How Do Answer Engines Describe Regions? A Guide for Economic Development Organizations
Michael Etheredge is VP of Product Development at QuestionFuel, where he leads how the firm measures and corrects the way AI answer engines describe companies. He was featured by the Southern Economic Development Council on how AI is changing the way companies research communities and regions.
Answer engines like ChatGPT now shape early business research, and published studies show their answers about places vary by engine, by repeat, and by region size. This guide pulls the evidence and the practical steps for economic development organizations into one place, and points to the article that goes deeper on each.
Key takeaways
- Answer engines can leave a qualified region out of an answer, or describe it with outdated or unsupported facts, and the organization often never sees it happen.
- Published studies show answers about places vary by engine and by repeat, and tilt toward wealthier and larger places. They do not isolate the causes.
- A twelve prompt test, run three times in new logged out chats, shows how ChatGPT describes your region today.
- Site selectors now screen on power, water, talent, and community acceptance, so accuracy on those facts matters most.
- Most organizations can start with an in house test. Whether an outside audit is worth it depends on who needs the baseline.
Quick answer: Answer engines like ChatGPT can leave a region out of an answer, or describe it with outdated or unsupported facts, and studies show answers vary by engine, by repeat, and by place. Economic developers can test their own region with twelve prompts and compare the results to the criteria site selectors now use.
What is Regional Silent Exclusion?
Regional Silent Exclusion: when a qualified region is left out of an answer engine's response to a relevant location question, or appears only with framing its own record does not support, and the region's economic development organization has no signal that it happened.
It is the regional version of Silent Exclusion, the pattern QuestionFuel has documented for companies. A region dropped late in a project can often ask why. A region that never appears in the first answer has nobody to ask.
AI Narrative Drift: the gap between what is currently true about a company or place and what answer engines say about it, because those answers update slowly and imperfectly when facts change.
Drift is a different failure. The region appears, but with outdated or incomplete facts. AI Narrative Drift and Silent Exclusion can happen together, and each calls for a different fix.
Why does this matter to economic development organizations now?
Because companies research with answer engines, and the facts they screen on are changing.
Forrester reported in January 2026 that 94% of business buyers used AI in their buying process. That measures purchasing research, not location decisions, and we found no published data on how many location decision makers use ChatGPT for early research.
The facts companies screen on are shifting. The Site Selectors Guild and Development Counsellors International released The State of Site Selection 2026 on September 15. Its central theme is a growing scarcity of certainty, and it points to power, talent, infrastructure, and community acceptance as defining competitiveness. In the Guild's 2026 pulse check, 76% of members named infrastructure capacity the top factor affecting location decisions.
If a first answer about your region misses or misstates those facts, the region may be set aside before certainty is ever tested. How often that happens is not yet measured, which is why testing matters.
What does research show about how answer engines describe places?
Four sources are worth knowing. None is a benchmark of how a specific region appears to a specific industry today.
| Source | Published | What it suggests for regions | Limit |
|---|---|---|---|
| University of Oxford, "The Silicon Gaze" | January 2026 | Answers favored wealthier, Western regions and can carry negative associations built from partial or outdated information | Subjective questions, not site selection; one model version |
| Virginia Tech | December 2023 | ChatGPT gave location specific answers for 515 of 3,018 counties, or 17%, with gaps for smaller and rural places | Older model; one topic |
| Site Selectors Guild and Development Counsellors International | Fall 2023 test, commentary November 2024 | Shortlists overlapped with human lists but deviated, differed by engine, and changed when the same engine was asked the same question again | 2023 models; two scenarios |
| Zen Media | September 2026 | One brand appeared in 15.5% to 59.5% of prompts depending on the engine | Company visibility, not regions; agency data |
The shared pattern is that answers about places are not fixed and not neutral. For the full evidence, see How Does AI Describe Your Region to Companies Deciding Where to Build?.
How can an organization check what answer engines say about its region?
Run a test, not a single question. The full method is in How to Audit What AI Says About Your Region. In short:
- •Build twelve prompts across four types: shortlist, comparison, criteria, and direct questions about your region.
- •Run each prompt three times, in new chats, logged out, because answers can change between runs.
- •Score three things: whether the region appears, how it is framed, and whether the stated facts match your published record.
- •Compare the results to competitor regions and to your own earlier run, not to a benchmark.
Why do answers change from one run to the next?
The studies document the variation but do not isolate its causes, so treat any single result as one sample. Why Does ChatGPT Give Different Site Selection Shortlists to the Same Question? explains how to measure it: run the same prompt five times and classify the region as consistent, frequent, occasional, or absent. Report ranges with dates and engine names, not one screenshot.
Do answers match the criteria site selectors use?
Check by comparing what ChatGPT says against your own documented evidence on power, water, talent, and community acceptance. Does AI Describe Your Region by the Criteria Site Selectors Now Use? provides a six row worksheet: document your evidence, ask the prompt, and mark each row as a match, a gap, or an error. Record emphasis gaps separately from factual errors, because they call for different fixes.
What are the options for responding?
Three realistic options exist. They are not exclusive, and many organizations begin with in house testing and add an audit.
| Option | What it is | Best for | Not best for |
|---|---|---|---|
| Staff training | A workshop on search and answer engine basics | Building internal skill and fixing basic web content | Measuring how the region is described today |
| In house testing | Staff run a prompt set themselves, using the guide above | A first baseline at low cost, and learning the method | Many industries, or repeated tracking over time |
| Regional narrative audit | An outside team runs a larger prompt set with repeat runs and checks accuracy against your documented facts | An independent baseline for the region and board reporting | Organizations that have not yet reviewed their own published record |
Whichever you choose, ask any partner how many prompts, how many repeat runs, which engines, and how accuracy is verified against documented facts. No honest partner can guarantee what ChatGPT will say. How Should an Economic Development Organization Choose an AI Visibility Program? has the full list of questions. QuestionFuel describes its own approach on our page for AI visibility for economic development organizations and in our methodology.
Where should an organization start?
| If this describes you | Start here |
|---|---|
| Your own web content is dated or thin | Training and a content refresh |
| You want proof before committing budget | The in house test, then the evidence on answer variation |
| A board or state partner wants an independent baseline | A regional narrative audit |
| You compete on power, water, and talent | The criteria worksheet, alongside any option |
What is not known yet?
- •We found no published data on what share of corporate location decision makers use ChatGPT for early research.
- •The studies above show variation but do not isolate why it happens.
- •Answers change as engines change, so any result is a snapshot with a date.
- •None of the research is a benchmark of how a specific region appears to a specific industry today. We have not tested your region.
When is this not the right place to start?
It may be premature if the region has not published current data on power, water, sites, and workforce, because there is little accurate material to improve or measure. It may also be premature if no one can own the results internally. In both cases, start with the evidence column of the criteria worksheet and the in house test.
Frequently asked questions
What is Regional Silent Exclusion?
It is when an answer engine leaves a qualified region out of an answer, or describes it with framing its own record does not support, and the region's economic development organization has no signal it happened. The full definition is at the top of this guide.
Do site selectors use ChatGPT to research locations?
Business buyers in general use AI heavily, but we found no published data on location decision makers specifically. Treat answer engines as an early research layer that can shape which regions get a second look, not as the place where the decision is made.
How often should a region test what answer engines say?
There is no published standard. A practical approach is to repeat the test after major changes to your published facts and compare the new run to your earlier one, always using the same prompts so the results can be compared.
Can an organization control what an answer engine says about its region?
No. No one controls what an answer engine says. An organization can make its own published record accurate, current, and clear, and it can measure what answer engines currently say. No partner can honestly guarantee a specific answer.
Does a region need an outside audit?
Not always. Many organizations start with an in house test. An outside audit is most useful when a board or state partner wants an independent baseline, or when the region needs repeat runs across many industries.
Sources and references
- •Forrester, blog post on zero click buying and AI use among business buyers, January 2026
- •University of Oxford, New study finds that ChatGPT amplifies global inequalities, January 2026
- •Virginia Tech, Researchers use environmental justice questions to reveal geographic biases in ChatGPT, December 2023
- •Site Selectors Guild, The Potential and Limitations of AI in Site Selection
- •Site Selection Magazine, AI Challenges Traditional Site Selection, November 2024
- •Site Selectors Guild and Development Counsellors International, The State of Site Selection 2026, September 2026
- •Site Selectors Guild, 2026 pulse check
- •Zen Media, One Brand's AI Visibility Tracking Ranged From 15.5% to 59.5%, September 2026
Related reading: How Does AI Describe Your Region to Companies Deciding Where to Build? · How to Audit What AI Says About Your Region · Why Does ChatGPT Give Different Site Selection Shortlists to the Same Question? · Does AI Describe Your Region by the Criteria Site Selectors Now Use? · How Should an Economic Development Organization Choose an AI Visibility Program?