good example. One quiet upside to the AI trend: Directories that charge communities for placement matter less every day. Scrapers don’t check who paid to be listed.
• A word on what information to share. Sometimes it makes sense not to lead with a detail that looks worse on a desktop review than it is in reality. That’s a legitimate call to make, but have a truthful answer for every question, and never leave a question with no answer at all. 
• Finally, the scrape is just step one. Expect a follow-up information request, increasingly drafted by AI itself. The communities that answer it completely are the ones that stay alive. Your online data gets you through the first screen; your follow-up answers get you to the short list.

2. Plug the gaps because a blank scores zero

Here’s the part nobody tells you: AI evaluation tools are unsympathetic. A human analyst with a missing data point might pick up the phone. The model just scores it, and missing information scores 0%.

So, provide an answer that’s directionally correct, and qualify it in the notes. Say you’re asked how long a water extension to your industrial park would take, and you don’t have an engineered answer on a short turnaround. But you remember a line extended to another park last year with a similar distance that required no right-of-way. That process took 10 months. Say 15 months to cover your bases, note the basis for the estimate, and that data point just went from scoring zero to maybe 75%. 

A defensible, clearly labeled assumption beats a blank every time.

The flip side matters just as much. If you put down assumptions that are simply wrong to make a site score well, it will be caught on a second review or on the site visit, and from that moment, the site selector and the company feel they have to verify every single data point you’ve provided. There’s a balance between representing your site well and reaching too far, and, frankly, it’s a new art we’re all going to have to learn. 

Nobody gets it right immediately, and that’s okay.

3. Ask AI about your community before someone else does

Open Claude, ChatGPT, Gemini, or whatever tool you like, and ask the questions companies would ask you about your community: Is this community business-friendly? Has there been union activity? What’s the sentiment toward industrial development? How’s the workforce? Is this a growing area? What do you know about this specific site?

Whatever comes back is, roughly, the first impression a project team gets. If something in there could look a bit off, address it by providing context to prospects, explaining why it wouldn’t affect a typical project, or framing it accurately where the AI’s version is stale or one-sided. It’s often smart to raise these things with site selectors before any project is in tow, so the context is already in their heads for everything they work on in your area.

A real example: In one community, union activity surfaced in the research. But it involved an automotive plant, a sector where national unions concentrate their organizing campaigns, and our client was an advanced manufacturing operation in a different industry, with a different labor pool and wage structure. With that context, it was a non-issue. Without it, a model just flags “union activity” and the company wonders why.

4. Tell the full workforce story

Workforce analysis completed by AI isn’t new math. It digests the same public census, labor and job-posting data the industry’s platforms have used for years. The problem is that the old flaws get worse: data that misses recent growth, uninformed drive times, and other issues. The only difference is that now site selectors and companies are one more step removed from the data, because so much of the analysis is automatic.

The good news is that correcting the record has never been easier. Your wage study, your labor shed analysis (the real geography your workers commute from), and your employer testimonials can now be dropped straight into an AI-driven analysis with a file upload. 

So do three things: 

1. Load that material into every submission’s supporting documents. 
2. Publish it on the open internet where scrapers can grab it.
3. Don’t be afraid to ask site selectors directly how workforce information feeds their analysis. It's a fair question, and the answer will sharpen everything you send.

5. Know where the human line is

If the first four tips show anything, it’s that this is still an art form. These tools are unsympathetic, literal, and occasionally wrong, which is exactly why we need sharp, passionate people at the front of these decisions, guiding companies to the right home. Relationships and trust are how all of us will figure out where these tools help and where they mislead, because the real goal hasn’t changed: getting companies to a place where they’ll thrive.

And there’s a whole category of things AI will never score, such as how a community shows up on visit day, the mayor who picks up the phone, and the trust built over years of straight answers. Since those can’t be measured any other way, the human element doesn't shrink in the age of AI site selection. It becomes the differentiator.