AI Retail Site Selection

The Problem

“Retail site selection is slow, costly, and risky”

Organizations face these key challenges:

Siloed data across brokers, internal sales systems, GIS tools, and third-party providers leads to inconsistent site scoring and duplicated work

Manual trade-area and competitor analysis is slow, subjective, and difficult to reproduce across markets and store formats

High financial irreversibility: long lease terms and large buildout costs make wrong site decisions expensive and slow to correct

Impact When Solved

Cuts site evaluation cycle time from 6–10 weeks to 2–4 weeks by automating screening, mapping, and underwriting inputsImproves new-store performance by identifying higher-demand trade areas and quantifying cannibalization and co-tenancy effectsStrengthens investment committee and landlord negotiations with transparent drivers, comparable evidence, and forecast confidence intervals

Real-World Use Cases

AI for Finding High-Potential Real Estate Investments

It’s like giving every real-estate investor their own tireless analyst that quietly scans thousands of properties and markets in the background, then taps you on the shoulder when it finds deals that match your strategy and are likely underpriced or high-potential.

Classical-SupervisedEmerging Standard

8.5

Transforming Commercial Real Estate Through Artificial Intelligence

This is about using AI as a super-analyst and super-assistant for commercial real estate: it scans market data, building information, and financials much faster than people can, then suggests better deals, pricing, layouts, and operations decisions for offices, retail, and industrial properties.

UnknownEmerging Standard

6.5