For anyone new to the term, location intelligence is the practice of turning “where”, where your customers live, where your competitors operate, where your next site should go, into a business decision, by layering spatial data like demographics, traffic, and foot-traffic against maps and analysis. Retailers use it to pick store locations, banks use it to assess branch and lending risk by area, logistics teams use it to plan routes. It’s not a new field. What’s changed dramatically is how the answer gets produced.
A few years ago, “location intelligence” meant a map. Someone in GIS pulled boundary files, layered on demographic data, and handed a static image to a decision-maker who then made the actual call. The map was the output. The thinking still happened in someone’s head.
That division of labour has quietly collapsed, and I think it’s worth pausing on how we got here because the shift says as much about how AI is changing decision-making generally as it does about maps specifically.
Stage one was infrastructure. Classic GIS gave us the plumbing coordinate systems, spatial joins, the ability to store and query “where” at scale. It was powerful, but it required specialists. If you wanted an answer, you filed a request and waited.
Stage two was analytics. Dashboards and choropleth maps started answering “what’s happening here?” footfall by suburb, competitor density, demographic overlays. This is where most organisations still sit today: better visibility, but the strategic judgment, where do we expand, which site wins, was still a human synthesising six spreadsheets and a gut feeling.
Stage three, the one we’re in now, is different in kind, not just degree. The system doesn’t just show you the data anymore, it scores the decision. We at INGRITY led a site-selection engagement earlier this year for a quick-service restaurant brand, and the difference from how this work used to be done was stark. Instead of a planner manually eyeballing a handful of candidate sites against a checklist, the model pulled in trade-area data, competitor proximity, demographic fit, and drive-time catchments, then produced a ranked shortlist with a defensible score attached to each option including where two candidate sites would likely cannibalise each other’s revenue. The team’s job shifted from producing the analysis to interrogating if the ranking made sense, what’s missing, where do we override the model. That’s a fundamentally different kind of work.
That shift isn’t isolated to one project. The geospatial AI market is projected to grow from roughly $60 billion in 2025 to close to $600 billion by 2035, and the reason is the same pattern showing up everywhere, tools that used to take months to implement and required a specialist now generate a usable answer in minutes, for a business user who has never opened a GIS package. Natural-language interfaces are a big part of why you can now ask a plain-English question like “which suburbs have the fewest clinics within a 15-minute drive?” and get back an actual spatial analysis, not a support ticket. Foundation models trained on satellite imagery are doing the same thing to remote sensing that large language models did to text, recognising flood risk or building footprints at a scale no analyst team could match manually. On the operations side, this isn’t theoretical. UPS’s route-optimisation system alone is estimated to save the company several hundred million dollars a year by shaving a few miles off every driver’s route, at scale, every single day.
None of this means the human role disappears. If anything, it gets more important, just further upstream. The risk with any model that hands you a confident-looking score is that confidence isn’t the same as correctness. Location models can hallucinate too like misplacing rural addresses, or systematically favouring the well-mapped, high-income areas where training data is richest. The organisations getting real value out of this aren’t the ones blindly trusting the ranked list, they’re the ones who’ve built in a human checkpoint. That’s also why regulation is catching up, the EU AI Act now treats several location-driven applications as high-risk systems requiring documentation and conformity checks. Governance isn’t a separate conversation from location intelligence anymore it’s part of the same one.
If you’re still treating location intelligence as “the map team,” it’s worth asking where in your organisation “where should we go” decisions actually get made today and whether the answer is still a person with six browser tabs open, or a system that’s already done the first pass for them.
This is exactly the kind of problem we work on at INGRITY, pairing industry-specific solutions with AI accelerators to turn location data into a decision engine, not just a dashboard, and building the governance layer in from the start rather than bolting it on later. If your team is weighing where to expand, invest, or consolidate and the current answer still lives in someone’s head, let’s talk.